Development and Testing of a Flexible Topology Micro ...
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The Pennsylvania State University The Graduate School College of Engineering DEVELOPMENT AND TESTING OF A FLEXIBLE TOPOLOGY MICRO-HYBRID PASSENGER VEHICLE POWERTRAIN FOR HARDWARE-IN-THE LOOP SIMULATION AND EDUCATION A Thesis in Mechanical Engineering by Timothy Paul Cleary 2010 Timothy Paul Cleary Submitted in Partial Fulfillment of the Requirements for the Degree of Master of Science May 2010
Transcript of Development and Testing of a Flexible Topology Micro ...
The Pennsylvania State University
The Graduate School
College of Engineering
DEVELOPMENT AND TESTING OF A FLEXIBLE TOPOLOGY MICRO-HYBRID
PASSENGER VEHICLE POWERTRAIN FOR
HARDWARE-IN-THE LOOP SIMULATION AND EDUCATION
A Thesis in
Mechanical Engineering
by
Timothy Paul Cleary
2010 Timothy Paul Cleary
Submitted in Partial Fulfillment
of the Requirements
for the Degree of
Master of Science
May 2010
The thesis of Timothy Paul Cleary was reviewed and approved* by the following:
Daniel C. Haworth
Professor of Mechanical Engineering
Thesis Co-Advisor
Joel R. Anstrom
Research Associate, Thomas D. Larson Pennsylvania Transportation Institute
Thesis Co-Advisor
H. Joseph Sommer III
Professor of Mechanical Engineering
Professor-In-Charge of Mechanical and Nuclear Engineering Graduate Programs
Karen A. Thole
Professor of Mechanical Engineering
Head of the Department of Mechanical and Nuclear Engineering
*Signatures are on file in the Graduate School
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ABSTRACT
Hardware-in-the-loop (HIL) simulations allow for rapid testing and validation of prototype
components and their mathematical models. Development time of these complex components and their
control systems, such as high voltage batteries, advanced internal combustion engines and electric motors
seen in hybrid electric vehicles, is also decreased. Teaching these skills for more than one specific
component or system of components is difficult given the effort to pull together complicated experimental
hardware and or software. This presents a need to develop a tool that adapts quickly to a desired test
configuration and performs multiple test modes for an assortment of prototype components. This thesis
discusses the development of such a tool and explores its ability to test and operate in multiple
configurations.
This thesis details the design and testing of an HIL test bench that performs high voltage battery,
internal combustion engine, electric-motor and controller HIL simulations. The same test bench is
designed to be tested in a small vehicle as an all electric, series hybrid, parallel hybrid, and conventional
powertrain.
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TABLE OF CONTANTS
LIST OF FIGURES ................................................................................................................. viii
LIST OF TABLES ................................................................................................................... xv
LIST OF EQUATIONS ........................................................................................................... xvi
LIST OF ABBREVIATIONS .................................................................................................. xviii
ACKNOWLEDGEMENTS ..................................................................................................... xix
Chapter 1 Introduction ............................................................................................................. 1
Chapter 2 Literature Review .................................................................................................... 3
2.1 Hybrid Electric Vehicles ............................................................................................ 3 2.2 Hardware-in-the-Loop in the Automotive Industry ................................................... 4 2.3 Hardware-in-the-Loop in Education .......................................................................... 4 2.4 Powertrain System Analysis Toolkit (PSAT) ............................................................ 5 2.4 Existing HIL Test Benches ........................................................................................ 6
Chapter 3 Hardware Summary ................................................................................................. 9
3.1 Solid Modeling ........................................................................................................... 10 3.2 Batteries ..................................................................................................................... 16
3.2.1 Lithium Technology Corporation (LTC) – Lithium Iron Phosphate ............... 16 3.2.2 Motorcraft – Nickel-Metal-Hydride ................................................................ 19 3.2.3 Saft - Nickel-Metal-Hydride ........................................................................... 20
3.3 Electric Motors ........................................................................................................... 20 3.3.1 Hi Performance - AC-12 AC Induction Motor ................................................ 20 3.3.2 Hi Performance - AC-15 AC Induction Motor ................................................ 21
3.4 Engine ........................................................................................................................ 21 3.4.1 Engine Throttle and Choke Mechanical Control ............................................. 21 3.4.2 Engine Speed Component Level Control ........................................................ 22
3.5 Controllers .................................................................................................................. 23 3.5.1 Master Vehicle Controller ............................................................................... 24 3.5.2 Electric Motor Controllers............................................................................... 24 3.5.3 Engine Servo Controller .................................................................................. 24 3.5.4 Battery Management System ........................................................................... 25
3.6 Electronics .................................................................................................................. 25 3.6.1 DC-DC Block .................................................................................................. 25 3.6.2 Relay Box ........................................................................................................ 26 3.6.3 Driver Interfaces .............................................................................................. 27
Chapter 4 Operational Modes .................................................................................................. 29
4.1 Passenger Vehicle Modes .......................................................................................... 30
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4.1.1 Electric Vehicle (EV) Mode ............................................................................ 30 4.1.2 Series Hybrid Electric Vehicle Mode .............................................................. 33 4.1.3 Parallel Hybrid Electric Vehicle Mode ........................................................... 35 4.1.4 Series/Parallel Hybrid Electric Vehicle Mode ................................................ 37 4.1.5 Conventional Vehicle Mode ............................................................................ 38
4.2 Hardware-In-The-Loop Modes .................................................................................. 39 4.2.1 Battery HIL Mode ........................................................................................... 39 4.2.2 Engine HIL Mode ............................................................................................ 40 4.2.3 Motor HIL Mode ............................................................................................. 41 4.2.4 Auxiliary Power Unit HIL Mode .................................................................... 42
Chapter 5 Battery HIL ............................................................................................................. 43
5.1 PSAT Battery Model .................................................................................................. 44 5.2 Characterization ......................................................................................................... 49 5.2 Model Setup ............................................................................................................... 58 5.3 Hardware Setup .......................................................................................................... 64 5.4 Test Setup ................................................................................................................... 66 5.5 Results of Battery Simulation, Co-Simulation, and HIL Tests .................................. 67 5.6 Other Electrical Components ..................................................................................... 74 5.7 Associated Laboratory for Classroom Education ....................................................... 75
Chapter 6 Engine HIL .............................................................................................................. 76
6.1 PSAT Engine Model .................................................................................................. 76 6.2 Characterization ......................................................................................................... 79
6.2.1 High Resolution Data Collection Procedure ................................................... 79 6.2.2 Low Resolution Automated Data Collection Procedure ................................. 80
6.3 Model Setup ............................................................................................................... 85 6.4 Hardware Setup .......................................................................................................... 88 6.5 Test Setup ................................................................................................................... 89 6.6 Results of Engine Simulation, Co-Simulation, and HIL Tests................................... 89 6.7 Associated Laboratory for Classroom Education ....................................................... 95
Chapter 7 Motor HIL ............................................................................................................... 96
7.1 PSAT Motor Model.................................................................................................... 96 7.1.1 Motor ............................................................................................................... 96 7.1.2 Generator ......................................................................................................... 98
7.2 Characterization ......................................................................................................... 99 7.3 Model Setup ............................................................................................................... 107 7.4 Hardware Setup .......................................................................................................... 111 7.5 Test Setup ................................................................................................................... 112 7.6 Results of Motor Simulation, Co-Simulation, and HIL Tests .................................... 112 7.7 Associated Laboratory for Classroom Education ....................................................... 118
Chapter 8 Controller HIL ......................................................................................................... 119
8.1 Model Setup ............................................................................................................... 119 8.1.1 Plant Models .................................................................................................... 120
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8.1.2 Controller Model ............................................................................................. 121 8.2 Hardware Setup .......................................................................................................... 124 8.3 Test Setup ................................................................................................................... 125 8.4 Results of Controller HIL Tests ................................................................................. 125 8.5 Associated Laboratory for Classroom Education ....................................................... 128
Chapter 9 Component Level Control and Communication...................................................... 129
9.1 Engine Control ........................................................................................................... 129 9.1.1 Engine Torque Control .................................................................................... 129 9.1.2 Engine Speed Control ...................................................................................... 130
9.2 Motor Control ............................................................................................................ 133 9.2.1 Motor Torque Control ..................................................................................... 142 9.2.2 Motor Speed Control ....................................................................................... 142 9.2.3 Dual Motor Control ......................................................................................... 143
9.3 Electronic Clutch Control .......................................................................................... 143 9.4 Battery Control ........................................................................................................... 144
9.4.1 State of Charge Calculation ............................................................................. 145 9.5 Digital Scale ............................................................................................................... 147 9.6 Vehicle Speed ............................................................................................................ 147 9.7 Drive Input ................................................................................................................. 148
Chapter 10 Full Vehicle Control .............................................................................................. 149
9.1 Electric Only Vehicle Control .................................................................................... 150 9.2 Series Hybrid Vehicle Control ................................................................................... 152
9.2.1 State of Charge Non-Depleting ....................................................................... 152 9.2.2 State of Charge Depleting – Plug in Hybrid Electric Vehicle (PHEV) ........... 154
9.3 Parallel Hybrid Vehicle Control ................................................................................. 155 9.4 Series / Parallel Hybrid Vehicle Control .................................................................... 155 9.5 Conventional Vehicle Control .................................................................................... 155
Chapter 11 Full Vehicle Testing .............................................................................................. 156
11.1 Electric Vehicle Mode Results ................................................................................. 156 11.1.1 Single Motor Electric Vehicle Mode ............................................................. 156 11.1.2 Dual Motor Electric Vehicle Mode ............................................................... 162
11.2 Series Engine Mode Results ..................................................................................... 166 11.3 Parallel Mode Results .............................................................................................. 169 11.4 Conventional Mode Results ..................................................................................... 170
Chapter 12 Conclusions and Future Work ............................................................................... 171
Appendix A Simulink Models ................................................................................................ 172
Appendix B Wire Diagrams .................................................................................................... 177
Appendix C Motor and Controller Dynamometer Results ..................................................... 182
Appendix D HIL Laboratory Instructions ............................................................................... 186
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Battery HIL Simulation .................................................................................................... 186 Hardware .................................................................................................................. 186 Procedure .................................................................................................................. 187 Analysis .................................................................................................................... 190
Engine HIL Simulation .................................................................................................... 190 Hardware .................................................................................................................. 190 Procedure .................................................................................................................. 190 Analysis .................................................................................................................... 192
Electric Motor HIL Simulation ........................................................................................ 193 Hardware .................................................................................................................. 193 Procedure .................................................................................................................. 193 Analysis .................................................................................................................... 195
Controller HIL Simulation ............................................................................................... 195 Hardware .................................................................................................................. 195 Procedure .................................................................................................................. 196 Analysis .................................................................................................................... 198
Full Vehicle Electric Mode Control Development .......................................................... 198 Full Vehicle Series HEV Control Development .............................................................. 198 Full Vehicle Parallel HEV Control Development ............................................................ 198
Appendix E PSAT Initialization Files .................................................................................... 199
NiMH (Motorcraft) Battery Pack Model ......................................................................... 199 Primary Motor Model ...................................................................................................... 203 Secondary Motor Model (as a motor) .............................................................................. 206 Secondary Motor Model (as a generator) ......................................................................... 209 Honda GS 190 Gasoline Engine Model ........................................................................... 212 Electrical Accessory Model ............................................................................................. 220 Power Converter Model ................................................................................................... 220 Final Drive Model ............................................................................................................ 221 Single Reduction Gear Ratio Model ................................................................................ 222 Berkeley Vehicle (body) Model ....................................................................................... 223 Berkeley Wheel Model .................................................................................................... 224
APPENDIX F ABC 150 Script ............................................................................................... 226
Pulse Power Testing ......................................................................................................... 226 Co-Simulation and HIL Simulation ................................................................................. 228
Appendix G Motor Controller Parameters .............................................................................. 229
Bibliography ............................................................................................................................ 236
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LIST OF FIGURES
Figure 2-1: Harbin Institute of Technology HIL Test Bench .................................................. 7
Figure 2-2: Harbin Institute of Technology HIL Test Bench Diagram ................................... 7
Figure 2-3: Argonne National Laboratory Modular Automotive Technology Testbed ........... 8
Figure 3-1: Penn State GATE Berkeley MMHEV Solid Model .............................................. 9
Figure 3-2: Penn State GATE Berkeley MMHEV .................................................................. 10
Figure 3-3: Powertrain Housing Factor of Safety .................................................................... 12
Figure 3-4: Lithium Ion Pack CFD Setup ................................................................................ 12
Figure 3-5: Lithium Ion Pack CFD Results – Air Temperature ............................................... 13
Figure 3-6: Lithium Ion Battery Pack CFD Results - Air Flow Side View ............................. 14
Figure 3-7: Lithium Ion Battery Pack CFD Results - Air Flow Top View .............................. 14
Figure 3-8: Lithium Ion Battery Pack CFD Results - Cell Temperature ................................. 15
Figure 3-9: Exposed Lithium Ion Battery Header ................................................................... 17
Figure 3-10: Exploded Lithium Ion Battery Header ................................................................ 18
Figure 3-11: System Test of the Lithium Ion Battery Pack ..................................................... 19
Figure 3-12: Engine Throttle and Choke Servos ..................................................................... 22
Figure 3-13: Controller Layout ................................................................................................ 23
Figure 3-14: Power Converter Module .................................................................................... 26
Figure 3-15: Potentiometer and Pedal Driver Interface Integration ......................................... 27
Figure 3-16: Dash Mounted Touch Screen Driver Interface ................................................... 28
Figure 4-1: Powertrain Layout CAD Model ............................................................................ 29
Figure 4-2: Integrated Powertrain ............................................................................................ 30
Figure 4-3: Primary Motor and Drive ...................................................................................... 31
Figure 4-4: Energy Flow - Electric Vehicle Mode................................................................... 32
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Figure 4-5: Secondary Motor and Engine APU ....................................................................... 34
Figure 4-6: Energy Flow - Series Hybrid Mode ...................................................................... 35
Figure 4-7: Energy Flow - Parallel Hybrid Mode .................................................................... 37
Figure 4-8: Energy Flow - Conventional Mode ....................................................................... 38
Figure 4-9: Battery Hardware-in-the-Loop Configuration ...................................................... 40
Figure 4-10: Motor Hardware-in-the-Loop Configuration ...................................................... 42
Figure 5-1: PSAT Battery Generic Map Model ....................................................................... 44
Figure 5-2: Motorcraft C/3.13 Discharge ................................................................................ 50
Figure 5-3: Hybrid Pulse Power Characterization Test (Start of Sequence)............................ 51
Figure 5-4: Hybrid Pulse Power Characterization Test (Complete HPPC Sequence) ............. 52
Figure 5-5: Motorcraft 80% SOC HTTC Test ......................................................................... 53
Figure 5-6: Motorcraft NiMH Battery Internal Resistance to Charging .................................. 54
Figure 5-7: Motorcraft NiMH Battery Internal Resistance to Discharging ............................. 55
Figure 5-8: Motorcraft Battery Maximum Charge and Discharge Power ............................... 56
Figure 5-9: Motorcraft Open-Circuit Voltage Map ................................................................. 57
Figure 5-10: Complete PSAT Model of the Berkeley MMHEV in Electric Vehicle Mode .... 59
Figure 5-11: Battery Co-Simulation and HIL Diagram ........................................................... 60
Figure 5-12: PSAT Battery Model HIL Changes .................................................................... 61
Figure 5-13: Measured Battery Voltage and Current Inputs .................................................... 62
Figure 5-14: Commanded Current Signal to the ABC 150 ...................................................... 62
Figure 5-15: Battery HIL Hardware Layout ............................................................................ 64
Figure 5-16: Voltage Divider ................................................................................................... 65
Figure 5-17: Battery Testing Voltage Results .......................................................................... 68
Figure 5-18: Battery Testing Voltage Results (snapshot) ........................................................ 69
Figure 5-19: Battery Testing Current Results .......................................................................... 70
Figure 5-20: Battery Testing Current Results (snapshot) ........................................................ 70
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Figure 5-21: Battery Testing State of Charge Results ............................................................. 72
Figure 5-22: Battery Testing State of Charge Results (snapshot) ............................................ 72
Figure 5-23: Battery Testing Vehicle Speed Results ............................................................... 73
Figure 5-24: Battery Testing Vehicle Speed Results (snapshot) ............................................. 74
Figure 6-1: PSAT Engine Hot Map Model Inputs and Outputs ............................................... 77
Figure 6-2: Wide Open Throttle Engine Torque Curve ........................................................... 83
Figure 6-3: Engine Efficiency .................................................................................................. 83
Figure 6-4: Engine Fuel Rate ................................................................................................... 84
Figure 6-5: Engine Fuel Rate and Maximum Torque Curve ................................................... 84
Figure 6-6: Engine Co-Simulation and HIL Diagram.............................................................. 85
Figure 6-7: PSAT Engine Model HIL Changes ....................................................................... 86
Figure 6-8: Secondary Motor and Engine State Control for Engine HIL and Co-Simulation . 87
Figure 6-9: Engine Hardware-in-the-Loop Hardware Layout ................................................. 88
Figure 6-10: Engine Testing Speed Results ............................................................................. 90
Figure 6-11: Engine Testing Speed Results (snapshot) ........................................................... 91
Figure 6-12: Engine Testing Torque Results ........................................................................... 92
Figure 6-13: Engine Testing Torque Results (snapshot)......................................................... 93
Figure 6-14: Engine Testing Fuel Consumption Results ......................................................... 93
Figure 6-15: Engine Testing Fuel Consumption Results (snapshot) ....................................... 94
Figure 6-16: Engine Testing Power Out .................................................................................. 94
Figure 7-1: PSAT General Map Voltage in Motor Model ....................................................... 97
Figure 7-2: PSAT General Generator Map Torque in Model .................................................. 99
Figure 7-3: Motor/Controller Dynamometer Control Setup .................................................... 101
Figure 7-4: Motor/Controller Dynamometer Hardware Layout .............................................. 102
Figure 7-5: Dynamometer Load Cell ....................................................................................... 103
Figure 7-6: Primary Motor Dynamometer Torque Results ...................................................... 104
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Figure 7-7: Secondary Motor Dynamometer Torque Results .................................................. 104
Figure 7-8: Primary Motor and Controller Dynamometer Efficiency Results ......................... 105
Figure 7-9: Secondary Motor and Controller Dynamometer Efficiency Results ..................... 105
Figure 7-10: Primary Motor Efficiency Map ........................................................................... 106
Figure 7-11: Secondary Motor Efficiency Map ....................................................................... 106
Figure 7-12: Motor HIL Simulation Model ............................................................................. 108
Figure 7-13: Motor HIL Dynamometer Speed Control ........................................................... 109
Figure 7-14: Motor Co-Simulation and HIL Diagram ............................................................. 110
Figure 7-15: Motor HIL Motor Model ..................................................................................... 111
Figure 7-16: Motor Torque Calculation ................................................................................... 111
Figure 7-17: Clutch and Motors of the Berkeley MMHEV ..................................................... 112
Figure 7-18: Motor Testing Current Results ............................................................................ 113
Figure 7-19: Motor Testing Current Results (snapshot) .......................................................... 113
Figure 7-20: Motor Testing Speed Results .............................................................................. 114
Figure 7-21: Motor Testing Speed Results (snapshot) ............................................................. 115
Figure 7-22: Motor Testing Torque Results ............................................................................ 115
Figure 7-23: Motor Testing Torque Results (snapshot) ........................................................... 116
Figure 7-24: Motor Testing Vehicle Speed Results ................................................................. 117
Figure 7-25: Motor Testing Vehicle Speed Results (snapshot) ............................................... 117
Figure 8-1: Berkeley MMHEV Series Mode Plant Models Only ............................................ 120
Figure 8-2: Manual Control Model .......................................................................................... 121
Figure 8-3: Automatic Control Model ..................................................................................... 122
Figure 8-4: Automatic Control Model Subsystems ................................................................. 122
Figure 8-5: Engine Speed Control Trigger .............................................................................. 123
Figure 8-6: Engine Speed Control ........................................................................................... 123
Figure 8-7: High Level Automatic Control Model Logic ........................................................ 124
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Figure 8-8: Controller HIL Hardware Layout .......................................................................... 125
Figure 8-9: Controller HIL Engine Speed Results ................................................................... 126
Figure 8-10: Controller HIL Generator Load Demand ............................................................ 127
Figure 8-11: Controller HIL Generator Power Output ............................................................ 127
Figure 9-1: Engine Speed Control ........................................................................................... 131
Figure 9-2: Engine Speed Control Enable/Disable .................................................................. 131
Figure 9-3: Engine Speed Control Results ............................................................................... 132
Figure 9-4: Curtis Controller Indirect Rotor Flux Orientation (Curtis Instruments) ............... 133
Figure 9-5: Curtis Motor Controller Inverter Switching (Curtis Instruments) ......................... 134
Figure 9-6: Curtis Motor Controller Logic (Curtis Instruments) ............................................. 134
Figure 9-7: Highest Level Motor Control ................................................................................ 135
Figure 9-8: Motor Control Sub-Sections ................................................................................. 135
Figure 9-9: Motor Control Read CAN Messages .................................................................... 136
Figure 9-10: Motor Control CAN Message Decoding ............................................................. 136
Figure 9-11: Motor Control CAN Message Data Read Looping ............................................. 138
Figure 9-12: Motor Control CAN Message Command Write Looping ................................... 138
Figure 9-13: Motor Control CAN Message Writing Switch (Single Motor) ........................... 139
Figure 9-14: Motor Control CAN Message Writing Switch Logic (Dual Motors) ................. 140
Figure 9-15: Motor Control CAN Message Writing Switch (Dual motors) ............................ 140
Figure 9-16: Motor Controller Communication Start Up and Shut Down Logic .................... 141
Figure 9-17: Motor Control CAN Message Building .............................................................. 141
Figure 9-18: Motor Speed Control ........................................................................................... 143
Figure 9-19: Electronic Clutch Control ................................................................................... 144
Figure 9-20: Electronic Clutch Control Logic ......................................................................... 144
Figure 9-21: Battery State of Charge Calculation .................................................................... 145
Figure 9-22: Initial Battery State of Charge Logic .................................................................. 146
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Figure 9-23: Battery State of Charge Current Integration Coulomb Counting ........................ 146
Figure 9-24: Digital Scale Communication ............................................................................. 147
Figure 9-25: Vehicle Speed Calculation .................................................................................. 148
Figure 9-26: Driver Input ......................................................................................................... 148
Figure 10-1: Component Force Relative to Road Load ........................................................... 150
Figure 10-2: Single Motor Electric Vehicle Control................................................................ 151
Figure 10-3: Dual Motor Electric Vehicle Control .................................................................. 151
Figure 10-4: Series Vehicle Control ........................................................................................ 153
Figure 10-5: Series Vehicle Control APU Logic ..................................................................... 154
Figure 11-1: Electric Vehicle Mode Run 1 Battery Voltage.................................................... 157
Figure 11-2: Electric Vehicle Mode Run 1 Battery Current .................................................... 158
Figure 11-3: Electric Vehicle Mode Run 1 Battery State of Charge ....................................... 159
Figure 11-4: Electric Vehicle Mode Run 1 Vehicle Speed ...................................................... 159
Figure 11-5: Electric Vehicle Mode Run 2 Battery Voltage.................................................... 160
Figure 11-6: Electric Vehicle Mode Run 2 Battery Current .................................................... 160
Figure 11-7: Electric Vehicle Mode Run 2 Battery State of Charge ....................................... 161
Figure 11-8: Electric Vehicle Mode Run 2 Vehicle Speed ...................................................... 161
Figure 11-9: Dual Motor Electric Vehicle Mode Battery Voltage........................................... 163
Figure 11-10: Dual Motor Electric Vehicle Mode Motor Currents ......................................... 163
Figure 11-11: Dual Motor Electric Vehicle Mode Battery State of Charge ............................ 164
Figure 11-12: Dual Motor Electric Vehicle Mode Motor Speeds ............................................ 165
Figure 11-13: Dual Motor Electric Vehicle Mode Vehicle Speed ........................................... 165
Figure 11-14: Series Engine Mode Battery Voltage ................................................................ 167
Figure 11-15: Series Engine Mode Motor Current .................................................................. 167
Figure 11-16: Series Engine Mode Battery State of Charge .................................................... 168
Figure 11-17: Series Engine Mode Engine Speed .................................................................. 168
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Figure 11-18: Series Engine Mode Vehicle Speed .................................................................. 169
A-1: Berkeley MMHEV Conventional Mode PSAT Model .................................................... 172
A-2: Berkeley MMHEV Electric Vehicle Mode PSAT Model ............................................... 173
A-3: Berkeley MMHEV Series Mode PSAT Model ............................................................... 174
A-4: Berkeley Electric Vehicle Control Model ....................................................................... 175
A-5: Berkeley Electric Vehicle Control Model (dual motor) .................................................. 175
A-6: Berkeley Series Vehicle Control Model .......................................................................... 176
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LIST OF TABLES
Table 5-1: PSAT Generic Map Battery Model Parameters ..................................................... 45
Table 5-2: Motorcraft Open-Circuit Voltage ........................................................................... 57
Table 5-3: Battery Testing Error Data ..................................................................................... 67
Table 6-1: PSAT Engine Hot Map Parameters ........................................................................ 77
Table 6-2: High Resolution Engine Mapping Procedure ......................................................... 80
Table 6-3: Low Resolution Engine Mapping Procedure .......................................................... 81
Table 7-1: PSAT General Map Voltage in Motor Model Parameters...................................... 97
Table 7-2: Motor and Controller Testing Procedure ................................................................ 100
Table 10-1: Road Load Vehicle Parameters ............................................................................ 150
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LIST OF EQUATIONS
Equation 3-1: Maximum Motor Power Calculation................................................................. 21
Equation 4-1: Primary Motor Gear Ratio ................................................................................ 31
Equation 5-1: PSAT Open-Circuit Voltage and Internal Resistance ....................................... 46
Equation 5-2: Battery Terminal Voltage .................................................................................. 46
Equation 5-3: Battery Power Lost to Heat ............................................................................... 47
Equation 5-4: Battery Temperature Calculation ...................................................................... 48
Equation 5-5: Battery State of Charge Calculation .................................................................. 48
Equation 5-6: Battery Capacity Calculation ............................................................................ 50
Equation 5-7: Battery Coulombic Efficiency Calculation ....................................................... 51
Equation 5-8: Battery Internal Resistance Calculation ............................................................ 53
Equation 5-9: Battery Power Calculation ................................................................................ 55
Equation 5-10: Measured Battery Voltage Scaling ................................................................. 63
Equation 5-11: Measured Battery Current Scaling .................................................................. 63
Equation 5-12: ABC 150 Current Command Scaling .............................................................. 63
Equation 5-13: Voltage Divider Gain Calculation ................................................................... 66
Equation 6-1: PSAT Engine Torque Calculation ..................................................................... 78
Equation 6-2: PSAT Instantaneous Fuel Consumption ........................................................... 78
Equation 6-3: PSAT Cumulative Fuel Rate ............................................................................. 78
Equation 6-4: Engine Torque ................................................................................................... 82
Equation 6-5: Engine Fuel Rate ............................................................................................... 82
Equation 6-6: Engine Efficiency .............................................................................................. 82
Equation 7-1: Motor Current.................................................................................................... 98
Equation 7-2: Motor Torque .................................................................................................... 98
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Equation 7-3: Maximum Torque.............................................................................................. 98
Equation 7-4: Maximum Mechanical Torque .......................................................................... 98
Equation 7-5: Maximum Electrical Torque ............................................................................. 98
Equation 7-6: Heat Index of the Electric Motor....................................................................... 98
Equation 7-7: Generator Torque Output .................................................................................. 99
Equation 7-8: Generator Speed ................................................................................................ 99
Equation 7-9: Measured Motor Torque ................................................................................... 102
Equation 7-10: Measured Motor and Controller Efficiency .................................................... 103
Equation 9-1: Throttle Position Signal Equation ..................................................................... 130
Equation 9-2: Battery State of Charge Equation ...................................................................... 146
Equation 9-3: Vehicle Speed Calculation ................................................................................ 148
Equation 10-1: Vehicle Drag Force ......................................................................................... 149
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LIST OF ABBREVIATIONS
HIL
EV
HEV
MMHEV
APU
SOC
PSAT
HPPC
CAD
FEA
CFD
NiMH
PID
PWM
VGA
PCI
MISO
MOSI
DC
AC
DOE
GATE
CAFE
CAN
Hardware-in-the-Loop
Electric Vehicle
Hybrid Electric Vehicle
Multiple Mode Hybrid Electric Vehicle
Auxiliary Power Unit
State of Charge
Powertrain System Analysis Toolkit
Hybrid Pulse Power Characterization
Computer Automated Design
Finite Element Analysis
Computational Fluid Dynamics
Nickel Metal Hydride
Proportional, Integral, Derivative
Pulse Width Modulated
Video Graphic Array
Peripheral Component Interconnect
Master in Slave out
Master out Slave in
Direct Current
Alternating Current
U.S. Department of Energy
Graduate Automotive Technology Education
Corporate Average Fuel Economy
Control Area Network
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ACKNOWLEDGEMENTS
I would like to thank the many people who supported me through my research endeavors.
First I would like to thank Dr. Daniel Haworth for his support through the Challenge X program
and funding from the Larson Transportation Institute. Thank you to Dr. Joel Anstrom for his personal
time and extensive guidance throughout this project. Thank you to Bryan Markovich and Theresa Maher
for their kind willingness to help when needed.
I’ve relied greatly on the Learning Factory, Thomas D. Larson Transportation Institute (LTI), and
The Penn State Advanced Vehicle Team facilities for tooling and manufacturing. But also I would like to
thank Carson Baird of the Learning Factory, Gary Neal, Matt Shirk, Brian Herrold, Eric Reischer and the
rest of the Advanced Vehicle Team veterans of the Challenge X and EcoCAR programs for their expertise
and support.
Thank you to Doug Picard for his donation of the 1958 Berkeley vehicle used in this research.
Thank you to Michael Bierly and the students at Pennsylvania College of Technology for their
remarkable paint and body work that radically transformed the vehicle. I would like to sincerely thank
Aymeric Rousseau and the PSAT team at Argonne Nation Laboratory as well as MathWorks for their
software donation. Thank you to Lithium Technology Corporation for their partial donation of prototype
Lithium Ion Iron Phosphate battery cells, also to Hi-Performance Golf Carts and Electric Motor Sports for
their partial donation of electric motors, controllers and hardware.
Finally, I would like to thank the most important people in my life. Thank you to my family and
friends, especially Allison, my mother, father, brother, and sister, for their encouragement and patience
throughout my college career.
Chapter 1
Introduction
Hardware-in-the-Loop (HIL) is a real-time simulation technique used for complex hardware
testing as well as the development of system or component level control. The process helps reduce
development time and cost (Crabb, 1998). While it is emerging as an engineering staple method of
development, instruction and education is required to teach current and future engineers the method and
application.
This thesis discusses the development and testing of a tool for instruction of HIL simulation. With
learning by doing in mind, the tool will allow students to take ideas learned about in lectures and apply
them quickly and safely.
HIL simulation is useful for studying many automotive systems. A benefit of HIL simulation is
the ability to experiment with single components to isolate and study their dynamics. Many experiments
are possible and will be exercised here, including high voltage, internal combustion engine, electric-motor
and controller HIL simulations. Including all individual tests in separate laboratory exercises over a
semester would involve many test bench setups and various laboratory visits. Another benefit of the tool
detailed in this thesis is the centralization of many HIL experiments into one test bench.
The design of this system allows for multiple HIL experiments by grouping many of the involved
components together in a way that allows for quick configuration changes. A system with this ability and
power is also useful as an interesting hybrid electric vehicle powertrain. This idea of grouping many
components together opens opportunities for control experiments with this unique multiple mode
powertrain while maintaining the function of a flexible test bench.
2
The configuration changes are achieved using a unique setup and automated control for each
component and the overall system. Using Matlab software, the control of the bench/powertrain and its
components offers more opportunities for student exercises. Students can practice developing control
algorithms for the components and the different system configurations of the test bench/powertrain.
The system design is on a small scale using inexpensive parts. While this sacrifices quality and
performance it provides an affordable system to replicate for education at other schools. Also, with a
smaller compact system, travel is easier and demonstrations are possible in smaller places such as a class
room.
The design, manufacturing and testing of the system presented in this thesis was performed
primarily by the author.
Chapter 2
Literature Review
Recent interests in reducing American dependency on foreign oil while increasing security and
over all energy independence have presented increased pressure to produce clean efficient automotive
transportation (Congress, Energy Independence and Security Act, 2007). The currently weak automotive
industry is in need of a strong product designed and built by American labor to get behind and help
rejuvenate the industry (Congress, American Recovery and Reinvestment Act, 2009). With a mandated
Corporate Average Fuel Economy (CAFE) of 35 mile per gallon (mpg) for 2020 passenger vehicles sold
in the United States (Congress, Energy Independence and Security Act, 2007) rapid and cheap
development of clean efficient vehicles is required. This high fuel economy standard will require
hybridization of many vehicle platforms before the 2020 model year.
