Model Based EnginegCalibration
Using State of the Art Software Support
2010 Motorcycle & Engine Key Technology SeminarTanjin University
June 2.-3.
Tony Gullitti, IAV Automotive Engineering, Inc
Don Nutter, A&D Technology, Inc
Dr. Jürgen Bredenbeck, A&D Europe GmbH

Introduction•Model based calibration– Use of models of the engine behavior for main calibration– Models are created using Design of Experiments (DoE) Methods•

D E iin engine
DoEi ddevelopment
lt iis more ththen jjustt experimentitd
designi– It is a synonym for a structured methodology of calibration•

Split natnature
re of the process– Statistical knowledge for analysis– Test cell automation for data gathering•

Typical end user understands engines / calibration– But is not a statistics expert– Does not specialize in test bed control systems

Objective••••

Objective– Demonstrate how to use the software tools to execute a typical calibrationtask– Ease of use
Calibration Goal– Optimize part of the speed/relative load map of a gasoline engine
Definition of Factors– Define optimal settings for available parameters• Variable Valve Timing• Spark Advance• Lambda
Optimization Objectives– Minimize brake specific fuel consumption (BSFC)– Minimize the BSFC and emissions– Maximize the torqueq

State of the Art Software Tools•

The use of state of the art software tools facilitates the process for theend user and organization– EasyDoE ToolSuite provides statistical methods– ORION provides procedures for automated testingDefinition of Factors and Responses•

The factors required are– Engine Speed– Relative Load
– Variable Valve Timing– Spark Advance– Lambda•

Optimization Constraints– Spark advance less thanor equal to MBT Spark•Monitor during data gathering– Knock Amplitude– Water,W t Oil Temperatures,Ttetc.

•The responses required are– Torque– Mass Fuel Flow
– Exhaust Temperature– Maximum Brake Torqueq ((MBT))
Spark– Emissions HC/CO/NOx– Coefficient of Variation of IndicatedMean Effective Pressure (COV ofIMEP)– BSFC (calculated)R  ƒ Spd,Ld,VVT
Spd Ld VVT,Spk,Lmd
Spk Lmd MBT  ƒ Spd, Ld,VVT , Lmd Set Up Project•Factors and responses are entered into EasyDoE ToolsuiteEasyDoE Test Plan
• The experiment design is entered, and 145 points are generated

A&D Technology’s R&D Test Cell
Test Cell Features:• ORION Test Automation• iTest Bench control• ADX rapid prototype ECU• Best Sokki Emissions BenchOther Tools• CAS C
Combustionb ti A
Analysisl iCalibration
Generation

Engine Features:
4 cyl gasoline• Production 4-cylengine• Variable Valve Timing
ADX
ECU Replacement

ORION
Automated Calibration

iCentralLab ManagementDoEModeling

iTEST
DAC - Bench Control

CASCombustion Analysis

Best-Sokki
BEX 8500 EGR
Raw Gas E-BenchiConnect
Distributed I/OAndromeda
Simulation Controller
FIC
Facilities Interface
CellMinder II
Cell Monitoring

Engine/Dyno

ORION Configuration• ORION MDA is the key interface for the user creating theconfiguration• Main configuration task is
Compiling the following
elements:
 Parameters – both from thetest cell and Calibration tool
 Sequence – action to be
executed in, flow-chart based TestT t Plan
Pl – allll valueslffrom
the DoE that the sequenceneeds to execute importedfrom Easy DoE

ORION Test Execution• MA is the key interface for the operator in the test cell Simple load the configuration file from MDA
 Connect to test cell control and calibration tool Execute sequenceqbyypressing “start”• Indicators and graphs keep
the operator informed onprogress and status• Test cell system collects thedata as directed by MA via
ORION “Measure” action• MA remembers state of test
point – measured successfully or not Easy to restart a test

Data Gathering Strategy
Limit of:
COV of IMEPKKnocking
ki Limit
Li it

Limit of:
COV of IMEP
Exhaust Temperaturetorquemax-21 °CA

Torqque• Save existing cal values• Set speed and load
• Set VVT
• Set Lambda• Sweep spark for MBT– Measure
M• Set offset spark value
relative to MBT Spark– Measure• Reset cal values+3 °CA

MBTDoEMeasure
1.
2.

Find MBT
S Spark
Set
S
Timing Offset
Offrelative toMBT(value given by test plan)

Ignition Angle

Data Gathering Strategy• Test cell run in speed / load mode
• Parallel control on spark advance during setting of speed / load and stepwise
setting of VVT and Lambda– CA50– Monitored limits of temperature and knock• Two data points taken for each Speed/Load/VVT/Lambda– On
On-lineline determination of MBT Spark using ORION optimization– Offset spark added to MBT• Repeatability points are added– Center point of factor ranges– Used to check verify model quality

Data Gathering in the Test Cell with ORIONPart 1: Parallel Control of Spark CA50, Set stepwise VVT••••••••

padvance for reset atStore the initial values for the spark
the end of the step.
Start the parallel control for spark advance.
Set the speed/load setpoint from the experiment design.
Store the VVT value for resetreset.
Store flags from the experiment design.
Turn on VVT permission and set the VVT stepwise.
pStabilize the temperature
Change the dyno mode to speed / alpha to lock the airpath.

