2007 SAE Commercial Vehicle Engineering Congress – MathWorks · Diesel Engine Calibration In...
Transcript of 2007 SAE Commercial Vehicle Engineering Congress – MathWorks · Diesel Engine Calibration In...
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2007 SAE Commercial Vehicle Engineering Congress – MathWorks Hosted Panel on Analytical Calibration
Analytical Calibration PanelAnalytical Calibration PanelIntroductory remarksIntroductory remarks
SAE Commercial Vehicle Conference, Rosemont, IL, October 31st 2007
Giorgio RizzoniDirector
The Ohio State University Center for Automotive Research
CalibrateCalibrate
“To check, adjust, or determine bycomparison with a standard.”
Σ+
−
CalibrateCalibrate
“To check, adjust, or determine bycomparison with a standard.”
Σ+
−
CalibrateCalibrate
“To check, adjust, or determine bycomparison with a standard.”
Σ+
−
CalibrateCalibrate
“To check, adjust, or determine bycomparison with a standard.”
Σ+
−
CalibrateCalibrate
“To check, adjust, or determine bycomparison with a standard.”
Σ+
−
So So ……Does Control = Calibration?Does Control = Calibration?Do we need Calibrators?Do we need Calibrators?
CalibrateCalibrate
“To check, adjust, or determine bycomparison with a standard.”
Σ+
−
We get this box …So So ……Does Control = Calibration?Does Control = Calibration?Do we need Calibrators?Do we need Calibrators?
No!No!
CalibrateCalibrate
“To check, adjust, or determine bycomparison with a standard.”
Σ+
−
… from our knowledgeof this box.
So So ……Does Control = Calibration?Does Control = Calibration?Do we need Calibrators?Do we need Calibrators?
No!No!
CalibrateCalibrate
“To check, adjust, or determine bycomparison with a standard.”
Σ+
−
So So ……Does Control = Calibration?Does Control = Calibration?Do we need Calibrators?Do we need Calibrators?
No!No!
Yes!Yes!
CalibrateCalibrate
“To check, adjust, or determine bycomparison with a standard.”
Σ+
−
So So ……Does Control = Calibration?Does Control = Calibration?Do we need Calibrators?Do we need Calibrators?
No!No!
Yes!Yes!Typically, Calibrators arethe plant experts!
Modeling Modeling →→ Control Control →→ CalibrationCalibration
How do we go from How do we go from thisthis……
Modeling Modeling →→ Control Control →→ CalibrationCalibration
To this?To this?
… and do it with a short development cycle, keeping in mind dimensionality, modularity, adaptability, scalability, all the while admitting a rigorous calibration process?
An Obvious Answer: Good ModelingAn Obvious Answer: Good Modeling
All models are lies (some are better than others)(Box)
The “real” answer goes beyond simply “modeling”
““ModelModel--Based ControlBased Control””–– a process, not a techniquea process, not a technique
Define the problem
Understand the plant
Pick a control theory
Control-Oriented model
Calculate a control law
Make it work
““ModelModel--Based ControlBased Control””–– a process, not a techniquea process, not a technique
Define the problem
Understand the plant
Pick a control theory
Control-Oriented model
Calculate a control law
Make it work
Manager(with help)
(the “real” problem)
““ModelModel--Based ControlBased Control””–– a process, not a techniquea process, not a technique
Define the problem
Understand the plant
Pick a control theory
Control-Oriented model
Calculate a control law
Make it work
“Modeler”
- Physics-Matlab simulation- some data
““ModelModel--Based ControlBased Control””–– a process, not a techniquea process, not a technique
Define the problem
Understand the plant
Pick a control theory
Control-Oriented model
Calculate a control law
Make it work
Control TheoristHow many academicians start here ….
““ModelModel--Based ControlBased Control””–– a process, not a techniquea process, not a technique
Define the problem
Understand the plant
Pick a control theory
Control-Oriented model
Calculate a control law
Make it workCalibrator andcontrol
practitioner
““ModelModel--Based ControlBased Control””–– a process, not a techniquea process, not a technique
Define the problem
Understand the plant
Pick a control theory
Control-Oriented model
Calculate a control law
Make it work
How many academicians start here ….
““ModelModel--Based ControlBased Control””–– a process, not a techniquea process, not a technique
Define the problem
Understand the plant
Pick a control theory
Control-Oriented model
Calculate a control law
Make it work
How many academicians start here ….
… and finish here?
““ModelModel--Based ControlBased Control””–– a process, not a techniquea process, not a technique
Define the problem
Understand the plant
Pick a control theory
Control-Oriented model
Calculate a control law
Make it work
How many academicians start here ….
How manypractitioners start
here ….
… and finish here?
““ModelModel--Based ControlBased Control””–– a process, not a techniquea process, not a technique
Define the problem
Understand the plant
Pick a control theory
Control-Oriented model
Calculate a control law
Make it work
How many academicians start here ….
How manypractitioners start
here ….
… and finishhere?
… and finish here?
““ModelModel--Based ControlBased Control””–– a process, not a techniquea process, not a technique
Define the problem
Understand the plant
Pick a control theory
Control-Oriented model
Calculate a control law
Make it work
How many academicians start here ….
