etas whitepaper ascmo 2011 16seiten
TRANSCRIPT
New Simulation and Automation Solutionsfor the Optimized Calibration of Complex Electronic Systems
Holger Ulmer (ETAS)
Thomas Kruse (ETAS)
Tobias Lang (Bosch)
2 COntEnt
Abstract 3
1 | Introduction 4
2 | Data-based Modeling for Calibration 4
2.1 the DoE Approach 4
2.2 Classical Data-based Models 5
2.3 new Statistical Learning Approaches 6
3 | Multi-criteria Optimization with Evolutionary Algorithms 6
4 | The ASC Environment for Model-based Calibration 8
5 | DoE Method for Prognosis and Optimization of Tail Pipe Emissions by Optimization of Catalyst Heating Duration on a Gasoline Engine 9
5.1 Exhaust System Modeling Environment 10
5.2 Model Verification 10
5.3 DoE Method for Model-based Calibration of Catalyst Heating 10
6 | Summary 14
7 | Outlook and Transfer to other Calibration Tasks 14
Acknowledgement and Bibliography 15
Content
3IntrO
Abstract
Driven by increasing system complexity, the calibration of engine control parameters has a growing impact
concerning engineering targets like emissions, driving dynamics as well as cost and duration of power train
development. Simulation methods in which the system behavior of the drive train is represented by models
can support the calibration of complex systems considerably. However, an essential prerequisite for the
practical application is that the models have high accuracy and can be configured with low measurement
and time effort.
In a joint project with calibration and research engineers of robert Bosch GmbH and EtAS GmbH the
Advanced Simulation for Calibration (ASC) tool suite was developed. A central element of the ASC tool
suite is the modeling of global engine behavior with high accuracy. the ASC modeling uses new data-
based methods which can identify central engine outputs such as consumption, raw emissions and exhaust
temperature dependent of operating conditions (speed, load, engine temperature) and calibration parameters
(e.g., ignition, fuel injection, camshaft positions, ...) on few measurements in a mostly automated manner.
On the basis of such a model, suggestions for optimal calibration parameters are automatically generated.
In case of conflicting objectives, for example between emissions and fuel consumption, the user can
choose the best compromise between the competing outputs from different proposals interactively.
Another essential element of the ASC tool suite is the simulation of the exhaust system based on a highly
accurate physicochemical catalyst model. the integration of the engine torque with the exhaust system
model facilitates the optimization of cycle-related exhaust emissions. this approach can provide for example
optimal strategies for heating up a three-way catalyst.
1 | Introduction
the calibration of ECU parameters has be-
come a crucial element for the overall ve-
hicle performance and is today an essential
part of the development process of new
engines and vehicles. A main challenge for
calibration is finding the best compromise
between contradictory requirements, such
as nOx versus particle or CO2 emissions in
a high dimensional space spanned over the
engine parameters, e.g., injection timings
and quantities, fuel pressure or exhaust-
gas recirculation (EGr) rate for modern
diesel engines (figure 1).
Usually, most parameters have strong in-
teractions, so that a separated optimization
of one parameter at a time leads to an in-
sufficient result. In addition, calibration
has to be done for a high number of
different vehicles or model variants which
are sold in different markets. to master
this task with acceptable expenditure of
time and costs, new calibration methods
are necessary. Besides automation, model-
based calibration is seen here as one ma-
jor solution.
Model-based calibration means to simu-
late the relevant engine or vehicle behavior
on a PC with a plant model, so that the
main calibration task, the parameter opti-
mization, can be done virtually. An addi-
tional benefit of this approach is the signifi-
cantly reduced demand of prototypes and
test bench resources, which are then only
required to parameterize the model once.