2.1 Hybrid Electric Vehicles
Hybrid electric vehicles (HEV) are being rapidly developed to keep up with the growing demand
for more efficient transportation. HEVs come in various configurations and have many benefits over
traditional conventional vehicles, all of which present opportunities for more efficient transportation. For
example, using stored electrical energy for all or a fraction of required traction power offsets the use of
petroleum fuels as well as recapturing energy through regenerative braking and easily recharging at home
(Omonowo D. Momoh, 2009). All of these present opportunities to adapt our transportation needs to a
more efficient method.
4
2.2 Hardware-in-the-Loop in the Automotive Industry
There are three main process for developing a HEV: software simulation, Hardware-in-the-Loop
(HIL) simulation, and complete vehicle testing (Zhao Hui, 2008). Software testing of systems is only
possible when all the dynamics are understood and represented by mathematical models of a component.
In the case where all models are not available then HIL simulation or full vehicle testing is required. HIL
simulation is necessary to keep up with the increasing demand because full vehicle testing is a time
intensive and expensive process and models are not always available.
The automotive industry has been using HIL techniques for years on control systems. Plant
models of a vehicle’s powertrain are simulated on a computer while testing the production powertrain
control module in real time with the computer simulation (S. Raman, 1999). Recently, new components
such as high voltage batteries and electric traction motors are finding their way into production vehicles
as part of HEV configurations. Before a vehicle reaches the preproduction phase, extensive HIL testing
should be performed to test designs and verify function. HIL testing has proven valuable to accomplish
these tasks in a rapid and cheap manner.
2.3 Hardware-in-the-Loop in Education
Because the automotive industry has a growing interest in HIL simulation, educating engineering
students and industry professionals in HIL techniques is also growing. The Pennsylvania State University
is currently offering one of the first advanced automotive HIL classes in the country as part of their
Department of Energy (DOE) sponsored Graduate Automotive Technology Education (GATE)
curriculum under EEREVT (Energy Efficiency and Renewable Energy Vehicle Technologies). To
support this education, a small and cheap automotive powertrain HIL test bench was conceived to help
students apply lecture theory and make the connection to practice.
5
This thesis presents such a test bench with the ability to simulate battery HIL, engine HIL,
electric motor HIL, and engine/motor generator set HIL, as well as full vehicle testing in all electric,
series HEV, parallel HEV and conventional configurations.
2.4 Powertrain System Analysis Toolkit (PSAT)
Running an HIL simulation for an automotive application does not require a complete system of
hardware components including an engine, transmission, etc. Instead, missing hardware is replaced with a
mathematical model. Using a model offers flexibility to the test setup. One could completely change the
vehicle configuration by simply rearranging its models and control algorithm while physically testing the
same component. For example, if an engine was the tested component, HIL simulation could easily adapt
from a conventional vehicle simulation to a series HEV or parallel HEV configuration by changing the
simulated plant models and their control algorithm using the same engine hardware setup. This thesis
presents a hardware design that also changes arrangement as quickly and easily as software changes.
This thesis discusses the design and testing of an HIL test bench which uses Matlab, Matlab
Simulink, and Matlab Stateflow to model hardware not present in a test. Matlab software is also used to
control the hardware involved in the HIL test or the full vehicle.
Models of HEV and conventional vehicle components are available but many of them require a
unique understanding and expertise in a specific field. Argonne National Laboratory’s Powertrain System
Analysis Toolkit (PSAT) offers the ability to run full vehicle simulations using predefined models with a
basic understanding of conventional and HEV component configurations and function (Rousseau, 2002).
PSAT offers the ability to quickly model full HEV vehicles of all configurations using predefined or user
defined models. This thesis relies heavily on the use of PSAT models while redefining their parameters to
correlate with the hardware selected for the designed test bench. PSAT is also a forward looking model
6
written to work with Matlab software in a co-simulation environment making it ideal for HIL simulations
(Rousseau, 2002).
2.4 Existing HIL Test Benches
Others have designed and built HIL test benches. Few are designed to run more than one test and
none have the ability to carry out multiple HIL tests and perform as the powertrain of the test stand on
which they are built.
The Harbin Institute of Technology has developed a system seen in Figure 2-1 with the ability to
test electric-motor and engine combinations while always simulating the transmission and high voltage
battery components (Zhao Hui, 2008). Figure 2-2 shows a diagram of the hardware used in this system.
The Harbin Institute of Technology system, though flexible in test configuration, is large and not portable.
Argonne National Laboratory (ANL) has developed a modular HIL test bench designed to
operate on a chassis dynamometer (Lohse-Busch, 2009). This system, the Modular Automotive
Technology Testbed (MATT), is mobile because it was integrated with automotive suspension
components but is not suitable for passenger vehicle operation. MATT is instrumented with laboratory
hardware and was built without passenger interfaces. Though unique, flexible, and mobile, MATT is
devoted to act as a test bench loaded by a chassis dynamometer in a laboratory setting. Figure 2-3 shows
MATT in a configuration exercising an engine, transmission, and final drive while high voltage battery
and electric traction motor hardware were emulated.
Both MATT and the Harbin Institute of Technology systems are able to perform engine, electric
motor, and controller HIL simulations. Neither of the above systems can perform the same tests while
outside of the laboratory environment.
7
Figure 2-1: Harbin Institute of Technology HIL Test Bench
Figure 2-2: Harbin Institute of Technology HIL Test Bench Diagram
8
Figure 2-3: Argonne National Laboratory Modular Automotive Technology Testbed
Both the Harbin Institute of Technology and ANL HIL systems test large, full size vehicle
components. This is useful for real world testing without the added complexity of component scaling. The
following thesis details the design and testing of an HIL test bench that is smaller but also tests without
scaling. This test bench performs battery, engine, electric motor, and controller HIL simulations, many
while mobile. The same test bench is then tested as the HEV powertrain of a small vehicle in all electric,
series HEV, parallel HEV, and conventional modes.
Chapter 3
Hardware Summary
Several unique prototype components were integrated into the HIL test bench and as the installed
HEV powertrain into a 1958 Berkeley 328 sports car making it a Multiple Mode Hybrid Electric Vehicle
(MMHEV). These components give it the versatility of a fully functional test bench and MMHEV. The
powertrain design allows for single component level HIL experimentation as well as component and
system level control software testing. This same powertrain design will also and primarily act as the
power plant for the Berkeley MMHEV. This chapter discusses the details of component selection and the
HEV system design for this project.
Figure 3-1: Penn State GATE Berkeley MMHEV Solid Model
Primary Motor
Secondary Motor
Engine
Master Vehicle Controller
High Voltage Battery
10
Figure 3-2: Penn State GATE Berkeley MMHEV
3.1 Solid Modeling
Three dimensional solid models were created in SolidWorks to design and integrate components
of the system into an existing vehicle as shown in Figure 3-1 and Figure 3-2. Figure 3-1 shows the solid
model while Figure 3-2 shows an image of the finished vehicle. Solid modeling is a common tool used
extensively in the automotive industry and has proved to increase design and integration productivity
while decreasing time to production. This is accomplished by using Finite Element Analysis (FEA) and
Computational Fluid Dynamics (CFD) studies along with physical layout planning to validate designs.
Layout planning, FEA, and CFD testing took place before components or systems were manufactured to
decrease the development time of the Berkeley MMHEV. These models are also used in this thesis to
explain designs and functional modes.
11
A solid model of the 1959 Berkeley 328 chassis and suspension was created using the current
dimensions of the LTI owned Berkeley vehicle. These vehicles originally were produced by hand using
predominantly fiberglass, which will flex and distort over time. The current condition after recent body
reconstruction is reflected in this solid model.
This modular and flexible design could also be adapted to other sub-compact platforms. The same
design is currently being adapted to a Geo Metro by the Auto Technology Program at the Central
Pennsylvania Institute of Technology.
Solid models of the components selected for this design were created using manufacturer
specifications and measured dimensions. This includes the primary and secondary motors and controllers
along with the selected internal combustion engine and electronic clutch. These components were selected
for the powertrain design and purchased, while other components required custom design specifically for
this vehicle’s powertrain and its function. Some components unique to the Berkeley MMHEV are the
powertrain housing, Lithium Ion battery pack packaging and enclosure, as well as the engine throttle and
choke control integration system.
Using the designed powertrain, battery pack, and electronics, a full vehicle solid model was
assembled and tested for fit and function. Figure 3-1 shows the vehicle model with many of the
components integrated. Along with the ability to design and integrate physical dimensions, CAD allows
for CFD and FEA analysis to validate models. Figure 3-3 shows the results of an FEA simulation
performed to validate the powertrain housing and design. Made of ¼ inch steel and loaded from the two
electric motors and the engine due to their weight and maximum torque output, the simulated minimum
factor of safety for the powertrain housing is 1.8. Given these results, the powertrain housing is
approximately twice as strong as needed to handle maximum loading, and deform by a maximum of 0.05
inches.
12
Figure 3-3: Powertrain Housing Factor of Safety
Figure 3-4: Lithium Ion Pack CFD Setup
Figure 3-4 illustrates the boundary conditions and heat sources for the CFD analysis of the
Lithium Ion battery pack design. Each cell in the pack was set to source 500 Watts of heat to simulate the
13
battery at its maximum rated continuous load. The simulation was run assuming an inlet fan that pushes
120 cubic feet per minute of ambient air at 70 degrees Fahrenheit and 50% relative humidity into the
enclosure. The outlet was set to ambient atmospheric pressure and temperature.
Figure 3-5: Lithium Ion Pack CFD Results – Air Temperature
14
Figure 3-6: Lithium Ion Battery Pack CFD Results - Air Flow Side View
Figure 3-7: Lithium Ion Battery Pack CFD Results - Air Flow Top View
15
Figure 3-8: Lithium Ion Battery Pack CFD Results - Cell Temperature
Figure 3-5 to Figure 3-8 show some of the results from CFD analysis of the Lithium Ion battery
pack design. In Figure 3-5 the cooling air is shown to enter the pack at 70 degrees Fahrenheit and leave at
approximately 90 degrees. Figure 3-6 and Figure 3-7 show an evenly distributed air flow throughout the
pack with higher velocities near the outsides of the outer cells. Figure 3-8 illustrates the surface
temperature of the cells and indicates peak temperatures on the cylindrical surface of approximately 165
degrees Fahrenheit, which is slightly above the recommended operating temperature for these cells. Given
the test setup and results, this pack would be useful for the Berkeley MMHEV operation conditions but
should be closely monitored for dangerous temperature conditions.
16
3.2 Batteries
Three different battery packs were used for this study. The best suited pack was selected for each
experiment given the availability and testing parameter constraints. Below is a list and several
specifications for the batteries used in the thesis.
3.2.1 Lithium Technology Corporation (LTC) – Lithium Iron Phosphate
Lithium-Iron-Phosphate cells were used in a prototype battery pack. The packaging was designed
for easy assembly and disassembly while isolating dangerous areas. This allows students to see the
internal components for future instruction. Figure 3-9 shows the assembled pack with a header exposed.
A header is the structure that holds a group of cells at each end. This unique header encases the high
voltage bus bars and channels for individual cell voltage sensing wires.
17
Figure 3-9: Exposed Lithium Ion Battery Header
An additional sheet of machined polypropylene caps the exposed copper bus bars and voltage
sensing wires. This header design isolates any energized terminals or wires from accidental shorting by
inexperienced operators.
This battery pack consists of two headers, top and bottom. Each header contains the bus bars and
individual voltage sensing wires while also acting as the structure of the battery pack. Figure 3-10 shows
a rendered three dimensional model of the design. Included are four sheets of polypropylene, copper bus
bars and all of the necessary hardware.
18
Figure 3-10: Exploded Lithium Ion Battery Header
Fifteen Lithium Technology or GAIA, 3.2 volt, 33 Ah, prismatic, iron-phosphate cells in a
series make up the 48 Volt nominal battery pack. With manufacturer specifications of 350 Amperes
continuous discharge and 990 Amperes for 10 seconds the battery has continuous and peak power ratings
of 16.8 kW and 47.5 kW, respectively. These power ratings are safely above the peak and continuous
ratings of all other electrical devices combined.
A Battery Management System (BMS) is required given the unstable nature of Lithium-Ion cells.
Details of the battery management system will be discussed later in section 3.5.4 Battery Management
System.
Figure 3-11 shows the complete battery pack during initial system testing. A laboratory power
supply simulates the 48 Volt to 24 Volt DC-DC power supply that would energize the BMS. The driver
interface and communication system was tested to display the status of the individual cells using the high
speed Controller Area Network (CAN) between the BMS and vehicle controller.
19
Figure 3-11: System Test of the Lithium Ion Battery Pack
3.2.2 Motorcraft – Nickel-Metal-Hydride
The instability of the prototype advanced Lithium-Ion battery pack required a back up battery to
be used during the development and testing of the vehicle. Two Nickel Metal Hydride (NiMH) battery
packs were selected. The first backup battery pack used was the Motorcraft, NiMH, air cooled modules
that are nominally 12 volts each with 10, 1.2 Volt cells. These modules have been on the shelf for years;
consequently, given the NiMH high self discharge rate, cycling and testing had to be done to revive the
modules. The testing also generated new parameters and maps to use for modeling and control. After
testing four modules in a series arrangement the pack proved to have approximately 63 Ah of capacity
remaining. Details of the results are shown in Chapter 5. Considering that when they were manufactured
20
more than ten years ago they were rated at 90 Ah, a 60Ah capacity is satisfactory and still useful for this
project.
3.2.3 Saft - Nickel-Metal-Hydride
A second back up battery pack consists of Saft, NiMH, liquid cooled modules which are
nominally 12 volts each with ten, 1.2 Volt cells. These modules were used primarily for motor
dynamometer testing because of their liquid cooling ability. Repeated and rapid charging and discharging
required a battery that could be safely cooled. These modules also had been on the shelf for years, and
required cycling to revive them.
3.3 Electric Motors
Initially, two Direct Current (DC) permanent magnet brushed machines were tested and proved to
be inadequate for the Berkeley MMHEV design. Their controllers did not allow shaft freewheeling or
dynamic regenerative braking. Two similar Alternating Current (AC) induction electric machines were
ultimately selected and have proven to be the best for the Berkeley MMHEV design. They work well with
the system because of their light weight and robust control with adequate communication, true
freewheeling, and dynamic regenerative abilities.
3.3.1 Hi Performance - AC-12 AC Induction Motor
The Hi Performance AC-12 and AC-15 AC Induction motors are virtually identical electric
machines. The only noticeable difference is the controller that powers them. The AC-12 machine is
operated by a Curtis Instruments AC 1236-6301, 48 Volt to 84 Volt, 300 Ampere controller. This
21
machine has the potential to put out approximately 14.4 kW of traction power when operated by a 48 Volt
battery without considering the efficiencies of the motor and controller. Equation 3-1 calculates the
potential power of this system.
𝑀𝑎𝑥𝑖𝑚𝑢𝑚 𝑀𝑜𝑡𝑜𝑟 𝑃𝑜𝑤𝑒𝑟 = 𝐵𝑎𝑡𝑡𝑒𝑟𝑦 𝑉𝑜𝑙𝑡𝑎𝑔𝑒 × 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑙𝑒𝑟 𝐶𝑢𝑟𝑟𝑒𝑛𝑡 𝐿𝑖𝑚𝑖𝑡 (𝐷𝐶)
Equation 3-1: Maximum Motor Power Calculation
3.3.2 Hi Performance - AC-15 AC Induction Motor
The AC-15 machine is operated by a Curtis Instruments AC 1238-6305, 48 Volt to 84 Volt, 550
Ampere controller. Again without considering the efficiencies of the motor and controller, Equation 3-1
gives the potential power of this system. Operated by a 48 Volt battery, this machine has the potential to
put out 26.4 kW of traction power.
3.4 Engine
A Honda GS 190, single cylinder, 6 horsepower engine was selected for the Berkeley MMHEV
design, because of its light weight, low cost, and robust operation.
3.4.1 Engine Throttle and Choke Mechanical Control
The engine was originally designed as a utility engine with a speed governor. Many
modifications were required to use the engine in the vehicle and for experimentation. The most significant
change was engine throttle control. After removing the original throttle and choke controls, two small
servos were integrated to the butterfly valves used for engine throttle and choke. The integrated servos
can be seen in Figure 3-12.
22
Figure 3-12: Engine Throttle and Choke Servos
The throttle and choke servos, along with an ignition coil and relay, give the vehicle controller
complete control of engine operation.
3.4.2 Engine Speed Component Level Control
The original engine was designed to mechanically operate in a single speed mode, so a
component level control was required for dynamic control of engine speed or torque. A Pulse Width
Modulated (PWM) signal is generated that drives the throttle and choke servos using a PIC
microcontroller. More detail about the controller is provided in Chapter 6.
23
3.5 Controllers
Several controllers are found throughout the Berkeley MMHEV. The master vehicle controller is
the master for all vehicle operations and modes. The electric motor and engine servo controllers carry out
requests from the master vehicle controller, while the electric motor controllers and battery management
systems broadcast the status and parameters of their corresponding components. Figure 3-13 shows the
layout of the key controllers in the vehicle.
Vehicle
Front
Secondary Motor
ControllerPrimary Motor
Controller
Vehicle
Controller
Battery Management
System
Servo
Controller
Mode Clutch
Relay Box
Figure 3-13: Controller Layout
24
3.5.1 Master Vehicle Controller
For overall control of the vehicle, an Advantech UNO 3072 fan-less Intel Pentium computer with
two PCI slots was chosen. Analog and Digital I/O were accomplished using an Advantech PCI-1716 card
while CAN communications was achieve using a Softing AC2 PCI card.
The computer runs a simple version of DOS as an operating system. This loads the embedded
Matlab xPC Target file, which is a compiled version of a Matlab Simulink code written to run the vehicle
and its components. The code will be discussed in detail in future chapters.
3.5.2 Electric Motor Controllers
Two electric motor controllers were selected to directly control the primary and
secondary motors. A Curtis Instruments 1238-6501 AC induction motor controller with a maximum DC
current rating of 550 Amperes and a Curtis Instruments 1236-6301 AC induction motor controller with a
maximum DC current rating of 300 Amperes control the primary and secondary motors, respectively. The
motor controllers are turned on using relays, and communicate using CAN messaging between
themselves and the master vehicle controller (Curtis Instruments, 2006).
3.5.3 Engine Servo Controller
The master vehicle controller sends analog voltage signals to the engine servo controller, which
generates PWM signals to drive the throttle and choke servo position on the engine. Controlling position
of the throttle and choke allow speed or torque control of the engine depending on the desired operational
mode. Other components associated with the control of the engine are the ignition relay and engine speed
25
sensor. Engine ignition is a simple relay that grounds the engine’s ignition to shut down the engine. The
engine’s speed sensor is a Hall Effect sensor and Red Lion frequency to voltage converter that converts
pulses from a toothed wheel and outputs an analog signal to the master vehicle controller proportional to
engine speed.
3.5.4 Battery Management System
For safety, Lithium Ion cells must be supervised to avoid over or under voltage or over
temperature. The selected I+ME BMS monitors cell voltage and temperature and has the ability to
discharge single cells to maintain a balanced pack. The CAN communicates the state of each individual
cell to the master vehicle controller. The BMS has the ability to send warnings and/or shut down the
battery if dangerous conditions are approached.
3.6 Electronics
Several electronic devices required for full automation of the many features of the Berkeley
MMHEV are described in the following subsections.
3.6.1 DC-DC Block
A custom power converter module was required to supply DC voltages to Berkeley MMHEV’s
components. This power module consists of three DC-DC power supplies, all inputting power from the
high voltage, 48 Volt battery pack. These power supplies output 24 Volt, 12 Volt, and 5 Volt DC at
varying current limits. Figure 3-14 is an image of the exposed power converter module designed for the
Berkeley MMHEV.
26
Figure 3-14: Power Converter Module
Wire diagrams and details of its function can be found in Appendix B, Wire Diagrams.
3.6.2 Relay Box
A National Instruments ER-16 containing 16 relays was selected to handle the switching
requirements of the Berkeley MMHEV. It interfaces TTL digital signals from the master vehicle
controller to switch relays. The operator must enable its function by manually switching on the dashboard
emergency stop. If the emergency switch is in an off state, all the relays go to a safe position in which all
components are shut down and de-energized.
27
3.6.3 Driver Interfaces
The driver interface consists of a 7 inch touch screen displaying the master vehicle controller
output to allow the operator to monitor desired parameters, and two potentiometers integrated into the
Berkeley MMHEV on the brake and acceleration pedals to drive the vehicle. The potentiometers
communicate with the master vehicle controller and represent the operator’s input for acceleration and
braking. The brake potentiometer is attached to the mechanical brake, allowing both mechanical and
regenerative braking to work together to slow the vehicle with only one input from the operator. These
potentiometers were attached to the original throttle and brake pedals of the Berkeley MMHEV. Figure
3-15 shows the integration of potentiometers into the original pedal system of the vehicle, while Figure
3-16 shows the integration of the touch screen into the dash board.
Figure 3-15: Potentiometer and Pedal Driver Interface Integration
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Figure 3-16: Dash Mounted Touch Screen Driver Interface
Chapter 4
Operational Modes
The most unique feature of the Berkeley MMHEV is its ability to quickly change operation
modes while being used as either a passenger vehicle or a test bench for HIL experimentation. This
chapter illustrates the Berkeley’s many modes of operation. Solid Works, a three dimensional solid
modeling software package, was used to model the Berkeley chassis and components. Figure 4-1
illustrates a solid model of the Berkeley MMHEV powertrain, and will be used in this chapter to
demonstrate the energy flow in each mode. Figure 4-2 shows the powertrain integrated into the vehicle.
Figure 4-1: Powertrain Layout CAD Model
Primary Motor
Electronic Clutch
Secondary Motor
Engine
Transmission
Differential
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Figure 4-2: Integrated Powertrain
4.1 Passenger Vehicle Modes
The following are operational modes of the Berkeley MMHEV when used as a passenger vehicle
powertrain. All the following images show mechanical, electrical, and chemical energy flow.
4.1.1 Electric Vehicle (EV) Mode
An Electric Vehicle (EV) is a vehicle in which the power is sourced solely by energy stored in an
electrochemical device (SAE, Feburary 2008). In electric only or electric vehicle mode, only electrical
energy from the battery is used to propel the vehicle through an electric traction motor. Electric mode in
the Berkeley MMHEV is achieved by using the battery to power only the primary electric motor for
31
traction. All other powertrain components are in an off state and at zero speed. Figure 4-3 illustrates the
primary traction motor and its belt speed reduction to the front axle and differential through an idler shaft.
The double reduction has an overall gear ratio of 1:5.64, calculated using Equation 4-1.
Figure 4-3: Primary Motor and Drive
𝑔𝑟 =𝑛𝑝𝑚
𝑛𝑡𝑖𝑛
×𝑛𝑡𝑜𝑢𝑡
𝑛𝑑𝑖𝑓𝑓
gr = gear ratio
npm=number of teeth, motor gear
ntin = number of teeth, transmission input
ntout = number of teeth, transmission output
ndiff = number of teeth, differential
Equation 4-1: Primary Motor Gear Ratio
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A gear ratio of 1:5.64 translates to 5.64 rotations of the electric motor for every single rotation of
the front driving axle. Given the motor’s maximum recommended speed, the vehicle’s tire diameter and
gear ratio of 5.64:1, the maximum vehicle speed is approximately 65 mph. Figure 4-4 shows the flow of
energy while the vehicle is in an EV mode. The green arrow indicates mechanical energy flowing from
the motor to the wheels during driving and from the wheels to the primary motor during regenerative
braking. The orange arrow indicates the flow of electrical energy, flowing from the high voltage battery
through the motor controller to the motor while driving and flowing from the motor through the motor
controller and into the battery during regenerative braking. The red circles and lines indicate a component
in an off state. M
M
Figure 4-4: Energy Flow - Electric Vehicle Mode
33
4.1.2 Series Hybrid Electric Vehicle Mode
A series hybrid electric vehicle is driven by an electric machine while a chemical energy
converter creates electrical power with or without a battery for energy storage. Typically, the chemical
energy converter is an internal combustion engine that uses fossil fuel and is coupled to an electric
generator. The traction motor is often coupled with a high voltage battery to absorb transient loads. The
energy converter is not necessarily an internal combustion engine, but could also be a fuel cell or turbine,
amongst others. Simply, a series hybrid vehicle is one in which both sources of energy go through a
single propulsion device. In this example fuel energy converted to electrical energy and battery electrical
energy go through a single drive motor (SAE, Feburary 2008).
The Berkeley MMHEV operating as a series hybrid utilizes the primary electric motor and drive
from Figure 4-3 for traction, as in the EV mode. The secondary motor and engine produce electricity to
charge the battery and/or power the traction motor. First, gasoline energy flows to the engine in which it
is converted to mechanical energy. The secondary motor then absorbs that energy and converts it to
electrical energy. Then, the electrical energy produced by the secondary motor joins the high voltage
electrical bus which is connected to the high voltage battery. Finally, both electrical power sources supply
the primary electric drive to propel the vehicle.
In this case, the engine is decoupled from the road speed and is only used as part of the generator
system or Auxiliary Power Unit (APU). The Berkeley MMHEV is a range extending series hybrid,
because its APU cannot produce enough power to meet the average road load on the highway. Figure 4-5,
secondary motor and engine APU, shows the two components grouped together to make the vehicle’s
auxiliary power unit (APU).
34
Figure 4-5: Secondary Motor and Engine APU
Combining both the Primary Motor and Drive with the APU in the Berkeley chassis creates a
series hybrid mode in the Berkeley MMHEV powertrain. An electric clutch connects the two motor
shafts, and in this mode is in an off state allowing the two systems to operate with independent speeds.
Figure 4-6 below illustrates the flow of energy during series hybrid operation. Similar to the electric
vehicle mode, the first, right, green arrow indicated energy to and from the primary motor and road, while
the corresponding orange arrow represents the electrical energy to and from the primary motor and
battery. The second, left, green arrow indicates mechanical energy flow from the secondary electric
motor to the engine during an engine start, and engine to secondary motor during a generation state. The
second orange arrow indicates electrical energy flow from the battery through the motor controller to the
35
secondary motor during engine starting, and secondary motor through its motor controller to the battery
during a generation state. Finally, the blue arrow indicates fuel energy into the engine.
Figure 4-6: Energy Flow - Series Hybrid Mode
4.1.3 Parallel Hybrid Electric Vehicle Mode
A parallel hybrid vehicle differs from a series hybrid because the engine or chemical converting
device can mechanically propel the vehicle in combination with or parallel with, the electrical machine. A
parallel hybrid is a vehicle in which multiple propulsion systems are used for traction and all can be
operated independently or together, depending on the design and mode (SAE, Feburary 2008).
Different parallel hybrid technologies use different primary movers, either electric motors or
engines. In our case the chief traction device will be the primary electric motor, but could very easily be
M
M
36
the engine given a simple change in controller code. The electric motor was selected as primary mover in
this case because of its superior power and efficiency compared to the small engine. If the engine in this
system was considerably larger, it would then be the primary mover device. With this setup, one could
easily experiment with the choice of primary mover.
For the Berkeley MMHEV, the only mechanical difference between series and parallel operation
is the state of the electric clutch. With the clutch in an engaged state, both electic motors and the engine
are tied to the road. The clutch, in combination with the freewheeling capable induction drives, gives
options for using many arrangements of components to drive the vehicle and opens up opportunities for
control optimization. Figure 4-7 illustrates the energy flow of the Berkeley MMHEV’s parallel
configuration. The green arows indicate mechanical energy flow to and from the engine and secondary
motor, to and from the differential or road and primary motor, as well as between the two primary drive
and APU systems. This configuration allows the engine and secondary motor to apply mechaincal energy
to the road. Orange arrows indicate electrical energy to and from the electic motors and high voltage
battery via their respecive controllers. The blue arrow indicates the flow of gasoline fuel energy to the
internal combustion engine.
37
Figure 4-7: Energy Flow - Parallel Hybrid Mode
4.1.4 Series/Parallel Hybrid Electric Vehicle Mode
A series/parallel hybrid is one that can switch between series and parallel operation. This vehicle
configuration takes advantage of the benefits of all three modes depending on the current vehicle
situation. For example, an interesting experiment would be to investigate whether series operation during
city driving and parallel operation during highway driving is more efficient than series or parallel
operation alone.
The electronic clutch is simply engaged or disengaged to mechanically achieve series/parallel
mode. The control algorithm is much more complicated than simply controlling an electronic clutch, and
requires special consideration when making the switch. An unstable state could easily be entered if care is
not taken to understand the dynamics involved.
M
M
38
4.1.5 Conventional Vehicle Mode
In the conventional mode, both the primary and secondary motors are in an off or neutral state.
The regenerative braking feature is inactive, but at low speeds the primary motor may be required to
propel the vehicle up to speed allowing the engine and clutch to fully engage. Figure 4-8 illustrates the
flow of energy and status of the major components during conventional operation mode.
The green arrows indicate mechanical energy flowing from the engine through the motors and
clutch to the wheels. The motors are in a neutral state and are not supplying or sinking mechanical energy
to propel or brake the vehicle in the conventional mode, unless needed to start the engine or for initial
vehicle movement. The blue arrow indicates the flow of gasoline fuel to the engine.
Figure 4-8: Energy Flow - Conventional Mode
M
M
39
4.2 Hardware-In-The-Loop Modes
4.2.1 Battery HIL Mode
The Berkeley MMHEV hardware configuration for battery HIL testing requires only three
components: the vehicle high voltage battery, vehicle controller, and the AeroVironment ABC 150 power
processing machine. The ABC 150 is capable of charging or discharging a battery at 150 kW and has an
updating frequency of approximately 10 Hz, which is fast enough to capture all of the important dynamics
of the battery in hybrid electric or electric vehicles. The vehicle controller acts as data recorder and signal
generator to the ABC 150.
The vehicle controller is also required to run the PSAT simulations to generate the current signal
for simulation, co-simulation, and HIL experiments. The controller sends a current signal to the ABC 150,
which in turn exercises the battery with the commanded current. Software and test setup will be discussed
in more detail in the Battery HIL chapter.
Figure 4-9 shows the configuration of the hardware required for the battery HIL test. The orange
arrow indicates the electrical energy to and from the battery being tested and the ABC 150 power
processor. The black lines indicate data being recorded from the battery and the PSAT generated current
signal sent to the ABC 150 power processor.
40
Figure 4-9: Battery Hardware-in-the-Loop Configuration
4.2.2 Engine HIL Mode
The Berkeley MMHEV hardware configuration for engine HIL requires the APU system, shown
in Figure 4-5, the vehicle’s high voltage battery, and the vehicle’s controller. Again, the PSAT simulation
will run on the vehicle controller. In this case the engine will be run as required by the PSAT simulation
outputs and the attached secondary motor will also be controlled by the vehicle controller to sink the
appropriate amount of power from the engine. Software and test setup will be discussed in more detail in
the Engine HIL chapter.
Vehicle Controller (computer)
High Voltage Battery Pack ABC 150 - Power Processor
41
All of the hardware required for engine testing is in the vehicle allowing for the test to be easily
mobile. This supports the idea that the Berkeley MMHEV is a powerful, flexible, and mobile instructional
tool.
4.2.3 Motor HIL Mode
The Berkeley MMHEV hardware configuration for motor HIL requires the primary motor,
secondary motor, electronic clutch, battery, and the vehicle’s controller. Again the PSAT simulation will
run on the vehicle controller. In this case the secondary motor is run as required by the PSAT demand for
the traction motor, and the larger primary motor is run as a dynamometer to simulate road speed. This
arrangement allows for the secondary motor to be fully exercised up to its peak torque and speed. The
software and test setup will be discussed in more detail in the Motor HIL chapter.
Figure 4-10 shows the basic configuration of the motor HIL setup. The orange arrows indicate
electrical energy into and out of each motor through their controllers to the battery pack. Separate battery
packs may be used for this test to isolate the dynamics of each motor, but one pack is satisfactory for
demonstration and testing. Also, both the primary and secondary belts must be disconnected to isolate the
motors from the rest of the drive train. The green arrow indicates the mechanical energy to and from each
electrical motor through the electronic clutch.
42
Figure 4-10: Motor Hardware-in-the-Loop Configuration
This test can be performed with only the Berkeley MMHEV and an additional battery pack, if
desired. This also supports the idea that the Berkeley MMHEV is a powerful, flexible, and mobile
instructional tool.