How manypractitioners start
here ….
… and finishhere?
… and finish here?
Usually, one person cannotplay all these roles …
A Preview of the PanelA Preview of the Panel
• Emissions legislation has significantly increased the complexity of the calibration problem, and extended this problem to heavy-duty and off-highway applications
• Complex combustion and exhaust aftertreatmentbehavior coupled with insufficient sensor information make the task of achieving open loop calibration for new emissions standard very challenging.
• Model-based approaches and computer-aided calibration tools can assist in this process, however the current state of models
• For example, the problem of transient system response is still a very challenging one: models of engine transient behavior, especially vis-à-vis emissions, are still inadequate.
Courtesy: Bosch, FKFS
Growth in complexity Growth in complexity --19901990--20052005
• Processor: 8-bit → 32-bit• Performance (MIPS): <1 → 300• Transistors: < 1M → 25M• Memory: 33 kB → 4,000 kB• Application parameters: 500 → 8,000
– Expected number in 2007: 20,000• Connector pins: 50 → 150• ECU manual ~5,000 pages!!
Courtesy: Bosch, FKFS
ECU mapping processECU mapping process
Courtesy: Bosch, FKFS
Manual mapping processManual mapping process
Courtesy: Bosch, FKFS
Automated mapping processAutomated mapping process
AcknowledgmentsAcknowledgments
• Steve Yurkovich, – Professor of Electrical and Computer Engineering, Center for
Automotive Research, CAR, The Ohio State University.
• Yann Guezennec, – Professor of Mechanical Engineering, Center for Automotive
Research, CAR, The Ohio State University.
• Michael Bargende, – Professor of Mechanical Engineering, Center for Automotive
Research Institute of Automotive Engineering and Vehicle Engines, FKFS, Universität Stuttgart
Analytical Calibration for Diesel EnginesLisa Farrell, PhDCummins Inc
2Cummins Inc.
Evolution of Complexity for HD Diesel Engine Calibration
0
5
10
15
0 0.1 0.25 0.6 1.0
1974 EPA (HC + NOx)
1988
199019911994
1998
Particulate [g/(HP-hr)]
NO
x[g
/(HP -
hr)]
20042010?
Up until 1990's, performancewas fixed by design – no realdegrees of freedom
In 1991, variable injection timing was a degree of freedom
In the late 1990's, electronic fuel systems added many degrees of freedomIn 2004,
VG, EGR added
3Cummins Inc.
Evolution of Complexity for HD Diesel Engine CalibrationIn 2007, robust particulate filter after-treatment was introduced for HD US on-highway enginesWhat is coming for 2010?
May introduce new degrees of freedomMay introduce new after-treatment challenges
Increasing complexity of Diesel engine systems requires the application of analytical calibration methods
4Cummins Inc.
Analytical Calibration Benefits – The Cummins Inc PerspectiveDesign of experiments has led to reduced data collection timesKey benefit is constant development cycles with increasing system complexityA means to optimize engine systems with many degrees of freedom outside the test cell environmentRigorous problem definition
Constraints – mechanical/emissionsObjective function – emissions/fuel consumption
5Cummins Inc.
400
1000
1400
1800
2230
80
770
020406080
100120
Turbo Speed (krpm)
Speed (rpm)Torque (ft-lb)
Global ModelTurbo Speed = f(Speed, Load,Timing, VG, EGR, Rail Pressure,Pilot, Post, etc.)
Engine Family Torque Curves
0
200
400
600
800
1000
1200
1400
1600
1800
2000
600 1000 1400 1800 2200
Speed (RPM)
Torq
ue (f
t-lb)
CalibrationPlanning
DOEData Collection
ModelDevelopment
Optimizationand Validation
Itera
tion
Engine Speed
Torq
ue
Variation inSpeed, Load, TimingVG, EGR, Rail PressurePilot, Post, etc.
Analytical Calibration DevelopmentSteady-state Performance
6Cummins Inc.
Benefits of Analytical CalibrationFuel Consumption is optimizedover a cycle while constraining cycle bsNOx and surface smoke targets.
Optimal set of control surfaces are determined.
Excellent benefit and capabilityfor steady-state performance.
7Cummins Inc.
Transient Calibration Development
The traditional approach for transient tuning has been centered on transient testingCurrent analytical calibration methods for transient tuning are limited, if available at allApproach for Cummins Inc is a quasi-steady cycle optimization
Captures transient NOx trends reasonablyDoes not capture transient PM trends
8Cummins Inc.
Transient Results
Comparison of measureddilution tunnel NOxemissionsvs. quasi-steady predictedNOx emissions
9Cummins Inc.
What is Needed for Dynamic Calibration of Diesel Engines?Can dynamic models for key performance parameters be incorporated into analytical techniques?What statistical or physical models are appropriate for transient response?What is the optimization method for transient response tuning parameters?
10Cummins Inc.
What is the Future of Diesel Engines?
