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Implementation of Model-Based Calibration for aGasoline Engine

2012-01-0722
Published
04/16/2012

Shugang Jiang and Donald Nutter
A&D Technology Inc.

Anthony Gullitti
IAV Automotive Engineering Inc.
Copyright © 2012 SAE International
doi:10.4271/2012-01-0722

ABSTRACTTo meet the ever increasing requirements in the areas ofperformance, fuel economy and emission, more and moresubsystems and control functions are being added to modernengines. This leads to a quick increase in the number ofcontrol parameters and consequently dramatic time and cost
increase for engine calibration. To deal with this problem, theautomotive industry has turned to model-based calibration fora solution. Model-based calibration is a method that uses
modern Design of Experiments (DoE), statistical modelingand optimization techniques to efficiently produce highquality calibrations for engines. There are two major enablers
for carrying out this method - fully automated engine control
and measurement system, and advanced mathematical tools
for DoE, modeling and optimization. This paper presents acase study of adopting this methodology for thedetermination of optimum steady state calibrations of ignition
timing, air-fuel ratio and intake cam phasing for a gasoline
engine. ORION automated engine control and measurement
system is used for testing data collection. EasyDoE Toolsuiteis used for DoE, engine response modeling and control
parameter optimization. Major features of these tools aredescribed. Each step in performing this process, including
definition of factors and responses, DoE, automatic
measurement on engine test bench, creation of engine models
of sufficient accuracy, and generation of control maps using
optimization techniques, is covered. The results demonstratethat the model-based approach is a well suited method forengine calibration, and the integrated system provides aneffective solution for implementing model-based calibration.

INTRODUCTION
In order to improve fuel economy and lower exhaustemissions, modern internal combustion engines have beenequipped with many new subsystems. More and more control
features are being integrated in the engine Electronic Control
Unit (ECU). When the number of control parameters isrelatively low, the full factorial mapping method may be aviable solution for some engine calibration tasks. However,as new degrees of freedom are introduced, the experimental
burden for the full factorial mapping method will increase
exponentially which makes the method impractical. Dealing
with this problem, the automotive industry has adopted themodel-based calibration methodology in the recent years
which has been proven very successful in dramaticallyshorten the time needed for various calibration tasks [1, 2, 3,4, 5, 6, 7, 8].
In model-based calibration a design of experiments (DoE) is
first performed to generate a matrix of engine test factors.
Then engine test and measurement are carried out at thesepoints. The number of engine test points should be minimal
but still sufficient for obtaining data to create a statisticalmodel that provides enough fidelity in describing the engine
responses to variations in the factors. This statistical model isthen used for the determination of optimum control
parameters of the engine.
One key element in performing model-based calibration is afully automated engine control and measurement system,
including engine ECU and calibration tool, dynamometer
control, measurement devices, and various test cell
equipments that need be coordinated. In this paper A&D's
ORION automated engine control and measurement system isGratis copy for Shugang Jiang
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used. On one hand, ORION communicates with engine ECU,
which in this study is a rapid prototyping controller, forengine control parameter adjustment; on the other hand, it
communicates with engine test cell control system for
dynamometer control and engine performance measurement.
ORION will perform the experiments designed from the DoE
tool, and runs the sequences to command both the test cellcontrol system and the ECU accordingly to collect the engine
performance data for building the statistical model.

algorithms needed to run the engine and all the functions canbe calibrated via ASAP3. By coordinating all the devices
involved, ORION maintains control of the test cell and theengine controller to collect the data from all sources for use
in the modeling process.

Another key element in model-based calibration is the toolfor DoE, statistical modeling and control parameter
optimization. In this study, IAV's EasyDoE Toolsuite is used.
EasyDoE Toolsuite enables users to generate the experiment
design and to perform data analysis, modeling, optimization,and map generation. First test points are created by EasyDoE
Toolsuite, which is subsequently imported into ORION forexecution. After ORION finishes the test sequences the test
results are sent back into EasyDoE Toolsuite for engine
modeling and optimum control map creation.
The engine used in this study is a 2.0L in-line 4 cylinder
gasoline engine with intake cam phasing, i.e., variable valve
timing (VVT). Engine equipped with cam phasing provides
more flexibility for the designers to strive for high volumetricefficiency under various engine operation conditions,enabling increased engine torque output with simultaneous
reductions in exhaust emission and fuel consumption. In thisstudy, the optimization objective is to find the optimal
combination of the major engine control parameters, i.e., todefine the optimal settings for spark advance, air-fuel ratio
and intake VVT across the interested operating region (speedand load), to minimize brake-specific fuel consumption(BSFC) and meanwhile keep engine exhaust emissions,engine roughness and exhaust temperature etc. within theconstraints. To limit spark inside the knocking boundary, an
optimization constraint of spark advance less than or equal toMaximum Brake Torque (MBT) spark is used as well.
The next sections will cover in more details the setup of thesystem, the design and execution of the tests, and the creationof modeling and calibrations.