4 IntrODUCtIOnDAtA-BASED MODELInG fOr CALIBrAtIOn
Engine models that will be applied in series
calibration have to fulfill different require-
ments. they have to be sufficiently accu-
rate: for many calibration tasks as accu-
rate as real measurements. Model para-
meterization must be fast and easy, i.e.,
the number of measurements required for
the parameterization should be as small as
possible and the parameterization should
not demand deep knowledge of modeling
techniques from the calibration engineer.
furthermore, the modeling approach has
to be universal in terms of application for
all relevant diesel and gasoline combus-
tion concepts and future electric engines.
these requirements exclude in most cases
physical modeling approaches. In this case,
data-based models using mathematical
approximation methods for the descrip-
tion of the relevant engine or vehicle be-
havior lead to much better results. Usually,
the data-based modeling approach is
combined with the “Design of Experi-
ment” (DoE) method, which can signifi-
cantly reduce the number of measure-
ments required for parameterization [1].
the paper is organized as follows: first,
the method of data-based modeling for
calibration is described. then we outline
the benefits of statistical modeling and
multicriteria optimization approaches,
which are included in the ASC modeling
environment. In chapter 5 we discuss the
application of the methods to the calibra-
tion task of optimizing a gasoline engine
with regard to tail pipe emissions. finally,
we conclude with a short summary and
give an outlook on other applications.
2 | Data-based Mode-ling for Calibration
2.1 The DoE Approach
the basic idea of Design of Experiments
(DoE) is to characterize an unknown system,
e.g., a combustion engine, by a data-based
mathematical model, whereas the meas-
urement effort is minimized by matching
the test plan to the used approximation
model (figure 2). the determination of the
calibration parameters from the model is
done subsequently by using methods of
mathematical optimization. Compared to
the standard full factorial procedure, where
all parameter combinations have to be
measured with a specific step width, the
measurement effort can be reduced by
order of magnitudes, especially for high
dimensional problems. the combination of
DoE with modern test bench automation
methods that allow a fast and simultaneous
variation of all parameters additionally
increases the efficiency [2].
5
Figure 1: Engine parameters and outputs of a modern diesel engine that must
be optimized during calibration
DAtA-BASED MODELInG fOr CALIBrAtIOn
Figure 2: The DoE process: Measurements of the real system based on a DoE
test plan.
2.2 Classical Data-based Models
the first applications of data-based
modeling and DoE in ECU calibration
started more than a decade ago [3]. Often
polynomials or neural networks are used
as mathematical approximation models.
Both types have specific advantages but
also significant disadvantages.
Polynomials are relatively easy to under-
stand and a number of established
commercial tools are available. their main
disadvantage is their limited flexibility, i.e.,
only very simple system behavior can be
described. nevertheless, their parameteri-
zation needs relatively high effort and
expertise. furthermore, polynomials are
very sensitive to single measurement
errors. As a consequence, if not detected
as outliers, measurement errors can
deteriorate the whole polynomial model.
neural networks are in principle able to
describe complex system behavior.
However, parameterization of neural
networks requires high expertise and
validation data to avoid overfitting. Even
with a high number of training data, the
accuracy of neural networks is often
insufficient for calibration purposes.
As a consequence of the drawbacks of
both model types, DoE methods are today
applied to a limited number of use cases
by a few experts only.
6 DAtA-BASED MODELInG fOr CALIBrAtIOnMULtI-CrItErIA OPtIMIzAtIOn wItH EVOLUtIOnAry ALGOrItHMS
2.3 New Statistical Learning Approa-
ches
to overcome the drawbacks of the classical
modeling approaches, a generic modeling
framework for the broad use in calibration
has been developed. the basic principle is
a superposition of basis functions Φ with
weights ω to describe the system output
f(x), depending on the D dimensional
parameter vector x as:
A statistical learning algorithm based on a
Bayesian approach determines automati-
cally that set n of basis functions Φ and
weights ω which represents the training
data with the maximum likelihood, as
described in [4]. As the main advantage of
this approach, the user gets the best fit on
defined statistical criteria without being
compelled to find any model parameter.
this feature, together with its insensitivity
against single outliers, makes this method
very robust and easy to handle for use in
calibration. the high performance of the
advanced modeling approach compared
to classical neural network approaches has
been demonstrated [5].
the new machine learning modeling
approach has a superior modeling perform-
ance and allows the calibration engineer
to reach a better model quality with fewer
measurements. furthermore, the model-
based approach can be extended to new
tasks which demand a very high accuracy.
with increasing number of training data
this approach reaches the accuracy of the
used measurement devices.