OxidationCatalyst
DieselParticulate
Filter
TT
SCR Catalyst
TT
NH3 Slip Cat
Temperature Sensors
Engine + After-treatment (TBD)
+
11Cummins Inc.
How Can Dynamic After-Treatment Performance Be Included?
0
50
100
150
200
250
300
350
400
450
0 200 400 600 800 1000 1200
Time [second]
Exha
ust
Tem
pera
ture
[C]
100 150 200 250 300 350 400 450 5000
20
40
60
80
100
Temperature [C]
New
Cat
alys
t Ef
ficie
ncy
[%]
ActualDesired
EngineControls
ExhaustConditioning
400°C = 752°F
After-treatment performance is linked to engine exhaust conditions
12Cummins Inc.
Summary
Analytical calibration methods have been very successful when applied to steady-state performanceDynamic tuning capability needs further development of analytical methodsInclusion of after-treatment modeling techniques would enhance analytical calibration methods for diesel engines in the future
®
1
Analytical Calibrationat
International Truck and Engine Corporation
2007 SAE Commercial Vehicle CongressEduardo Nigro
®
2
DoE Automation
CALGEN
Map Plots
Physical Model Fitting
Job Split
Progress Report
Database
Calibration Compare
Combustion Development
Aftertreatment Calibration
Steady State Calibration Transient Calibration
Vehicle Calibration
Desktop Calibration
Diagnostic Calibration
AUTOMATION &
OPTIMIZATION TOOLS
CALIBRATION PROCESS
CALIBRATION MANAGEMENT
Calibration Process Overview
®
3
DoE Automation
CALGEN
Map Plots
Physical Model Fitting
Job Split
Progress Report
Database
Calibration Compare
Combustion Development
Aftertreatment Calibration
Steady State Calibration Transient Calibration
Vehicle Calibration
Desktop Calibration
Diagnostic Calibration
AUTOMATION &
OPTIMIZATION TOOLS
CALIBRATION PROCESS
CALIBRATION MANAGEMENT
Calibration Process Overview
®
4
Steady State Optimization Process
• Procedure built on CAMEO (AVL) and CALGEN (in-house) provides state-of-the-art tool for steady-state calibration optimization
CREATE DOEs
BUILD LOCAL MODELS
DEFINE KEY POINTS / KEY
REGIONS
RUN DoEs IN TEST
CELL
BUILD GLOBAL MODELS
LOCAL OPTIMIZATION
GENERATE CALIBRATION
MAPS
EXPORT TABLES TO
ECU
GLOBAL OPTIMIZATION
KEY POINT
KEY REGION
SOI FUP
EGR
P
KEY POINT FACTORS
OPTIMIZATION POINTS
SURFACE FIT MODEL BSFC
BSNOx
BSS
OO
T
OPTIMAL MAP OPTIMAL FACTORS
AUTOMATIC DoE ADAPTATION
CAMEO
CALGEN
®
5
+ +
Analytical Tools
Analytical tools must be end-user-oriented
CALGEN is used by engine calibrators
+LOW COST FASTPROCESS
ORIENTEDEASY TO
USE
®
6
Vision for Tool Development
Steady-State Modeling
Static Modeling of Transient Parameters
Transient Modeling with Dynamic Systems Identification
Transient Modeling with Dynamic Systems Models (Model-Based)
TOOL DEVELOPMENT TIMELINE
CALGEN
Under Development
®
7
Analytical Models
Analytical models strengthen the calibration process
Benefits in process flow, data quality and development time
Systematic Approach
Better Knowledge Transfer
Easy-to-do Offline Calibration Modifications
Clear Hardware Limit Determination
Optimal Calibration Generation
ANALYTICAL MODELS
12007 SAE Commercial Vehicle Panel on Analytical CalibrationY. He10/31/07
MathMath--based Control Development based Control Development and Analytical Calibration and Analytical Calibration
Yongsheng HeGeneral Motors Research and Development
October 31, 2007
22007 SAE Commercial Vehicle Panel on Analytical CalibrationY. He10/31/07
OutlineOutlineIntroductionIntroduction
Math-based control developmentAnalytical calibration
MathMath--based control developmentbased control developmentDetailed 1D engine modelMean value engine model
Results and DiscussionResults and DiscussionStep transientsFTP cycle
SummarySummaryCurrent capabilitiesFuture outlook
32007 SAE Commercial Vehicle Panel on Analytical CalibrationY. He10/31/07
IntroductionIntroductionMathMath--based control development in automotive industrybased control development in automotive industry
Much of control design and development process could be done off-line using computer simulationsDramatically reduce development time and risk
Integrated engine and control system model valuable Integrated engine and control system model valuable Accurately evaluate control algorithmsExplore different control strategies & study parameter sensitivity
Before experiments conductedBefore hardware selected and built
Analytic calibration critical to develop modern embedded Analytic calibration critical to develop modern embedded powertrainpowertrain controllers (complexity, speedcontrollers (complexity, speed--toto--market, etc.)market, etc.)