4.2.4 Auxiliary Power Unit HIL Mode
The Berkeley MMHEV hardware configuration for APU HIL simulation requires the vehicle’s
APU and the ABC 150 power processor. This test uses the APU setup to generate electricity which is then
absorbed by the ABC 150 power processor.
Chapter 5
Battery HIL
This chapter explains high voltage battery pack HIL procedures and required pre-testing.
First, this chapter introduces the PSAT battery model. Then the battery characterization is
completed. Next, the PSAT mathematical model is created to simulate the battery. Then, a full vehicle
model is created using PSAT. The PSAT toolkit groups the battery and other hybrid electric vehicle
component models together to form a complete vehicle model. Several changes are made to allow for HIL
simulations using the complete vehicle model. Then, the required hardware is arranged in accordance
with the vehicle HIL model. Next, a test procedure is defined and performed. Finally, results are
analyzed.
Three different high voltage battery packs were used in the development of the vehicle and test
bench procedures: a Saft NiMH pack, a Lithium Technology Li-Ion pack, and a Motorcraft NiMH pack.
For each of the packs the cells and modules were on the shelf for a long period of time before HIL testing.
Self-discharging and aging affect battery packs, requiring proper re-habilitation before the pack
should be modeled or used in a HIL simulation. Similarly, the Lithium Technology cells required proper
characterization, because they lack full definition in the form of parameters and maps from the
manufacturer. The Motorcraft NiMH battery pack was selected for the following tests, because of its
stable performance and availability.
The only non-virtual component used in battery HIL testing is the high voltage battery pack. All
other components are simulated using PSAT models and existing data. Simulating other components in
real-time allows the battery to be exercised as if it was within the vehicle without the time and cost of
building a full prototype. This demonstrates how HIL testing can provide more realistic results than
simulation alone in less time than full prototype tests, shortening development time and reducing cost.
44
5.1 PSAT Battery Model
Several battery models exist for use in programs such as PSAT and Matlab, including generic
map and equation fit models. For this thesis the generic map model was selected because of its ability to
adapt to many battery chemistries as opposed to an equation fit model that is designed specifically for a
given chemistry. Figure 5-1 shows the generic map battery model input and output. The model outputs
battery voltage, temperature, and state of charge requiring only battery current as an input. Since the
PSAT vehicle model is forward looking, the battery current input to the battery model originates from the
road load and driver demand. This value then goes through all the component models and their
efficiencies such as the wheel, final drive and motor models. In the last stage the electric motor model
outputs the required battery current which is then fed to the input of the battery model. All other
component models use the current or previous samples of the battery model output to complete the loop.
Greater detail of the full system model can be found in Appendix A, Simulink Models, and in the
associated digital files.
The PSAT generic map battery model requires several battery parameters. Table 5-1 lists the key
parameters and the method for obtaining them.
Battery Current
Required for
Motor Operation
Voltage
State of Charge
Figure 5-1: PSAT Battery Generic Map Model
Temperature
45
Parameter SI Unit Procedure to Gather Data
Initial State of Charge Dimensionless User or test defined
Number of Cells Number Predefined
Cell Mass Kilogram Manufacturer Defined
Nominal Voltage Volt Manufacturer Defined
Minimum Voltage Volt Manufacturer Defined
Maximum Voltage Volt Manufacturer Defined
Minimum State of Charge Dimensionless User or test defined
Maximum State of Charge Dimensionless User or test defined
State of Charge Index Dimensionless User or test defined
Temperature Index ºCelsius User or test defined
Maximum Capacity Ampere-hour Cycle Testing
Columbic Efficiency Dimensionless Cycle Testing
Map of a Cells Resistance to Discharge Ohm HTTC Testing
Map of a Cells Resistance to Charge Ohm HTTC Testing
Map of a Cells Open Circuit Voltage Volt Cycle / HTTC Testing
Maximum Charging Current Ampere Manufacture Defined
Maximum Discharging Current Ampere Manufacture Defined
Table 5-1: PSAT Generic Map Battery Model Parameters
Using the above parameters, the following equations represent how the PSAT generic
map battery model determines battery voltage, temperature, and state of charge. Battery voltage and
internal resistance are functions of battery temperature and state of charge. Look up tables of test data are
used to define these functions. Assuming all batteries are in series,
Equation 5-1 represents the battery open circuit voltage and internal resistance. Because all cells
are connected in series, it is assumed that all cell temperatures and states of charge are the same
throughout the pack. PSAT assumes a positive current to be energy into the battery pack. In the following
set of equations, Rint is taken from the charging map when current is greater than zero, and from the
discharging map when current is less than zero.
46
𝑉𝑜𝑐 = 𝑉𝑜𝑐 𝑝𝑒𝑟 𝑐𝑒𝑙𝑙 × 𝐾𝑛𝑢𝑚 𝑐𝑒𝑙𝑙
𝑉𝑜𝑐 𝑝𝑒𝑟 𝑐𝑒𝑙𝑙 = 𝑓 𝑇𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒, 𝑆𝑂𝐶
𝑅𝑖𝑛𝑡 = 𝑅𝑖𝑛𝑡 𝑝𝑒𝑟 𝑐𝑒𝑙𝑙 × 𝐾𝑛𝑢𝑚 𝑐𝑒𝑙𝑙
𝑅𝑖𝑛𝑡 𝑝𝑒𝑟 𝑐𝑒𝑙𝑙 = 𝑓 𝑇𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒, 𝑆𝑂𝐶
𝑉𝑜𝑐 𝑎𝑛𝑑 𝑉𝑜𝑐 𝑝𝑒𝑟 𝑐𝑒𝑙𝑙 = 𝑜𝑝𝑒𝑛 𝑐𝑖𝑟𝑐𝑢𝑖𝑡 𝑣𝑜𝑙𝑡𝑎𝑔𝑒 𝑜𝑓 𝑡𝑒 𝑏𝑎𝑡𝑡𝑒𝑟𝑦 𝑎𝑛𝑑 𝑎 𝑠𝑖𝑛𝑔𝑙𝑒 𝑐𝑒𝑙𝑙, 𝑟𝑒𝑠𝑝𝑒𝑐𝑡𝑖𝑣𝑙𝑦
𝑅𝑖𝑛𝑡 𝑎𝑛𝑑 𝑅𝑖𝑛𝑡 𝑝𝑒𝑟 𝑐𝑒𝑙𝑙 = 𝐼𝑛𝑡𝑒𝑟𝑛𝑎𝑙 𝑟𝑒𝑠𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑜𝑓 𝑡𝑒 𝑝𝑎𝑐𝑘 𝑎𝑛𝑑 𝑎 𝑠𝑖𝑛𝑔𝑙𝑒 𝑐𝑒𝑙𝑙, 𝑟𝑒𝑠𝑝𝑒𝑐𝑡𝑖𝑣𝑖𝑙𝑦
𝐾𝑛𝑢𝑚 𝑐𝑒𝑙𝑙 = 𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑐𝑒𝑙𝑙𝑠 𝑖𝑛 𝑡𝑒 𝑏𝑎𝑡𝑡𝑒𝑟𝑦 𝑝𝑎𝑐𝑘 𝑎𝑙𝑙 𝑐𝑜𝑛𝑛𝑒𝑐𝑡𝑒𝑑 𝑖𝑛 𝑠𝑒𝑟𝑖𝑒𝑠
𝑇𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒 = 𝑇𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒 𝑜𝑓 𝑡𝑒 𝑏𝑎𝑡𝑡𝑒𝑟𝑦 𝑝𝑎𝑐𝑘
𝑆𝑂𝐶 = 𝐵𝑎𝑡𝑡𝑒𝑟𝑦 𝑝𝑎𝑐𝑘 𝑠𝑡𝑎𝑡𝑒 𝑜𝑓 𝑐𝑎𝑟𝑔𝑒 (0 𝑡𝑜 1)
Equation 5-1: PSAT Open-Circuit Voltage and Internal Resistance
When a battery is discharging or current input is negative, the model assumes that the current
input into the model is achieved. When the input current is positive, the charging current is multiplied by
the mapped Coulombic efficiency. Equation 5-2 represents the terminal voltage of the battery pack,
which is the open-circuit voltage minus the drop due to the internal resistance of the battery and current
due to Ohms law.
𝑉𝑜𝑢𝑡 = 𝑉𝑜𝑐 − 𝑅𝑖𝑛𝑡 × 𝐼𝑒𝑠𝑠
𝐼𝑒𝑠𝑠=𝑖𝑛𝑡𝑒𝑟𝑛𝑎𝑙 𝑏𝑎𝑡𝑡𝑒𝑟𝑦 𝑐𝑢𝑟𝑟𝑒𝑛𝑡 (𝑎𝑐𝑐𝑜𝑢𝑡𝑠 𝑓𝑜𝑟 𝑐𝑜𝑢𝑙𝑜𝑚𝑏𝑖𝑐 𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 𝑑𝑢𝑟𝑖𝑛𝑔 𝑐𝑎𝑟𝑔𝑖𝑛𝑔 )
Equation 5-2: Battery Terminal Voltage
47
The temperature calculation utilizes a simple single-node lumped-parameter thermal
model to predict internal battery and exiting air temperature. All cells are assumed to receive the same air
flow and maintain the same temperature. This is not a reliable model, and is not sufficiently accurate for
individual cell temperature management. For the case of the Lithium Ion pack, individual temperature
sensors are distributed across the pack. The recorded sensor data are not expected to match well with the
PSAT thermal model. Equation 5-3 calculates Qgen, the power lost by the battery in heat with the above
assumptions.
𝑄𝑔𝑒𝑛 = (𝑅𝑖𝑛𝑡 × 𝐼𝑖𝑛2) − (𝑉𝑖𝑛 × 𝐼𝑖𝑛 × 1 − 𝜂𝑐𝑜𝑢𝑙𝑜𝑚𝑏𝑖𝑐 )
𝜂𝑐𝑜𝑢𝑙𝑜𝑚𝑏𝑖𝑐 = 𝐶𝑜𝑢𝑙𝑜𝑚𝑏𝑖𝑐 𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 𝑜𝑓 𝑡𝑒 𝑏𝑎𝑡𝑡𝑒𝑟𝑦
𝑅𝑖𝑛𝑡 = 𝐼𝑛𝑡𝑒𝑟𝑛𝑎𝑙 𝑟𝑒𝑠𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑜𝑓 𝑡𝑒 𝑏𝑎𝑡𝑡𝑒𝑟𝑦 𝑝𝑎𝑐𝑘
𝑉𝑖𝑛 = 𝐵𝑎𝑡𝑡𝑒𝑟𝑦 𝑝𝑎𝑐𝑘 𝑣𝑜𝑙𝑡𝑎𝑔𝑒
𝐼𝑖𝑛 = 𝐵𝑎𝑡𝑡𝑒𝑟𝑦 𝑝𝑎𝑐𝑘 𝑐𝑢𝑟𝑟𝑒𝑛𝑡
Equation 5-3: Battery Power Lost to Heat
Equation 5-4 completes the calculation used to predict battery temperature, Tess, using the above
equation for wasted heat.
𝑇𝑒𝑠𝑠 =𝑄𝑔𝑒𝑛 + 𝑄𝑐𝑎𝑠𝑒
𝑀𝑎𝑠𝑠𝑒𝑠𝑠 × 𝐶𝑝 𝑒𝑠𝑠
𝑄𝑐𝑎𝑠𝑒 =(𝑇𝑒𝑠𝑠 + 𝑇𝑎𝑖𝑟 )
𝑅𝑡𝑒𝑟𝑚𝑎𝑙
𝑇𝑎𝑖𝑟 = 𝑇𝑎𝑚𝑏𝑖𝑒𝑛𝑡 −0.5 + 𝑄𝑐𝑎𝑠𝑒
𝑑𝑚𝑎𝑖𝑟𝑑𝑡
× 𝐶𝑝 𝑎𝑖𝑟
𝑀𝑎𝑠𝑠𝑒𝑠𝑠 = 𝑀𝑎𝑠𝑠 𝑜𝑓 𝑡𝑒 𝑏𝑎𝑡𝑡𝑒𝑟𝑦
48
𝐶𝑝 𝑒𝑠𝑠 = 𝑇𝑒𝑟𝑚𝑎𝑙 𝑒𝑎𝑡 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦 𝑜𝑓 𝑡𝑒 𝑏𝑎𝑡𝑡𝑒𝑟𝑦
𝑇𝑎𝑚𝑏𝑖𝑒𝑛𝑡 = 𝐴𝑚𝑏𝑖𝑒𝑛𝑡 𝑡𝑒𝑚𝑝𝑒𝑎𝑡𝑢𝑟𝑡𝑒 𝑜𝑓 𝑡𝑒 𝑒𝑛𝑣𝑖𝑟𝑜𝑛𝑚𝑒𝑛𝑡
𝑑𝑚𝑎𝑖𝑟
𝑑𝑡= 𝑀𝑎𝑠𝑠 𝑓𝑙𝑜𝑤 𝑟𝑎𝑡𝑒 𝑜𝑓 𝑡𝑒 𝑐𝑜𝑜𝑙𝑖𝑛𝑔 𝑎𝑖𝑟
𝐶𝑝 𝑎𝑖𝑟 = 𝐻𝑒𝑎𝑡 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦 𝑜𝑓 𝑡𝑒 𝑐𝑜𝑜𝑙𝑖𝑛𝑔 𝑎𝑖𝑟
Equation 5-4: Battery Temperature Calculation
Equation 5-5 calculates the batteries’ state of charge (SOC) by counting Coulombs. Capacity
used, Capacityused , is calculated based on the maximum capacity (Capacitymaximum ) and initial state of
charge (SOCinitial ). Then absolute and usable state of charge are determined.
𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝑢𝑠𝑒𝑑 = 𝐼𝑖𝑛 𝑑𝑡 + 1 − 𝑆𝑂𝐶𝑖𝑛𝑖𝑡𝑖𝑎𝑙 × 𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝑚𝑎𝑥𝑖𝑚𝑢𝑚
𝑆𝑂𝐶𝑎𝑏𝑠 =𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝑚𝑎𝑥 − 𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝑢𝑠𝑒𝑑
𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦𝑚𝑎𝑥
𝑆𝑂𝐶𝑢𝑠𝑎𝑏𝑙𝑒 =𝑆𝑂𝐶𝑎𝑏𝑠 − 𝑆𝑂𝐶𝑚𝑖𝑛
𝑆𝑂𝐶𝑚𝑎𝑥 − 𝑆𝑂𝐶𝑚𝑖𝑛
Equation 5-5: Battery State of Charge Calculation
Given the above equations, the model is complete, but requires calibration to a particular battery.
Characterization is needed to define battery SOC vs. Open-Circuit Voltage, as well as maps of internal
battery charge and discharge resistances.
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5.2 Characterization
The following section explains the process of battery characterization for a PSAT generic map
model using the Motorcraft NiMH battery pack. The characterization data required are open-circuit
voltage and internal battery resistances to charging and discharging, all relative to battery state of charge.
The FreedomCAR Hybrid Pulse Power Characterization (HPPC) test was used to calculate pulse
power and energy capability under FreedomCAR operating conditions (FreedomCAR, 2003).
The HTTC test seen in Figure 5-3 and Figure 5-4 starts by fully charging and discharging the
battery pack using the ABC 150 power processor. To revive the Motorcraft NiMH cells, this first step of
cycling was repeated several times. Each cycle brought the cells into a tighter average voltage. After a
few cycles, the battery’s capacity increased to approximately 63 Ahr.
Figure 5-2 shows an example of a C/4.5 or 20 ampere discharge. Note that the manufacturer rated
capacity when new was 90 Ahr. After multiple cycles, the pack proved to have a capacity of only 63 Ahr,
making the actual discharge rate C/3.15 or C/(63Ahr/20Ahr).
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Figure 5-2: Motorcraft C/3.13 Discharge
This test is useful for estimating parameters for capacity and efficiency. For this battery, precise
capacity and turn around efficiencies can be calculated because the energies in and out were carefully
recorded. Equation 5-6 shows the method for calculating the battery’s measureable capacity during the
above test.
𝐵𝑎𝑡𝑡𝑒𝑟𝑦 𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦 𝐴𝑟 = 𝐶𝑢𝑟𝑟𝑒𝑛𝑡[𝐴𝑚𝑝𝑒𝑟𝑒] × 𝑇𝑖𝑚𝑒 [𝐻𝑜𝑢𝑟𝑠]
Equation 5-6: Battery Capacity Calculation
The total energy used to charge, and the total energy drained to discharge, are used to calculate
Coulombic turnaround efficiency according to Equation 5-7.
51
𝜂𝐶𝑜𝑢𝑙𝑜𝑚𝑏𝑖𝑐 = 𝐼𝑜𝑢𝑡 𝑑𝑡
𝐼𝑖𝑛 𝑑𝑡
Equation 5-7: Battery Coulombic Efficiency Calculation
With cycling complete, the batteries’ resistance to charging and discharging at a given state of
charge is required. Figure 5-3 and Figure 5-4 below illustrate the HPPC cycle test procedure. The first
step in the HTTC test is a full charge and discharge cycle, followed by rest for an hour. After an hour rest,
the battery is to be discharged 10% at a C/1 rate followed by rest for another hour. For the Motorcraft
pack, a C/1 rate and 10% SOC correspond a to a 60 Ampere discharge for 6 minutes. After another hour
of rest, the battery is subjected to a high rate pulse discharge, a short rest, then a high rate pulse charge, at
the given SOC. This generates the data required to obtain the batteries’ peak power and internal resistance
while charging and discharging at the given SOC.
Figure 5-3: Hybrid Pulse Power Characterization Test (Start of Sequence)
52
Figure 5-4: Hybrid Pulse Power Characterization Test (Complete HPPC Sequence)
Figure 5-5 shows the results of the HTTC pulse charge and discharge test at 80% SOC. The
discharge pulse is shown by the decrease in voltage and is achieved by a 100 Ampere discharge for 10
seconds. The charge pulse is shown by an increase in pack voltage and is achieved by a 75 ampere charge
for 10 seconds.
53
Figure 5-5: Motorcraft 80% SOC HTTC Test
To calculate the battery’s internal resistance, Rint, Equation 5-8 below from the FreedomCAR
Electric Vehicle Battery Test Procedures Manual was used. The changes in voltage and current were
taken between points 1 and 2 in Figure 5-5 for discharging resistance, and points 3 and 4 for charging
resistance.
𝑅𝑖𝑛𝑡 −𝑑𝑖𝑠𝑐 𝑎𝑟𝑔𝑒 = Δ 𝑉𝑜𝑙𝑡𝑎𝑔𝑒 (1 𝑡𝑜 2)
Δ Current (1 𝑡𝑜 2)
𝑅𝑖𝑛𝑡 −𝑐𝑎𝑟𝑔𝑒 = Δ 𝑉𝑜𝑙𝑡𝑎𝑔𝑒 (3 𝑡𝑜 4)
Δ Current (3 𝑡𝑜 4)
Equation 5-8: Battery Internal Resistance Calculation
Using Equation 5-8, Figure 5-6 represents the results of the HTTC test, a map of the battery’s
internal resistance to charging, while Figure 5-7 represents the battery’s internal resistance to discharging.
49
50
51
52
53
54
55
56
0 20 40 60 80 100
Bat
tery
Vo
ltag
e [
Vo
lts]
Time [seconds]
80 % SOC HTTP Test
1
2
3
4
54
The data are presented to represent an individual cell. The pack is made of 40 cells in series; measured
resistance was then divided by 40 to attain the plots below.
Figure 5-6: Motorcraft NiMH Battery Internal Resistance to Charging
55
Figure 5-7: Motorcraft NiMH Battery Internal Resistance to Discharging
Using the battery’s internal resistance to charging and discharging as well as the maximum
manufacturer rated current during charging and discharging, Equation 5-9 calculates the battery’s
maximum power. Figure 5-8 shows the maximum power during charging and discharging indexed by
battery state of charge.
𝑃𝑚𝑎𝑥 −𝑑𝑖𝑠𝑐𝑎𝑟𝑔𝑒 =𝑉2𝑓𝑢𝑛𝑐𝑡𝑖𝑜𝑛(𝑆𝑂𝐶)
𝑅𝑖𝑛𝑡 −𝑑𝑖𝑠𝑐 𝑎𝑟𝑔𝑒
𝑃𝑚𝑎𝑥 −𝑐𝑎𝑟𝑔𝑒 =𝑉2𝑓𝑢𝑛𝑐𝑡𝑖𝑜𝑛(𝑆𝑂𝐶)
𝑅𝑖𝑛𝑡 −𝑐𝑎𝑟𝑔𝑒
Equation 5-9: Battery Power Calculation
56
Figure 5-8: Motorcraft Battery Maximum Charge and Discharge Power
Battery voltage was recorded during the last minutes of the HTTC testing one hour rests. This
value and the corresponding SOC were then used to generate the open-circuit voltage vs. SOC map. Table
5-2 and Figure 5-9 below show the results of the voltage recording during the HTTC test as the SOC vs.
Open-Circuit Voltage Map.
57
% SOC Voltage
100 56
90 52.53
80 51.4
70 50.9
60 50.5
50 49.92
40 49.42
30 49.08
20 48.7
10 47.98
0 40.94
Table 5-2: Motorcraft Open-Circuit Voltage
Figure 5-9: Motorcraft Open-Circuit Voltage Map
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This completes characterization of all battery pack data required to populate the generic map
model in PSAT.
5.2 Model Setup
This section details the steps to set up a full vehicle PSAT model for HIL testing of the Berkeley
MMHEV. Step one is to complete a PSAT model of the Berkeley MMHEV and run the model on a drive
cycle. Figure 5-10 shows a full PSAT model of the Berkeley MMHEV in an EV mode. Results are saved
for comparison with co-simulation and HIL results. The model is exercised and data recorded at a rate of
100 Hz.
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Figure 5-10: Complete PSAT Model of the Berkeley MMHEV in Electric Vehicle Mode
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Figure 5-11 shows the flow of data in a battery co-simulation or HIL simulation test. The battery
model was removed and replaced by a real battery and the ABC150 power processing machine. This
setup allows for the real battery to be exercised by the input model data, and output data are sent back into
the model in real time.
Figure 5-11: Battery Co-Simulation and HIL Diagram
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Figure 5-12: PSAT Battery Model HIL Changes
Figure 5-12 shows the changes made to the PSAT generic map battery model to allow for HIL
testing. The two circles highlight the areas of change. The left circle shows the manual switch for battery
current input. This switch allows the user to switch the model input from simulated battery to actual
battery current measured from the battery being exercised. The current signal fed to the power processor
will always be the current required to meet the vehicle’s demand, which is calculated by the PSAT
vehicle model. The ability to use actual current as an input to the battery model ensures there will be no
error between commanded current to the power processing machine and what current is actually achieved.
Having a manual switch instead of fixed wiring also allows for easy test setup changes for future runs.
The second change to the model is highlighted by the circle on the right. This switch changes
voltage output. The voltage signal output to the rest of the vehicle model is either that predicted by the
PSAT generic map model, or the actual battery voltage of the battery being exercised. This switch allows
for two modes. The first mode is simulation and co-simulation in which the actual and model battery
voltages are recorded, but only the model’s predicted voltage is outputted to the rest of the model. The
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second mode is an HIL simulation mode. This mode records model forecasted voltage, but forwards the
actual measured battery voltage to the rest of the vehicle model.
To achieve the I/O required, drivers for the Advantech PCI-1716 analog and digital I/O card were
included in the new vehicle model as xPC blocks. These drivers and the sub-systems that are used for
battery voltage and current measurement as well, as the current command, can be seen in Figure 5-13 and
Figure 5-14 below. Analog inputs of the Advantech PCI-1716 were used for this measurement of voltage
and current.
Figure 5-13: Measured Battery Voltage and Current Inputs
Figure 5-14: Commanded Current Signal to the ABC 150
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Hardware gains were used along with Equation 5-10 to scale the measured voltage signals to
actual battery voltage and current. Likewise, Equation 5-11 scales the current command signal. The
ABC150 required Equation 5-11to offset, scale, and command the desired current. Equation 5-10 shows
the formula that converts the measured voltage from the output of the isolated voltage divider measuring
the exercised battery voltage.
Battery Voltage = Hardware Gain × Measured Voltage
Equation 5-10: Measured Battery Voltage Scaling
Battery Current = Hardware Gain × Measured Voltage
Equation 5-11: Measured Battery Current Scaling
For safety reasons, the ABC150 power processor has a 0.5 Volt offset from 0 Volts for current
commands; the resulting operation range is -0.5 to 4.5 and -0.5 to -4.5. Also, the PSAT model and ABC
150 power processing machine have opposite sign conventions. This sub-system also limits the range of
the command signal; so that the battery is only operated within the manufacturer defined safe operational
range. Equation 5-12 represents the conversion from desired current to ABC 150 current command. All
hardware gains are detailed in the next section, 5.3 Hardware Setup.
𝐴𝐵𝐶 150 𝐶𝑢𝑟𝑟𝑒𝑛𝑡 𝑆𝑖𝑔𝑛𝑎𝑙 = −(𝑃𝑆𝐴𝑇 𝐶𝑜𝑚𝑚𝑎𝑛𝑑𝑒𝑑 𝐶𝑢𝑟𝑟𝑒𝑛𝑡 × 𝐻𝑎𝑟𝑑𝑤𝑎𝑟𝑒 𝐺𝑎𝑖𝑛) + 0.5
Equation 5-12: ABC 150 Current Command Scaling
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5.3 Hardware Setup
The section details the hardware and its setup required for high voltage battery HIL
experimentation. Primary components are the ABC 150 power processing machine, vehicle controller
computer, and a high voltage battery pack to be tested. Several electronics components are also required
to complete the setup. First, a relay box is required to turn on and off signals and hardware automatically
and rapidly in case of an emergency shutdown. Second, an isolation board is required to isolate high
voltage signals from the computer. Finally, current and voltage sensors are used to read real time battery
parameters. Figure 5-15 shows the arrangement of these hardware components for battery HIL
experimentation with the battery test laboratory at The Larson Transportation Institute.
Figure 5-15: Battery HIL Hardware Layout
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To the left of the ABC 150 power processing machine are four, 12 volt, Motorcraft, NiMH
battery modules, an Advantech computer with the relay box, PCI 1716 breakout board, current and
voltage sensors and isolation boards. On top of the ABC 150 is a power supply powering the isolation
board and relay box. The desk in front of the ABC 150 holds a monitor displaying the current trace of the
battery as recorded by the ABC 150 and a laptop computer. The laptop computer is running a Matlab
Simulink Gauges program which displays real time the important vehicle related values such as speed,
battery voltage, current, and state of charge as well as the brake and throttle signals. For simulation these
signals are determined by the PSAT vehicle simulation running real time on the Advantech computer, but
during HIL simulation the battery signals are from the tested pack. The Matlab Gauges dash board like
display helps students connect with the experiment because it shows the results of the current that is being
pushed and pulled from the battery in terms of familiar variables.
Two important sensors were required to safely measure voltage and current data by the
Advantech computer running PSAT vehicle simulations. An isolated voltage divider is required because
the computer’s channels are limited to +/- 10 Volts, and to isolate high voltage signals from the computer.
A voltage divider is required to drop the 48 Volt pack voltage to within a measureable range. Figure 5-16
below details a voltage divider.
Figure 5-16: Voltage Divider
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R1 and R2 have been selected to be 12 and 1 kilo-ohms, respectively. R1 and R2 give a hardware
gain of 13 using Equation 5-13 below for voltage dividers.
𝑉𝑖𝑛 = 𝑉𝑜𝑢𝑡 𝑅2 + 𝑅1
𝑅2
Equation 5-13: Voltage Divider Gain Calculation
The voltage signal is then fed through a unity gain isolation amplifier before being measured by
the Advantech computer and input to the PSAT vehicle model. The current sensor has an output of +/- 4
volts and an input of +/- 300 Amperes, with a linear relationship between the input and output, giving the
hardware gain a value of -75 or (-300/4). A negative value is used because the sensor and PSAT have
opposite sign conventions for current flow.
5.4 Test Setup
The battery simulation, co-simulation, and HIL simulations all have the same test setup. Each test
was run with the same Simulink model generated by PSAT, and switches used as mentioned previously to
allow for co-simulation and HIL simulation.
The Berkeley MMHEV model in EV mode was used. The model runs the Urban Dynamometer
Drive Schedule (UDDS) with an 80% initial state of charge, and using the Motorcraft battery pack and
model.
For the simulation the vehicle model used battery model inputs and outputs. The co-simulation is
similar to the simulation run with the battery model outputs used by the rest of the vehicle model, except
the current input to the battery model. The current signal is taken from the actual battery being exercised.
This ensures there will be no error between the input to the battery model and actual battery current. This
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method makes the corresponding voltage, temperature, and state of charge values comparable because
both systems have the exact same input. The last step is the HIL test, which completely removes the
battery models from the vehicle simulation model. The battery voltage measured is fed directly to the
output of the battery model using the switch discussed above. Again, the actual battery current is fed into
the battery model to allow for the use of its state of charge calculation and temperature predictions. Initial
capacities are always set to 80% in the model and the real battery. Current is the only variable in the state
of charge calculation, allowing the battery model calculation of SOC and temperature be used without
introducing error.
5.5 Results of Battery Simulation, Co-Simulation, and HIL Tests
This section compares the results of the three battery tests, simulation, co-simulation, and HIL.
These results highlight the battery parameters predicted from the model and recorded from the actual
exercised battery. Figure 5-17 plots a comparison of battery voltage for all three tests and over the full test
time, while Figure 5-18 isolates the voltage data to the snapshot high vehicle speed section of the test
cycle. Table 5-3 below shows the average and maximum error for the outputs of the model relative to the
simulation output data. Note that the error is relatively low. This confirms the function of the PSAT
generic map battery model and HIL testing to be accurate, while demonstrating the ability of the test
bench to perform HIL testing.
Voltage SOC Speed
Co-Simulation Average 0.71% 5.36% 0%
Co-Simulation Maximum 7.32% 12.20% 0%
HIL Average 1.12% 5.05% 0%
HIL Maximum 9.37% 11.54% 0%
Table 5-3: Battery Testing Error Data
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Figure 5-17: Battery Testing Voltage Results
69
Figure 5-18: Battery Testing Voltage Results (snapshot)
The test results above show that the battery voltage is slightly higher at some points than the
simulated voltage during the co-simulation test while the battery voltage is slightly lower than the
simulated voltage at some points during the HIL test. Also, the HIL test data for voltage have slightly
more noise than the co-simulation voltage data. This may be a result of voltage divider calibration,
temperature change or a movement in wire location that is closer to the high current noise source.
Regardless, average percentage errors of 0.71% and 1.12% for co-simulation and HIL tests represent
results from a very repeatable and validated model.
Figure 5-19 compares battery current for all three tests over the full test time, while Figure 5-20
isolates current data to a snap shot during a high speed section of the test cycle.
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Figure 5-19: Battery Testing Current Results
Figure 5-20: Battery Testing Current Results (snapshot)
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A slight offset is seen in the current plots above for both the co-simulation and HIL current
signals. This may be caused again by an improperly calibrated component used in the testing, most likely
the ABC 150 power processor. As a result, the PSAT generic map battery model was modified to use the
actual battery current for model input of both co-simulation and HIL tests instead of PSAT model
predicted current, which may be slightly different. This eliminates any 5% current offset error that would
have been seen in the predicted and measured state of charge, voltage and temperature outputs. Note in
Table 5-3 that there is no difference in the speed trace. Changing the input current allows the vehicle
enough power to reach and maintain the trace speed.
Also seen in the current plots above is a limit on current in the co-simulation and HIL tests. This
is because of the ABC150’s single channel current limit of approximately 264 Amperes. This limitation is
also accounted for by using actual battery current as the model’s input.
In summary, using the actual battery current for model input may result in the battery pulling high
current for slightly longer than the full simulation predicts, but it eliminates a large source of simulation
setup error. Figure 5-21 shows a comparison of battery state of charge for all three tests and over the full
test time, while Figure 5-22 isolates the state of charge data to the snapshot high vehicle speed section of
the test cycle.
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Figure 5-21: Battery Testing State of Charge Results
Figure 5-22: Battery Testing State of Charge Results (snapshot)
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In the above state of charge data, the co-simulation and HIL tests show slightly higher state of
charge readings compared to the simulation tests. Both co-simulation and HIL test data for state of charge
and current are almost identical. Most likely the cause of this discrepancy is because of the slightly
decreased current draw compared to the simulation current.
Figure 5-23 shows a comparison of vehicle speed for all three tests and over the full test time,
while Figure 5-24 isolates vehicle speed data to the snapshot high vehicle speed section of the test cycle.
