Steady-State Engine Modeling for Calibration: A ... · Steady-State Engine Modeling for...

Steady-State Engine Modeling for Calibration: A Productivity and Quality StudyMathWorks Automotive Conference 2007

Hyatt Regency, Dearborn, MIJune 20, 2007

Toyota Motor Engineering and Manufacturing, NA

Toyota Motor Corporation

A&D TechnologyIAV GmbH

Ken ButtsSatoru WatanabeJohn ReevesUlrike Schoop

June 20, 2007IAV GmbH, A&D Technology, TMC, TEMA

2

Presentation Outline

1. Motivation and Introduction

2. Advanced Calibration Process Considerations

3. Engine Test-bench Infrastructure

4. Engine Modeling: Productivity and Quality Assessment

5. Future Work


3

Motivation

New powertrain technologies benefit society …

*

1. EFl : Electronic Fuel lnjection *2. VVT-i : Variable Valve Timing-intelligent *3. D-4 : Direct lnjection 4 stroke gasoline engine *4. D-4S : Direct lnjection 4 stroke gasoline engine superior version

(Source: Toyota Motor Corporation - http://www.toyota.co.jp/en/tech/environment/)

but their development carries planning implications• Complexity • Uncertainty

Engine development is already on Toyota’s critical development path !

4

Consider Engine Mapping

Engine Map: • Steady-state engine response to control inputs

– Establishes operating limits– Used to set control input bias versus operating point (steady-state calibration)

• Typically an empirical model due to accuracy requirements• Traditional Methods use Full-Factorial Experiment Design

Development Planning Implication:

2,940

26,460

238,140476,280

1

10

100

1,000

10,000

100,000

1,000,000

Base Engine12 x 7 x 7 x 5

+ Intake VVT9 levels

+ Exhaust VVT9 levels

+ Manifold Tuning2 levels

# of

Mea

sure

men

ts .

Engine technology vs. Measurement requirements

We need a different way !


5

Model-based Calibration

Our Process Model :

Definition of factors

and responses

Optimization & evaluationof DoE- models

Experimental design

MBC Toolbox (TMW)

ATLAS (A&D)Measurements

on the test bench

ModelingFilling tables and fitting models of ECU

CAGE (TMW)and scripts (IAV)

X XX not considered in this presentation

Model-based Calibration Toolbox

AtlasModel-based Calibration Toolbox


6

Purpose of our study

1. Investigate (and Develop where necessary) Model-based Calibration processes, methods, and tools infrastructure for engine mapping to increase productivity.

2. Quantify productivity benefit and verify quality

1. Relative to known metrics from previous production development

1. I-4 engine system with Intake VVT

2. Super-Ultra-Low-Emission-Vehicle (SULEV) emission target


7

Advanced Calibration Process Considerations

1. Model Structure

2. Experiment Design

3. Model Generation

8

Model Structure – based on IAV experience

1. One-Stage Model

2. Engine Map is composed of five over-lapping regional models with interpolation

6000

Load

/ %

Speed / rpm

WOT

1000 2000 3000 4000 5000600

Region 1

Region 2

Region 5

Region 3

Lambda < 1 (catalyst temperature)

limited CAM timing

Region 4


9

Response Models

scalingspeed, load, VVTECU calculated engine load

constraintspeed, load, VVT, λλλλ, spark timingengine roughness (COV of IMEP)

constraintspeed, load, VVT, λλλλ, spark timingHC, NOx

constraintspeed, load, VVT, λλλλ, spark timingcatalyst temperature

constraintspeed, load, VVT, λλλλ, spark timingexhaust gas temperature

constraintspeed, load, VVT, λλλλknocking limit spark timing

constraintspeed, load, VVT, λλλλMBT spark timing

optimizationspeed, load, VVT, λλλλ, spark timingCA50 (crank angle @ 50% burn)

optimizationspeed, load, VVT, λλλλ, spark timingfuel consumption

optimizationspeed, load, VVT, λλλλ, spark timingtorque

PurposeModel InputsEngine Model Output


10

Method to coordinate the regional experiment designs

1. Analyze the initial preparation measurements and engineering knowledge (i.e. full load, zero torque, VVT, spark timing, and ��limits) to determine the experiment constraints for each region. –Capture these constraints in five corresponding test-plans in a single Model-Based Calibration Toolbox Project.