SYSTEM SETUP
Figure 1 shows the overall setup of the automated calibration
system. On the top of the system is ORION, which provides
the capability of designing and performing customized test
cell data gathering processes. On one hand, ORION accesses
the A&D iTest Data Acquisition and Control (DAC) systemthrough ASAP3, thus controls the operation of thedynamometer as well as various test cell equipments, such as
combustion analysis system and emission bench etc; On theother hand, ORION communicates with the ADX Rapid
Prototyping Controller which realizes the engine control
functionalities. The ADX controller includes all the typicalFigure 1. Automated Calibration System Setup

DESIGN OF EXPERIMENTIn this study the interested operating region of the engine isthe medium to high speed (3000 to 5000 rpm) and medium tohigh load (50% to 100%) range. The optimization objective isto find the optimum spark advance, air-fuel ratio and intake
VVT that minimizes BSFC. The engine test factors areengine speed, relative load, spark advance offset, air-fuelratio, and intake VVT. The engine responses are torque, massfuel flow, exhaust temperature, MBT spark, emissions
HC/CO/NOx, and COV of Indicated Mean Effective Pressure(IMEP). Other than using the absolute values for spark
timing, in this study the spark advance offset is used as a testfactor. It refers to the offset from the MBT spark at each
operating point, while the MBT spark is obtained during thedata gathering process using an online optimization functionof ORION.
Good judgment for the space of likely optimum control
values is always critical for effective implementation ofmodel-based calibration. After all the factors and responses
are identified, ranges of the test factors need be firstGratis copy for Shugang Jiang
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determined. Then experiment constraints of the factors andresponses are specified based on experience and preliminary
tests. For example, at certain engine speeds that are relatively
low, higher values of relative load cannot be reached.
Experiment constraints are expressed using either equations
or 2D tables.
Next the model structure need be specified, which will affectthe minimum number of measurement necessary for building
the engine statistical model. While some responses can bemodeled very well using low order polynomials, responses
like emissions etc. need higher order polynomials formodeling, which determines the number of test points
needed. In this study, the model structure is selected to be 4thorder polynomials with 3rd order interactions, which results
in a polynomial of 61 terms. Based on experience, thisstructure can efficiently model engine responses with highfidelity. To avoid over-fitting, for each response, various
methods are used and compared during model fitting andsome of the 61 terms will be removed.
After the range, constraints, and model structure informationis entered into EasyDoE Toolsuite, the set of target points tobe measured are created based on the design method selected.
D-optimality minimizes the generalized variance of theparameter estimates. V-optimality minimizes the variance ofthe predicted response. In this study, first sufficient numberof D-optimal points are specified, then V-optimal points andspace-filling points are added to arrive at the final number ofexperiment points. Altogether 269 experiment points aregenerated. As an example, Figure 2 shows the DoE result for
intake VVT with respect to engine rpm and relative load. The
results of DoE are exported to a file which will be read into
ORION test plan for execution.

EXECUTION OF TEST PLAN
ORION automated control and measurement system imports
the DoE results and performs the engine tests accordingly.
ORION facilitates the engine calibration process by taking
control of both the ECU calibration tool and the test cellsystem to run experiments as part of an automated calibration
process. ORION is composed of two applications: theMeasurement Definition Application (MDA) and theMeasurement Application (MA). The MDA provides a set ofstandard actions to create the sequences used to direct test
cell systems to perform test and measurement tasks. The
sequence is then run on the MA real-time system in a test
cell, commanding the test cell system and calibration toolaccording to the actions specified in the sequence. During theprocess, the MA directs the calibration tool and the cell dataacquisition system to collect data to characterize the engine.
After the test completes, the data that has been logged by thetest cell system can be used in the subsequent modeling andoptimization process.
For this study, the test cell is configured to run in speed/
relative load mode. A typical steady-state test sequence isused and a set of actions are executed in a specific order toaccomplish the experiment. For this study, a brief descriptionof the test sequence is as follows:
1. Set speed, load, VVT and air-fuel ratio according to thetest plan.
2. Find MBT spark using the online optimization function.
3. Take measurement and recording of all data items to beused in the model, including data from test cell, ECU,combustion analysis system, and emission bench etc.
4. Perform test for each spark offset value defined in the test
plan. Take measurements and recording of all data items.
5. Repeat for all test points in the test plan.
During the execution of the experiments, a lot of details need
be considered, such as storing original values for future reset,
maintaining the emission bench, stabilizing the system forsufficiently long before triggering the measurement, handlingof persistent error, and repeatability check etc. ORION
provides the capability and flexibility for taking care of allthese details. When the test is executed, the progress of thetest and the status of the system will be displayed on MAwindows. Figure 3 shows a MA window while executing thetest. After the whole test plan is finished, the test results areread by the EasyDoE Toolsuite for engine modeling andparameter optimization.