One significant advantage of this new
approach for calibration purposes is its
ability to describe the global engine
behavior including the complex influence
of engine speed and load. this means that
one single model can describe the engine
behavior in the whole operation range.
As an additional benefit, the new statistical
approach provides automatically the local
variance of the model, giving the confi-
dence interval of the model prediction at
each setting of input quantities.
3 | Multi-criteria Optimization with Evolutionary Algorithms
Besides the model itself, also the available
optimization methods were often insuffi-
cient for calibration purposes. Adequate
methods for multi-criteria optimization in
calibration were missing.
while in single criterion optimization, the
optimal solution is usually clearly defined,
this does not hold for multi-criteria optimiza-
tion. the optimization problem is charac-
terized by the fact that multiple conflicting
target values (e.g., exhaust emission, fuel
economy and engine torque) have to be
considered simultaneously. for this reason,
a single global optimum does not exist,
but a set of equivalent compromise
solutions, called Pareto optimal solutions.
figure 3 explains the concept of Pareto
optimality for a multi-criteria optimization,
where two conflicting targets f1 and f2
have to be minimized.
Solution B and C are equivalent, since B is
superior concerning target f2 while C is
superior concerning target f1. the Pareto
optimal solution A is superior or domi-
nates B, C, and D. Pareto optimality means
that it is not possible to improve one target
without worsen at least one other. the
Pareto front shows the entity of all Pareto
optimal solutions from which the individual
compromise between the conflicting targets
can be selected.
MULtI-CrItErIA OPtIMIzAtIOn wItH EVOLUtIOnAry ALGOrItHMS 7
Classical methods as gradient based or
simplex algorithms aggregate all criteria
into a single weighted objective function.
thus, they can only consider one solution
per optimization run and are not able to
handle problems with concave Pareto
fronts. Multi-criteria optimization prob-
lems can therefore not be solved effi-
ciently by classical optimization methods.
Evolutionary algorithms are stochastic
optimization methods, which are inspired
by the gradual adaptation process of the
natural biological evolution. they try to
mimic the natural evolution by applying
selection and mutation operators on the
given set of solutions represented by a
population of individuals (figure 4).
Possible solutions for a given problem are
represented by individuals who accumu-
late to a population P. from a parent
population Pp a child population Pc is
generated by applying different evolution-
ary operators. the quality of these new
solutions is determined by evaluating the
objective functions which have to be
optimized. the population of the next
parent population is a selection of the
best individuals out of the child population.
Figure 4: Operation principle of evo-
lutionary algorithms.
Figure 3: Illustration of Pareto opti-
mality. Goal is to minimize the tar-
gets f1 and f2.
the population-based principle of evolu-
tionary optimization allows a parallel
search in the decision space. Evolutionary
algorithms are able to capture multiple
Pareto optimal solutions in a single optimiza-
tion run and can be used for multi-criteria
optimization, if a multi-criteria selection
method is used and the diversity of the
population is maintained to improve the
distribution on the Pareto front. there
exist many different implementations for
multi-criteria evolutionary optimization.
the presented optimization module uses
an archive of solutions to maintain the
best solutions in order to improve conver-
gence. It is based on the nSGA-II algo-
rithm [6].
8 tHE ASC EnVIrOnMEnt fOr MODEL-BASED CALIBrAtIOn
4 | the ASC Environ-ment for Model-based Calibration
the new modeling and optimization algo-
rithms were implemented in the ASC
modeling environment. the environment
provides an interactive experimental de-
sign module for DoE plan generation and
an interactive visualization to study and
optimize the modeled system.
the graphs in figure 5 show the depend-
ence of four relevant engine outputs from
seven calibration parameters. the calibra-
tion engineer can choose any operating
point of the engine – in the example 2000
rpm speed and 40 nm torque – and ana-
lyze the influence of the calibration pa-
rameter on the relevant engine outputs. In
the example, the seven calibration param-
eters are injection and ignition timing, fuel
pressure, EGr rate, timing of exhaust and
inlet camshaft, and a swirl control valve
(SCV) to influence in-cylinder air motion.