2. Use the Model-Based Calibration Toolbox to sequentially design an experiment for each test-plan while enforcing common measurement-points in overlapping areas.

3. Export the regional experiment designs and combine them into a single design. Sort the measurement points by speed and then load in ascending order.

4. Include repeatability measurement points at regular intervals toallow the test-automation procedures, test engineers, and modeling engineers to assess data quality.


11

Experiment Regional Design Recipe

Measure the engine response at:

1. the spark timing that yields either the Maximum Brake Torque (MBT) or the engine knock limit and

2. some spark timing delta from the MBT / knock limit spark timing as scheduled by the experiment design.

Specify a 4th order polynomial with 3rd order interaction model then:

1. generate a D-optimal design with 20% more points than is minimally required. The D-optimal design gives good test coverage at the borders of the model.

2. add 20% more points with a V-optimal design. The V-optimal design adds coverage of the interior of the model.

3. add 20% more validation points with a V-optimal design. These measurements are not used for the fitting of the model.


12

Model Generation Recipe

Semi-automated scripts that use the Model-Based Calibration Toolbox Command Line Interface:

1. automatically generate alternative polynomial and Radial Basis Function models that are initially evaluated based on validationRoot-Mean-Square-Error.

2. manually tune candidate polynomial models based on condition number and the effect of transformation.

3. manually inspect candidate models using single-influence plots to ensure that the responses match physical intuition.


13

Engine Test-bench Infrastructure

1. Combustion analysis system to measure knock, misfire, and Coefficient of Variation of Indicated Mean Effective Pressure (COV of IMEP) .

2. Test-bench automation

3. Engine water and oil temperature conditioning

Engine Dyno.

DAC

Emissions Analyzer Prototype

ECU

CRAMAS & Rtype ECU Prototyping Tool

ASAP3 ASAP3 CAS

CAL CAL CAS

Combustion Analaysis ADAPT Data Acquisition and Control ATLAS

Calibration Tool


14

Test-bench Automation using Atlas by A & D Technology

1. Atlas communicates with test-bench data-acquisition and control and engine ECU calibrations systems via ASAM standard protocols.Our set-up allowed ECU calibration command and response rates at up to 10HZ.

2. Atlas readily imports and executes experiment designs from the Model-Based Calibration Toolbox.

3. Atlas can dynamically access MATLAB for on-line data processing. We use this feature to fit a spark sweep from as few as three measurements.

4. Atlas can execute parallel threads of execution. We separate our‘engine safe operation monitor’ and our ‘test-execution’ processes into parallel, communicating execution threads.


15

Test-bench Automation Example

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16

Engine Modeling: Productivity

# points: X 0.17Test time: X 0.28With heaterImproved COV of IMEPAs above.

3

# points: X 0.17Test time: X 0.36As above.

General Improvements:

Parallel program.

5 dependent regions;

Overlap Commonality

2

# points: X 0.24Test time: X 0.47Heat exchanger only

Baseline;

Sequential program flow.

5 independent regions;

No overlap matching

1

# of points & Test Time (compared to baseline)

Test BenchTest AutomationExperiment DesignRun#


17

Engine Modeling: Quality Assessment

0.000

2.000

4.000

6.000

8.000

10.000

Torque Fuel Flow CA50 Cat Temp In Cat Temp Rear HC NOx COV of IMEP ECU KL

Per

cent

Run2 Ave Repeatability (Std/Mean)Run2 Model Error (RMS/Range)Run3 Ave Repeatability (std/Mean)Run3 Model Error (RMSE/Range)


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Given Successful Demonstration: Future Work

1. Installation of the test-bench infrastructure in Toyota facilities for detailed evaluation.

2. Confirmation of the methods on a second engine application in close cooperation with Toyota mass-production engineers.

3. Porting the test-bench automation logic to a new Simulink / Stateflowbased test-automation environment from A&D.

4. Extending the test-bench automation methods in a general way to address more advanced engine technologies.

5. Connecting the test-automation to Toyota standard ECU communication tools.


19

Acknowledgements

1. Frank Biens, Tony Gullitti, Mirko Knaak, and Karsten Roepke, (IAV).

2. Andy Hall, Brian Moore, and Ray Skinner (A&D Technology).

3. Allen Lock (Denso International of America).

4. Kotaro Tanaka, Harunaga Uozumi (Fujitsu-Ten)

5. Harufumi Muto and Masato Ehara (Toyota Motor Corporation.)

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