Figure 5-23: Battery Testing Vehicle Speed Results
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Figure 5-24: Battery Testing Vehicle Speed Results (snapshot)
The above plots of vehicle speed for the three tests show that all tests were able to follow the
test cycle. The error between the three tests as seen in the figures above and Table 5-3, is zero. All three
tests follow the exact trace speed. This confirms that the slight difference in battery current had no effect
on the full vehicle model.
5.6 Other Electrical Components
Several other electrical energy storage devices can be tested with similar procedures and
equipment. For example, a high energy capacitor could use the HPPC test for characterization and
PSAT’s generic map for capacitors to run HIL simulations. Again, using a full vehicle model the
simulation, co-simulation, and HIL tests could be performed. The same would apply to flywheels, fuel
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cells, or other energy storage devices. Using the proper characterization procedures and models allow for
the comparison of outputs for the discussed test setups.
5.7 Associated Laboratory for Classroom Education
A laboratory experiment procedure is recommended based on the work of this chapter. That is
provided in Appendix D, HIL Laboratory Instructions.
Chapter 6
Engine HIL
This chapter explores internal combustion gasoline engine (ICE) HIL experimentations and the
pre-testing required before performing these tests. The engine used in this study was a six horsepower,
four stroke, Honda GS190 utility engine which was adapted from its speed governed control to a
computer controlled throttle and ignition.
6.1 PSAT Engine Model
PSAT contains four engine models: hot map, hot and cold map, hot map interpolation, and neural
net. The hot map model uses only steady state maps of engine data taken after the engine reaches its
running temperature. The hot and cold map model uses both a map for cold operation and a map for hot
operation, and interpolates between the two maps by a term defining the warm up state of the engine. The
hot map interpolation model uses only the hot map data but interpolates between lean and rich operation
conditions. The neural net model requires proprietary software that is not available. The simple hot map
model was chosen and used during the modeling and HIL simulations to reduce time and complexity
required for students to populate the engine model.
The PSAT hot map engine model outputs engine torque, fuel consumption, and engine-out
emissions given engine speed and throttle position. Figure 6-1 illustrates the engine model inputs and
outputs.
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The parameters included in the PSAT Engine Hot Map Model as well as the units used and
procedures to gather the data are listed in Table 6-1.
Parameter SI Unit Procedure to Gather Data
Time Constant Rising Second Dynamometer Testing
Time Constant Falling Second Dynamometer Testing
Idle Speed Radian/Second Manufacture Defined
Exhaust Gas Heat Capacity J/kg/K N/A
Fuel Heating Value J/kg EIA
Inertia Kilogram-Meter2 Calculation
Start Speed Radian/Second Dynamometer Testing
Speed Index Radian/Second Dynamometer Testing
Torque Index Newton-Meter Dynamometer Testing
Max Torque Map (wide open throttle) Newton-Meter Dynamometer Testing
Minimum Torque Map (closed throttle) Newton-Meter Dynamometer Testing
Fuel Mass Flow Rate Map Kilogram/Second Dynamometer Testing
Table 6-1: PSAT Engine Hot Map Parameters
The PSAT Engine Hot Map Model also predicts engine-out emissions, similar to the torque and
fuel maps, using predefined maps of Hydrocarbon, Nitrogen Oxide, Particulate Matter, and Oxygen
Fuel Consumption
Engine On/Off
Engine Throttle
Engine Speed
Engine Torque
Figure 6-1: PSAT Engine Hot Map Model Inputs and Outputs
Engine-Out Emissions
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exhaust mass flow rates. Considering the scope and available time for the project, here we disregard the
emissions and thermal aspect of the model. Also, the steady state nature of engine emissions maps ignores
the complexity and dynamics of real engine operating points, leading to substantially reduced accuracy.
Using the parameters listed in Table 6-1, the following equations represent the model’s method
for determining engine torque and fuel consumption. If the engine is in an off state (engine on/off =0) or
at zero throttle, torque output is zero. Similarly, for the fuel rate calculation, if the engine is in an off state,
fuel consumption is zero.
Equation 6-1 uses a combination of the wide open throttle torque map, closed throttle torque map,
and throttle position to calculate engine torque output.
𝑇𝑜𝑟𝑞𝑢𝑒𝑜𝑢𝑡 = 1 − 𝑇𝑟𝑜𝑡𝑡𝑙𝑒 ∗ 𝑇𝑜𝑟𝑞𝑢𝑒𝑐𝑙𝑜𝑠𝑒𝑑 𝑡𝑟𝑜𝑡𝑡𝑙𝑒 + (𝑇𝑟𝑜𝑡𝑡𝑙𝑒 × 𝑇𝑜𝑟𝑞𝑢𝑒𝑤𝑖𝑑𝑒 𝑜𝑝𝑒𝑛 𝑡𝑟𝑜𝑡𝑡 𝑙𝑒 )
Equation 6-1: PSAT Engine Torque Calculation
Equation 6-2 uses a map of engine instantaneous fuel rate that is indexed by engine speed and
engine torque if the engine is running and above the closed throttle torque curve. If the torque map is not
defined at the operation point, the model will interpolate between the defined map point and the closed
and wide open throttle curves. Otherwise, the engine torque is below the closed throttle curve and
considered to be in an off state where instantaneous fuel rate equals zero.
𝐼𝑛𝑠𝑡𝑎𝑛𝑡𝑎𝑛𝑒𝑜𝑢𝑠 𝐹𝑢𝑒𝑙 𝑅𝑎𝑡𝑒 = 𝑓𝑢𝑛𝑐𝑡𝑖𝑜𝑛(𝐸𝑛𝑔𝑖𝑛𝑒 𝑆𝑝𝑒𝑒𝑑, 𝑇𝑜𝑟𝑞𝑢𝑒𝑜𝑢𝑡 )
Equation 6-2: PSAT Instantaneous Fuel Consumption
Cumulative Fuel Rate= 𝐼𝑛𝑠𝑡𝑎𝑛𝑡𝑎𝑛𝑒𝑜𝑢𝑠 𝐹𝑢𝑒𝑙 𝑅𝑎𝑡𝑒 𝑑𝑡
Equation 6-3: PSAT Cumulative Fuel Rate
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Given the above equations, engine characterization is needed to define engine torque and
instantaneous fuel rate maps.
6.2 Characterization
For purposes of modeling, HIL testing and/or control design, it is vital to know certain engine
parameters such as engine mass, peak power, and efficiency at a variety of operating points. Detailed
efficiency maps and torque curves are not available from the manufacturer because the engine selected for
the Berkeley MMHEV, a Honda GS190, was designed for use as a utility engine. Testing was required to
gather some of this information.
SAE publication J1312 (SAE, 1995) procedures were referenced for test procedures and setup.
Two procedures were developed to fit available hardware, time and requirements. The first was for rapid
and high resolution data collection of fuel consumption rates and engine torque. The second procedure
was developed with student instruction in mind, emphasizing increased safety but resulting in reduced
resolution of collected data.
6.2.1 High Resolution Data Collection Procedure
Hardware used in the high resolution engine mapping procedure includes many of the
components integrated into the Berkeley MMHEV: the tested engine, secondary motor and controller,
high voltage battery, master vehicle controller, relay box, and engine speed sensor along with associated
electronics. Additional non-vehicle components required include a digital scale, graduated cylinder and
large cooling fan.
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The Berkeley MHEV was parked in a well ventilated area while a large cooling fan forced
ambient air over the Berkeley MMHEV engine bay to cool the air cooled engine and secondary motor.
To start and run the test, Simulink code was developed to automatically start, warm up the
engine, run, and stop the test. The same code monitored for any unsafe conditions, such as engine over
speed. The following procedure represents the steps or states of the Stateflow diagram used in the
Simulink model.
High Resolution Engine Mapping Procedure:
1) Run engine at idle speed and zero load for five minutes or until running temperature is
reached
2) Measure fuel level in the graduated cylinder and record as the starting point (manually)
3) Go to the engine test speed by speed controlling the dynamometer (secondary) motor
4) Set throttle to test position
5) Run the engine at test speed and throttle position for 10 minutes or until 10 g of fuel has been
used
6) Measure fuel level in the graduated cylinder and record as the end point (manually)
7) Rest for a minimum of 15 minutes; then the test can be repeated for another engine operating
point
Table 6-2: High Resolution Engine Mapping Procedure
6.2.2 Low Resolution Automated Data Collection Procedure
For demonstration and student involvement, a safer and completely automated procedure was
developed using available hardware. The automated test reduces the possibility of an untrained student
damaging the hardware while handling fuel.
Hardware used in the completely automated engine mapping procedure was the same as in the
previous set up, except fuel flow was measured with a digital scale that communicates serially with the
master controller with a resolution of one gram (Acculab).
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Simulink code was reused to run the automatic test with minimal changes, since it was written
with provisions for both procedures.
Low Resolution Engine Mapping Procedure:
1) Run engine at idle speed and zero load for five minutes or until running temperature is
reached.
2) Initialize the scale weight reading to the beginning of the tests measurement
3) Go to the engine test speed by speed controlling the dynamometer motor.
4) Set throttle to test position
5) Run the engine at test speed and throttle position for ten minutes or until 10g of fuel are used
6) Record the scale reading and compare the starting point to calculate total fuel used
7) Rest for a minimum of 15 minutes; then the test can be repeated for another engine operating
point
Table 6-3: Low Resolution Engine Mapping Procedure
Maps of engine torque, fuel rate, and efficiency were produced for use in the PSAT Engine Hot
Map Model using the high resolution method. Generated electrical power was used to estimate
mechanical power produced by the engine using the secondary motor as a dynamometer. The following
formulas were used to calculate engine torque, fuel consumption rates and efficiency given the motor’s
voltage, current, and speed as well as the output of the digital scale. Equation 6-4 uses the motor power
and its measured efficiency to calculate engine torque given the relationship between engine power,
torque and speed. Interpolated points are calculated for values in-between test points. Equation 6-5 uses
the digital scale’s output to measure fuel used during the test and calculate the test point’s fuel
consumption rate. Equation 6-6 calculates engine efficiency.
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𝑀𝑜𝑡𝑜𝑟 𝑃𝑜𝑤𝑒𝑟𝑀𝑒𝑐 𝑖𝑛 =𝐶𝑢𝑟𝑟𝑒𝑛𝑡 × 𝑉𝑜𝑙𝑡𝑎𝑔𝑒
𝑀𝑜𝑡𝑜𝑟 𝑎𝑛𝑑 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑙𝑒𝑟𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦@𝑜𝑝𝑒𝑟𝑎𝑡𝑖𝑛𝑔 𝑝𝑜𝑖𝑛𝑡
𝐸𝑛𝑔𝑖𝑛𝑒 𝑃𝑜𝑤𝑒𝑟𝑜𝑢𝑡 = 𝑀𝑜𝑡𝑜𝑟 𝑃𝑜𝑤𝑒𝑟𝑀𝑒𝑐 𝑖𝑛 = 𝐸𝑛𝑔𝑖𝑛𝑒 𝑇𝑜𝑟𝑞𝑢𝑒 × 𝐸𝑛𝑔𝑖𝑛𝑒 𝑆𝑝𝑒𝑒𝑑
𝐸𝑛𝑔𝑖𝑛𝑒 𝑇𝑜𝑟𝑞𝑢𝑒 =𝑀𝑜𝑡𝑜𝑟 𝑃𝑜𝑤𝑒𝑟𝑀𝑒𝑐 𝑖𝑛
𝐸𝑛𝑔𝑖𝑛𝑒 𝑆𝑝𝑒𝑒𝑑
*assumes the belt drive between the engine and motor is 100% efficient
Equation 6-4: Engine Torque
𝐸𝑛𝑔𝑖𝑛𝑒 𝐹𝑢𝑒𝑙 𝑈𝑠𝑒𝑑 = 𝐹𝑢𝑒𝑙 𝑊𝑒𝑖𝑔𝑡𝐹𝑖𝑛𝑎𝑙 − 𝐹𝑢𝑒𝑙 𝑊𝑒𝑖𝑔𝑡𝐼𝑛𝑖𝑡𝑖𝑎𝑙
Equation 6-5: Engine Fuel Rate
𝐸𝑛𝑔𝑖𝑛𝑒 𝐸𝑛𝑒𝑟𝑔𝑦𝑜𝑢𝑡 = 𝐸𝑛𝑔𝑖𝑛𝑒 𝑃𝑜𝑤𝑒𝑟𝑜𝑢𝑡 𝑑𝑡
𝐹𝑢𝑒𝑙 𝐸𝑛𝑒𝑟𝑔𝑦𝑖𝑛 = 𝐸𝑛𝑔𝑖𝑛𝑒 𝐹𝑢𝑒𝑙 𝑈𝑠𝑒𝑑 × 𝐹𝑢𝑒𝑙𝐸𝑛𝑒𝑟𝑔𝑦 𝐷𝑒𝑛𝑠𝑖𝑡𝑦
𝐸𝑛𝑔𝑖𝑛𝑒 𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 =𝐸𝑛𝑔𝑖𝑛𝑒 𝐸𝑛𝑒𝑟𝑔𝑦 𝑜𝑢𝑡
𝐹𝑢𝑒𝑙 𝐸𝑛𝑒𝑟𝑔𝑦 𝑖𝑛 × 100
Equation 6-6: Engine Efficiency
Using the above equations and recoded data, the following plots represent the engine
characterization. Using a combination of both high and low resolution data sets, 0, 25, 50, 75, and 100%
throttle runs were recorded at 1400, 2000, 2500, 3000, and 3300 RPM. Figure 6-2 shows the peak torque
and closed throttle torque curves, while Figure 6-3 shows a contour plot of its efficiency. The most
efficient point of operation based on the measured efficiency is approximately 75% throttle at 2300 RPM.
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Figure 6-2: Wide Open Throttle Engine Torque Curve
Figure 6-3: Engine Efficiency
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Figure 6-4: Engine Fuel Rate
Figure 6-5: Engine Fuel Rate and Maximum Torque Curve
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Figure 6-4 shows the measured fuel consumption rate for the Honda GS190 engine with the
modifications made to integrate it into the Berkeley MMHEV. Figure 6-5 shows the same fuel rate data
as Figure 6-4, but in a contour plot format with the wide open throttle and closed throttle torque curves
from Figure 6-2 overlaid.
6.3 Model Setup
For Engine HIL testing, the series HEV configuration mode of the Berkeley MMHEV was
modeled in PSAT, simulated, and the results recorded. The model is exercised and data recorded at a rate
of 100 Hz. During the modeling process, changes were made to the Simulink model of the Berkeley
MMHEV to allow for co-simulation and HIL Simulation. This section details the changes made and the
method of running the same model in different modes. Figure 6-6 shows the data flow for a co-simulation
and HIL simulation.
Figure 6-6: Engine Co-Simulation and HIL Diagram
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Figure 6-7 shows the PSAT Engine Hot Map model; the circles highlight the changes made to
allow for simulation, co-simulation, and HIL simulation using the same Simulink model. All changes
integrate a manual switch which controls the flow of data. Either model predicted values and commands
or actual measured engine parameters and commands are allowed to pass the switch, depending on the
desired test configuration.
Figure 6-7: PSAT Engine Model HIL Changes
During simulation of the Berkeley MMHEV in a series configuration, the PSAT control
algorithm typically operates the engine at only one operating point, usually the most efficient operating
point. The most efficient point is calculated based on the maps and data defining the engine and motor or
generator models. For the safety of students involved in testing, a Stateflow control model was also
integrated into the model to protect the engine against running in a dangerous or destructive manner. This
Stateflow model controls the engine and generator operations, including starting the engine and getting to
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a state where the engine and generator produce electricity in a safe and stable manner. This model
replaces the PSAT engine control algorithm, which was not completely designed for actual engine
operation. Figure 6-8 shows the new Stateflow model controlling engine and generator operation. The
series PSAT model still controls the on and off state of the engine, but Figure 6-8 controls how the engine
is started and operated along with the generator. Also, Figure 6-8 keeps the APU on and generating for a
minimum of sixty seconds, while the engine is held in an off state and not generating for a minimum of
thirty seconds. These minimum settings keep the engine from rapidly starting and stopping. This is
intended to fix the tendency of PSAT to rapidly change the on and off state of the engine during series
HEV simulations. This code protects the HIL hardware against unnecessarily rapid changes in state.
Figure 6-8: Secondary Motor and Engine State Control for Engine HIL and Co-Simulation
Component level control of both the engine and secondary motor are built into the series
Berkeley MMHEV model to allow the model to run and measure data from the real components. This
gives the model the ability to run a co-simulation or hardware-in-the-loop simulation. Because the
vehicle’s engine and secondary motor systems were used to run these tests, the model component level
configuration and setup are identical to the control algorithm to operate the vehicle during series
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operation. Details of the component level control for both simulation and vehicle operation are the same,
and are discussed in Chapter 9, Component Level Control and Communication.
6.4 Hardware Setup
This section details the setup of hardware for the engine HIL simulations. The primary
components are the Honda GS190 gasoline engine, Hi-Performance secondary motor and Curtis
controller, Advantech computer vehicle controller, and a digital scale measuring the mass of the engine’s
gasoline supply. Exhaust was safely vented and a large fan was used to cool the Berkeley engine bay.
Figure 6-9 shows the arrangement of these hardware components for engine HIL testing.
Figure 6-9: Engine Hardware-in-the-Loop Hardware Layout
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The engine and secondary motor are arranged as shown in Figure 4-5 using the set up discussed
above and in 4.2.2 Engine HIL. The digital scale and gasoline supply tank, as well as the laptop
communicating with the master vehicle controller, are seen to the right of the Berkeley MMHEV.
6.5 Test Setup
The engine HIL, co-simulation and simulation runs all use similar test setups. Each test is also run
with the same Simulink model as previously described; only changing positions of manual switches in the
model. The drive cycle chosen was again the Urban Dynamometer Drive Schedule with initial battery
state of charge of 80%.
PSAT simulation runs are performed first. The new engine model uses engine on/off, throttle
command, and speed inputs from the original PSAT model. The outputs of the engine model are fed back
into the full PSAT vehicle model while all data are recoded. Then a co-simulation test is performed.
During co-simulation testing, the inputs to the engine model are fed to the engine model as well as the
actual engine setup and the engine is exercised. Only the engine model results are fed back to the full
PSAT vehicle model. Finally, HIL test are performed. During the HIL test, the PSAT engine model was
completely bypassed. Engine model inputs are sent to the actual engine while the measured engine
parameters are fed back to the full PSAT vehicle model.
6.6 Results of Engine Simulation, Co-Simulation, and HIL Tests
This section compares the results from the three tests above. These results show both model
predicted and actual behavior of the Honda GS190 gasoline engine. Both full cycle data and smaller snap
shots of data are shown. The snapshots of data are the first few minutes of the UDDS cycle, while the full
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data shows the entire UDDS cycle. Note that the results may not match well because of the difference in
PSAT command and actual engine command due to the Stateflow model put in place for student safety.
Figure 6-10 shows results of engine RPM. Clearly, during HIL testing, the engine was
commanded to run longer and stop less frequently than the simulation run. Also, the cumulative engine
run time of the HIL tests was considerably higher. This is the result of Stateflow code integrated for
student and hardware safety. The energy loss of the PSAT model unnecessarily reduces battery SOC by
continually starting the engine and not completely recouping start energy. Despite its faults, these results
do show the system’s ability to exercise a simulation, co-simulation, and HIL simulation. A more
desirable control strategy may be tested with further work to improve PSAT control algorithms.
Figure 6-10: Engine Testing Speed Results
Looking closer at the snapshot of data in Figure 6-11, engine speed for simulation and HIL tests
matches closely, but the co-simulation data shows a slightly lower engine RPM seen in Figure 6-12 and
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Figure 6-13. The reason for this reduced speed is possibly due to different battery state of charge, which
through the motor loaded the speed controlled engine differently in the HIL and co-simulation tests. As
shown in Figure 6-12 and Figure 6-13, battery SOC during co-simulation increased the battery current,
and in turn torque increased of the secondary motor controller. The opposite occurred during HIL testing:
a high SOC limited battery current and in turn motor torque, resulting in a reduced brake load on the
engine and reduced engine power output.
Figure 6-11: Engine Testing Speed Results (snapshot)
As expected, the co-simulation run uses more fuel because of its increased power output. While
the HIL test uses considerably less, the simulation and co-simulation runs correlate relatively closely.
When comparing energy output, the co-simulation and simulation runs follow similar, but not exact,
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patterns. Figure 6-14 and Figure 6-15 show the relationship between measured and predicted engine fuel
use.
Figure 6-12: Engine Testing Torque Results
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Figure 6-13: Engine Testing Torque Results (snapshot)
Figure 6-14: Engine Testing Fuel Consumption Results
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Figure 6-15: Engine Testing Fuel Consumption Results (snapshot)
Figure 6-16: Engine Testing Power Out
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Figure 6-16 shows the output power of the engine during simulation, co-simulation, and HIL
simulation. To follow the simulation path in engine power and speed, corrections to the PSAT model
would be required. These fixes are beyond the scope of this thesis.
6.7 Associated Laboratory for Classroom Education
A laboratory experiment procedure is recommended based on the work of this chapter. That is
provided in Appendix D, HIL Laboratory Instructions.
Chapter 7
Motor HIL
This chapter explores electric motor HIL experimentations and the pre-testing required before
performing these tests. The motors used during this testing are Hi-Performance AC induction machines
with Curtis 1238 and 1236 controllers for the primary and secondary motor systems, respectively. The
primary motor acts as a dynamometer while the secondary motor system is tested as the simulated
vehicle’s traction motor.
7.1 PSAT Motor Model
The following section discusses two motor models, a traction machine and dedicated generator.
In the Berkeley MHEV, either the primary or secondary motor systems could operate as a traction motor
or a generator, depending on the configuration selected by the user. In previous chapters the secondary
motor has been modeled as the generator while the primary motor was modeled as the only traction
motor.
7.1.1 Motor
Figure 7-1 shows the input and output signals, while Table 7-1 lists parameters of the PSAT
voltage in generic map model. Equation 7-1 uses an efficiency look up table indexed by motor torque and
speed to calculate motor power output. This look up table integrates efficiency data gathered from motor
dynamometer testing. A torque request input to the motor model is assumed to be equal to output torque
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of the model. The current used to supply this torque is calculated using the modeled battery’s voltage and
above power calculation.
Equation 7-2 represents the motor torque request and output, given the maximum available torque
calculated by Equation 7-3, Equation 7-4, and Equation 7-5. This torque calculation assumes the available
torque to be the smallest value calculated using either the mechanical or electrical methods of calculating
motor torque. The heat index of the motor is calculated using Equation 7-6.
Figure 7-1: PSAT General Map Voltage in Motor Model
Parameter SI Unit Procedure to Gather Data
Motor Inertia Kilogram-meter2 Static Weight Drop Test
Motor Command Rate Limit Dimensionless User Defined
Heat Index Time Constant ºCelsius Manufacturer Defined
Speed Index radian/second Manufacturer Defined
Continuous Torque Map Newton Dynamometer Testing
Maximum Torque Map Newton Dynamometer Testing
Continuous Regenerative Torque Map Newton Dynamometer Testing
Maximum Regenerative Torque Map Newton Dynamometer Testing
Table 7-1: PSAT General Map Voltage in Motor Model Parameters
Motor Speed
DC Current
Motor Torque
DC Voltage
Motor Torque Command
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𝐶𝑢𝑟𝑟𝑒𝑛𝑡 =𝑃𝑜𝑤𝑒𝑟𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑎𝑙
𝑉𝑜𝑙𝑡𝑎𝑔𝑒
𝑃𝑜𝑤𝑒𝑟𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑎𝑙 = 𝐹𝑢𝑛𝑐𝑡𝑖𝑜𝑛 𝑀𝑜𝑡𝑜𝑟 𝑇𝑜𝑟𝑞𝑢𝑒, 𝑀𝑜𝑡𝑜𝑟 𝑆𝑝𝑒𝑒𝑑 𝜂(𝑚𝑜𝑡𝑜𝑟)
Equation 7-1: Motor Current
𝑀𝑜𝑡𝑜𝑟 𝑇𝑜𝑟𝑞𝑢𝑒 = 𝑇𝑜𝑟𝑞𝑢𝑒𝑀𝑎𝑥 × 𝑀𝑜𝑡𝑜𝑟 𝐶𝑜𝑚𝑚𝑎𝑛𝑑
Equation 7-2: Motor Torque
𝑇𝑜𝑟𝑞𝑢𝑒𝑚𝑎𝑥 = 𝑀𝑖𝑛(𝑇𝑜𝑟𝑞𝑢𝑒𝑀𝑎𝑥 𝑀𝑒𝑐𝑎𝑛𝑖𝑐𝑎𝑙 , 𝑇𝑜𝑟𝑞𝑢𝑒𝑀𝑎𝑥 𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑎𝑙 )
Equation 7-3: Maximum Torque
𝑇𝑜𝑟𝑞𝑢𝑒𝑀𝑎𝑥 𝑀𝑒𝑐𝑎𝑛𝑖𝑐𝑎𝑙 = 𝑇𝑜𝑟𝑞𝑢𝑒𝐶𝑜𝑛𝑡𝑖𝑛𝑢𝑜𝑢𝑠 × 𝐻𝑒𝑎𝑡 𝐼𝑛𝑑𝑒𝑥 + (𝑇𝑜𝑟𝑞𝑢𝑒𝑃𝑒𝑎𝑘 × 1 − 𝐻𝑒𝑎𝑡 𝐼𝑛𝑑𝑒𝑥 )
Equation 7-4: Maximum Mechanical Torque
𝑇𝑜𝑟𝑞𝑢𝑒𝑀𝑎𝑥 𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑎𝑙 = 𝐹𝑢𝑛𝑐𝑡𝑖𝑜𝑛(𝑆𝑝𝑒𝑒𝑑𝑚𝑜𝑡𝑜𝑟 , 𝑃𝑜𝑤𝑒𝑟𝐸𝑙𝑒𝑐𝑡𝑟𝑖𝑐𝑎𝑙 )
Equation 7-5: Maximum Electrical Torque
𝐻𝑒𝑎𝑡 𝐼𝑛𝑑𝑒𝑥 = −0.3 + 0.3
𝑡×
𝑀𝑜𝑡𝑜𝑟 𝑇𝑜𝑟𝑞𝑢𝑒
𝑇𝑜𝑟𝑞𝑢𝑒𝐶𝑜𝑛𝑡𝑖𝑛𝑢𝑜𝑢𝑠− 1 𝑑𝑡
Equation 7-6: Heat Index of the Electric Motor
7.1.2 Generator
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Figure 7-2: PSAT General Generator Map Torque in Model
Figure 7-2 shows inputs and outputs, while Table 7-1 list of model parameters for the PSAT
generator model. Generator current is also calculated using Equation 7-1, while generator torque and
speed are calculated using Equation 7-7 and Equation 7-8. The generator torque calculation is simply a
proportion of available mapped continuous torque. Generator speed is calculated by integrating the
angular acceleration of the machine, which is its torque divided by its mass moment of inertia.
𝑇𝑜𝑟𝑞𝑢𝑒𝑂𝑢𝑡 = 𝐺𝑒𝑛𝑒𝑟𝑎𝑡𝑜𝑟 𝐶𝑜𝑚𝑚𝑎𝑛𝑑 × (𝑇𝑜𝑟𝑞𝑢𝑒𝐶𝑜𝑛𝑡𝑖𝑛𝑢𝑜𝑢𝑠 𝑃𝑟𝑜𝑝𝑒𝑙 𝑜𝑟 𝑇𝑜𝑟𝑞𝑢𝑒𝐶𝑜𝑛𝑡𝑖𝑛𝑢𝑜𝑢𝑠 𝑅𝑒𝑔𝑒𝑛𝑒𝑟𝑎𝑡𝑖𝑣𝑒 )
Equation 7-7: Generator Torque Output
𝐺𝑒𝑛𝑒𝑟𝑎𝑡𝑜𝑟 𝑆𝑝𝑒𝑒𝑑 = 𝑇𝑜𝑟𝑞𝑢𝑒𝑖𝑛 + 𝑇𝑜𝑟𝑞𝑢𝑒𝑜𝑢𝑡
𝐼𝑛𝑒𝑟𝑡𝑖𝑎𝑖𝑛 + 𝐼𝑛𝑒𝑟𝑡𝑖𝑎𝐺𝑒𝑛𝑒𝑟𝑎𝑡𝑜𝑟
Equation 7-8: Generator Speed
7.2 Characterization
A similar test procedure was developed for electric motor characterization following the methods
from SAE J1312 for small ICE engines. Both the primary motor, secondary motor and their controllers
were dynamometer tested using the following procedure to populate their respective efficiency maps, as
well as their peak torque curves. Runs were made with different nominal battery pack voltages. For each
Generator Command
DC Current
Motor Speed
Torque / Inertia
DC Voltage
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test voltage, the battery pack consisted of different numbers of the liquid cooled Saft batteries in series.
Packs used included 48 Volt, 72 Volt, and 84 Volt arrangements. As expected each pack gave different
motor and controller system characterization parameters. Only 48 Volt data were used in modeling for
this project, but additional 72 Volt and 84 Volt data can be seen in Appendix C, Motor and Controller
Dynamometer Results. Table 7-2 represents the procedure for motor and controller dynamometer testing.
Motor and Controller Testing Procedure:
1) Ensure the high voltage battery pack is approximately at or above 80% state of charge
2) Enable the motor controller and link the computer to log motor and battery data
3) Using throttle and brake inputs run the motor to the desired speed
4) Apply dynamometer load until maximum throttle or brake is reached at stable speed
5) Record load cell reading and motor data after ten seconds of running at stable speed
6) Charge the battery and rest for at least fifteen minutes
Table 7-2: Motor and Controller Testing Procedure
The following figures illustrate the hardware used to test the motor and controller systems. The
key hardware used in the motor and controller characterization include an Eddy current dynamometer
with a load cell to measure torque and apply a load, the motors and controllers, a high voltage battery
pack, and computers to record measured data from the motor controllers and dynamometer.
Figure 7-3 shows the dynamometer power supply, throttle and brake potentiometers and their
power supply, as well as the computer used to log motor parameters directly from the motor controller
being tested. Parameters recorded include motor speed and temperature, controller temperature, DC
current and voltage, and dynamometer load cell force. Motor torque, power, and efficiency were then
calculated using the following equations.
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Figure 7-3: Motor/Controller Dynamometer Control Setup
Figure 7-4 shows the layout of hardware during motor and controller characterization. The motor
is bolted to a bracket that is welded to the base of the dynamometer stand, giving the system a solid
mount. This bracket also has integrated slots allowing for adjustments in belt tension. The motor
controller is mounted to an aluminum heat sink with cooling fan and sits on a cool concrete floor. A gear
reduction between the motor and dynamometer consists of a 34 tooth pulley, a belt, and a 90 tooth
dynamometer pulley giving the system a gear ratio of 34/90, or 1:0.3777.
Dynamometer Power Supply
Motor Throttle and Brake Potentiometers
Motor Controller Data Logging
Potentiometer Power Supply
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Figure 7-4: Motor/Controller Dynamometer Hardware Layout
Figure 7-5 shows the Eddy current dynamometer (an 8000 Series Enclosed Absorption
Dynamometer) and load cell; a Daytronic 3200 digital indicator and load cell (Daytronic, November
1996) are used to measure the torque applied by the motor. The load cell is mounted between the
dynamometer torque reaction arm and ground to measure the force of the Eddy current dynamometer
coils (Dyne System, Inc., 2009). The load cell is mounted 17 inches from the center of the dynamometer
pulley. Equation 7-9 calculates measured motor torque.
𝑀𝑜𝑡𝑜𝑟 𝑇𝑜𝑟𝑞𝑢𝑒 = 𝐹𝑜𝑟𝑐𝑒𝐿𝑜𝑎𝑑 𝐶𝑒𝑙𝑙 × 𝐺𝑒𝑎𝑟 𝑅𝑎𝑡𝑖𝑜 × 𝑅𝑒𝑎𝑐𝑡𝑖𝑜𝑛 𝐴𝑟𝑚 𝐿𝑒𝑛𝑔𝑡
Equation 7-9: Measured Motor Torque
High Voltage Battery (NiMH)
Motor Controller and Cooling Fan
Tested Motor
Gear Reduction
Eddy Current Dynamometer
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Figure 7-5: Dynamometer Load Cell
Using data logged by the motor controller and measured by the load cell, Equation 7-10
calculates motor and controller system efficiency.