Physical dyno and/or vehicle testing to be minimizedComputer simulations also to be reduced
42007 SAE Commercial Vehicle Panel on Analytical CalibrationY. He10/31/07
Model Accuracy Model Accuracy vsvs Model SpeedModel SpeedFastFast--running engine model with sufficient accuracyrunning engine model with sufficient accuracy
Efficient evaluation of control algorithms and control strategiesExploration of the classical trade-off in the modeling process
Detailed 1D engine modelDetailed 1D engine modelPredict gas dynamics and engine performance within 3-5%Run speed on the order of 100~1000 times slower than real time
Mean value engine modelMean value engine modelCapture dynamics over one or more engine cyclesRun speed close to or faster than real time
Model AccuracyModel Accuracy Model SpeedModel Speed
52007 SAE Commercial Vehicle Panel on Analytical CalibrationY. He10/31/07
Integrated Engine & Control System SimulationIntegrated Engine & Control System Simulation
fuel temp (C)
coolant temp (C)
mph
Intake_oxy_frac_out
Exh_tmp_K_out
Pexh_bar_out
MAP_eng_out
MAF_eng_out
fqc_q_desired_out
pos_itv_target_out
power_eng_out
BMEP_eng_out
trq_eng_out
turbo_speed_out
Exh_oxy_frac_out
pos_vnt_target_out
pos_egr_target_out
PPS (%)
Intake tmp (K)
rpm
EGR cooler tmp (K)
EGR lift (%open)
VNT pos (%close)
fuel (mm^3/st)
eng spd (rpm)
baro (bar)
IAT (K)
EGR cooler out temp (K)
MAF (kg/s)
MAP (bar)
Pexh (bar)
Exh tmp (K)
Intake O2 frac
Exh O2 frac
Turbo spd (rpm)
Trq (Nm)
BMEP (bar)
Power (kW)
Diesel Engine
MAF (kg/s)
MAP (bar)
PPS (%)
rpm_eng
mph_veh
Baro (bar)
fuel_temp (C)
tmp_air_intake (K)
coolant_temp (C)
egr_target_lift (%)
vnt_position (%)
itv_position (%)
fuel (mm^3/st)
Controller (ECM)
Baro (bar)
(SAE Paper 2006-01-0439)
62007 SAE Commercial Vehicle Panel on Analytical CalibrationY. He10/31/07
FTP Cycle: FTP Cycle: Simulation Simulation ResultsResults
BlowBlow--up of the up of the FTP results to FTP results to compare compare simulations and simulations and experiments experiments (200(200--300 s)
200 210 220 230 240 250 260 270 280 290 3000
0.05
0.1
MA
F (k
g/s)
200 210 220 230 240 250 260 270 280 290 300
1
1.2
1.4
MA
P(b
ar)
200 210 220 230 240 250 260 270 280 290 300
1
1.5
2
2.5
Pex
h (bar
)
200 210 220 230 240 250 260 270 280 290 300400
600
800
1000
T exh (K
)
200 210 220 230 240 250 260 270 280 290 3000
0.1
0.2
0.3In
t O2
200 210 220 230 240 250 260 270 280 290 3000
0.1
0.2
0.3
Exh
O2
200 210 220 230 240 250 260 270 280 290 3000
2
4
6
8x 104
Turb
o S
peed
(rpm
)
Time (sec)
(a)
(b)
(c)
(d)
(e)
(f)
(g)
SimulationExperiment
Simulated Thermocouple Temp
300 s)ExperimentSimulation
(SAE Paper 2006-01-0439)
72007 SAE Commercial Vehicle Panel on Analytical CalibrationY. He10/31/07
Detailed 1D Engine ModelDetailed 1D Engine Model
Inlet
Outlet
Turbine CylindersExhaust Manifold
Compressor IntercoolerIntakeThrottle
IntakeManifold
EGRValve
EGRCooler
(SAE Paper 2007-01-1304)
82007 SAE Commercial Vehicle Panel on Analytical CalibrationY. He10/31/07
Input Variables and DOEInput Variables and DOETurbocharged V6 Turbocharged V6 diesel engine with diesel engine with external EGRexternal EGR
Focus on the Focus on the control of fueling, control of fueling, EGR, and VNT EGR, and VNT
DOE: Constrained DOE: Constrained Latin Hypercube Latin Hypercube
Consider the physical constraints of engine operations
500 1000 1500 2000 2500 30000
20
40
60
Fuel
(mg/
cycl
e)
500 1000 1500 2000 2500 30000
0.2
0.4
0.6
0.8
1
Engine Speed (rpm)
EG
R L
ift F
ract
ion
500 1000 1500 2000 2500 30001
1.1
1.2
1.3
1.4
Boo
st P
ress
ure
(bar
)
500 1000 1500 2000 2500 30001
1.5
2
2.5
Engine Speed (rpm)
Bac
k P
ress
ure
(bar
)
Engine Speed (rpm) [530 3000]Total Fueling (mg/cycle) [0 55]EGR Valve Lift Fraction [0 1]
Boost Pressure (bar) [1 1.4]Back Pressure (bar) [1 2.4]
(SAE Paper 2007-01-1304)
92007 SAE Commercial Vehicle Panel on Analytical CalibrationY. He10/31/07
Mean Value Engine Modeling Mean Value Engine Modeling –– Final ModelFinal Model
