Paper Info
Title
Multi-criteria Optimization for Parameter Estimation of Physical Models in Combustion Engine Calibration.
Abstract
In the automotive industry the calibration of the engine control unit is getting more and more complex because of many various boundary conditions, like the demand on CO2 and fuel reduction. One important calibration problem is the parameter estimation for the model of the intake system of the combustion engine. This system is modeled by a physically motivated system, which can be parameterized by black-box models, like neural nets and characteristic diagrams, whose parameters must be set by an intelligent optimizer. Further, two contradictory aims must be considered and the engineer expects at the end of the optimization a pareto-front, where he can choose the best settings for the application from the pareto-optimal parameter estimations. To solve this multi-criteria optimization task an evolutionary algorithm is used, which is a combination of a genetic algorithm and an evolutionary strategy. This evolutionary algorithm is like all other stochastic searching methods leaned on the naturally biological evolution. It combines the well-known covariance matrix adaption for the mutation of the individuals with the S-metric selection for the multi-criteria fitness assignment of the individuals. It also improves these combination by the use of many subpopulations, which work parallel on various clusters, and by the use of an intelligent DoE-strategy for the initialization of the start individuals. With these improvements the developed evolutionary algorithm can easily fit the model of the intake system to test bed measurements and can provide the user a pareto-optimal set of parameters, on which he can choose on his own that ones, which he find most plausible.
Year
DOI
Venue
2013
10.1007/978-3-642-37140-0_47
Lecture Notes in Computer Science
Keywords
Field
DocType
Evolutionary algorithms,Multi-criteria optimization,Adaptive algorithm,intake system,Automobile industry,Engine modeling,Global optimization,Simulation,Engine control
Mathematical optimization,Global optimization,Evolutionary algorithm,Control theory,Computer science,Evolution strategy,Engine control unit,Adaptive algorithm,Initialization,Artificial neural network,Genetic algorithm
Conference
Volume
ISSN
Citations 
7811
0302-9743
1
PageRank 
References 
Authors
0.36
6
2
Name
Order
Citations
PageRank
Susanne Zaglauer141.67
Michael Deflorian211.04