𝑀𝑜𝑡𝑜𝑟 𝑎𝑛𝑑 𝐶𝑜𝑛𝑡𝑜𝑙𝑙𝑒𝑟 𝐸𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 =𝑃𝑜𝑤𝑒𝑟 𝑂𝑢𝑡
𝑃𝑜𝑤𝑒𝑟 𝐼𝑛=
𝑀𝑜𝑡𝑜𝑟 𝑇𝑜𝑟𝑞𝑢𝑒 × 𝑀𝑜𝑡𝑜𝑟 𝑆𝑝𝑒𝑒𝑑
𝐵𝑎𝑡𝑡𝑒𝑟𝑦 𝐶𝑢𝑟𝑟𝑒𝑛𝑡 × 𝐵𝑎𝑡𝑡𝑒𝑟𝑦 𝑉𝑜𝑙𝑡𝑎𝑔𝑒
Equation 7-10: Measured Motor and Controller Efficiency
Figure 7-6 plots torque results from the above procedure using the primary motor and associated
controller. Each trace represents a fixed throttle command. The tests were performed using four different
throttle settings (100, 75, 50, and 25%) resulting in four different torque curves. Figure 7-7 plots similar
results from testing the secondary motor and its associated controller.
Eddy Current Dynamometer
Load Cell
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Figure 7-6: Primary Motor Dynamometer Torque Results
Figure 7-7: Secondary Motor Dynamometer Torque Results
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Figure 7-8: Primary Motor and Controller Dynamometer Efficiency Results
Figure 7-9: Secondary Motor and Controller Dynamometer Efficiency Results
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Figure 7-10: Primary Motor Efficiency Map
Figure 7-11: Secondary Motor Efficiency Map
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Figure 7-8 and Figure 7-9 show traces of efficiency relative to speed and throttle position for both
the primary and secondary motor systems, while Figure 7-10 and Figure 7-11 show mapped results of
dynamometer testing in the form of efficiency. These efficiency maps, indexed by speed and torque,
represent the lookup tables used in Equation 7-1 above to account for motor efficiency when calculating
motor power.
7.3 Model Setup
This section details the steps taken to use a PSAT model of the Berkeley MMHEV in an EV
mode for a motor HIL simulation experiment.
Communications and dynamometer controls are added to a full vehicle PSAT model, along with
switches on motor model inputs and outputs. The switches allow for model and test setups to change from
simulation to co-simulation and/or HIL simulation easily. This model runs at an increased rate of 1 kHz to
ensure control of the dynamometer machines.
Two motors are required for the motor HIL simulation. One motor is exercised as the simulated
traction machine, and the other is used as a dynamometer to apply a positive or negative torque
representing road load. The primary motor is more powerful and is selected as the dynamometer motor so
that the secondary motor can be fully exercised without scaling. Figure 7-12 shows the PSAT model with
subsystems for running both the primary and secondary motors along with the electric clutch.
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Figure 7-12: Motor HIL Simulation Model
The secondary motor is controlled using the input torque command of the PSAT motor model. A
gain and signal conditioning are applied to convert the PSAT torque signal range of -1 to 1 to the
hardware required motor signal of 0 to 100 for both throttle and brake. A negative value in a PSAT motor
command represents a braking command while a positive value represents a throttle command. Figure
7-13 shows the speed controller used by the primary motor to maintain the secondary motor at test speed.
Motor test speed is taken from PSAT model vehicle speed. This speed controller first calculates the error
between measured motor speed and motor model input speed. It then normalizes error by the maximum
motor speed as input to a PID controller. Finally, the signal is sorted and separated into throttle and brake
commands.
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Figure 7-13: Motor HIL Dynamometer Speed Control
Figure 7-15 shows the motor model and the changes required to run simulation, co-simulation
and HIL simulation. The black circles highlight the switches put in place to change the flow of data for a
given test. These switches pass model simulated data through during a simulation run, while model inputs
are allowed through to hardware drivers during a co-simulation or HIL simulation. For co-simulation
runs, only model predicted values are used. During an HIL simulation, both the inputs and outputs are
switched to supply PSAT model commands to the motors and the measured motor parameters back into
the PSAT model, completing the experimental loop.
The PSAT vehicle model generates motor speed, battery voltage, and motor command inputs to
the motor model. The motor model generates battery current and motor torque outputs. For simulation
runs, all PSAT motor model inputs and outputs are to and from the rest of the full model. During co-
simulation runs, the motor speed is fed to the dynamometer speed controller and motor commands to the
tested traction motor. Only in HIL simulation runs are the measured current and motor torque fed back
into the PSAT model, replacing the motor model’s output. Figure 7-14 show a diagram of data flow
during motor HIL testing.
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Figure 7-14: Motor Co-Simulation and HIL Diagram
During co-simulation and HIL simulation testing, motor torque is estimated by using a
combination of measured battery voltage and current along with measured motor speed and throttle
position. Estimated motor torque is then calculated using a look up table of tested efficiency, as shown in
Figure 7-16.
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Figure 7-15: Motor HIL Motor Model
Figure 7-16: Motor Torque Calculation
7.4 Hardware Setup
The hardware setup for this test is straight forward and requires only the components already
installed in the Berkeley MMHEV. Physical test set up was completed by removing both the generator
and drive belts, allowing the motors to spin freely. The only other change required is engagement of the
electronic clutch seen in Figure 7-17. This figure shows the setup for motor to motor testing, with the
dynamometer or primary motor on the right and the tested or secondary motor on the left. The clutch is
engaged at the beginning of the test and locks both motors together with a one to one ratio.
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Figure 7-17: Clutch and Motors of the Berkeley MMHEV
7.5 Test Setup
The motor HIL simulation, co-simulation and simulation are all performed using the same model
discussed above. The first 130 seconds of the UDDS cycle is run as the vehicle speed trace. This
considerably short cycle is used because of some instability observed in motor to motor testing. Before
full cycle testing should be performed, further work is required to tune controllers and optimize this
system including a more accurate measure of motor torque. The current test is meant to demonstrate the
capability of the test bench and its current hardware.
7.6 Results of Motor Simulation, Co-Simulation, and HIL Tests
This section presents the comparison of simulation, co-simulation, and HIL simulation runs using
the above models and test setup. Figure 7-18 show the battery current during these three tests, while
Figure 7-19 shows a small snapshot of current data.
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Figure 7-18: Motor Testing Current Results
Figure 7-19: Motor Testing Current Results (snapshot)
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Figure 7-20 shows the results of motor speed during these three tests, while Figure 7-21 shows a
snapshot of the same data. The HIL simulation data indicates that the motor speed is not stable as
expected. The challenge here is accurately controlling motor speed with another motor of similar size and
dynamics. The dynamometer motor should respond faster than, and easily absorb any torque from, the
tested motor. Noisy data measured from the drive and dynamometer motors that feeds through the control
system and results in unstable control signals is another challenge. Further refinement and optimization
are required in the PSAT models to correct this issue. An even faster responding speed controller may
help, but the best solution would include a considerably larger dynamometer motor.
Figure 7-20: Motor Testing Speed Results
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Figure 7-21: Motor Testing Speed Results (snapshot)
Figure 7-22: Motor Testing Torque Results
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Figure 7-23: Motor Testing Torque Results (snapshot)
Figure 7-22 shows the torque results for these three tests, while Figure 7-23 shows a snapshot of
the same data. Similar unstable results are seen in the HIL data, suggesting that further optimization is
required. Even though the motor speed, current and torque are rather noisy and unstable, PSAT predicts a
vehicle speed that closely matches the trace. Vehicle speed results can be seen in Figure 7-24, with a
snapshot of the same data in Figure 7-25.
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Figure 7-24: Motor Testing Vehicle Speed Results
Figure 7-25: Motor Testing Vehicle Speed Results (snapshot)
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7.7 Associated Laboratory for Classroom Education
A laboratory experiment procedure is recommended based on the work of this chapter. That is
provided in Appendix D, HIL Laboratory Instructions.
Chapter 8
Controller HIL
This chapter explores the hardware and software required, as well as results and benefits of
controller HIL simulation.
The following tests utilize PSAT component plant models of the Berkeley MMHEV in a series
engine hybrid configuration. PSAT plant models of components such as the engine and generator of the
vehicle APU are simulated without a control system. This simulation of plant models is run on a
dedicated computer. Another computer runs experimental control algorithms, sending control parameters
to and reading simulation data from the computer simulating the plant models. This offers a safe method
for development of trial control algorithms and the controllers on which they run.
8.1 Model Setup
For controller HIL testing, all mechanical components of an HEV were simulated using PSAT
models previously discussed. These plant models of the vehicle were useful for predicting and testing
component dynamics and system behavior during full and HIL simulations. Here models previously used
in past chapters are being exercised differently. Their controllers and control logic have been completely
removed. The purpose for removing PSAT control models from the component plant models is to allow
student written control algorithms to be tested in a safe way before applying them to actual hardware.
Two sets of models were required for this experimentation; first, an assembly of plant models
including each of the components for the vehicle being simulated: and second, a control algorithm model
to operate these virtual components.
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8.1.1 Plant Models
Figure 8-1 shows the PSAT plant models for the Berkeley MMHEV arranged as a series hybrid,
with the addition of subsystems that allow for communication between two computers.
Figure 8-1: Berkeley MMHEV Series Mode Plant Models Only
The controller input subsystem of the model gathers instructions from the external control model
and forwards them to the component plant models being exercised. Signals in the plant model are tagged
using the xPCTag functions for feedback of data to the controller model. This method sends signals to the
controller model running on a separate PC using the xPC Target toolbox drivers over an Ethernet
connection.
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8.1.2 Controller Model
For testing of student written core control, two controller models were written to communicate
with the plant model discussed above. First, a simple model was built with manual inputs to experiment
with the function of the plant models. Then a more sophisticated model was written to automatically run
these plant model components as if they were in a real vehicle, starting and running the engine based on
simulated data input. Figure 8-2 shows the manual control model with information coming from the
simulation plant models on the left and commands being sent to the plant model on the right. This simple
manual control uses the Simulink Library digital displays and constants for operation. Note in this setup,
constants are changed manually during model execution, while displays are updated every time a data
packet is received from the computer running the plant models.
Figure 8-3 shows the automatic control algorithm tested. This model takes the previous manual
model and adds an automatic control subsystem with an engine speed controller and system management
model.
Figure 8-2: Manual Control Model
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Figure 8-3: Automatic Control Model
Figure 8-4: Automatic Control Model Subsystems
Figure 8-4 shows the subsystem layout of the automatic control model. Rate translations were
required to allow the plant and control models to run at different speeds on opposite machines. Figure 8-6
shows the engine speed control subsystem, which is triggered by Figure 8-5 to operate when the engine
speed measured is above 50 radians per second. The engine speed control is a discrete PID controller with
an error signal calculated from the difference of engine speed measured and commanded. The signal is
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then normalized by the maximum engine speed and fed into a PID controller. Its output is saturated to
match the plant model input restrictions.
Figure 8-5: Engine Speed Control Trigger
Figure 8-6: Engine Speed Control
Figure 8-7 shows the high level, or system management, control that starts the engine, idles, and
then applies the generator load. This logic is designed to be simple, because testing is concentrated on the
engine speed controller and its ability to control speed.
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Figure 8-7: High Level Automatic Control Model Logic
With the above models, users can quickly and easily test different PID gains in the engine speed
controller under different loading situations. This presents an opportunity for quick and productive testing
with limited risk to hardware and personal safety.
8.2 Hardware Setup
Two computers were required to run the above two models independently. For the plant model an
Advantech fan-less computer was selected, while the control algorithm ran on an independent laptop. This
setup allows for quick changes of the control model setup and parameters, while the plant model can
continue to run safely. Figure 8-8 illustrates the hardware layout of the controller HIL tests. The two
machines communicate with a single Ethernet connection transmitting data to and from each machine.
The Advantech computer was setup using Matlab Simulink xPC Target software, and loaded with the
plant models. The laptop is running the control algorithms with blocks for communicating to the plant
model, as discussed above.
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Figure 8-8: Controller HIL Hardware Layout
8.3 Test Setup
Tests using similar hardware and model setup prove to be useful for control development. One
could easily test many different control algorithms in different running conditions. As discussed above,
manual and automatic control models were developed to test the engine speed controller used in this
project for engine component level control. Also, motor torque or speed control could be examined using
the same plant models and a similar speed or torque control model.
To validate the engine speed controller, the engine was started then step inputs of generator load
were increased continually after the engine speed settled. During this test, the response of the engine’s
speed, generator’s command and power, along with other data was recorded.
8.4 Results of Controller HIL Tests
Figure 8-9 plots the engine’s command and simulation speeds, while the generator’s commands
are shown in Figure 8-10 and the generator power output in Figure 8-11. These results show a relatively
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stable speed controller when load is increased in a step manner. Engine speed recovers rather quickly, and
generator power steps smoothly relative to its simulated command.
Figure 8-9: Controller HIL Engine Speed Results
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Figure 8-10: Controller HIL Generator Load Demand
Figure 8-11: Controller HIL Generator Power Output
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8.5 Associated Laboratory for Classroom Education
A laboratory experiment procedure is recommended based on the work of this chapter. That is
provided in Appendix D, HIL Laboratory Instructions.
Chapter 9
Component Level Control and Communication
This chapter describes the main control algorithm designed for communication and control of the
Berkeley MMHEV components. These sections of code were designed to directly control their component
with minimal input from higher level control such as throttle, brake and on/off state. Typically, each
component level control code handles boot up, run state, shut down, and data recording.
These algorithms are shown in their Simulink and Stateflow application format.
9.1 Engine Control
The engine used for the Berkeley MMHEV was originally designed to run at a constant speed
using a mechanical governor. The Berkeley MMHEV project required a method of changing engine
operation between a speed control and torque control without mechanical input. The master vehicle
controller operates relays for ignition and a throttle valve under servo control to allow rapid test setup
changes for the class room environment. Both modes were not typically used during the same test or
operation mode, but given a proper transition in the control code, this is also a possibility.
9.1.1 Engine Torque Control
Engine torque control was required for engine mapping as well as conventional and parallel
vehicle operation, while engine speed control was used for series vehicle operation.
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To achieve electronic control of an engine that was designed for mechanical speed control,
electronic servos were adapted to the throttle and choke valves. Details about this modification are
discussed in section 3.4.1 Engine Throttle and Choke Mechanical Control, while the control hardware
associated with the electronic servos is detailed in section 3.5.3 Engine Servo Controller.
Closed throttle represents a 2.75 volts signal while wide open throttle corresponds to 5 volts. Any
value in-between is a linear relationship with user input of 0 to 100% throttle position. Equation 9-1
represents throttle position signal.
𝑇𝑟𝑜𝑡𝑡𝑙𝑒 𝑆𝑖𝑔𝑛𝑎𝑙 = −0.0225 × 𝑈𝑠𝑒𝑟 𝑇𝑟𝑜𝑡𝑡𝑙𝑒 𝐼𝑛𝑝𝑢𝑡 + 5
Equation 9-1: Throttle Position Signal Equation
9.1.2 Engine Speed Control
Engine speed control was used for series vehicle operation of the Berkeley MMHEV. This mode
of engine control is again achieved by using the electronic servos and controllers in combination with a
Simulink control algorithm running on the master vehicle controller. At the heart of this algorithm is a
classical PID controller. Wrapped around the PID controller are safety checks that ensure stable operation
and prevent over speed. These safety checks will also shut down the engine if an event such as a broken
belt or severe unstable operation is experienced.
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Figure 9-1: Engine Speed Control
Figure 9-1 shows the closed loop control of engine throttle using measured and actual engine
speed to calculate the error signal. The error signal was normalized by multiplying the error signal by the
maximum engine RPM, giving the error signal a range of 0 to 1. A lower level of the PID controller block
is a triggered subsystem that activates operation of the PID block when engine control is desirable. This
eliminates the dangerous accumulation of integral error, because the subsystem is reinitialized to zero
every time it is entered or exits. Figure 9-2 shows the triggered subsystem containing the PID controller
block (with the derivative gain set to zero) and the logic used to enable or disable the system. If measured
engine speed is below 500 RPM, the engine speed controller will not be active.
Figure 9-2: Engine Speed Control Enable/Disable
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The engine speed controller was shown to respond positively to a proportional gain of 1 and an
integral gain of 0.5 after empirical testing under loaded and unloaded conditions. The selected gains and
speed signal are adequate for procedural testing, but more work tuning gains and filtering engine speed
would improve performance and result in smoother operation. Figure 9-3 shows a series of step inputs of
commanded engine speed vs. actual engine speed while a fixed load was applied to the generator. Note
that in the same test, the engine was started by the same generator and stopped by the engine ignition
relay. Algorithms for generator and motor control are detailed in future sections of this chapter.
Figure 9-3: Engine Speed Control Results
Closed loop speed control is made more difficult by the torque and speed variations of a four
cycle, one cylinder engine. One cylinder engines have significant speed fluctuations between the
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combustion and compression strokes. Also, electromagnetic interference from the engine ignition adds
noise to the servo’s PWM signal.
9.2 Motor Control
A level of control is already built into the hardware of the Hi-Performance motors and Curtis
controllers. They accept throttle and brake signals to run the motors in a torque mode, while speed control
required additional algorithms. Controlling these motors consisted of CAN communication algorithms
between the master vehicle controller and each motor system. CAN was the method used to send throttle
and brake signals while reading motor and battery parameters.
The Curtis motor controller provides field oriented control, or the three phase AC motor from a
DC power source. Figure 9-4 illustrates the indirect rotor flux orientation algorithm used by Curtis
controllers, while Figure 9-5 shows the high frequency inverter switching schematic (Curtis Instruments,
Inc., 2008).
Figure 9-4: Curtis Controller Indirect Rotor Flux Orientation (Curtis Instruments)
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Figure 9-5: Curtis Motor Controller Inverter Switching (Curtis Instruments)
Figure 9-6 shows how the Curtis motor controller uses throttle input and closed loop PI control
on motor current to operate the motor. This controller has been tuned by the manufacturer. While
parameters can be changed by the user, they were not for any of the tests performed in this thesis.
Figure 9-6: Curtis Motor Controller Logic (Curtis Instruments)
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A Simulink algorithm was developed to communicate the throttle and brake signals while reading
and sorting motor and battery parameters received over CAN. The following figures show in detail the
steps taken to communicate with one and/or two of the motor controllers. Figure 9-7 illustrates this high
level flow of information, while Figure 9-8 shows all the sub-systems required to accomplish this task of
component level CAN communication and sorting of data when using the Curtis AC motor controllers.
Figure 9-7: Highest Level Motor Control
Figure 9-8: Motor Control Sub-Sections
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Figure 9-9 shows two CAN messages that the primary motor controller will write to the CAN
bus. First are the “Master In Slave Out” (MISO) messages, then the heartbeat message. The MISO using
Curtis Instruments CAN Open logic is the format in which the motor controller will respond to a request
for data. A request for data is a CAN message that is written to the bus by the master vehicle controller
and referred to as a “Master out Slave in” (MOSI) message.
Figure 9-9: Motor Control Read CAN Messages
Figure 9-10: Motor Control CAN Message Decoding
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Figure 9-10 shows the decoding of a CAN Message sent from a motor controller. This format is
specific to Curtis Instruments products. First the message is received by the master vehicle controller.
Then the message is sorted by the length in bytes of each parameter. The control signal, which indicates
the origin of the message, is the first byte (byte 0, bits 0 through 7). The CAN object index, identifying
the data included in the message, is the second and third byte (bytes 1 and 2, bits 8 through 23). The CAN
object sub-index, further defining the data include in the message, is the forth byte or byte 3, bits 24 to 31.
Finally the data requested are represented in the fifth and sixth bytes (byte 4 and byte 5, bits 32 to 47).
These data are first sorted as an unsigned data set, then again as a signed data set. Depending on the
parameter being received, a signed or unsigned data set is required. For example a battery current signal is
signed while a battery voltage signal is unsigned.
In order to read various parameters using one message, the control algorithm must loop through a
request, sorting, and then acknowledgment of each parameter before moving on to the next. Figure 9-11
shows the Stateflow logic that will request the next parameter after acknowledging that the current one
has been received and sorted. Figure 9-12 performs a similar task, except but for writing the throttle and
brake command signals.
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Figure 9-11: Motor Control CAN Message Data Read Looping
Figure 9-12: Motor Control CAN Message Command Write Looping
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Figure 9-13: Motor Control CAN Message Writing Switch (Single Motor)
Figure 9-13 shows the switch that allows either a read request for data to be written or a write
request to write throttle and brake signals to the bus. This switch is necessary because the CAN message
ID for throttle and brake writing is a high priority; if that is written during the time a data message is
being sent, the data message will be missed by the master vehicle controller. This switch ensures all data
are written and read before the next parameter will be requested or written. During a mode in which both
the primary and secondary motors are communicating, it is necessary to adapt this same switch to handle
both motors’ data and commands to ensure no messages are missed. Figure 9-14 shows the logic for
controlling the switch used in a dual motor mode. Figure 9-15 shows the switch used for the dual motor
mode.
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Figure 9-14: Motor Control CAN Message Writing Switch Logic (Dual Motors)
Figure 9-15: Motor Control CAN Message Writing Switch (Dual motors)
Figure 9-16 shows the logic used to start up and shut down communication between the motor
controller and master vehicle controller. Also, this Stateflow code will handle an error that disables the
communication temporarily and restart the communication when the controllers recover.
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Figure 9-16: Motor Controller Communication Start Up and Shut Down Logic
Figure 9-17: Motor Control CAN Message Building
Figure 9-17 shows the building of a CAN message for writing the throttle and brake signals to the
bus. The throttle signal is a signed signal with reverse rotation when negative, and forward rotation when
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the value is positive. The lower right section of code handles this switching between signed throttle and
unsigned brake using the above Stateflow loop’s MOSI state.
9.2.1 Motor Torque Control
Motor torque control is used for electric, series, and parallel vehicle operation as well as the
engine and motor HIL. Torque control of the primary and secondary motors was easily achieved by
simply sending the throttle and brake signals using the above Simulink and Stateflow code.
9.2.2 Motor Speed Control
Motor speed control is used for engine dynamometer testing and motor HIL. Motor speed control
is achieved by defining the requested motor speed and using the PID control in Figure 9-18 to generate
the throttle and brake signals, and to write using the above communication.
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Figure 9-18: Motor Speed Control
9.2.3 Dual Motor Control
Special consideration to communication was required to obtain control of both the primary and
secondary motors. A loop was put in place to ensure all data were written and read because of the high
priority of both the primary and secondary motors’ throttle and brake commands. Without this loop,
primary and secondary data are missed when a throttle or brake command is written to the network.
9.3 Electronic Clutch Control
Clutch control is relatively simple and only requires a relay to activate. Figure 9-19 shows the
input of primary motor speed and the two outputs, a relay signal and state of the logic. Figure 9-20 details
the logic of the clutch control. In this code, the clutch stays closed after it is activated. This code is used
when the dual traction motor setup is desired. For parallel or conventional operation, different code is
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required. The electronic clutch must be engaged at a substantial differential speed, because a zero
differential will cause an unbalanced engagement.
Figure 9-19: Electronic Clutch Control
Figure 9-20: Electronic Clutch Control Logic
9.4 Battery Control
The Lithium Technology battery pack includes a Battery Management System (BMS) with CAN
communication requiring similar coding as motor communication. The NiMH battery packs do not have
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active management or any sensors in the vehicle. All battery data for these packs were measured using the
motor controllers.
9.4.1 State of Charge Calculation
The Lithium Technology battery management system sends calculated state of charge to the
master vehicle controller over CAN. The NiMH packs used do not have a BMS therefore required an
algorithm is required to initialize and calculate battery SOC.
Figure 9-21 shows the sections of code used to measure battery SOC. The SOC calculation
includes initial SOC calculation using open circuit voltage, then applies Coulomb counting to calculate
battery SOC once current starts to flow.
Figure 9-21: Battery State of Charge Calculation
Figure 9-22 shows the logic used to detect initial battery voltage. This initial battery voltage,
assumed to be a relaxed voltage, is then sent to an open circuit voltage vs. SOC map to determine the
initial SOC. When the final state in the logic string is reached, the flow of current from each motor is
summed and integrated to determine battery SOC.
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Figure 9-22: Initial Battery State of Charge Logic
Figure 9-23: Battery State of Charge Current Integration Coulomb Counting
Figure 9-23 shows the integration of summed current that is initialized by the initial SOC in
capacity form. Then current capacity is divided by total capacity to calculate current SOC. Equation 9-2
represents the formula used in Figure 9-23.
𝐵𝑎𝑡𝑡𝑒𝑟𝑦 𝑆𝑡𝑎𝑡𝑒 𝑜𝑓 𝐶𝑎𝑟𝑔𝑒 = 𝐼𝑛𝑖𝑡𝑎𝑙 𝑆𝑂𝐶 + 𝐶𝑢𝑟𝑟𝑒𝑛𝑡 × 𝑇𝑖𝑚𝑒 𝑑𝑡
𝑀𝑎𝑥𝑖𝑚𝑢𝑚 𝐵𝑎𝑡𝑡𝑒𝑟𝑦 𝐶𝑎𝑝𝑎𝑐𝑖𝑡𝑦
Equation 9-2: Battery State of Charge Equation
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9.5 Digital Scale
A digital scale was utilized to measure fuel use and to calculate fuel rate during an engine test.
Figure 9-24 shows the Simulink code used to read RS232 scale data. A rate translation is in place because
the Baud rate of the scale is less than the 100 Hz frequency vehicle controller code.
Figure 9-24: Digital Scale Communication
9.6 Vehicle Speed
The vehicle speed calculation uses the primary motor speed, because the primary motor is always
on line and moving if the vehicle is moving. Figure 9-25 shows the Simulink code to calculate vehicle
speed from primary motor speed, and Equation 9-3 details the parameters.
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Figure 9-25: Vehicle Speed Calculation
𝑉𝑒𝑖𝑐𝑙𝑒 𝑆𝑝𝑒𝑒𝑑 𝑀𝑃𝐻
= −1 × 𝐺𝑒𝑎𝑟 𝑅𝑎𝑡𝑖𝑜𝑀𝑜𝑡𝑜𝑟 𝑡𝑜 𝑇𝑟𝑎𝑛𝑠 × 𝐺𝑒𝑎𝑟 𝑅𝑎𝑡𝑖𝑜𝑇𝑟𝑎𝑛𝑠 𝑡𝑜 𝐷𝑖𝑓𝑓
× 𝐶𝑜𝑛𝑣𝑒𝑟𝑠𝑖𝑜𝑛 𝑓𝑟𝑜𝑚 𝑅𝑃𝑀 𝑡𝑜 𝑀𝑃𝐻
Equation 9-3: Vehicle Speed Calculation
9.7 Drive Input
Throttle and brake pedal position signals were needed to allow the operator to input requests to
operate the vehicle. Figure 9-26 shows the input signals from the throttle and brake potentiometers, which
are converted to 0 to 100% signals.
Figure 9-26: Driver Input
Chapter 10
Full Vehicle Control
This chapter details the combination of the component level control algorithms and a high level
control to allow for full vehicle operation. These integrate the throttle and brake signals from the driver
pedals, as well as handle the start up and shut down of the components automatically without any other
driver input.
A key decision in series, parallel and series/parallel control algorithms is when and how to run the engine; this
engine; this has a great effect on fuel economy. Knowing each component’s power relative to road load is important.
important. Figure 10-1 shows the available force of each component relative to road load and speed. This schedule is
useful in deciding which component should be used for traction force in a given situation. Table 10-1 lists the
parameters measured and used to calculate vehicle road load. Road load is the summation of vehicle drag resistance and
rolling resistance. Vehicle drag is calculated using
Equation 10-1 and rolling resistance is calculated using a 4th degree polynomial defined in the
PSAT initialization file of Appendix E.
𝑉𝑒𝑖𝑐𝑙𝑒 𝐷𝑟𝑎𝑔 = 0.5 × 𝐷𝑒𝑛𝑠𝑖𝑡𝑦 𝑜𝑓 𝐴𝑖𝑟 × 𝐷𝑟𝑎𝑔 𝐶𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡 × 𝐹𝑟𝑜𝑛𝑡𝑎𝑙 𝐴𝑟𝑒𝑎 × 𝑆𝑝𝑒𝑒𝑑2
Equation 10-1: Vehicle Drag Force
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Figure 10-1: Component Force Relative to Road Load
Berkeley Road Load Vehicle Parameters
Glider Mass 235 kg
Frontal Area 0.5 m2
Drag Coefficient 0.33
Table 10-1: Road Load Vehicle Parameters
9.1 Electric Only Vehicle Control
Electric vehicle operation requires a simple system level control algorithm. The main task of this
algorithm is to measure user input in the form of throttle and brake pedal position, and then forward these
signals to the motor controller. The second task of this algorithm is to read, record, and display data to the
operator. To perform these tasks, the primary motor controller, driver input, SOC calculation, and vehicle
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speed blocks are integrated together. There are no higher level controls with the single motor electric
vehicle mode.
Figure 10-2: Single Motor Electric Vehicle Control
Figure 10-3: Dual Motor Electric Vehicle Control
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The same simple control algorithm is used for dual motor operation. The only differences are the
addition of the clutch block, additional driver block and the combined dual motor operation block
replacing the primary motor block. Figure 10-3 shows these blocks. Again there is no high level control,
only an enable and disable signal based on clutch engagement. This was performed by limiting the
secondary motor system driver block to an active state when the primary motor speed was greater than
200 RPM and the clutch engaged.
9.2 Series Hybrid Vehicle Control
Two vehicle control algorithms were developed to operate the Berkeley MMHEV in a series
hybrid mode. The first series control algorithm is for a Plug-In Hybrid Electric Vehicle (PHEV), charge
depleting SOC operation and the second is an HEV charge sustaining SOC operation. The differences are
minimal: simply different SOC values to command on and off states of the APU. Typically an PHEV is
assumed to be at high initial SOC and will drop to a region where SOC is then sustained. An HEV will
always be operated in the region where SOC is sustained.
9.2.1 State of Charge Non-Depleting
The non-depleting or HEV series operation of the Berkeley MMHEV is one in which the APU
attempts to maintain an 80% SOC. Given the relatively low peak power output of the APU, this is
accomplished by running the APU at a high duty cycle. Figure 10-4 shows the sections of code that are
assembled to make the series HEV control algorithm. The algorithm includes the combination of the dual
motor control, driver input, SOC calculation, and vehicle speed calculation Simulink blocks. Also, a new
high level APU control block is integrated to control engine starting, stopping, and generation states
automatically with no input from the operator.
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Figure 10-4: Series Vehicle Control
Figure 10-5 illustrates the logic used to initialize and start the engine below 80% SOC, generate
at one of three states, and shut down the APU if 85% SOC is reached. The three levels of power
generation are guesses and need further tuning. The basic principle is to generate at low power while at
low speed to minimize noise, then run at higher power levels at higher speed where noise is more
acceptable. When vehicle speed is increased, power is required and louder operation is also more
tolerable.
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Figure 10-5: Series Vehicle Control APU Logic
9.2.2 State of Charge Depleting – Plug in Hybrid Electric Vehicle (PHEV)
The all electric range of the Berkeley MMHEV using the Motorcraft NiMH pack is
approximately 20 miles. The PHEV charge depleting algorithm is similar to the HEV algorithm, except
the vehicle operates within its all electric range (AER) until the SOC depletes from 100% to a much lower
SOC of 30%, where the APU comes on to maintain SOC. The SOC value in which the APU is
commanded on in this state was 30% and off at 40%. This mode enables all electric operation and is
intended for trip distances less than the remaining AER.
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9.3 Parallel Hybrid Vehicle Control
A parallel control algorithm could be developed to run the Berkeley MMHEV using electric
control of the clutch and APU systems in a unique way. The most efficient use of the engine at high
speeds would be to clutch it to the road and use its power to propel the vehicle. The events of coupling
and decoupling are challenging with the current hardware. To run the Berkeley MMHEV in this mode,
the engine must couple to the road at speeds greater than 30 MPH and decouple at speeds lower than 25
MPH. First matching engine speed and drive line speed then engaging smoothly requires extensive
testing. The current Simulink code to perform this task requires more tuning before repeated cycling can
be tested. Successful testing in this vehicle mode has not yet been achieved.
9.4 Series / Parallel Hybrid Vehicle Control
Using the above parallel and series controls, an algorithm could be developed to switch between
the two to use the most efficient mode under the current operating conditions. This vehicle mode cannot
be tested until the parallel vehicle mode control is complete.
9.5 Conventional Vehicle Control
Conventional operation is difficult in the Berkeley MMHEV because of the high road load
relative to the engine power output. But it is possible in a blended parallel mode: using the primary motor
to get the vehicle to high enough speeds so that the road load is low enough that the engine can continue
to power the vehicle alone. This vehicle mode cannot be exercised until the engine engagement algorithm
is reliable. Currently the engagement event is dangerously violent and should be calibrated on a chassis
dynamometer before full vehicle testing is performed.
Chapter 11
Full Vehicle Testing
This chapter presents results of full vehicle testing in different modes of operation. The
above full vehicle and component level control algorithms were loaded into the Berkeley MMHEV. The
vehicle was then driven in a safe environment on asphalt roads. During the multiple trips, data were
recorded for each mode loaded into the vehicle. With the traces of vehicle speed, a PSAT full vehicle
model of the corresponding mode was exercised to compare actual vehicle results with the full PSAT
vehicle model. Differences in results may be present because the Berkeley MMHEV does not always use
the same control algorithm as PSAT models.