Hybrid RBF (ModelHybrid RBF (Model--Based Based Calibration Toolbox, MATLAB) Calibration Toolbox, MATLAB) to approximate cylinder to approximate cylinder quantities for better accuracyquantities for better accuracy
Hybrid RBFHybrid RBFVolumetric EfficiencyVolumetric Efficiency 0.9990.999
Indicated EfficiencyIndicated Efficiency 0.9670.967
Exhaust Energy FractionExhaust Energy Fraction 0.9790.979
2R
(SAE Paper 2007-01-1304)
Integrated Engine & Controller Model Integrated Engine & Controller Model –– Updated Updated with Mean Value Modelwith Mean Value Model
fuel temp (C)
coolant temp (C)
mph
Intake_oxy_frac_out
Exh_tmp_K_out
Pexh_bar_out
MAP_eng_out
MAF_eng_out
fqc_q_desired_out
pos_itv_target_out
power_eng_out
BMEP_eng_out
trq_eng_out
turbo_speed_out
Exh_oxy_frac_out
pos_vnt_target_out
pos_egr_target_out
PPS (%)
Intake tmp (K)
rpm
EGR cooler tmp (K)
EGR lift (%open)
VNT pos (%close)
fuel (mm^3/st)
eng spd (rpm)
baro (bar)
IAT (K)
EGR cooler out temp (K)
MAF (kg/s)
MAP (bar)
Pexh (bar)
Exh tmp (K)
Intake O2 frac
Exh O2 frac
Turbo spd (rpm)
Trq (Nm)
BMEP (bar)
Power (kW)
Diesel Engine
MAF (kg/s)
MAP (bar)
PPS (%)
rpm_eng
mph_veh
Baro (bar)
fuel_temp (C)
tmp_air_intake (K)
coolant_temp (C)
egr_target_lift (%)
vnt_position (%)
itv_position (%)
fuel (mm^3/st)
Controller (ECM)
Baro (bar)
102007 SAE Commercial Vehicle Panel on Analytical CalibrationY. He10/31/07
(SAE Paper 2007-01-1304)
112007 SAE Commercial Vehicle Panel on Analytical CalibrationY. He10/31/07
Model Validation: Vehicle TestingModel Validation: Vehicle TestingSeries of different cruising and acceleration conditionsSeries of different cruising and acceleration conditions
Selected for validation: 3 step transients (ST)
0 100 200 300 400 500 600 700 800 900 1000 11000
20
40
60
80
Ped
al (%
)
0 100 200 300 400 500 600 700 800 900 1000 1100500
1000
1500
2000
2500
3000
Eng
ine
spee
d (rp
m)
0 100 200 300 400 500 600 700 800 900 1000 11000
20
40
60
80
Veh
icle
spe
ed (m
ph)
Time (sec)
(a)
(b)
(c)
PPS
RPM
MPH
ST3 ST1 ST2
(SAE Paper 2007-01-1304)
Step Transient: Simulation Results (1/3)Step Transient: Simulation Results (1/3)
0 5 10 15 201500
2000
2500
3000
Eng
ine
Spe
ed (r
pm) (a)
0 5 10 15 200
20
40
60
80
Ped
al (%
)
(b)
0 5 10 15 200
20
40
60
80
100
120
EG
R L
ift (%
)
Time (sec)
(c)
0 5 10 15 200
20
40
60
80
Fuel
(mm
3 /st)
(d)
0 5 10 15 2020
40
60
80
100
120
VN
T P
ositi
on (%
) (e)
0 5 10 15 200
0.05
0.1
0.15
MA
F (k
g/s)
Time (sec)
(f)
Mean Value (RBF)Detailed ModelST2
(SAE Paper 2007-01-1304)
122007 SAE Commercial Vehicle Panel on Analytical CalibrationY. He10/31/07
132007 SAE Commercial Vehicle Panel on Analytical CalibrationY. He10/31/07
Step Transient: Simulation Results (2/3)Step Transient: Simulation Results (2/3)
0 5 10 15 200.8
1
1.2
1.4
Inta
ke P
ress
ure
(bar
)
(a)
0 5 10 15 20
1
1.5
2
Exh
aust
Pre
ssur
e (b
ar)
(b)
0 5 10 15 20
200
400
600
800
1000
Exh
aust
Tem
pera
ture
(K)
Time (sec)
(c)
0 5 10 15 200.05
0.1
0.15
0.2
0.25
0.3
Inta
ke O
xyge
n Fr
actio
n
(d)
0 5 10 15 20-0.1
0
0.1
0.2
0.3
Exh
aust
Oxy
gen
Frac
tion
(e)
0 5 10 15 200
2
4
6x 104
Turb
ine
Spe
ed (r
pm)
Time (sec)
(f)
Mean Value (RBF)Detailed Model
ST2
(SAE Paper 2007-01-1304)
Step Transient: Simulation Results (3/3)Step Transient: Simulation Results (3/3)
0 5 10 15 20-50
0
50
100
150
Eng
ine
Pow
er (k
W)
(a)
0 5 10 15 20
-100
0
100
200
300
400
500
Eng
ine
Torq
ue (N
m)
(b)
0 5 10 15 200
20
40
60
80
100
EG
R R
ate
(%)
Time (sec)
(c)
0 5 10 15 200
20
40
60
80
100
120
Vol
umet
ric E
ffici
ency
(%)
(d)
0 5 10 15 200
20
40
60
80
100
Indi
cate
d E
ffici
ency
(%)
(e)
0 5 10 15 200
20
40
60
80
100
Exh
aust
Fra
ctio
n (%
)
Time (sec)
(f)
Mean Value (RBF)Detailed Model
ST2
(SAE Paper 2007-01-1304)
142007 SAE Commercial Vehicle Panel on Analytical CalibrationY. He10/31/07
152007 SAE Commercial Vehicle Panel on Analytical CalibrationY. He10/31/07
Model Validation: FTP CycleModel Validation: FTP Cycle
0 200 400 600 800 1000 1200 14000
20
40
60
80
Ped
al (%
)
0 200 400 600 800 1000 1200 14000
1000
2000
3000
Eng
ine
spee
d (rp
m)
0 200 400 600 800 1000 1200 14000
20
40
60
Veh
icle
spe
ed (m
ph)
Time (sec)
(a)
(b)
(c)
PPS
RPM
MPH
(SAE Paper 2007-01-1304)