Besides fuel consumption, the most rele-
vant outputs here are engine smoothness
(CoV), soot, and nOx emissions. the val-
ues of the calibration parameters, indicat-
ed by the vertical dashed lines, can be
changed interactively. the dashed lines
around the prediction lines indicate the
confidence interval of the result and give a
measure for the quality of the model.
In addition, the calibration engineer can
perform an automatic optimization over
the whole engine operation range, for ex-
ample minimizing the fuel consumption
while keeping specific limits for other out-
puts. As a result, he gets a proposal for
the complete calibration of all the seven
maps which are essential for cycle optimi-
zation in a very efficient way.
Figure 5: Visualization of the global engine
behavior depending on seven calibration
parameters visible at the bottom of the
screenshot.
DOE MEtHOD fOr PrOGnOSIS AnD OPtIMIzAtIOn Of tAIL P IPE EMISSIOnS 9
5 | DoE Method for Prognosis and Optimization of tail Pipe Emissions by Optimization of Catalyst Heating Duration on a Gasoline Engine
the flexibility of modern gasoline fuel in-
jection systems facilitates the minimization
of catalyst heating duration by, e.g., multi-
ple injections. to ensure minimum catalyst
heating duration, which is the key factor
for optimized tail pipe emissions of a
gasoline engine, an optimized set of the
relevant ECU parameters must be found.
the conventional calibration method is
based on measurements of tail pipe emis-
sions on a roller test bench. for each vari-
ation of ECU parameters, the accumulated
tail pipe emissions during one test driving
cycle (e.g., ECE cycle) must be identified.
with model-based calibration, the amount
of roller test bench measurements can be
considerably reduced and at the same
time more ECU parameter combination
can be tested. Modeling engine raw emis-
sions in the complete engine operation
range and a three way catalyst model with
high precision are the key components of
model-based calibration of the exhaust
system with the ASC tool suite at robert
BOSCH GmbH.
5.1 Exhaust System Modeling Environ-
ment
figure 6 gives an overview of the exhaust
system modeling environment. MAtLAB®/
Simulink® is used as simulation environment.
figure 7 shows the simulation of the ex-
haust system of a 2.0l GDI turbo charged
engine with two catalysts (precatalyst plus
main catalyst) including lambda probes
and exhaust pipes.
5.2 Model Verification
the successful usage of model-based cali-
bration for series projects is based on ex-
cellent modeling quality of engine raw
emissions and tail pipe emissions. to en-
sure the required modeling quality, model
verification is required before using the
models for calibration.
Engine raw emissions, tail pipe emissions
and traces of the relevant ECU signals are
measured during a test driving cycle (e.g.,
ECE cycle) on the roller test bench. the
traces of the relevant ECU signals are used
as stimulation signals for the exhaust cycle
simulation. then the simulated exhaust
values can be verified with the measured
exhaust values from the roller test bench
measurement.
figure 8 shows the procedure for verifica-
tion of the engine raw emission model
and the catalyst model.
the verification results (based on a ECE
cycle) of the engine raw emission model
from a 2.0l GDI turbo charged engine are
shown in figure 9. figure 10 shows the
accumulated emission values of the verifi-
cation.
the verification results (based on a ECE
cycle) of the tail pipe emission values after
the first catalyst of the exhaust gas system
are shown in figure 11. figure 12 shows
the accumulated emission values of the
verification. the ECU catalyst heating
function was deactivated and an aged
catalyst was used.
the engine raw emission and the three
way catalyst ASC models fulfill the mode-
ling accuracy requirements for series project
calibration. In the following we describe
by an example, how model-based calibra-
tion is used at the robert BOSCH GmbH
in series projects.