Data Gathering in the Test Cell with ORIONPart 2: Set Stepwise Lambda•••

Store the initial values for the Lambda for reset atthe end of the step.
Set the Lambda permission and set Lambda
stepwise.
Stop the parallel control for spark advance.

Data Gathering in the Test Cell with ORIONPart 3: Optimization•••••

Find optimal torque by sweeping spark. Exhaust
temperature and knock are monitored to defineboundaries.
Alternatively, if this is a repeatability point, then set
to the desired spark in the test plan. After every 10
experiment design points a repeatability point is
run using the center point for each region todetermine the variation of the response values.
Stabilize for 10 seconds and then measure.
Reset the values if this is a repeatability point.
Otherwise continue to measure offset spark.

15

Data Gathering in the Test Cell with ORIONPart 4: Measure Offset Spark, Reset Starting Values•••padvance byy the offset sparkp
Increment the sparkvalue from the experiment design.
Stabilize and measure.
Reset the initial values and proceed to the nextstep.

Data Review• The data is imported into EasyDoE Toolsuite and reviewed via a userinterface

Data Review• Temperature limits during data gathering set to 750°C– This was conservative; difficultly reaching lambda = 1• Aftermarket Lambda sensor used for AFR feedback control– AFR calculated from bench was more reliable– Resulted in variation in the repeatability measurements for emissions

Lambda < 1 as speed / load increases

Modeling• The data is associated with the factor definition and modeled• A best model is selected for each response and stored as a result model

EasyDoE Fitting Methods• Model fitting is done automatically in EasyDoE Toolsuite• The following
gp
polynomial
yfittingg methods are run for each model
Polynomial Fitting Method

Description

1.

Standard Regression

Least Squares Estimation

2.

Minimize PRESS

The PRESS value is used to select the model terms.

3.

Stepwise Fit

Stepwise regression for term selection

4.

OLS

Orthogonal Least Squares Estimation

5.

T-testTests each coefficient to be zero with a specific probability (model structure).
If the coefficient is likely to be zero it is taken out.

6.

Robust Regression

Detects the bad data points and build models without these points.

7
7.

R b t Regression
RobustRi+ Minimize
Mi i i PRESS

M d l isModeli built
b ilt without
ith t badb d datad t points
i t andd trainedt i d with
ith theth 'best''b t' termstselected by 'Minimize PRESS' algorithm.

8.

Robust Regression + Stepwise Fit

Model is built without bad data points and trained with the 'best' terms
selected by 'Stepwise Fit' algorithm.

9.

Stagewise Regression

Incremental Forward Stagewise Algorithm i.e. incremental coefficient
adaptation in direction of highest correlation to the current residuals.

Model Quality Analysis
Model Quality Analysis
6.00%

4.00%
Normalized RMSEE  
Repeatability (%)  Average mean * of the repeatability points

5.00%

RepeatabilityNorm RMSE Fit
Norm RMSE Valid

3.00%

2.00%

Model Quality (%) RMSERange

1.00%

0.00%
TorqueTHCNOxFuel Flow

Output Factor

Repeatability  Model Quality Fit  Model Quality Valid & Ver  5%%`
• Repeatability and Model Quality should correlate• The variability of the AFR sensor resulted in higher repeatability values for emissions

Optimization Requirements• In Model Evaluation a grid of speed / load points is defined:– Speed3000 to 5000 in 200 RPM increments– Relative Load
50 to 100% in 10% increments• A weightedgsum g
gradient descent method is selected.
– +1
Maximize the response– -1
Minimize the response– 0
No optimization on the response• Three optimizations:– Minimize BSFC:– Minimize BSFC :• Min HC/CO/NOx– Maximum torque:

BSFC weightg is set to -1
BSFC weight is set to -0.5.
HC/CO/NOx weights set to -0.05/-0.05/-0.4
Torque weight is set to +1• A constraint is set to restrict the factor of– Spark advance < MBT spark

Model Evaluation – Map Creation• Maps for each optimization are created in the map editor– VVT, Spark, Lambda

Model Evaluation - Optimization• The optimization is performed in Model Evaluation

Model Evaluation – Map Editor• After the optimization the maps can be edited graphically or in the table

Model Evaluation
Objective
jBSFC

Torqueq

LambdaNOxSpark

BSFCVVTConclusion• EasyDoE Toolsuite and ORION provide effective methods for
implementing DoE methods– Their GUIs make DoE easy to use– The results match the physical expectations

Tony Gullitti
IAV Automotive Engineering, Inc15620 Technology Drive
Northville MI 48168
Northville,Phone: +1(734) 233-3352
anthony.gullitti@iav-usa.com

Th k youThank

Don Nutter
A&D Technology,gy, Inc
4622 Runway Blvd
Ann Arbor, MI 48108
Phone: +1(734) 822-9564
dnutter@aanddtech.com
Dr. Jürgen Bredenbeck
A&D Europe GmbHIm leuschnerpark 464347 Griesheim
Germany
Phone: +49(6155) 60 52 50
bredenbeck@aanddeurope.com