162007 SAE Commercial Vehicle Panel on Analytical CalibrationY. He10/31/07
FTP Cycle: Simulation Results (1/2)FTP Cycle: Simulation Results (1/2)
0 200 400 600 800 1000 1200 14000
20
40
60
80
Fuel
(mm
3 /st) (a)
0 200 400 600 800 1000 1200 1400
60
80
100
VN
T P
ositi
on (%
)
(b)
0 200 400 600 800 1000 1200 14000
50
100
EG
R L
ift (%
)
Time (sec)
(c)
Mean Value (RBF)Detailed Model
(SAE Paper 2007-01-1304)
172007 SAE Commercial Vehicle Panel on Analytical CalibrationY. He10/31/07
0 200 400 600 800 1000 1200 14000
0.1
0.2
MA
F (k
g/s) (a)
0 200 400 600 800 1000 1200 14001
1.5
MA
P (b
ar) (b)
0 200 400 600 800 1000 1200 14001
1.5
2
Pex
h (b
ar)
(c)
0 200 400 600 800 1000 1200 14000
500
1000
Texh
(K)
(d)
0 200 400 600 800 1000 1200 14000.1
0.2
0.3In
t O2
(e)
0 200 400 600 800 1000 1200 1400
0
0.1
0.2
Exh
O2
(f)
0 200 400 600 800 1000 1200 14000
5
x 104
Turb
ine
Spe
ed (r
pm)
Time (sec)
(g)
Mean Value (RBF)Detailed Model
FTP Cycle: FTP Cycle: Simulation Simulation Results (2/2)Results (2/2)
(SAE Paper 2007-01-1304)
182007 SAE Commercial Vehicle Panel on Analytical CalibrationY. He10/31/07
FTP Cycle: Simulation Results BlowFTP Cycle: Simulation Results Blow--up (1/2)up (1/2)
200 210 220 230 240 250 260 270 280 290 3000
20
40
60
80
Fuel
(mm
3 /st)
200 210 220 230 240 250 260 270 280 290 300
60
80
100
VN
T P
ositi
on (%
)
200 210 220 230 240 250 260 270 280 290 3000
50
100
EG
R L
ift (%
)
Time (sec)
Mean Value (RBF)Detailed Model
DetailedMean Value
BlowBlow--up of the FTP results for comparison (200up of the FTP results for comparison (200--300 s)300 s)
(SAE Paper 2007-01-1304)
192007 SAE Commercial Vehicle Panel on Analytical CalibrationY. He10/31/07
200 210 220 230 240 250 260 270 280 290 3000
0.1
0.2
MAF
(kg/
s)
200 210 220 230 240 250 260 270 280 290 3001
1.5
MAP (b
ar)
200 210 220 230 240 250 260 270 280 290 3001
1.5
2
Pex
h (b
ar)
200 210 220 230 240 250 260 270 280 290 3000
500
1000
Texh
(K)
200 210 220 230 240 250 260 270 280 290 3000.1
0.2
0.3In
t O2
200 210 220 230 240 250 260 270 280 290 300
0
0.1
0.2
Exh
O2
200 210 220 230 240 250 260 270 280 290 3000
5
x 104
Turb
ine
Spe
ed (r
pm)
Time (sec)
Mean Value (RBF)Detailed ModelFTP Cycle: FTP Cycle:
Simulation Simulation Results Results BlowBlow--up (2/2)up (2/2)
BlowBlow--up of the up of the FTP results for FTP results for comparison comparison (200(200--300 s)300 s)
DetailedMean Value
(SAE Paper 2007-01-1304)
202007 SAE Commercial Vehicle Panel on Analytical CalibrationY. He10/31/07
Model Accuracy Model Accuracy vsvs Model Speed (Summary)Model Speed (Summary)Mean value engine model developed in this studyMean value engine model developed in this study
Accuracy slightly compromised (cylinder quantities)About 40 times faster than the detailed model
0
20
40
60
80
100
Model Run Time (x Real Time)
Detailed 1D Model [15-16] Mean Value Model (RBF)
Model Error (%)(SAE Paper 2007-01-1304)
212007 SAE Commercial Vehicle Panel on Analytical CalibrationY. He10/31/07
SummarySummaryA fastA fast--running mean value engine model with sufficient running mean value engine model with sufficient accuracy developed for control applications accuracy developed for control applications
Reduced from a detailed engine model in GT-PowerConstrained Latin Hypercube to consider physical constraints Hybrid RBF to approximate cylinder quantities for better accuracyCompletely simplified (cylinders, intake & exhaust system)
Model development time & model throughput minimized
The developed mean value model integrated with a The developed mean value model integrated with a comprehensive controller model for control analysiscomprehensive controller model for control analysis
The integrated engine and control system model extensively validated with satisfactory accuracy achieved
1 Step change, 3 Step transients, 1 FTP cycleControl strategies development & preliminary calibrations beforehardware availability and testing
222007 SAE Commercial Vehicle Panel on Analytical CalibrationY. He10/31/07
SummarySummaryCurrent Capabilities:Current Capabilities:
Provide fast-running models for control developmentExplore control strategies and study control parameter sensitivities Generate preliminary calibrations before hardware availability and testing Use for air-EGR system calibrationsAllow easy adaptation to hardware changes …
Future Outlook:Future Outlook:Analytic calibration critical and integral part of modern embedded powertrain controllers development process, but more important in the early development phasePhysical dyno and/or vehicle testing still needed, but to be minimized Computer simulations more accurate, powerful and standardized, but model development time, model throughput, and model runs to be reduced
John Deere Power SystemsAnalytical Engine Calibration at John Deere
John Deere Power SystemsAnalytical Engine Calibration at John Deere
Jason SchneiderEngine Engineering
MotivationMotivation• Performance Optimization
• Off-Road Market– Number of applications – Over 1000 internal and external
• Application Variation– Different Usage Profiles– Different Optimization Objective
• Complexity– HPCR– Cooled EGR – VTG and EGR Valve
Worldwide Engine CustomersWorldwide Engine CustomersENGINE BUSINESS OVERVIEW
External ApplicationsExternal Applications
≅ 50% Engine VolumeIndustrial, Power Generation
and Marine Applications
Internal ApplicationsInternal Applications≅ 50% Engine Volume
Ag, C&F, CC&E Division Applications
Usage Profile ExamplesUsage Profile Examples
Application 1 Application 2
750
900
1150
1350
1500
1600
1700
1800
1900
2000
2100
2200
2300
2412
.5
2475
0
10
20
30
40
50
60
70
80
90
100
110
120
Speed [rpm]
Pow
er [%
of R
ated
]
Islands of Operation
750
900
1150
1350
1500
1600
1700
1800
1900
2000
2100
2200
2300
2400
2450
0
10
20
30
40
50
60
70
80
90
100
110
120
Speed [rpm]
Pow
er [%
of R
ated
]
Islands of Operation
Analytical Calibration ObjectiveAnalytical Calibration Objective
• Generate calibration tables off line from test bed– Comply with emission legislation– Minimize BSFC, subject to application, base engine and
calibration constraints
• Deere developed empirical engine models are used– DOE – Matlab MBC Toolbox– Matlab (MBC Toolbox), Statistica, Table Curve 3D
• Deere Optimized Table Generator (DOTG) interface is used to enter calibration optimizer settings
– Excel driven Matlab optimization
• Final results are calibration set point tables
DOTG Calibration Process Flow
Implementation
Data ModelingDOE Design Data Collection
Deere Optimization GUI
Table ExportTable Import
CalibratorSettings
Calibrations not released without test bed confirmation
Virtual Calibration Lab
TimingBSFCTorque
Fuel Pressure
Input and Output Scheme
Fuel/Air Ratio
Turbo. SpeedPeak PressureVTG Position
ConstraintsAnd
Optimal Tables
Objective
Optimal Tables
EGR
NOx+HCTC AFR
Constraints
PMSmokeEGR Cooler TComp. Out TComp. Out PEOI TimingCoolant HRIntercooler HRPress. Ratio
RPM
Fuel Mass/Inj
Table Set PointComp. Mass
DieselEngine Models
BenefitsBenefits• Performance optimization given application constraints
– 1-3% improvement in application specific fuel consumption compared to conventional techniques
• Reduction of needed testing and associated expense– Measured in hundreds of thousands of dollars compared to
conventional techniques
• Control of constraint usage to minimize errors– Example – peak firing pressure or exhaust temperature– Consistent reliability performance across applications
• Calibration methodology is controlled – NTE compliance– Similar performance output of engine across applications
Optimization Potential –Optimization Potential –8530 Ag Tractor
1 2 3 5 6 7
Emission Mode
BSF
C, g
/kW
h
Fully Optimized Traditional Calibration
0.7%
1.1%
6.6%
1.4%
1.1%1.0%
Industry Record, Nebraska Test 2005:Most Fuel Efficient Row-Crop TractorIndustry Record, Nebraska Test 2005:Most Fuel Efficient Row-Crop Tractor• 8430 Series Tractor / 9.0L PowerTech Plus
– 8.8% more fuel efficient with 40% less emissions
– Engine optimization
– Vehicle efficiency improvement
JOHN DEERE ENGINES
AccuracyAccuracy
Example Results – OEM RatingExample Results – OEM RatingNOxHC
0 1 2 3 4 5 6 7 8