11.1 Electric Vehicle Mode Results
This section presents results recorded from driving the Berkeley MMHEV in a single traction
motor mode and a dual traction motor mode. The dual motor mode was achieved by using two motors
along with an electronic clutch to increase available traction power.
11.1.1 Single Motor Electric Vehicle Mode
Using the single motor electric vehicle control algorithm in Figure 10-2, the Berkeley MMHEV
was driven and data recoded. Multiple cycles were recorded and archived, while two are presented for
results comparison.
Also, using the recorded speed during the single motor EV operation, an EV PSAT model was
exercised and model results recorded. The following figures compare results from driving the Berkeley
MHEV in an EV mode and PSAT results of the Berkeley MMHEV in a similar EV mode. The data
157
shown were recorded while the motors were in a limited regenerative mode. This limit was set
intentionally to protect the drive belts from breaking if overloaded due to high regenerative braking
torques. Figure 11-1 and Figure 11-5 show battery voltage data and the simulation of battery voltage from
the similar PSAT model for EV runs 1 and 2. Note that PSAT battery voltage recovers quickly, while the
real battery takes some time to recover after a large current draw; PSAT data also show significant
regenerative braking while the recorded data do not.
Figure 11-1: Electric Vehicle Mode Run 1 Battery Voltage
Figure 11-2 and Figure 11-6 show battery current and the simulation of battery current from the
similar PSAT model for EV runs 1 and 2. The PSAT results show a lower current draw than the actual
vehicle uses. This may be because the vehicle road load is under estimated.
Figure 11-3 and Figure 11-7 show battery SOC and the simulation of battery SOC from the
similar PSAT model for EV runs 1 and 2. The PSAT SOC decreases at a slower rate because of the
decrease in current draw. Again, this is probably caused of an under estimate in vehicle road load.
158
Figure 11-4 and Figure 11-8 show the vehicle speed during vehicle testing. The same vehicle
speed is fed into the PSAT model for simulations.
Figure 11-2: Electric Vehicle Mode Run 1 Battery Current
159
Figure 11-3: Electric Vehicle Mode Run 1 Battery State of Charge
Figure 11-4: Electric Vehicle Mode Run 1 Vehicle Speed
160
Figure 11-5: Electric Vehicle Mode Run 2 Battery Voltage
Figure 11-6: Electric Vehicle Mode Run 2 Battery Current
161
Figure 11-7: Electric Vehicle Mode Run 2 Battery State of Charge
Figure 11-8: Electric Vehicle Mode Run 2 Vehicle Speed
162
11.1.2 Dual Motor Electric Vehicle Mode
The Berkeley MMHEV was then loaded with the dual motor electric vehicle control algorithm
from Figure 10-3. Multiple trips were recorded and archived. One test is presented here, but was not
modeled in PSAT. Only real vehicle results are presented. Because of hardware failure, a full exercise of
the dual motor mode at high power was not possible. In this mode the vehicle has a maximum power of
40.8 kW, compared to the 26.4 kW in a single electric motor mode. The power is contributed by the
primary and secondary motor controllers with 550 and 300 Amperes, respectively. Because the battery
can handle currents of 900 amps for periods of 10 seconds, the system is motor controller limited. High
drive torque caused a drive shaft key failure and limited testing. Figure 11-9 shows the battery voltage
during the dual motor test, while Figure 11-10 shows total battery current for each machine. Future testing
an analysis will be required to determine what peak drive torque the hardware can safely sustain and what
power level is safe and practical for the dual motor mode.
163
Figure 11-9: Dual Motor Electric Vehicle Mode Battery Voltage
Figure 11-10: Dual Motor Electric Vehicle Mode Motor Currents
164
Figure 11-11 shows the battery SOC during the test while Figure 11-12 shows each motor’s speed
output. The SOC plot shows a zero SOC until initial SOC is calculated. In Figure 11-12 the early
difference in motor speed represents an open clutch. The point highlighted with a circle indicates the time
in which the electric clutch was activated and the motors were locked together.
Figure 11-11: Dual Motor Electric Vehicle Mode Battery State of Charge
165
Figure 11-12: Dual Motor Electric Vehicle Mode Motor Speeds
Figure 11-13: Dual Motor Electric Vehicle Mode Vehicle Speed
166
Figure 11-13 shows a trace of vehicle speed with a section showing 0 to 50 MPH acceleration in
approximately 12 seconds with less than a 30 % throttle input for each motor. Acceleration time with
maximum throttle is estimated to be approximately 8 seconds 0 to 60. This must be tested with a stronger
differential allowing for the high power transmission.
11.2 Series Engine Mode Results
This section presents results from driving the vehicle using the series HEV (non-depleting)
control algorithm from Figure 10-4. A PSAT model of the Berkeley MMHEV hardware was exercised
using the trace speed recoded during series HEV operation. The real vehicle and PSAT model use the
same hardware but slightly different control algorithms. For the following test, 90% SOC was used for
the on state of the APU to show engine operation. Figure 11-14 plots the results of battery voltage, with
PSAT predicting a slightly lower voltage. Figure 11-15 plots the results of motor currents while Figure
11-16 shows battery SOC. The PSAT model did not ever get to a point where the engine would run and
the generator produce power. The model continued to try to start the engine unsuccessfully. This
unnecessarily reduced battery SOC. As mentioned before, the PSAT series control algorithm does not
operate in a reliable manner, and attempts to improve it have been unsuccessful.
167
Figure 11-14: Series Engine Mode Battery Voltage
Figure 11-15: Series Engine Mode Motor Current
168
Figure 11-16: Series Engine Mode Battery State of Charge
Figure 11-17: Series Engine Mode Engine Speed
169
Figure 11-17 plots engine speed , and shows the failure for the PSAT engine to start while the
real engine starts and runs smoothly and the APU produces electrical power.
Figure 11-18: Series Engine Mode Vehicle Speed
Figure 11-18 shows the speed fed into the PSAT simulation, and indicates a good match with
little deviation. This is possible because the series operation is separate from the traction drive and is not
effected by low SOC.
11.3 Parallel Mode Results
Successful parallel test were not performed due to time constraints. This will be discussed in
future work.
170
11.4 Conventional Mode Results
Successful conventional test were not performed due to a time constraints, as discussed in future
work.
Chapter 12
Conclusions and Future Work
This thesis has shown the design, characterization, component level testing and modeling, as well
as full system testing of an HIL test bench and HEV powertrain. Many model and data results compare
positively. A useful tool was developed for instruction of HIL techniques. This system has also been
shown to be useful as a teaching tool for control development and testing on a new unique multiple mode
HEV.
For future work, optimization would greatly benefit the performance and robustness of the
system. Many possibilities are present for future work in control development and testing on this unique
test bench and vehicle.
The system has tested to show substantial fuel efficiency but more improvements are
recommended that will increase the value. Electric clutch engagement strategies would allow for parallel
and conventional modes. An optimal APU gear ratio or increased engine power output would make the
vehicle more charge sustaining.
Additional sensor integration would help refine characterization data and allow for less noise
feedback during HIL simulations. Integration of a torque sensor in place of the electronic clutch would
allow for more accurate engine and motor HIL testing.
Appendix A
Simulink Models
High level models are shown here. For greater detail see the associated digital file.
A-1: Berkeley MMHEV Conventional Mode PSAT Model
173
A-2: Berkeley MMHEV Electric Vehicle Mode PSAT Model
174
A-3: Berkeley MMHEV Series Mode PSAT Model
175
A-4: Berkeley Electric Vehicle Control Model
A-5: Berkeley Electric Vehicle Control Model (dual motor)
176
A-6: Berkeley Series Vehicle Control Model
Appendix B
Wire Diagrams
178
Po
we
r C
on
ve
rte
r
Mo
du
le
Ad
va
nte
ch
Co
mp
ute
r
Ve
hic
le C
on
tro
ller
Red
Lio
n -
Fre
quen
cy to
Vol
tage
Con
vert
er
Ve
hic
le C
on
tro
ller
Ad
va
nte
ch
Co
mp
ute
r
Bre
ak-O
ut B
oa
rd
Re
lay B
ox
Fo
r D
eta
ils
Se
e R
ela
y B
ox W
ire
Dia
gra
m
5 V
olt –
DC
-DC
ou
tpu
t
Un-f
use
d
so
lid g
ree
n (
+)
gre
en
/wh
ite
(-)
Gro
un
d S
trip
Primary Motor Fan
Fuse # 1 (5 amps)
Secondary Motor Fan
Fuse # 2 (5 amps)Fuse # 3
Monitor
Fuse # 4 (2 amps)Fuse # 5
Primary Motor
Can Isolator
Fuse # 6 (2 amps)
Secondary Motor
Can Isolation
Fuse # 7 (2 amps)
12
Vo
lt F
use
Blo
ck
24
Vo
lt F
use
Blo
ck
12 Volt – DC-DC output
Un-fused
solid red (+)
red/white (-)
24 Volt – DC-DC output
Un-fused
solid yellow (+)
yellow/white (-)
Gro
un
d S
trip
Vehicle Controller
Fuse # 1 (10 amps)Fuse # 2 Fuse # 3 Fuse # 4 Fuse # 5
Fuse # 6 Fuse # 7
24 volt input
ground
Hig
h V
olta
ge
In
pu
t
(-)
gro
un
d
Hig
h V
olta
ge
In
pu
t
(+)
48
vo
lts
ca
se
gro
un
d
iso
late
d fro
m h
igh
vo
lta
ge
all
DC
-DC
in
pu
ts a
nd
ou
tpu
ts
Manual Switch
Enable Relay Box
USB – Touch
Screen
12 Volt Power
Monitor Power
PCI 1716 AI AD DI DO
CA
N #
1
CA
N #
2
VG
A
VGA
CA
N Is
olat
ion
/ Rep
eate
r
Prim
ary
Mot
or
Inte
rnal
ly T
erm
inat
ed
On
Bot
h E
nds
Gro
un
d
10
– 3
0 V
olts D
CCA
N Is
olat
ion
/ Rep
eate
r
Sec
onda
ry M
otor
Inte
rnal
ly T
erm
inat
ed
On
Bot
h E
nds
CA
N G
rou
nd
Gro
un
d
10
– 3
0 V
olts D
C
CA
N H
igh
CA
N L
ow
CA
N G
rou
nd
CA
N H
igh
CA
N L
ow
Manual Switch
CA
N G
rou
nd
CA
N H
igh
CA
N L
ow
48
Vo
lts
48
Vo
lts
Prim
ary
Mo
tor
Ga
ug
eS
eco
nd
ary
Mo
tor
Ga
ug
e
Se
co
nd
ary
Mo
tor
Ha
rne
ss
Prim
ary
Mo
tor
Ha
rne
ss
12
Vo
lt P
ow
er
Mo
tor
Fa
ns
Re
lay S
witch
es
48
Vo
lt P
ow
er
Su
pp
ly
Co
nta
cto
rs
Secondary Motor Harness
Primary Motor Harness
High Voltage Battery
CA
N G
rou
nd
CA
N H
igh
CA
N L
ow
En
gin
e Ig
nitio
n
Engine Ignition
Ju
nctio
n B
ox
Fo
r D
eta
iled
Dia
gra
m S
ee
Ju
nctio
n B
ox W
ire
Dia
gra
m
Motor Cooling Fans
12 Volt Power to Junction Box
12
Vo
lt P
ow
er
to C
AN
Iso
lato
rs
Vehicle Controller Power
Dig
ita
l O
ut
Re
lay C
on
tro
l
Gro
un
d
9 –
32
Vo
lts D
C
Gro
un
d
Ou
tpu
t S
ign
al
Inp
ut
Inp
ut
En
gin
e S
pe
ed
Se
nso
r
Engine Speed Signal
12 Volt Power From Vehicle Controller
Co
ntr
ol S
yste
m W
ire
Dia
gra
m
To
En
gin
e B
ay W
irin
g
To
Hig
h V
olta
ge
Ba
tte
ry
Pa
ck W
irin
g
179
12 Volt DC Fan
0.3 Amp
Lineage PowerModel # QBW018A0B1Z
36 - 75 Volt, 7 Amp (Max.) DC input12 Volt, 18 Amp (Max.) DC output
LambdaModel # PAH200S 48-24/V
36 - 76 Volt, 7 Amp (Max.) DC input24 Volt, 8.4 Amp (Max.) DC output
Cin
con
EC3SA
-48S053
6-7
5v 7
7m
A in
pu
t
5v 6
00
mA
ou
tpu
t
(-) 48 Volts
High Voltage
Ground
(Black)
V+ input
V- input
Ceramic Capacitor
2KVAC 4700pF
2 Amp Fuse
250 V, 15 Amp
Fuse
10 Amp Fuse
2 Amp Fuse
Fan Power
Supply
CTRL
V- output
V+ output
NC
NC
Cin
con
EC3SA
-48S053
6-7
5v 7
7m
A in
pu
t
5v 6
00
mA
ou
tpu
t
V+ input
V- input
CTRL
V- output
V+ output
NC
NC
V+ input
V- input
CNT
V- output
V+ output
CASE
TRM
V- output
V+ output
on/off
V+ input
V- input
-S
+S
Electrolytic Capacitor
100V 33uF
470uFFilm Capacitor
50V 1uF
Air F
low
Case Ground
1 Amp Fuse
(+) 48 Volts
High Voltage
Positive
(White)
+ 2
4 V
olts
- 2
4 V
olts
+ 1
2 V
olts
- 1
2 V
olts
+ 5
Vo
lts (
1)
- 5
Vo
lts (
1)Power Converter Module
Orange Wires -> 48 Volts DC, Orange (+), Black (-), (high voltage battery)
Yellow Wires -> 24 Volts DC, Yellow (+), Yellow and White (-)
Red Wires -> 12 Volts DC, Red (+), Red and White (-)
Green Wires -> 5 Volts DC, Green (+), Green and White (-)
All Output Grounds Are
Tied Together
180
Be
rke
ley –
Ju
nctio
n B
ox
Fu
se
d 4
8 V
olt P
ow
er
(Fu
se
# 2
)
Fu
se
d 4
8 V
olt P
ow
er
(Fu
se
# 1
)
Se
co
nd
ary
Mo
tor
Ke
y
Se
co
nd
ary
Mo
tor
Co
nta
cto
r 1
Se
co
nd
ary
Mo
tor
Co
nta
cto
r 2
Se
co
nd
ary
Mo
tor
Ga
ug
e
Bu
tto
n In
pu
t
Prim
ary
Mo
tor
Ke
y
Prim
ary
Mo
tor
Co
nta
cto
r 1
Prim
ary
Mo
tor
Co
nta
cto
r 2
Prim
ary
Mo
tor
Ga
ug
e
Bu
tto
n In
pu
t
En
gin
e Ig
nitio
n
Re
lay 1
En
gin
e Ig
nitio
n
Re
lay 2
Se
co
nd
ary
Mo
tor
Ke
y
Re
lay 1
of 2
Prim
ary
Mo
tor
Ke
y
Re
lay 1
of 2
Se
co
nd
ary
Mo
tor
Ke
y
Re
lay 2
of 2
Ba
tte
ry C
on
tacto
r
(Se
co
nd
ary
) 1
Ba
tte
ry C
on
tacto
r
(Se
co
nd
ary
) 2
Prim
ary
Mo
tor
Ke
y
Re
lay 2
of 2
Ba
tte
ry C
on
tacto
r
(Prim
ary
) 1
Ba
tte
ry C
on
tacto
r
(Prim
ary
) 2
En
gin
e Ig
nitio
n 1
En
gin
e Ig
nitio
n 2
1 2 3 4 5 6 7 8 9 10
11
12
Se
co
nd
ary
Mo
tor
Fa
n
Re
lay 2
12
Vo
lt G
rou
nd
Prim
ary
Mo
tor
Fa
n
Re
lay 2
12
Vo
lt G
rou
nd
12
Vo
lt G
rou
nd
Prim
ary
Mo
tor
Fa
n
Re
lay 1
Se
co
nd
ary
Mo
tor
Fa
n
Re
lay 1
En
gin
e T
hro
ttle
Mo
du
le
Po
we
r In
pu
t 1
(V
eh
.co
nt)
En
gin
e T
hro
ttle
Mo
du
le
Po
we
r In
pu
t 2
(V
eh
.co
nt)
Fu
se
d 4
8 V
olt P
ow
er
(Fu
se
# 4
)
Fu
se
d 4
8 V
olt P
ow
er
(Fu
se
# 3
)
Se
co
nd
ary
Mo
tor
Fa
n (
+)
Se
co
nd
ary
Mo
tor
Fa
n (
-)
Prim
ary
Mo
tor
Fa
n (
+)
Prim
ary
Mo
tor
Fa
n (
-)
Gro
un
d (
Co
mm
on
with
48
.12
. a
nd
5 v
olt g
rou
nd
)
Fu
se
d 1
2 V
olt P
ow
er
(Fu
se
# 1
)
Fu
se
d 1
2 V
olt P
ow
er
(Fu
se
# 2
)
Fu
se
d 1
2 V
olt P
ow
er
(Fu
se
# 3
)
En
gin
e T
hro
ttle
Mo
du
le
Re
lay 1
En
gin
e T
hro
ttle
Mo
du
le
Re
lay 2
1 2 3 4 5 6 7 8 9 10
11
12
Te
rmin
al #
1
Te
rmin
al #
2
Se
co
nd
ary
Mo
tor
Ha
rne
ss
Prim
ary
Mo
tor
Ha
rne
ss
En
gin
e Ig
nitio
n &
12
Vo
lt P
ow
er
Po
we
r to
Fa
ns &
En
gin
e T
hro
ttle
Mo
du
le
Sw
itch
Re
lay B
ox H
arn
ess
48
Vo
lt P
ow
er
& C
on
tacto
rs
181
Appendix C
Motor and Controller Dynamometer Results
183
184
185
Appendix D
HIL Laboratory Instructions
This appendix includes all of the suggested laboratory experiments to be exercised using the
Berkeley MMHEV powertrain / HIL test bench system.
Battery HIL Simulation
This laboratory experimentation is designed to exercise a high voltage battery used in electric or
hybrid electric vehicles with known characteristics using hardware-in-the-loop techniques
Hardware
Berkeley MMHEV battery pack – 4, 12 Volt NiMH modules in series
ABC150
Advantech Computer
Advantech PCI 1716 Analog and Digital I/O Board (with break out board and Tera Soft Dirvers)
National Instruments ER-16 Relay Box
Current Sensor (with power supply)
Voltage Divider
Analog Signal Isolation
Laboratory Power Supply
Laptop or PC with Matlab Simulink xPC Target, Real Time Workshop, and PSAT Software
187
Procedure
1) Using the laptop or PC load PSAT software and import the Berkeley MMHEV Initialization
files given. (if all Berkeley MMHEV models are loaded simply open PSAT, “Light Duty”
and continue)
a. Copy all Berkeley MMHEV initialization files to their correlating directory under the
PSAT installed directory (default C:PSATv62_SP1\component\initialization\...)
Example- copy the battery initialization file, ess_nimh_60_40_Mastercraft, to the
energy storage folder. (default
C:PSATv62_SP1\component\initialization\energy_storage)
b. Open PSAT, selecting “Light Duty”
c. Select Setup then components from the top menu
d. Use this GUI to select each component, its Model/Technology, and then the Related
Files tab and use the Add… button to add the corresponding initialization file
e. Save and continue until all Berkeley MMHEV components are loaded. (It may be
necessary to restart PSAT to use the new models)
2) Using given initialization files for the Berkeley MMHEV build a PSAT model of the desired
test mode: EV, Series HEV , Parallel HEV, or Series/Parallel HEV
a. Select desired vehicle configuration under Vehicle then Drivetrain Configuration
188
b. Select each Berkeley MMHEV component for the selected configuration under the
Vehicle then Drivetrain Components, select initial conditions for each such as initial
SOC
c. Select desired control strategy under Vehicle then Controller/Strategy
d. At this point the PSAT model can be saved for future use
3) Select a desired drive cycle (UDDS, HWFET, etc…) under Simulation Setup
4) Select the Run Simulation Tab then load rerun00.m and select Run the Simulation… button
to build and run the model (DO NOT CLOSE WHEN MODEL IS FINISHED RUNNING)
5) When results are seen and the model is finished running select Matlab then Launch Desktop
on the top menu
6) With Matlab open navigate to the current model (default – saved simulations\(model with
configuration cycle and time stamp))
7) With PSAT still open select the model that had just run and make changes to allow for
simulation, co-simulation, and HIL simulation
a. Input required drivers for hardware used to communicate and measure data
i. Using Advantech 1716 Tera Soft drivers are required
ii. Insert analog inputs for real battery current and voltage
iii. Insert an analog output to command the ABC 150. Insert commands for a
relay to turn on and off the analog signal to the ABC150 ( upon boot up the
Advantech outputs -5 volts, this could cause an undesired command to get to
the ABC 150 without a relay controlling when it does)
b. Select the battery model in the powertrain_model subsystem and input switches on
inputs and outputs to allow for switching from simulation to co-simulation and HIL
modes
189
c. Mux all relavant data together and send to a single ouput on the highest level of the
simulink model
d. Save a mode in each set up (simulation, co-simulation, and HIL) as a new model but
do not close PSAT or Matlab and do not delete the workspace
8) In the models just saved select Simulation then Configuration Parameters
a. Under Data Import/Export uncheck Limit data points to last:
b. Under Real-Time Workshop change System target file: to xpctarget.tlc
c. Select Apply and close window
9) With a properly setup compiler and xPC Target defined (see xPC Target setup in Matlab
Help) select Tools, Real-Time Workshop, then Build Model in each of the Simulink Models
10) Setup hardware and sensors to measure data
a. Connect the 48 Volt battery pack to the leads of the ABC 150
b. Connect sensors to the required ports of the break out board and isolation boards ( be
sure to isolate all signals into the Advantech Computer)
c. Connect the analog output through a relay to the ABC 150 analog input
11) Run the Compiled model in simulation mode, record data and ensure the battery current and
voltage are within a range that can be safely tested on the ABC 150
12) Cycle the tested battery to the SOC set in the model, let rest for one hour
13) Load ABC150 script that allows for Advantech control (see Appendix F ABC 150 Script)
14) Run the compiled model in co-simulation mode and record data
15) Cycle the tested battery to the SOC set in the model, let rest for one hour
16) Load ABC150 script that allows for Advantech (see Appendix F ABC 150 Script)
17) Run the compiled model in HIL mode and record data
190
Analysis
Using recorded data from simulation, co-simulation, and HIL runs compare, pack voltage,
current, and SOC. Does the PSAT model for the tested battery accurately represent the pack?
Engine HIL Simulation
This laboratory experimentation is designed to exercise an internal combustions engine similar to
one used in hybrid electric vehicles with known characteristics using hardware-in-the-loop techniques
Hardware
Berkeley MMHEV
Digital Scale with serial output
Tank of Gasoline
Laptop or PC with Matlab Simulink xPC Target, Real Time Workshop, and PSAT Software
Procedure
1) Using the laptop or PC load PSAT software and import the Berkeley MMHEV Initialization
files given. (if all Berkeley MMHEV models are loaded simply open PSAT, “Light Duty”
and continue)
a. Copy all Berkeley MMHEV initialization files to their correlating directory under the
PSAT installed directory (default C:PSATv62_SP1\component\initialization\...)
Example- copy the engine initialization file, eng_si_190_5_berkeley, to the engine
folder. (default C:PSATv62_SP1\component\initialization\engine)
191
b. Open PSAT, selecting “Light Duty”
c. Select Setup then components from the top menu
d. Use this GUI to select each component, its Model/Technology, and then the Related
Files tab and use the Add… button to add the corresponding initialization file
e. Save and continue until all Berkeley MMHEV components are loaded. (may be
necessary to restart PSAT to use the new models)
2) Using given initialization files for the Berkeley MMHEV build a PSAT model of the desired
test mode. (Series, Parallel, or Series Parallel) (series is recommended for engine HIL)
a. Select desired vehicle configuration under Vehicle then Drivetrain Configuration
b. Select each Berkeley MMHEV component for the selected configuration under the
Vehicle then Drivetrain Components, select initial conditions for each (initial SOC)
c. Select desired control strategy under Vehicle then Controller/Strategy
d. At this point the PSAT model can be saved for future use
3) Select a desired drive cycle (UDDS, HWFET, etc…) under Simulation Setup
4) Select the Run Simulation Tab then load rerun00.m and select Run the Simulation… button
to build and run the model (DO NOT CLOSE WHEN MODEL IS FINISHED RUNNING)
5) When results are seen and the model is finished running select Matlab then Launch Desktop
on the top menu
6) With Matlab open navigate to the current model (default – saved simulations\(model with
configuration cycle and time stamp))
7) With PSAT still open select the model that had just run and make changes to allow for
simulation, co-simulation, and HIL simulation
a. Input required drivers for hardware used to communicate and measure data, see
Engine HIL chapter for details
192
b. Select the engine model in the powertrain_model subsystem and input switches on
inputs and outputs to allow for switching from simulation to co-simulation and HIL
modes
c. Mux all relevant data together and send to a single output on the highest level of the
simulink model
d. Save a mode in each set up (simulation, co-simulation, and HIL) as a new model but
do not close PSAT or Matlab and do not delete the workspace
8) In the models just saved select Simulation then Configuration Parameters
a. Under Data Import/Export uncheck Limit data points to last:
b. Under Real-Time Workshop change System target file: to xpctarget.tlc
c. Select Apply and close window
9) With a properly setup compiler and xPC Target defined (see xPC Target setup in Matlab
Help) select Tools, Real-Time Workshop, then Build Model in each of the Simulink Models
10) Run the Compiled model in simulation mode, record data and ensure the engine will operate
in a desired manor (check eng_on_simu parameter, often starts more that necessary)
11) Setup the scale and tank to record fuel used during the test (see Engine HIL chapter for driver
details)
12) Run the compiled model in co-simulation mode and record data
13) Run the compiled model in HIL mode and record data
Analysis
Using recorded data from simulation, co-simulation, and HIL runs compare, engine speed, torque,
and fuel rate. Does the PSAT engine model accurately represent the tested engine?
193
Electric Motor HIL Simulation
This laboratory experimentation is designed to exercise an induction electric motor similar to one
used in electric and hybrid electric vehicles with known characteristics using hardware-in-the-loop
techniques.
Hardware
Berkeley MMHEV
Laptop or PC with Matlab Simulink xPC Target, Real Time Workshop, and PSAT Software
Procedure
1) Using the laptop or PC load PSAT software and import the Berkeley MMHEV Initialization
files given. (if all Berkeley MMHEV models are loaded simply open PSAT, “Light Duty”
and continue)
a. Copy all Berkeley MMHEV initialization files to their correlating directory under the
PSAT installed directory (default C:PSATv62_SP1\component\initialization\...)
Example- copy the motor initialization file, mc_id_4_12_hi_performance_AC_9 to
the motor folder. (default C:PSATv62_SP1\component\initialization\motor)
b. Open PSAT, selecting “Light Duty”
c. Select Setup then components from the top menu
d. Use this GUI to select each component, its Model/Technology, and then the Related
Files tab and use the Add… button to add the corresponding initialization file
e. Save and continue until all Berkeley MMHEV components are loaded. (may be
necessary to restart PSAT to use the new models)
194
2) Using given initialization files for the Berkeley MMHEV build a PSAT model of the desired
test mode: Series HEV, Parallel HEV, or Series/Parallel HEV, EV is recommended for motor
HIL
a. Select desired vehicle configuration under Vehicle then Drivetrain Configuration
b. Select each Berkeley MMHEV component for the selected configuration under the
Vehicle then Drivetrain Components, select initial conditions for each (initial SOC)
c. Select desired control strategy under Vehicle then Controller/Strategy
d. At this point the PSAT model can be saved for future use
3) Select desired test cycle (UDDS, HWFET, etc…) under Simulation Setup
4) Select the Run Simulation Tab then load rerun00.m and select Run the Simulation… button
to build and run the model (DO NOT CLOSE WHEN MODEL IS FINISHED RUNNING)
5) When results are seen and the model is finished running select Matlab then Launch Desktop
on the top menu
6) With Matlab open navigate to the current model (default – saved simulations\(model with
configuration cycle and time stamp))
7) With PSAT still open select the model that had just run and make changes to allow for
simulation, co-simulation, and HIL simulation
a. Input required drivers for hardware used to communicate and measure data, see
Motor HIL chapter for details
b. Select the motor model in the powertrain_model subsystem and input switches on
inputs and outputs to allow for switching from simulation to co-simulation and HIL
modes
c. Mux all relevant data together and send to a single output on the highest level of the
simulink model
195
d. Save a mode in each set up (simulation, co-simulation, and HIL) as a new model but
do not close PSAT or Matlab and do not delete the workspace
8) In the models just saved select Simulation then Configuration Parameters
a. Under Data Import/Export uncheck Limit data points to last:
b. Under Real-Time Workshop change System target file: to xpctarget.tlc
c. Select Apply and close window
9) With a properly setup compiler and xPC Target defined (see xPC Target setup in Matlab
Help) select Tools, Real-Time Workshop, then Build Model in each of the Simulink Models
10) Run the compiled model in simulation mode, record data and ensure the motor will operate in
a desired manor
11) Run the compiled model in co-simulation mode and record data
12) Run the compiled model in HIL mode and record data
Analysis
Using recorded data from simulation, co-simulation, and HIL runs compare, motor speed and
torque. Does the PSAT motor model accurately represent the tested motor?
Controller HIL Simulation
Hardware
Advantech Computer
Laptop or PC with Matlab Simulink xPC Target, Real Time Workshop, and PSAT Software
196
Procedure
1) Using the laptop or PC load PSAT software and import the Berkeley MMHEV Initialization
files given. (if all Berkeley MMHEV models are loaded simply open PSAT, “Light Duty”
and continue)
a. Copy all Berkeley MMHEV initialization files to their correlating directory under the
PSAT installed directory (default C:PSATv62_SP1\component\initialization\...)
Example- copy the motor initialization file, mc_id_4_12_hi_performance_AC_9 to
the motor folder. (default C:PSATv62_SP1\component\initialization\motor)
b. Open PSAT, selecting “Light Duty”
c. Select Setup then components from the top menu
d. Use this GUI to select each component, its Model/Technology, and then the Related
Files tab and use the Add… button to add the corresponding initialization file
e. Save and continue until all Berkeley MMHEV components are loaded. (may be
necessary to restart PSAT to use the new models)
2) Using given initialization files for the Berkeley MMHEV build a PSAT model of the desired
test mode: Series HEV, Parallel HEV, or Series/Parallel HEV, Series is recommended for
controller HIL
a. Select desired vehicle configuration under Vehicle then Drivetrain Configuration
b. Select each Berkeley MMHEV component for the selected configuration under the
Vehicle then Drivetrain Components, select initial conditions for each (initial SOC)
c. Select desired control strategy under Vehicle then Controller/Strategy
d. At this point the PSAT model can be saved for future use
3) Select a desired drive cycle (UDDS, HWFET, etc…) under Simulation Setup
197
4) Select the Run Simulation Tab then load rerun00.m and select Run the Simulation… button
to build and run the model (DO NOT CLOSE WHEN MODEL IS FINISHED RUNNING)
5) When results are seen and the model is finished running select Matlab then Launch Desktop
on the top menu
6) With Matlab open navigate to the current model (default – saved simulations\(model with
configuration cycle and time stamp))
7) With PSAT still open select the model that had just run and make changes to allow for
controller HIL
a. Delete all controller blocks, input constants for parameters that will come from the
separate control algorithm, and tag signals desired for experimental control (see
Matlab Help, xPC Target, User Guide, Graphical User Interfaces for help)
b. Save the model
8) In the model just saved select Simulation then Configuration Parameters
a. Under Data Import/Export uncheck Limit data points to last:
b. Under Real-Time Workshop change System target file: to xpctarget.tlc
c. Select Apply and close window
9) With a properly setup compiler and xPC Target defined (see xPC Target setup in Matlab
Help) select Tools, Real-Time Workshop, then Build Model in each of the Simulink Models
10) Develop a separate control algorithm for controlling the desired component in using the
PSAT plant models. (see Controller HIL chapter for details)
11) Run the compiled model
12) Run test controller
198
Analysis
Analysis results, is the designed control stable? Is it robust enough for real hardware testing?
Full Vehicle Electric Mode Control Development
Use PSAT software, the Berkeley MMHEV, and given control models to develop a controller that
will operate the vehicle in an EV mode.
Full Vehicle Series HEV Control Development
Use PSAT software, the Berkeley MMHEV, and given control models to develop a controller that
will operate the vehicle in a series HEV mode.