5.3 DoE Method for Model-based Cali-
bration of Catalyst Heating
the ASC modeling environment is used to
generate a data-based behavior model of
the complete exhaust system control path.
the model contains the dependency of
the system from all ECU parameters of the
catalyst heating function relevant for the
tail pipe emissions. Every data point of the
model is generated by an exhaust cycle
simulation (ECE cycle) with different ECU
parameters for catalyst heating.
By using the ASC modeling environment,
a space filling design of the parameter var-
iations for the different exhaust cycle sim-
ulations ensure best modeling quality.
figure 13 gives an overview of the DoE
method used for model-based calibration
of catalyst heating.
By using modern machine learning algo-
rithms, the training of the ASC model is
automated without any manual parameter
settings by the calibration engineer [5][7]
[8][9].
figure 14 shows a behavior model of the
complete exhaust system control path as a
function of ECU catalyst heating parameters.
10 DOE MEtHOD fOr PrOGnOSIS AnD OPtIMIzAtIOn Of tAIL P IPE EMISSIOnS
Figure 7: Example: 2.0l GDI TC engine with two catalyst systems.
Figure 6: Overview of the exhaust system modeling environment (ASCMEX).
In the next step, this model is the used for
multi-criteria optimization (see chapter 3)
of the tail pipe emissions HC, CO and nOx.
the results of the multi-criteria optimiza-
tion are shown in figure 15.
the parameter combinations colored in
blue, green and red give the best results.
the red marked parameter combinations
represent the optimal calibration. the vis-
ualization of the parameter combination is
shown in figure 16.
finally, the parameter combination is used
for the verification with a vehicle ECE driv-
ing cycle test on a roller test bench.
11
Figure 9: Engine raw emission verification results.
Figure 10: Verification of the accumulated engine raw emission signals.
DOE MEtHOD fOr PrOGnOSIS AnD OPtIMIzAtIOn Of tAIL P IPE EMISSIOnS
Figure 8: Model verification procedure.
Figure 13: Overview of the DoE method for model-based calibra-
tion of catalyst heating.Figure 11: Verification of the tail pipe emissions after the
precatalyst.
12 DOE MEtHOD fOr PrOGnOSIS AnD OPtIMIzAtIOn Of tAIL P IPE EMISSIOnS
Figure 12: Verification of the accumulated tail pipe emissi-
ons after precatalyst.
Figure 14: ASC system behaviour model of ECU catalyst heating.
13DOE MEtHOD fOr PrOGnOSIS AnD OPtIMIzAtIOn Of tAIL P IPE EMISSIOnS
Figure 15: Results of multi-criteria optimization.
Figure 16: Visualization of the parameter combination as result of the
multi-criteria ooptimization.
6 | Summary
the described example demonstrates the
possibility to use DoE methodology for
modeling the behavior of the complete
exhaust system control path. It shows the
usage of multi-criteria optimization of the
relevant ECU parameters. Accurate models
for engine raw emissions and three way
catalysts as well as the design of param-
eter variation are the basis of the DoE
method used.
the ASC modeling environment is
enabling the calibration engineers of the
robert BOSCH GmbH to use this method
in series calibration projects.
the method described could be used for
modeling the system behavior of a control
path and optimizing the relevant ECU
function parameters in general.
the quality of the used control path models
is the key factor for the success of this
method. the efficient parameterization of
the three way catalyst model is the prerequi-
site for the high benefit of this method.
the ASC models are also integrated in
hardware-in-the-loop systems at Bosch
and could be used for calibration and opti-
mization of other ECU function parameters.
7 | Outlook and transfer to other Calibration tasks
14 SUMMAry OUtLOOk AnD trAnSfEr tO OtHEr CALIBrAtIOn tASkS
15
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Acknowledgement
the authors want to thank the colleagues
at Bosch who contributed to the presented
results, especially Ingo Hein, wolfgang
Lengerer, Sven Meßy and Heiner Markert.
ACknOwLEDGEMEntBIBLIOGrAPHy
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