Modal Point
NO
xHC
, g/k
Wh
-10
-8
-6
-4
-2
0
2
4
6
8
10
% E
rror
Prediction Actual % Error
Example Results – OEM RatingExample Results – OEM RatingSmoke
0 1 2 3 4 5 6 7 8
Modal Point
Smok
e, R
B
-10
-8
-6
-4
-2
0
2
4
6
8
10
% E
rror
Prediction Actual % Error
Example Results – OEM RatingExample Results – OEM RatingBSFC
0 1 2 3 4 5 6 7 8
Modal Point
BSF
C, g
/kW
h
-5
-4
-3
-2
-1
0
1
2
3
4
5
% E
rror
Prediction Actual % Error
Example Results – OEM RatingExample Results – OEM RatingVTG Position
0 1 2 3 4 5 6 7 8
Modal Point
VTG
Pos
ition
-5
-4
-3
-2
-1
0
1
2
3
4
5
% E
rror
Prediction Actual % Error
Transient Certification TestsTransient Certification Tests
• NRTC in effect for Interim Tier 4
– New problem statement for Deere
• Methods heavily depended on emissions technology chosen
– Number of independent variables• EGR vs Non-EGR• After-treatment and interaction
Transient Certification TestsTransient Certification Tests• Perturbation of calibration tables for sequence of transient test
runs
– Select value for calibration tables that minimizes emission tradeoff point by point
– Fuel Pressure, Injection Timing, EGR rate, etc.
• Steady state points weighted to for correlation to transient test
– Calibration process as outlined can be used to reach targets
– System control tuning for refinement of NOx and PM tradeoff for cooled EGR engine
• Transiently accurate emission models
– Most elegant
FutureFutureFirst Steps
• Elimination of confirmation runs for steady state
– Allows further release of expensive calibration resources to earlier stages of product development
• Accurate transient emission models for transient optimization
– Fits well with need for embedded models for systems with AT to predict state of system
FutureFuture
Needs
• Better integration of calibration process with ECU software development process
– Collective mind set
– Comprehensive tool set• Controller Models Engine Models Calibration
• Predictive emission models that are accurate to drive process further upstream
– Engine cycle simulation environment
Thank You
©20
07 T
he M
athW
orks
, Inc
.
® ®
Analytical Calibration and What it Means
2
Analytical Calibration Process
3
Current Benefits of Analytical Calibration
Enables calibration process prototyping before hardware availability
Provides diagnostic data for later hardware testing
Provides fast-running statistical engine model for control development
Can be used for calibrations related to engine-breathing (e.g., EGR, VE)
Provides a non-hardware training environment for new calibrations
Acts as an executable specification of company calibration processes
Provides a means of determining minimum DoE testing requirements
4
Analytical Calibration Workflow Example
5
Identify Future Physical Test Setup
VGT Pos.
EGR Pos. Brake TorqueEngine Out NO
RPM Fuel Mass/Inj
SOI
Fuel Press.
Exhaust AFRTurbo. SpeedPeak PressureEGR Fraction
Closed-LoopEGR Fraction ControllerEGR Fraction Cmd.
6
Define Optimization Model Setup
VGT Pos.
EGR Pos.
Brake TorqueEngine Out NO
RPM Fuel Mass/Inj
SOI
Fuel Press.
Minimize mode-weighted brake specific fuel consumption,subject to multiple mode-based output constraints
Exhaust AFRTurbo. SpeedPeak PressureEGR Fraction
BSFC
Used toBuildExtraTable
Objective
Used To BuildOptimal Tables
7
Design Experiment
8
VGT Pos.
EGR Pos. Brake TorqueEngine Out NO
RPM Fuel Mass/Inj
SOI
Fuel Press.
Exhaust AFRTurbo. SpeedPeak PressureEGR Fraction
Closed-LoopEGR Fraction ControllerEGR Fraction Cmd.
High FidelityEngine Model
Execute Virtual Testingwith Distributed Computing
9
Statistically Model Engine Responses
10
Set Up Optimizations With Constraints
11
SOI Table Fuel Mass Table
Fuel Pressure Table VGT Rack Position Table
EGR Mass Fraction Table
Generate Optimal Calibration Tables
12
Future Benefits of Analytical Calibration
Inexpensive calibration adaptation to late program hardware changes
Tighter feedback between engine hardware design and control design using model sharing
Improvement of predictive quality of CAE engine models resultingfrom calibrator feedback