Full Vehicle Parallel HEV Control Development
Use PSAT software, the Berkeley MMHEV, and given control models to develop a controller that
will operate the vehicle in a parallel HEV mode.
Appendix E
PSAT Initialization Files
NiMH (Motorcraft) Battery Pack Model
%% File description
% Name : ess_li_60_40_Mastercraft
% Author : Timothy Cleary - Pennsylvania State University
% Description :
% Initialize the parameters used in the Mastercraft NiMH air cooled
pack
% Capacity = 60Ah, Cell number = 40
% Proprietary: Public
% Model : lib_ess_generic_map
% Technology : nimh
% Vehicle Type : Light, Heavy
%% File content
ess.list.init = {'soc_min','soc_max','soc_init','num_cell_series',...
'num_module_parallel','packaging_factor'};
ess.init.num_module_parallel = 1;
ess.init.soc_init = 0.7;
ess.init.element_per_module = 10;
ess.init.num_module = 4; % value for number of modules
ess.init.num_cell_series =...
ess.init.num_module * ess.init.element_per_module;
ess.init.volt_nom = 1.2;
ess.init.volt_min = 0.970;
ess.init.volt_max = 1.42;
ess.init.mass_module = 20.4; % (kg), mass of a single ~12 V
module
ess.init.mass_cell = 2.04;
ess.init.soc_min = 0.1;
ess.init.soc_max = 1.0;
ess.init.packaging_factor = 1.05;
ess.init.soc_min = ...
overwrite_parameters('simulation.drivetrain.ess','soc_min',...
ess.init.soc_min);
%Removed Overwrite ess.init.num_cell_series
ess.init.charge_max = 300; % amps
ess.init.discharge_max = 500; % amps
% LOSS AND EFFICIENCY parameters
200
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
ess.init.soc_index = [0 10 20 30 40 50 60 70 80 90 100]/100; ...
% SOC RANGE over which data is defined
ess.init.temp_index = [0 25 50];...
% Temperature range over which data is defined (C)
ess.init.cap_max_map = [59 60 61];...
% (A*h), max. capacity at C/5 rate, indexed by ess.init.temp_index
ess.init.eff_coulomb = [0.968 0.99 0.992];...
% average coulombic (a.k.a. amp-hour) efficiency below,...
% indexed by ess.init.temp_index
% cell's resistance to being discharged,...
% indexed by ess.init.soc_index and ess.init.temp_index
ess.init.rint_dis_map=[...
0.0370 0.0370 0.0344 0.0328 0.0324 0.0314 0.0322...
0.0314 0.0324 0.0328 0.0344;
0.0370 0.0370 0.0344 0.0328 0.0324 0.0314 0.0322...
0.0314 0.0324 0.0328 0.0344;
0.0370 0.0370 0.0344 0.0328 0.0324 0.0314 0.0322...
0.0314 0.0324 0.0328 0.0344]/ess.init.num_cell_series; % (ohm)
% cell's resistance to being charged,...
% indexed by ess.init.soc_index and ess.init.temp_index
ess.init.rint_chg_map=[...
0.03600 0.03600 0.03360 0.03280 0.03227 0.03147 0.03120 0.03227...
0.03147 0.03307 0.03387;
0.03600 0.03600 0.03360 0.03280 0.03227 0.03147 0.03120 0.03227...
0.03147 0.03307 0.03387;
0.03600 0.03600 0.03360 0.03280 0.03227 0.03147 0.03120 0.03227...
0.03147 0.03307 0.03387]/ess.init.num_cell_series;% (ohm)
% cell's open-circuit (a.k.a. no-load) voltage,...
% indexed by ess.init.soc_index and ess.init.temp_index
ess.init.voc_map=[...
32.60 50.48 51.08, 51.56 52.10 52.06 52.24 52.50 52.88 53.68
56.00;
32.60 50.68 51.28, 51.76 52.12 52.26 52.44 52.70 53.08 53.88
56.00;
32.60 50.88 51.48, 51.96 52.32 52.46 52.64 52.90 53.28 54.08
56.00;]...
/ess.init.num_cell_series; % (V) per cell
%% Max current and power when charging/discharging
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%
ess.init.curr_chg_max =...
-max(max((ess.init.volt_max-
ess.init.voc_map)./ess.init.rint_chg_map));
ess.init.curr_dis_max =...
201
max(max((ess.init.voc_map-
ess.init.volt_min)./ess.init.rint_dis_map));
%check the ess.calc.pwr_chg & ess.calc.pwr_dis because...
% they're a vector and in the database for the plot we
%need maps
ess.calc.pwr_chg = -max((ess.init.volt_max-ess.init.voc_map)...
.*ess.init.volt_max./ess.init.rint_chg_map);%per cell
ess.calc.pwr_dis = max((ess.init.voc_map-ess.init.volt_min)...
.*ess.init.volt_min./ess.init.rint_dis_map);%per cel
% gain factor to modify ess.calc.pwr_chg and ess.calc.pwr_dis
% discharge is brought to 0 at low SOC and charge is brought to 0 at
high
% SOC
% modification by vfreyermuth on 9/8/06
ess.calc.pwr_chg = ess.calc.pwr_chg.*double(ess.init.soc_index...
<= ess.init.soc_max);
ess.calc.pwr_dis = ess.calc.pwr_dis.*double(ess.init.soc_index...
>= ess.init.soc_min);
ess.init.pwr_chg = -max(max((ess.init.volt_max-ess.init.voc_map)...
.*ess.init.volt_max./ess.init.rint_chg_map));%per cell
ess.init.pwr_dis = max(max((ess.init.voc_map-ess.init.volt_min)....
*ess.init.volt_min./ess.init.rint_dis_map));%per cell
%% battery thermal model
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
ess.init.therm_on = 1; ...
% 0=no ess thermal calculations, 1=do calc's
ess.init.therm_cp_module = 795; ...
% J/kgK ave heat capacity of module (estimated for NiMH)
ess.init.temp_reg = 35; ...
% C thermostat temp of module when cooling fan comes on
ess.tmp.area_module = .032; ...
% m^2 total module surface area exposed to cooling air...
% (typ rectang module)
ess.init.flow_air_mod = .07/12; ...
% kg/s cooling air mass flow rate across module...
% (20 cfm=0.01 kg/s at 20 C)
ess.tmp.mod_flow_area = .0011; ...
% m^2 cross-sec flow area for cooling air per module...
(assumes 10-mm gap btwn mods)
ess.tmp.case_thk = .001; ...
% m thickness of module case (typ from Optima)
ess.tmp.therm_case_cond = 15; ...
% W/mK thermal conductivity of module case material...
% (typ polyprop plastic - Optima)
ess.tmp.speed_air = ess.init.flow_air_mod/...
202
(1.16*ess.tmp.mod_flow_area); % m/s ave velocity of cooling air
ess.tmp.therm_air_htcoef = 30*(ess.tmp.speed_air/5)^0.8;...
% W/m^2K cooling air heat transfer coef.
ess.init.therm_res_on = ((1/ess.tmp.therm_air_htcoef)...
+(ess.tmp.case_thk/ess.tmp.therm_case_cond))/...
ess.tmp.area_module; % K/W tot thermal res key on
ess.init.therm_res_off = ((1/4)+(ess.tmp.case_thk/...
ess.tmp.therm_case_cond))/ess.tmp.area_module;...
% K/W tot thermal res key off (cold soak)
ess.init.flow_air_mod = max(ess.init.flow_air_mod,0.001);
ess.init.therm_res_on = ...
min(ess.init.therm_res_on,ess.init.therm_res_off);
ess = rmfield(ess,'tmp');
% Battery density
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
ess.init.pwr_dis_nom = max(max(...
(ess.init.volt_nom-ess.init.volt_min).*ess.init.volt_min./...
ess.init.rint_dis_map));%per cell
ess.init.pwr_density = ess.init.pwr_dis_nom/ess.init.mass_cell;
ess.init.energy_density = ...
mean((ess.init.volt_nom*ess.init.cap_max_map))/ess.init.mass_cell;
%Values should only be used to calculate the number of cells
ess.init.num_cell_series = ...
overwrite_parameters('simulation.drivetrain.ess','num_cell_series',...
ess.init.num_cell_series);...
% need to update to make sure we have 0 power at SOC_min
ess.init.num_module_parallel = ...
overwrite_parameters('simulation.drivetrain.ess','num_module_parallel'
,...
ess.init.num_module_parallel);...
% need to update to make sure we have 0 power at SOC_min
ess.init.num_cell =
ess.init.num_module_parallel.*ess.init.num_cell_series;
203
Primary Motor Model
%% File description
% Name : mc_id_5_15_hi_performance_motor_AC_15
% Author : Timothy Cleary - Pennsylvania Stae University
% Description : Initialize an AC motor
% Continuous Power = 5kW, Peak Power = 15kW
% Data provided by Timothy Cleary Pennsylvania Stae University
% Proprietary : Public
% Model : lib_mc_map_Pelec_funTW_volt_in,lib_mc_map_Pelec_funTW_pwr_in
% Technology : id
% Vehicle Type : Light, Heavy
%% File content
mc.list.init =
{'motor_mass','controller_mass','inertia','time_response',...
'coeff_regen','volt_min','curr_max','cont_to_peak_ratio'};
mc.init.coeff_regen = 1;
mc.init.volt_min = 37; % volts
mc.init.time_response = 0.05;
mc.init.t_max_trq = 180; % Time motor can remain at max
torque
mc.init.inertia = 0.01; % kg-m^2
mc.init.cont_to_peak_ratio = 3;
mc.init.motor_mass = 21; % kg
mc.init.controller_mass = 4.12; % kg
mc.init.cont_to_peak_ratio = overwrite_parameters(...
'simulation.drivetrain.mc','cont_to_peak_ratio',...
mc.init.cont_to_peak_ratio);
mc.init.spd_base = conversion_calc...
('rotational_speed','rpm','rad/s',2700);% rad/s
mc.init.curr_max = 300;...
% (A), maximum current allowed by the controller and motor
mc.init.spd_max_index = [0,700,1400,2100,2800,3500,4200,...
4900,5600,6300,7000,7700,8400;]*(2*pi)/60; % rad/s
mc.init.trq_max_map =
[89.7125,88.8965,84.3262,69.5030,51.9721,...
37.9729,28.6598,22.4460,17.8495,14.2850,11.1268,0,0]; % N-m
mc.init.trq_max = max (mc.init.trq_max_map);
mc.init.spd_cont_index = mc.init.spd_max_index;
mc.init.trq_cont_map = mc.init.trq_max_map./...
mc.init.cont_to_peak_ratio;
mc.init.spd_min_index = mc.init.spd_max_index; % rad/s
mc.init.trq_min_map = -mc.init.trq_max_map;
204
mc.init.spd_eff_index = mc.init.spd_cont_index(1:end-2);% rad/s
mc.init.trq_eff_index = [.00,.03,.06,.09,.12,.15,.18,.27,.36,...
.45,.55,.64,.73,.82,.91,1.00]*mc.init.trq_max;
mc.init.eff_trq_map=...
[00.0000 00.0000 00.0000 00.0000 00.0000 00.0000...
00.0000 00.0000 00.0000 00.0000 00.0000 00.0000...
00.0000 00.0000 00.0000 00.0000;
00.0000 14.8398 25.3926 35.5711 43.3223 46.7505...
47.1773 46.2372 44.7523 44.1951 44.1334 42.1679...
39.7602 38.2171 37.0050 37.0050;
00.0000 18.2077 30.5984 39.0709 51.1220 62.0840...
71.2814 78.6293 76.6557 74.5511 72.3020 70.7005...
69.1002 66.7967 64.5632 64.5632;
00.0000 18.2077 35.7728 45.9420 53.5495 64.3718...
76.6967 88.0093 86.0393 84.0591 82.4167 81.0597...
78.0174 71.9112 64.5632 64.5632;
00.0000 18.2077 36.4155 53.8090 61.2178 68.6021...
78.0464 89.8881 87.8293 85.8418 83.0578 77.9324...
72.3398 71.9112 64.5632 64.5632;
00.0000 18.2077 36.4155 54.6232 68.9490 76.5422...
83.7482 89.5026 86.3483 83.1457 78.3522 77.9324...
72.3398 71.9112 64.5632 64.5632;
00.0000 18.2077 36.4155 54.6232 72.8310 84.5611...
91.6174 89.3483 82.6642 79.6984 78.3522 77.9324...
72.3398 71.9112 64.5632 64.5632;
00.0000 18.2077 36.4155 54.6232 72.8310 90.2138...
93.4372 84.4024 80.4972 79.6984 78.3522 77.9324...
72.3398 71.9112 64.5632 64.5632;
00.0000 18.2077 36.4155 54.6232 78.2855 91.6920...
88.4751 81.0885 80.4972 79.6984 78.3522 77.9324...
72.3398 71.9112 64.5632 64.5632;
00.0000 18.2077 37.7662 70.6604 89.4479 85.7844...
81.6660 81.0885 80.4972 79.6984 78.3522 77.9324...
72.3398 71.9112 64.5632 64.5632;
00.0000 25.4339 57.3440 72.7856 82.0577 78.7266...
81.6660 81.0885 80.4972 79.6984 78.3522 77.9324...
72.3398 71.9112 64.5632 64.5632;];
mc.init.spd_prop_cont_index = [-
fliplr(mc.init.spd_cont_index(2:end))...
-eps 0 eps mc.init.spd_cont_index(2:end)];
mc.init.trq_prop_cont_map = [-fliplr(mc.init.trq_cont_map(2:end))...
-mc.init.trq_cont_map(2) mc.init.trq_cont_map(2)...
mc.init.trq_cont_map(2) mc.init.trq_cont_map(2:end)];
mc.init.pwr_prop_cont_map = mc.init.spd_prop_cont_index...
.*mc.init.trq_prop_cont_map;
205
mc.init.spd_prop_max_index = [-
fliplr(mc.init.spd_max_index(2:end))...
-eps 0 eps mc.init.spd_max_index(2:end)];
mc.init.trq_prop_max_map = [-fliplr(mc.init.trq_max_map(2:end))...
-mc.init.trq_max_map(2) mc.init.trq_max_map(2)...
mc.init.trq_max_map(2) mc.init.trq_max_map(2:end)];
mc.init.pwr_prop_max_map = mc.init.spd_prop_max_index...
.*mc.init.trq_prop_max_map;
mc.init.spd_reg_cont_index = [-
fliplr(mc.init.spd_cont_index(2:end))...
-eps 0 eps mc.init.spd_cont_index(2:end)];
mc.init.trq_reg_cont_map = [fliplr(mc.init.trq_cont_map(2:end))...
mc.init.trq_cont_map(2) -mc.init.trq_cont_map(2)...
-mc.init.trq_cont_map(2) -mc.init.trq_cont_map(2:end)];
mc.init.pwr_reg_cont_map = mc.init.spd_reg_cont_index...
.*mc.init.trq_reg_cont_map;
mc.init.spd_reg_max_index = [-
fliplr(mc.init.spd_max_index(2:end))...
-eps 0 eps mc.init.spd_max_index(2:end)];
mc.init.trq_reg_max_map = [fliplr(mc.init.trq_max_map(2:end))...
mc.init.trq_max_map(2) -mc.init.trq_max_map(2)...
-mc.init.trq_max_map(2) -mc.init.trq_max_map(2:end)];
mc.init.pwr_reg_max_map = mc.init.spd_reg_max_index...
.*mc.init.trq_reg_max_map;
mc.init.spd_eff_index = [-
fliplr(mc.init.spd_eff_index(2:end))...
mc.init.spd_eff_index];
mc.init.trq_eff_index = [-
fliplr(mc.init.trq_eff_index(2:end))...
mc.init.trq_eff_index];
mc.init.eff_trq_map = ...
[flipud(fliplr(mc.init.eff_trq_map(2:end,2:end)))...
flipud(mc.init.eff_trq_map(2:end,:));...
fliplr(mc.init.eff_trq_map(:,2:end)) mc.init.eff_trq_map];
206
Secondary Motor Model (as a motor)
%% File description
% Name : mc_id_4_12_hi_performance_motor_AC_9
% Author : Timothy Cleary - Pennsylvania Stat University
% Description : Initialize an AC motor
% Continuous Power = 4kW, Peak Power = 12kW
% Data provided by Timothy Cleary Pennsylvania Stat University
% Proprietary : Public
% Model : lib_mc_map_Pelec_funTW_volt_in,lib_mc_map_Pelec_funTW_pwr_in
% Technology : id
% Vehicle Type : Light, Heavy
%% File content
mc.list.init =
{'motor_mass','controller_mass','inertia','time_response'...
,'coeff_regen','volt_min','curr_max','cont_to_peak_ratio'};
mc.init.coeff_regen = 1;
mc.init.volt_min = 37; % volts
mc.init.time_response = 0.05;
mc.init.t_max_trq = 180; % Time motor can remain at max
torque
mc.init.inertia = 0.01; % kg-m^2
mc.init.cont_to_peak_ratio = 3;
mc.init.motor_mass = 21; % kg
mc.init.controller_mass = 4.12; % kg
mc.init.cont_to_peak_ratio = overwrite_parameters(...
'simulation.drivetrain.mc','cont_to_peak_ratio',...
mc.init.cont_to_peak_ratio);
mc.init.spd_base = conversion_calc('rotational_speed',...
'rpm','rad/s',2700);% rad/s
mc.init.curr_max = 300; ...
% (A), maximum current allowed by the controller and motor
mc.init.spd_max_index = [0,700,1400,2100,2800,3500,4200,4900,..
5600,6300,7000;]*(2*pi)/60; % rad/s
mc.init.trq_max_map = [45.6831 45.3501 45.6363 45.4493 ...
41.4355 33.0929 25.1557 19.6587 15.9633 0 0]; % N-m
mc.init.trq_max = max (mc.init.trq_max_map);
mc.init.spd_cont_index = mc.init.spd_max_index;
mc.init.trq_cont_map = mc.init.trq_max_map./...
mc.init.cont_to_peak_ratio;
mc.init.spd_min_index = mc.init.spd_max_index; % rad/s
mc.init.trq_min_map = -mc.init.trq_max_map;
mc.init.spd_eff_index = mc.init.spd_cont_index(1:end-2);% rad/s
207
mc.init.trq_eff_index =
[.00,.03,.06,.09,.12,.15,.18,.27,.36,.45,...
.55,.64,.73,.82,.91,1.00]*mc.init.trq_max;
mc.init.eff_trq_map=...
[00.0000 00.0000 00.0000 00.0000 00.0000 00.0000...
00.0000 00.0000 00.0000 00.0000 00.0000 00.0000...
00.0000 00.0000 00.0000 00.0000;
00.0000 13.8095 26.7821 35.6096 42.6190 48.7910...
52.5569 53.7973 54.8229 55.4440 53.7280 51.4256...
49.1512 47.5373 45.9584 45.9584;
00.0000 13.8095 27.6189 41.4284 54.0160 63.5265...
71.3808 77.3222 76.5666 74.8497 73.1049 71.7418...
70.4441 69.3241 68.2055 68.2055;
00.0000 13.8095 27.6189 41.4284 55.2379 68.9864...
81.3011 88.7760 87.5156 86.2137 84.7568 83.3785...
82.0147 80.6548 79.2949 79.2949;
00.0000 13.8095 27.6189 41.4284 55.2379 69.0474...
82.8568 94.1712 93.0417 91.8567 90.4912 89.1751...
87.7139 85.2762 79.2949 79.2949;
00.0000 13.8095 27.6189 41.4284 55.2379 69.0407...
82.4880 94.7922 93.6492 92.5062 91.2078 88.1534...
85.6104 85.2762 79.2949 79.2949;
00.0000 13.8095 27.6189 41.4284 55.1431 68.1254...
81.4553 94.1375 92.4620 90.8016 87.8708 88.1534...
85.6104 85.2762 79.2949 79.2949;
00.0000 13.8095 27.6189 40.9485 54.6459 71.1033...
89.6995 92.2676 90.4301 86.2388 87.8708 88.1534...
85.6104 85.2762 79.2949 79.2949;
00.0000 13.6659 29.1802 48.5065 68.6308 88.1875...
92.6692 90.5864 89.2125 86.2388 87.8708 88.1534...
85.6104 85.2762 79.2949 79.2949;];
mc.init.spd_prop_cont_index = [-
fliplr(mc.init.spd_cont_index(2:end))...
-eps 0 eps mc.init.spd_cont_index(2:end)];
mc.init.trq_prop_cont_map = [-fliplr(mc.init.trq_cont_map(2:end))...
-mc.init.trq_cont_map(2) mc.init.trq_cont_map(2)...
mc.init.trq_cont_map(2) mc.init.trq_cont_map(2:end)];
mc.init.pwr_prop_cont_map = mc.init.spd_prop_cont_index.*...
mc.init.trq_prop_cont_map;
mc.init.spd_prop_max_index = [-
fliplr(mc.init.spd_max_index(2:end))...
-eps 0 eps mc.init.spd_max_index(2:end)];
mc.init.trq_prop_max_map = [-fliplr(mc.init.trq_max_map(2:end))...
-mc.init.trq_max_map(2) mc.init.trq_max_map(2) ...
mc.init.trq_max_map(2) mc.init.trq_max_map(2:end)];
mc.init.pwr_prop_max_map = mc.init.spd_prop_max_index.*...
208
mc.init.trq_prop_max_map;
mc.init.spd_reg_cont_index = [-
fliplr(mc.init.spd_cont_index(2:end))...
-eps 0 eps mc.init.spd_cont_index(2:end)];
mc.init.trq_reg_cont_map = [fliplr(mc.init.trq_cont_map(2:end))...
mc.init.trq_cont_map(2) -mc.init.trq_cont_map(2)...
-mc.init.trq_cont_map(2) -mc.init.trq_cont_map(2:end)];
mc.init.pwr_reg_cont_map = mc.init.spd_reg_cont_index.*...
mc.init.trq_reg_cont_map;
mc.init.spd_reg_max_index = [-
fliplr(mc.init.spd_max_index(2:end))...
-eps 0 eps mc.init.spd_max_index(2:end)];
mc.init.trq_reg_max_map = [fliplr(mc.init.trq_max_map(2:end))...
mc.init.trq_max_map(2) -mc.init.trq_max_map(2)...
-mc.init.trq_max_map(2) -mc.init.trq_max_map(2:end)];
mc.init.pwr_reg_max_map = mc.init.spd_reg_max_index.*...
mc.init.trq_reg_max_map;
mc.init.spd_eff_index = [-
fliplr(mc.init.spd_eff_index(2:end))...
mc.init.spd_eff_index];
mc.init.trq_eff_index = [-
fliplr(mc.init.trq_eff_index(2:end))...
mc.init.trq_eff_index];
mc.init.eff_trq_map = [flipud(fliplr(...
mc.init.eff_trq_map(2:end,2:end))) flipud...
(mc.init.eff_trq_map(2:end,:));fliplr(mc.init.eff_trq_map(:,2:end))...
mc.init.eff_trq_map];
209
Secondary Motor Model (as a generator)
%% File description
% Name : gc_id_4_12_AC9_berkeley
% Author : Timothy Cleary - PSU
% Description : Initialize an induction electric generator
% Continuous Power = 4kW, Peak Power = 12kW
% Proprietary : Public
% Model : lib_gc_generator_map_trq_in
% Technology : id
% Vehicle Type : Light, Heavy
%% File content
gc.list.init = {'inertia','time_response','str_trq','spd_opt',...
'ratio','cont_to_peak_ratio'};
gc.init.inertia = 0.01; % N m s^2/rad
gc.init.time_response = 0.05;
gc.init.t_max_trq = 180; ...
% Time the generator can remain at max torque
gc.init.str_trq = 5; ...
%Torque provided by the generator required to start the engine ...
% (when the generator is used as a engine starter device)
gc.init.spd_opt =
conversion_calc('rotational_speed','rpm',...
'rad/s',2800);
gc.init.ratio = 1;
gc.init.motor_mass = 21;
gc.init.controller_mass = 4.12;
gc.init.curr_max = 300; ...
% (A), maximum current allowed by the controller and motor
gc.init.cont_to_peak_ratio = 3;
gc.init.cont_to_peak_ratio = overwrite_parameters(...
'simulation.drivetrain.gc','cont_to_peak_ratio',...
gc.init.cont_to_peak_ratio);
gc.init.spd_max_index = [0,700,1400,2100,2800,3500,4200,4900,...
5600,6300,7000;]*(2*pi)/60;
gc.init.trq_max_map = [45.6831 45.3501 45.6363 45.4493 41.4355...
33.0929 25.1557 19.6587 15.9633 0 0]; % N-m
gc.init.trq_max = max (gc.init.trq_max_map);
gc.init.spd_cont_index = gc.init.spd_max_index;
gc.init.trq_cont_map = (1/gc.init.cont_to_peak_ratio) ...
* gc.init.trq_max_map;
gc.init.spd_min_index = gc.init.spd_cont_index; % rad/s
gc.init.trq_min_map = -gc.init.trq_cont_map;
210
gc.init.spd_loss_index =
[0,700,1400,2100,2800,3500,4200,4900,5600,...
6300,7000;]*(2*pi)/60;
gc.init.trq_loss_index = [0,21,43,65,87,109,130,152,174,196,218,...
239,261,283,305,327,349,370,392]/392*gc.init.trq_max;...
% ensures that the maximum torque is included in the efficiency
map
gc.init.spd_eff_index = gc.init.spd_cont_index(1:end-2);% rad/s
gc.init.trq_eff_index =
[.00,.03,.06,.09,.12,.15,.18,.27,.36,.45,...
.55,.64,.73,.82,.91,1.00]*gc.init.trq_max;
gc.init.eff_trq_map=...
[00.0000 00.0000 00.0000 00.0000 00.0000 00.0000...
00.0000 00.0000 00.0000 00.0000 00.0000 00.0000 ...
00.0000 00.0000 00.0000 00.0000;
00.0000 13.8095 26.7821 35.6096 42.6190 48.7910...
52.5569 53.7973 54.8229 55.4440 53.7280 51.4256 ...
49.1512 47.5373 45.9584 45.9584;
00.0000 13.8095 27.6189 41.4284 54.0160 63.5265 ...
71.3808 77.3222 76.5666 74.8497 73.1049 71.7418 ...
70.4441 69.3241 68.2055 68.2055;
00.0000 13.8095 27.6189 41.4284 55.2379 68.9864...
81.3011 88.7760 87.5156 86.2137 84.7568 83.3785...
82.0147 80.6548 79.2949 79.2949;
00.0000 13.8095 27.6189 41.4284 55.2379 69.0474...
82.8568 94.1712 93.0417 91.8567 90.4912 89.1751...
87.7139 85.2762 79.2949 79.2949;
00.0000 13.8095 27.6189 41.4284 55.2379 69.0407...
82.4880 94.7922 93.6492 92.5062 91.2078 88.1534...
85.6104 85.2762 79.2949 79.2949;
00.0000 13.8095 27.6189 41.4284 55.1431 68.1254...
81.4553 94.1375 92.4620 90.8016 87.8708 88.1534...
85.6104 85.2762 79.2949 79.2949;
00.0000 13.8095 27.6189 40.9485 54.6459 71.1033...
89.6995 92.2676 90.4301 86.2388 87.8708 88.1534...
85.6104 85.2762 79.2949 79.2949;
00.0000 13.6659 29.1802 48.5065 68.6308 88.1875...
92.6692 90.5864 89.2125 86.2388 87.8708 88.1534...
85.6104 85.2762 79.2949 79.2949;]/100;
gc.init.spd_prop_cont_index = [-
fliplr(gc.init.spd_cont_index(2:end))...
-eps 0 eps gc.init.spd_cont_index(2:end)];
gc.init.trq_prop_cont_map = [-fliplr(gc.init.trq_cont_map(2:end))...
-gc.init.trq_cont_map(2) gc.init.trq_cont_map(2)...
gc.init.trq_cont_map(2) gc.init.trq_cont_map(2:end)];
gc.init.pwr_prop_cont_map = gc.init.spd_prop_cont_index.*...
gc.init.trq_prop_cont_map;
211
gc.init.spd_prop_max_index = [-
fliplr(gc.init.spd_max_index(2:end))...
-eps 0 eps gc.init.spd_max_index(2:end)];
gc.init.trq_prop_max_map = [-fliplr(gc.init.trq_max_map(2:end))...
-gc.init.trq_max_map(2) gc.init.trq_max_map(2)...
gc.init.trq_max_map(2) gc.init.trq_max_map(2:end)];
gc.init.pwr_prop_max_map = gc.init.spd_prop_max_index.*...
gc.init.trq_prop_max_map;
gc.init.spd_reg_cont_index = [-
fliplr(gc.init.spd_cont_index(2:end))...
-eps 0 eps gc.init.spd_cont_index(2:end)];
gc.init.trq_reg_cont_map = [fliplr(gc.init.trq_cont_map(2:end))...
gc.init.trq_cont_map(2) gc.init.trq_cont_map(2)...
-gc.init.trq_cont_map(2) -gc.init.trq_cont_map(2:end)];
gc.init.pwr_reg_cont_map = gc.init.spd_reg_cont_index.*...
gc.init.trq_reg_cont_map;
gc.init.spd_reg_max_index = [-
fliplr(gc.init.spd_max_index(2:end))...
-eps 0 eps gc.init.spd_max_index(2:end)];
gc.init.trq_reg_max_map = [fliplr(gc.init.trq_max_map(2:end))...
gc.init.trq_max_map(2) gc.init.trq_max_map(2) ...
-gc.init.trq_max_map(2) -gc.init.trq_max_map(2:end)];
gc.init.pwr_reg_max_map = gc.init.spd_reg_max_index.*...
gc.init.trq_reg_max_map;
gc.init.spd_eff_index = [-
fliplr(gc.init.spd_eff_index(2:end))...
gc.init.spd_eff_index];
gc.init.trq_eff_index = [-
fliplr(gc.init.trq_eff_index(2:end))...
gc.init.trq_eff_index];
gc.init.eff_trq_map = [flipud(fliplr...
(gc.init.eff_trq_map(2:end,2:end))) flipud(...
gc.init.eff_trq_map(2:end,:));fliplr(...
gc.init.eff_trq_map(:,2:end)) gc.init.eff_trq_map];
212
Honda GS 190 Gasoline Engine Model
%% File description
% Name : eng_si_190_5
% Author : Timothy Cleary - Pennsylvania State University
% Description : Initialize the 190cc 5kW pump gasoline engine
% Data provided by Timothy Cleary - Pennsylvania State University
% Proprietary : Public
% Model : lib_eng_map_hot
% Technology : si
% Vehicle Type : Light
%% File content
eng.list.init =
{'time_response','eng_mass','tank_mass','fuel_mass'};
eng.init.technology = 'si';
eng.init.num_cyl_init = 1;
eng.init.material = 'Aluminum';
% source - ORNL/TM-2008/117
eng.init.fuel_density_val = 0.745; % (kg/L)
eng.init.fuel_heating_val = 42906796; % (J/kg)Specific
LHV
eng.init.fuel_carbon_ratio = 0.861; % (kg/kg)
eng.init.eng_mass = 14;...
%eng.init.mass_block + eng.init.mass_radiator +
eng.init.mass_vol...
+ eng.init.tank_mass;
eng.init.tank_mass = 2;
eng.init.fuel_mass = conversion_calc(...
'volume','gallon','liter',10)*eng.init.fuel_density_val;
% eng.init.fuel_mass = 7.45;
eng.init.time_response = 0.01;
eng.init.spd_idle =
conversion_calc('rotational_speed'...
,'rpm','rad/s',1000);
% eng.init.spd_idle = 1400*(2*pi())*(1/60);
eng.init.warmup_init = 0; % This should normally be
0
eng.init.pwr_max = 5000; % Watts
eng.init.ex_gas_heat_cap = 1089; ...
% J/kgK ave sens heat cap of exh gas (SAE #890798)
eng.init.displ_init = 190; % cc
eng.init.inertia = 0.07; ..
% kg-m^2 - approximate value
213
eng.init.spd_str =
conversion_calc('rotational_speed',...
'rpm','rad/s',1000);
% eng.init.spd_str = 1000*(2*pi())*(1/60);
eng.init.specific_pwr =
eng.init.pwr_max./eng.init.eng_mass;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%
% maximum curves at each speed (closed and wide open throttle)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%
% hot max wide open throttle curves
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
eng.init.spd_max_hot_index =
conversion_calc('rotational_speed','rpm',...
'rad/s',[0 1000:350:4150]);
% eng.init.spd_max_hot_index = ([0 1000:350:4150]*(2*pi())*(1/60));
eng.init.trq_max_hot_map = [0 5 19.17 20.46 17.53 14.93 13.09 8.38 2
0 0];
% Mid speed is used in logic to limit closed and wide open torque
curves
eng.init.spd_avg = 0.5 * (eng.init.spd_max_hot_index(1) +
...
eng.init.spd_max_hot_index(length(eng.init.spd_max_hot_index)));
% hot max closed throttle curves
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
eng.init.spd_min_hot_index = eng.init.spd_max_hot_index;
eng.init.trq_min_hot_map = [0 -5*ones(1,size...