Figure 2. Intake VVT Experiment Points

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Table 1. Torque Modeling Performance of Different
Fitting Methods

Figure 3. MA Window while Executing the Test

MODELING, OPTIMIZATION AND
MAP CREATION
The measurement data taken from the test cell is imported
into the EasyDoE Toolsuite for engine modeling. As
mentioned before, the model form is specified as 4th orderpolynomial with 3rd order interactions. For each response,multiple fitting methods are used and the quality of eachmodel fitting is evaluated. After the statistical characteristicsof each candidate model are studied, a best fit model isselected which will be used for the subsequent parameter
optimization and map creation. As an example, Table 1
summarizes the torque modeling performance of 9 different
fitting methods. Number of terms of the polynomial and Root
Mean Square Error (RMSE) of the fitting data are listed foreach method. In this study, Polynomial 7 which gives thesmallest root mean square error (RMSE) for the fitting data ischosen. The number of terms in this polynomial is reduced
from 61 to 37. Figure 4 further shows the more detailed
performance of this selection. Data for 249 test points is usedfor calculating the model, while another 20 points are usedfor validating the model. The measured torque and model
calculated torque are compared against each other. Thevalidation points show that the model gives good
performance and the normalized validation RMSE is less than
2.5%.

Figure 4. Evaluation of Torque ModelingAfter the models for all the responses are determined, theEasyDoE Toolsuite can perform the optimization of the test
factors based on the optimization criterion. For this study, thecriterion is set to minimize BSFC and meanwhile keep engine
exhaust emissions, engine roughness, which is indicated by
COV of IMEP, and exhaust temperature within theconstraints. Another constraint is that the spark advanceshould be less than or equal to the MBT spark. Figure 5
shows the BSFC contour map calculated by EasyDoEToolsuite. By carrying out the optimization algorithms,EasyDoE Toolsuite generates maps for all the test factors ofspark, air-fuel ration, and intake VVT using the models. As
an example, Figure 6 shows the optimized VVT map. Afterthe optimized maps are obtained, some verification tests needbe performed to check the optimization results. Typically agrid of speed and load points is run with the obtained
optimized control maps and the engine performance isstudied.

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2. Schlosser, A., Kinoo, B., Salber, W., Werner, S. et al.,“Accelerated Powertrain Development Through Model BasedCalibration,” SAE Technical Paper 2006-01-0858, 2006, doi:
10.4271/2006-01-0858.
3. Lumsden, G., Browett, C., Taylor, J., and Kennedy, G.,“Mapping Complex Engines,” SAE Technical Paper
2004-01-0038, 2004, doi:10.4271/2004-01-0038.
4. Suzuki, K., Nemoto, M., and Machida, K., “Model-BasedCalibration Process for Producing Optimal Spark Advance ina Gasoline Engine Equipped with a Variable Valve Train,”
SAE Technical Paper 2006-01-3235, 2006, doi:
10.4271/2006-01-3235.
Figure 5. BSFC Contour Map

5. Röpke, K., von Essen, C.: DoE in engine development,
Quality and Reliability Eng. Int. 24(6): 643-651, Volume 24,
Number 6 October 2008
6. Röpke, K. et al.: DoE-Design of Experiments, Methods
and Applications In Engine Development, published byVerlag Moderne Industrie, Munich, 2005
7. Schoop, U., Reeves, J., Watanabe, S., Butts, K.: SteadyState Engine Modeling for Calibration: A Productivity andQuality Study, Automotive Conference 2007, Dearborn, MI,USA
8. Gullitt, A., Nutter, D., Demonstration of the DoE Processwith Software Tools, Design of Experiments (DoE) in Engine
Development IV, Expert-Verlag 2009.

Figure 6. Optimized VVT Map

SUMMARY
A model-based calibration of spark, air-fuel ratio and intake
variable valve timing is carried out for a gasoline engine
using ORION and EasyDoE ToolSuite. The application
indicates that ORION automated engine control andmeasurement system and EasyDoE ToolSuite provide
effective methods for implementing model-based calibration.

REFERENCES
1. Guerrier, M. and Cawsey, P., “The Development of Model
Based Methodologies for Gasoline IC Engine Calibration,”
SAE Technical Paper 2004-01-1466, 2004, doi:
10.4271/2004-01-1466.

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Downloaded Wednesday, March 28, 2012 04:01:07 PMThe Engineering Meetings Board has approved this paper for publication. It has
successfully completed SAE's peer review process under the supervision of the sessionorganizer. This process requires a minimum of three (3) reviews by industry experts.
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