(eng.init.spd_max_hot_index,2)-1)];
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%
% consumption table
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%
eng.init.spd_fuel_hot_index = [146.6077 168.7152 190.8227
212.9302...
235.0377 257.1452 279.2527 301.3602 323.4677 345.5752];
eng.init.trq_fuel_hot_index = [0.1032 1.9041 3.7050 5.5059...
7.3068 9.1077 10.9087 12.7096 14.5105 16.3114
18.1123...
19.9132]; % (Eqn B)
% Rows represent speed (rad/s)
% Columns represent torque (N-m) Table is fuel rate (kg/s)
214
eng.init.fuel_hot_map = 1e-4*[...
0.558750000000000,1.01370662741118,1.29373086719970,1.44877456652358,.
..
1.52878957254084,1.58372773240951,1.66354089328762,1.78036266207578,..
.
1.82211058001517,1.80602220844150,1.76558839816602,1.73430000000000;..
.
1.500000000000000,1.30805753042053,1.41844300798210,1.42813495705093,.
..
1.39006112170558,1.33979514936847,1.72552098389961,1.93730737443319,..
.
2.01416318223787,2.00927384329399,1.98921443420548,1.500000000000000;.
..
1.500000000000000,3.27467029606142,1.28333826932813,1.08206409142853,.
..
0.933579475257697,1.39328526548741,1.86007456000625,2.03466300467808,.
..
2.23702522818617,2.24203171995344,1.500000000000000,1.500000000000000;
...
1.500000000000000,4.97478444707829,1.87230527748809,1.12351838408039,.
..
1.26364773198841,1.66981981494138,2.01830677203253,2.18660203730605,..
.
2.36266442419350,2.44121536322743,1.500000000000000,1.500000000000000;
...
1.500000000000000,4.05748804686011,1.50346179867969,1.46890629467775,.
..
1.55818397018010,1.91983832878112,2.24126418878559,2.39124587349737,..
.
2.60350785530969,1.500000000000000,1.500000000000000,1.500000000000000
;...
1.500000000000000,0.631664131091731,1.34005529798764,1.49786888704158,
...
215
1.86678511241439,2.23127694696984,2.47997713543944,2.63464155196909,..
.
2.84925080339583,1.500000000000000,1.500000000000000,1.500000000000000
;...
1.500000000000000,0.685914376493082,1.02697066845726,1.27204946657949,
...
1.72945722712099,2.27313821485474,2.70270411187335,2.92954925176639,..
.
1.500000000000000,1.500000000000000,1.500000000000000,1.50000000000000
0;...
1.500000000000000,0.684817818116623,0.560816363604910,1.10918924868283
,...
1.78691342197422,2.23323129561172,3.09436872272422,1.500000000000000,.
..
1.500000000000000,1.500000000000000,1.500000000000000,1.50000000000000
0;...
1.500000000000000,1.38069408084618,1.20942106922148,1.69231498299360,.
..
1.95133790469533,1.500000000000000,1.500000000000000,1.500000000000000
,...
1.500000000000000,1.500000000000000,1.500000000000000,1.50000000000000
0;...
1.500000000000000,1.500000000000000,1.500000000000000,1.50000000000000
0,...
1.500000000000000,1.500000000000000,1.500000000000000,1.50000000000000
0,...
1.500000000000000,1.500000000000000,1.500000000000000,1.50000000000000
0;];
% Expanding the map at low torque (0 0.03 0.05)
for i=1:length(eng.init.spd_fuel_hot_index)
tmp_map(i,:)=interp1(eng.init.trq_fuel_hot_index,...
eng.init.fuel_hot_map(i,:),[0 0.03 0.05...
eng.init.trq_fuel_hot_index],'linear','extrap');
end
eng.init.trq_fuel_hot_index=[0 0.03 0.05 eng.init.trq_fuel_hot_index];
216
% Maps negative values are replaced by the average of non negative
% closest values
for i =1:length(eng.init.spd_fuel_hot_index)
for j=1:length(eng.init.trq_fuel_hot_index)
if tmp_map(i,j)<0 && i>1
tmp_map(i,j)=abs(0.5*(tmp_map(i-1,j)+tmp_map(i+1,j)));
end
if tmp_map(i,j)<0 && i==1
tmp_map(i,j)=abs(0.5*(tmp_map(i,j+1)+tmp_map(i+1,j)));
end
end
end
eng.init.fuel_hot_map=tmp_map;
clear tmp_map
eng.init.fmep_hot_map = zeros(length(eng.init.spd_fuel_hot_index),...
length(eng.init.trq_fuel_hot_index));
% engine torque for fuel rate kg/s
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%GASOLINE ENGINE
% Friction Torque for a 1.7 L Engine Based on Heywood.
% "Internal Combustion Fundamentals,"
% Section 13.6.1 Figure 13-14 (a) P726
%Code for fmep calculation Gasoline
eng.init.spd_zero_fuel_hot_index
=(0:20:max(eng.init.spd_min_hot_index));
eng.init.fmep_hot_map = polyval([4.2337e-4 -3.41045e-3 62.831],...
\(0:20:max(eng.init.spd_min_hot_index)));
eng.init.fmep_hot_map(1) =0;
eng.init.trq_zero_fuel_hot_index = -eng.init.fmep_hot_map *...
eng.init.displ_init/1000 / 4/ pi;
%Emissions in percentage of fuel rate (kg/s)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%
eng.init.spd_co_hot_index = eng.init.spd_fuel_hot_index;
eng.init.trq_co_hot_index = eng.init.trq_fuel_hot_index;
eng.init.co_hot_map =
zeros(length(eng.init.spd_co_hot_index),...
length(eng.init.trq_co_hot_index));
eng.init.spd_hc_hot_index = eng.init.spd_fuel_hot_index;
eng.init.trq_hc_hot_index = eng.init.trq_fuel_hot_index;
eng.init.hc_hot_map =
zeros(length(eng.init.spd_hc_hot_index),...
length(eng.init.trq_hc_hot_index));
217
eng.init.spd_nox_hot_index = eng.init.spd_fuel_hot_index;
eng.init.trq_nox_hot_index = eng.init.trq_fuel_hot_index;
eng.init.nox_hot_map =
zeros(length(eng.init.spd_nox_hot_index),...
length(eng.init.trq_nox_hot_index));
eng.init.spd_pm_hot_index = eng.init.spd_fuel_hot_index;
eng.init.trq_pm_hot_index = eng.init.trq_fuel_hot_index;
eng.init.pm_hot_map =
zeros(length(eng.init.spd_hc_hot_index),...
length(eng.init.trq_nox_hot_index));
% O2
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%
eng.init.spd_o2_hot_index = eng.init.spd_fuel_hot_index;
eng.init.trq_o2_hot_index = eng.init.trq_fuel_hot_index;
eng.init.o2_hot_map =
zeros(length(eng.init.spd_fuel_hot_index),...
length(eng.init.trq_fuel_hot_index));
% exhaust table
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%
eng.init.spd_equiv_hot_index = eng.init.spd_fuel_hot_index;
eng.init.trq_equiv_hot_index = eng.init.trq_fuel_hot_index;
eng.init.equiv_hot_map = ones(length...
(eng.init.spd_equiv_hot_index),length(eng.init.trq_equiv_hot_index));
% Heat rejection variable Presid data table
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%
eng.init.spd_htrej_hot_index = eng.init.spd_fuel_hot_index;
eng.init.trq_htrej_hot_index = eng.init.trq_fuel_hot_index;
eng.init.htrej_hot_map =
zeros(length(eng.init.spd_fuel_hot_index)...
,length(eng.init.trq_fuel_hot_index));
eng.init.htrej_hot_map =
zeros(length(eng.init.spd_htrej_hot_index)...
,length(eng.init.trq_htrej_hot_index));
% Heat Transfer
% the following is a new thermal model of the engine
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%
eng.init.spd_ex_gas_flow_hot_index = eng.init.spd_fuel_hot_index;
eng.init.trq_ex_gas_flow_hot_index = eng.init.trq_fuel_hot_index;
eng.init.ex_gas_flow_hot_map = eng.init.fuel_hot_map *(1+20);
% g/s ex gas flow map: for CI engines,
218
% exflow=(fuel use)*[1 + (ave A/F ratio)]
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%
%eng.init.v0x\fuel use, thermal and emissions\thermal\fc heat net
% calculation
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%
eng.init.ex_pwr_map = eng.init.spd_fuel_hot_index'*...
eng.init.trq_fuel_hot_index;
eng.init.ex_temp_map = eng.init.ex_pwr_map./...
(eng.init.ex_gas_flow_hot_map *1089) + 20;
% W EO ex gas temp = Q/(MF*cp) + Tamb (assumes engine tested ~20 C)
eng.init.spd_ex_temp_index = eng.init.spd_fuel_hot_index;
eng.init.trq_ex_temp_index = eng.init.trq_fuel_hot_index;
eng.init.temp_operating = 90;
eng.init.ex_temp_operating = mean(mean(eng.init.ex_temp_map));
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%
%Calculations
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%
% maximum and minimum calculations
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%
eng.init.trq_hot_max = max(eng.init.trq_max_hot_map); % N-m
[eng.init.pwr_hot_max,Idx] = max(eng.init.spd_max_hot_index.* ...
eng.init.trq_max_hot_map); % W
eng.init.pwr_max_hot_map = eng.init.spd_max_hot_index.*...
eng.init.trq_max_hot_map; % W
%Code to compute maximum speed of the engine. Speed at 80% of peak
power.
eng.init.spd_max = eng.init.spd_max_hot_index(Idx);
if Idx < length(eng.init.pwr_max_hot_map)
eng.init.spd_max =
min(interp1(eng.init.pwr_max_hot_map(Idx:end)...
+1e-6*(1:length(eng.init.pwr_max_hot_map(Idx:end))),...
eng.init.spd_max_hot_index(Idx:end),eng.init.pwr_hot_max *...
0.80,'linear','extrap'),max(eng.init.spd_max_hot_index));
end
% Calculate the max engine efficiency in within the max torque curve
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%
eng.init.eff_hot_map = (eng.init.spd_fuel_hot_index'*...
eng.init.trq_fuel_hot_index)/eng.init.fuel_heating_val./...
219
(eng.init.fuel_hot_map);
eng.tmp.max_trq = interp1(eng.init.spd_max_hot_index,...
eng.init.trq_max_hot_map,eng.init.spd_fuel_hot_index);
eng.tmp.max_trq = eng.tmp.max_trq(:)*ones(1,length...
(eng.init.trq_fuel_hot_index));
eng.tmp.max_trq = (eng.init.trq_fuel_hot_index(:) *...
ones(1,length(eng.init.spd_fuel_hot_index)))' > eng.tmp.max_trq;
eng.init.eff_hot_map(eng.tmp.max_trq) = 0;
eng.init.eff_max = max(max(eng.init.eff_hot_map));
eng = rmfield(eng,'tmp');
220
Electrical Accessory Model
%% File description
% Name : accelec_200_berkeley
% Author : Timothy Cleary - PSU
% Description : Initialize the electrical accessory power losses to
200W
% Proprietary: Public
% Model :
lib_accelec_constant_pwrloss_volt_in,lib_accelec_constant_pwrloss_pwr_
in
% Vehicle Type : Light
%% File content
accelec.list.init = {'pwr','ess_12v_mass','acc_mass'};
accelec.init.pwr = 200.0;
accelec.init.ess_12v_mass = 18;
accelec.init.acc_mass = 0;
%accelec.init.mass = 20;
Power Converter Model
%% File description
% Name : pc_99_24
% Author : Timothy Cleary PSU
% Description : Initialize the power converter model
% Efficiency = 0.99, Output voltage = 24V
% Proprietary : Public
% Model : lib_pc_P2P_constant_eff,lib_pc_V2V_constant_eff,
% lib_pc_perc_constant_eff,lib_pc_P2V_constant_eff
% Vehicle Type : Light
%% File content
pc.list.init = {'mass','eff','volt_out'};
pc.init.eff = 0.99; % efficiency
pc.init.volt_out = 24; %constant voltage output for the accessory pc
(V)
pc.init.mass = 0.5;
221
Final Drive Model
%% File description
% Name : fd_252_berkeley
% Author : Timothy Cleary - Pennsylvania State University
% Description : Initialize the final drive for the Berkeley
% Ratio = 2.52
% Proprietary : Public
% Model : lib_fd_map_trqloss_funTW
% Vehicle Type : Light
%% File content
fd.list.init = {'mass','ratio','inertia','spd_thresh'};
fd.init.ratio = 2.52; % will be optimized in scaling
algorithm
fd.init.inertia = 0;
fd.init.mass = 5;
fd.init.spd_thresh = 20;
fd.init.trq_eff_index =
[51.40,52.40,104.7,157.1,209.4,261.8,...
314.2,366.5,418.9,471.2,523.6];
fd.init.spd_eff_index =
[0.500,6.000,33.90,67.80,101.7,135.6,...
169.5,203.4,237.3,271.2,305.1,339];
fd.init.eff_trq_map = ones(size(fd.init.trq_eff_index,2),...
size(fd.init.spd_eff_index,2)).* 0.99;
fd.init.trq_loss_index = fd.init.trq_eff_index;
fd.init.spd_loss_index = fd.init.spd_eff_index;
fd.init.trq_loss_map =
zeros(length(fd.init.trq_loss_index),...
length(fd.init.spd_loss_index));
% create final drive loss tables
for count=1:size(fd.init.trq_loss_index,2)
for count2=1:size(fd.init.spd_loss_index,2)
fd.init.trq_loss_map(count,count2) = ...
(1-fd.init.eff_trq_map(count,count2))*...
fd.init.trq_loss_index(count);
end
clear count2
end
clear count
% calculate the maximum efficiency
fd.init.eff_max=max(max(fd.init.eff_trq_map));
222
Single Reduction Gear Ratio Model
%% File description
% Name : tc_224_berkeley
% Author : Timothy Cleary PSU
% Description : Initialize a single gear - Ratio = 2.24
% Proprietary : Public
% Model : lib_tc_map_trqloss_funTW
% Vehicle Type : Light, Heavy
%% File content
tc.list.init = {'mass','ratio','inertia','spd_thresh'};
tc.init.ratio = 2.24;
tc.init.inertia = 0;
tc.init.mass = 5;
tc.init.spd_thresh = 10;
tc.init.trq_eff_index =
[51.40,52.40,104.7,157.1,209.4,261.8,314.2,...
366.5,418.9,471.2,523.6];
tc.init.spd_eff_index =
[0.500,6.000,33.90,67.80,101.7,135.6,169.5,...
203.4,237.3,271.2,305.1,339];
tc.init.eff_trq_map = ones(size(tc.init.trq_eff_index,2),...
size(tc.init.spd_eff_index,2)).*0.97;
tc.init.trq_loss_index = tc.init.trq_eff_index;
tc.init.spd_loss_index = tc.init.spd_eff_index;
tc.init.trq_loss_map = zeros(length(tc.init.trq_loss_index),...
length(tc.init.spd_loss_index));
% create axle1 drive loss tables
for count=1:size(tc.init.trq_loss_index,2)
for count2=1:size(tc.init.spd_loss_index,2)
tc.init.trq_loss_map(count,count2) = (1-tc.init.eff_trq_map...
(count,count2))*tc.init.trq_loss_index(count);
end
clear count2
end
clear count
% calculate the maximum efficiency
tc.init.eff_max=max(max(tc.init.eff_trq_map));
223
Berkeley Vehicle (body) Model
%% File description
% Name : veh_235_05_033_berkeley
% Author : Timothy Cleary - Pennsylvania State University
% Description : Initialize a 1959 Berkeley
% Body mass = 235 kg, Frotal area = 0.5, Drag coeff = 0.33
% Proprietary : Public
% Model : lib_veh_equation_losses
% Vehicle Type : Light
% Technology:
%% File content
veh.list.init = {'frontal_area','coeff_drag','cg_height',...
'ratio_weight_front'};
veh.init.body_mass = 235; %vehicle mass without
powertrain
veh.init.frontal_area = 0.5;
veh.init.coeff_drag = 0.33;
veh.init.axle_base = 1.25;% Vehicle wheel base, (m)
veh.init.cg_height = 0.25;% Vehicle CG height, (m)
veh.init.ratio_weight_front = 0.70;
%ratio of the weight to the front wheels
veh.init.ratio_weight_front = overwrite_parameters(...
'simulation.drivetrain.veh','ratio_weight_front',...
veh.init.ratio_weight_front);
veh.init.ratio_weight_rear = (1-veh.init.ratio_weight_front);
veh.init.cargo_mass = 80;
Berkeley Wheel Model
%% File description % Name : wh_0203_berkeley % Author : Timothy Cleary - Pennsylvania State University % Description : Initialize a wheel with a radius of 0.2032 m % Rolling resistance coefficients are estimated % Proprietary : Public % Model : lib_wh_2wd,lib_2wd_f0f1f2 % Vehicle Type : Light
%% File content wh.list.init =
{'number_wheels','mass_per_wheel','trq_brake_max','inertia_per_wheel',
'theoretical_radius','radius_correction_factor','radius','coeff_roll1'
,'coeff_roll2','coeff_roll3','coeff_roll4'};
if strcmp(simulation.building.axle,'2 wheel drive'), wh.init.number_wheels = 4; else wh.init.number_wheels = 2; end
wh.init.trq_brake_max = 2000; % N-m wh.init.inertia_per_wheel = 0.25; % kg-m^2 wh.init.theoretical_radius =
overwrite_parameters('simulation.drivetrain.wh','theoretical_radius',0
.2032); % m wh.init.radius_correction_factor = 0.95; % correction factor
used to take into account the impact of vehicle weight on actual
radius wh.init.radius =
wh.init.theoretical_radius*wh.init.radius_correction_factor;
% m wh.init.mass_per_wheel = 2; % kg wh.init.spd_thresh = 1;% Parameter for the blending block
used in rolling resistance calculation.
% Rolling Resistance Coefficient as a polynomial function of speed. % wh.init.coeff_roll1 + wh.init.coeff_roll2*w +
wh.init.coeff_roll3*w^2 + wh.init.coeff_roll4*w^3 wh.init.coeff_roll1 = 0.006; wh.init.coeff_roll2 = 0.00012; wh.init.coeff_roll3 = 0; wh.init.coeff_roll4 = 0;
wh.init.friction_coefficient = [0.97 0.97 0.95 0.9 0.85 0.65 0.5 0.5
0.5 0.5];% values for Dry Alsphalt
225
wh.init.vehicle_speed =
conversion_calc('linear_speed','mile/h','m/s',[0 10 20 30 40 50 60 70
80 90]);
APPENDIX F
ABC 150 Script
Pulse Power Testing
/* Script for running 48 Volt 60 Ah Air Cooled NimH pack Pulse Power
Test */
void main()
{
/* Define constants and variables */
/* This list sends the limit values to the machine at the start of the
test */
/* When the values hit the limit the test stops; The ABC will just
hold that value no matter what is requested until it decreases within
range. */
ABCVmin = 20;
ABCVmax = 65;
ABCImin = -100;
ABCImax = 75;
ABCPmin = -10;
ABCPmax = 5;
ChangeLimits();
OutputFileInterval(1.0);
/* Battery testing starts here */
/* Step 1 -- Rest at OCV */
Standby(0, CommandTime>=5.0);
OutputFileInterval(0.1);
/* Step 2 -- Pulse Discharge */
Current(-100, CommandTime>=10 || ABCVoltage <= 25);
OutputFileInterval(1.0);
/* Step 3 -- Rest at OCV */
Standby(0, CommandTime>=60.0);
227
OutputFileInterval(0.1);
/* Step 4 -- Pulse charge */
Current(75, CommandTime>=10 || ABCVoltage >= 65);
OutputFileInterval(1.0);
/* Step 5 -- Rest at OCV */
Standby(0, CommandTime>=10.0);
/* Step 6 -- Discharge 10% SOC at 1C */
Current(-60, CommandTime>= 360);
OutputFileInterval(60.0);
/* Step 5 -- Rest at OCV for 1 hour*/
Standby(0, CommandTime>=3600);
/* End Test */
}
Co-Simulation and HIL Simulation
/* Script for running 48 Volt 60 ah Motorcraft NimH pack in a co-sim or HIL test */
void main()
{
/* Define constants and variables */
/* This list sends the limit values to the machine at the start of the test */
/* When the values hit the limit the test stops; The ABC will just hold that value no matter what is
requested until it decreases within range. */
ABCVmin = 32;
ABCVmax = 56.8;
ABCImin = -265;
ABCImax = 265;
ABCPmin = -17;
ABCPmax = 17;
ChangeLimits();
OutputFileInterval(0.01);
/* Battery testing starts here */
/* Step 1 -- Rest at OCV */
Standby(0, CommandTime>=5.0);
/* Step 2 -- allow -5 to 5 Volt signal to command current */
Current(0, CommandTime>=1400 || ABCVoltage >= 58 || ABCVoltage <= 31);
/* End Test */
}
Appendix G
Motor Controller Parameters
Example of primary motor parameters for normal operation
Battery Information
Setting Min Max
One Hour Rate
6000 500 0 500
Five Hour Rate
6001 500 0 500
Reset Voltage
6002 60 0 120
Speed
Motor Speed
6003 7500 200 8000
Regen Threshold
6004 50 20 400
Control Mode Select
600A 2 0 2 0 - Speed Mode Express
Max Speed
600B 7500 100 8000 rpm
Kp
600C 30 0 100 %
Ki
600D 10 5 100 %
Accel Rate
600E 3.5 0.1 30 Seconds
Decel Rate
600F 7 0.1 30 Seconds
Brake Rate
6010 1 0.1 30 Seconds
Pump Enable
6011 1 0 1
1 - Speed Mode
Speed Controller
Max Speed 6012 7500 100 8000 rpm
Kp
6013 30 0 100 %
Ki
6014 10 5 100 %
Vel Feedforward
Kvff 6015 0 0 500 Ampere
Build Rate 6016 1 0.1 5 Seconds
Release Rate 6017 0.4 0.1 2 Seconds
Acc Feedforward
Kaff 6018 0 0 500 Ampere
Kbff 6019 0 0 500 Ampere
Build Rate 601B 1 0.1 5 Seconds
Release Rate 601C 0.4 0.1 2 Seconds
Response
Full Accel Rate HS 601D 7 0.1 30 Seconds
Full Accel Rate LS 601E 3.5 0.1 30 Seconds
Low Accel Rate 601F 15 0.1 30 Seconds
230
Neutral Decel Rate HS 6020 7 0.1 30 Seconds
Neutral Decel Rate LS 6021 12 0.1 30 Seconds
Full Brake Rate HS 6022 1 0.1 30 Seconds
Full Brake Rate LS 6023 2 0.1 30 Seconds
Low Brake Rate 6024 5 0.1 30 Seconds
Fine Tuning
Partial Decel Rate 6025 30 0.1 30 Seconds
HS (High Speed) 6026 70 0 100 %
LS (Low Speed) 6027 30 0 100 %
Reversal Soften 6028 20 0 100 %
Max Speed Accel 6029 1 0.1 30 Seconds
Max Speed Decel 602A 10 0.1 30 Seconds
Pump Enable
6031 1 0 1
2 - Torque Mode
Speed Limiter
Max Speed 6032 7500 500 8000 rpm
Kp
6033 25 0 100 %
Ki
6034 20 5 100 %
Kd
6035 5 0 100 %
Response
Accel Rate 6036 0.1 0.1 30 Seconds
Accel Release Rate 6037 0.1 0.1 2 Seconds
Brake Rate 6038 0.1 0.1 5 Seconds
Brake Release Rate 6039 0.1 0.1 2 Seconds
Neutral Braking 603A 0 0 100 %
Neutral Taper Speed 603B 2000 200 6000 rpm
Fine Tuning
Creep Torque 603C 0 0 100 %
Brake Full Creep Cancel 603D 50 25 100 %
Creep Build Rate 603E 0.1 0.1 5 Seconds
Creep Release Rate 603F 3 0.1 5 Seconds
Creep Release Rate Rollback 6040 10 0.1 30 Seconds
Gear Soften 6041 20 0 100 %
Brake Taper Speed 6042 1000 200 6000 rpm
Reversal Soften 6043 20 0 100 %
Max Speed Decel 6044 10 0.1 30 Seconds
Restraint
Restraint Forward
6045 0 0 100 %
Restraint Back
6046 0 0 100 %
Soft Stop Speed
6047 0 0 500 rpm
Position Hold
Position Hold Enable 6048 0 0 1
231
Kp
6049 10 2 100 %
Kp Deadband (motor degrees) 604A 0 0 720
Kd
604B 15 0 100 %
Set Speed Threshold 604E 30 5 100 rpm
Entry Rate 604F 50 5 100 %
Exit Rollback Reduction 6050 20 0 100 %
Current Limits
Drive Current Limit
6055 100 5 100 %
Regen Current Limit
6056 100 5 100 %
Brake Current Limit
6057 100 5 100 %
EMR Current Limit
6058 100 5 100 %
Interlock Brake Current Limit 6059 100 5 100 %
Power Limiting Map
Base Speed 605A 1500 100 4000 rpm
Delta Speed 605B 500 50 1000 rpm
Drive Limiting Map
Nominal 605C 100 0 100 %
Base Plus Delta 605D 100 0 100 %
Base Plus 2xDelta 605E 100 0 100 %
Base Plus 4xDelta 605F 100 0 100 %
Base Plus 8xDelta 6060 100 0 100 %
Regen Limiting Map
Nominal 6061 100 0 100 %
Base Plus Delta 6062 100 0 100 %
Base Plus 2xDelta 6063 100 0 100 %
Base Plus 4xDelta 6064 100 0 100 %
Base Plus 8xDelta 6065 100 0 100 %
Throttle
Throttle Type
6066 5 1 5
Forward Deadband
6067 0.3 0 5 Volt
Forward Map
6068 35 0 100 %
Forward Max
6069 3.6 0 5 Volt
Forward Offset
606A 0 0 100 %
Reverse Deadband
606B 0.3 0 5 Volt
Reverse Map
606C 35 0 100 %
Reverse Max
606D 3.6 0 5 Volt
Reverse Offset
606E 0 0 100 %
Throttle Filter
606F 10 2 125 Hz
HPD/SRO Enable
6070 0 0 1
Sequencing Delay
6071 0.1 0 5 Seconds
232
VCL Throttle Enable
6072 1 0 1
Brake
Brake Pedal Enable
6073 1 0 1
Brake Type
6074 5 1 5
Brake Deadband
6075 0.5 0 5 Volt
Brake Map
6076 50 0 100 %
Brake Max
6077 4.5 0 5 Volt
Brake Offset
6078 0 0 100 %
Brake Filter
6079 10 2 125 Hz
VCL Brake Enable
607A 1 0 1
EM Brake Control
EM Brake Set Upon Fault 607B 1 0 1
Brake Type
607C 0 0 2
Pull In Voltage
607D 100 0 100 %
Holding Voltage
607E 50 0 100 %
Battery Voltage Compensated 607F 1 0 1
Set Speed Threshold 6080 30 5 100 rpm
Release Delay
6081 48 40 2000 ms
Set Speed Settling Time 6082 3012 0 5000 ms
Torque Preload Delay 6083 200 0 800 ms
Torque Preload Enable 6084 1 0 1
Torque Preload Cancel Delay 6085 0 0 120 Seconds
Drivers
Main Contactor
Main Enable 6086 1 0 1
Pull In Voltage 6087 90 0 100 %
Holding Voltage 6088 50 0 100 %
Battery Voltage Compensated 6089 1 0 1
Interlock Type 608A 2 0 2
Open Delay 608B 0.1 0 40 Seconds
Checks Enable 608C 1 0 1
Main DNC Threshold 608D 5 0 84 Volt
Precharge Enable 608E 1 0 1
Proportional Driver
PD Enable 608F 0 0 1
Hyd Lower Enable 6090 0 0 1
PD Max Current 6091 2 0 2 Ampere
PD Min Current 6092 0.05 0 2 Ampere
PD Dither % 6093 0 0 100 %
PD Dither Period 6094 16 16 112 ms
PD Kp
6095 10 0 100 %
233
PD Ki
6096 10 0 100 %
Hydraulic Contactor
Contactor Enable 6097 0 0 1
Pull In Voltage 6098 100 0 100 %
Holding Voltage 6099 80 0 100 %
Fault Checking
Driver 1 Checks Enable 609A 0 0 1
Driver 2 Checks Enable 609B 0 0 1
Driver 3 Checks Enable 609C 0 0 1
Driver 4 Checks Enable 609D 0 0 1
PD Checks Enable 609E 0 0 1
External Supply Max 609F 200 5 200 mAmpere
External Supply Min 60A0 5 5 200 mAmpere
Motor
Typical Max Speed
60A1 8000 500 8000 rpm
Swap Encoder Direction 60A2 1 0 1
Swap Two Phases
60A3 0 0 1
Encoder Steps
60A4 64 32 256
Encoder Fault Setup
Fault Detection Enable 60A9 1 0 1
Fault Stall Time 60AA 5 1 10 Seconds
LOS Upon Encoder Fault 60AB 1 0 1
LOS Max Speed 60AC 800 100 2000 rpm
LOS Max Current 60AD 400 100 650 Ampere
LOS Max Mod Depth 60AE 50 15 100 %
LOS Accel Rate 60AF 7 2 15 Seconds
LOS Decel Rate 60B0 3 2 15 Seconds
Temperature Control
Sensor Enable 60B5 1 0 1
Sensor Type 60B6 3 1 5
Sensor Offset 60B7 0 -20 20 deg C
Temperature Hot 60B8 145 0 250 deg C
Temperature Max 60B9 160 0 250 deg C
MotorTemp LOS Max Speed 60BA 800 100 3000 rpm
Battery
Nominal Voltage
60BB 48 24 84 Volt
Undervoltage Cutback Range 60BC 7 2 14 Volt
User Overvoltage
60BD 123 115 200 %
User Undervoltage
60BE 77 50 80 %
Reset Volts Per Cell
60C0 2 0.9 3 Volt
234
Full Volts Per Cell
60C1 3 0.9 3 Volt
Empty Volts Per Cell 60C2 2 0.9 3 Volt
Discharge Time
60C3 60 0 600 Minutes
BDI Reset Percent
60C4 80 0 100 %
Vehicle
Metric Units
60C5 0 0 1
Speed to RPM
60C6 10 10 3000
Capture Speed
60C7 4500 0 8000 rpm
Capture Distance 1
60C8 22 1 1320
Capture Distance 2
60C9 100 1 1320
Capture Distance 3
60CA 150 1 1320
Emergency Reverse
EMR Enable
60CB 0 0 1
EMR Type
60CC 1 0 1
EMR Dir Interlock
60CD 0 0 1
EMR Time Limit
60CE 3 0 30 Seconds
EMR Speed
60CF 1000 50 6000 rpm
EMR Accel Rate
60D0 0.1 0.1 3 Seconds
EMR Decel Rate
60D1 0.1 0.1 3 Seconds
Interlock Braking
Enable
60D2 0 0 1
Decel Rate HS
60D3 2 0.1 30 Seconds
Decel Rate LS
60D4 3 0.1 30 Seconds
Interlock Brake Timeout 60D5 5 0 8 Seconds
CAN Interface
CANopen Interlock
60D6 0 0 1
Master ID
60D7 1 0 3
Slave ID
60D8 6 0 31
Baud Rate
60D9 2 0 2
Heartbeat Rate
60DA 100 16 200 ms
PDO Timeout Period 60DB 0 0 200 ms
Emergency Message Rate 60DC 16 16 200 ms
Suppress CANopen Init 60DD 0 0 1
Motor Control Tuning
Motor Characterization Tests
Test Enable 60E7 0 0 1
Test Throttle 60E8 0 -1 1
Motor Poles 60E9 4 2 8
Max Test Speed 60E1 1000 500 3000 rpm
Max Test Current 60EA 70 40 100 %
SlipGain
60EB 3.3 0 200
235
Field Weakening Control
Base Speed 60EC 3000 200 6000 rpm
Field Weakening 60ED 50 0 100 %
Weakening Rate 60EE 25 0 100 %
Motor Type
60F9 2 0 200
Reset Controller
613B 0 0 1
Note, for motor to motor testing response rates are adjusted. Drive motor response is reduced
while dynamometer motor response rate is increased.
Bibliography
Acculab. (n.d.). SVI Series Instructional Manual SVI -20B. Edgewood, New York: Acculab
USA.
Congress, U. S. (2009). American Recovery and Reinvestment Act. H.R. 1-24.
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