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Power Train Model Refinement Linked with Parameter Updating Through Nonlinear Optimization

Journal Article
ISSN: 1946-3995, e-ISSN: 1946-4002
Published June 09, 2010 by SAE International in United States
Power Train Model Refinement Linked with Parameter Updating Through Nonlinear Optimization
Citation: Girstmair, J., Priebsch, H., Reich, F., and Zehetner, J., "Power Train Model Refinement Linked with Parameter Updating Through Nonlinear Optimization," SAE Int. J. Passeng. Cars – Mech. Syst. 3(1):916-928, 2010,
Language: English


In the virtual development process validated simulation models are requested to accurately predict power train vibration and comfort phenomena. Conclusions from refined parameter studies enable to avoid costly tests on rigs and on the road. Thereby, an appropriate modeling approach for specific phenomena has to be chosen to ensure high quality results. But then, parameters for characterizing the dynamic properties of components are often insufficient and have to be roughly estimated in this development stage. This results in a imprecise prediction of power train resonances and in a less conclusive understanding of the considered phenomena. Conclusions for improvements remain uncertain.
This paper deals with the two different aspects of model refinement and parameter updating. First an existing power train model (predecessor power train) is analyzed whether the underlying modeling approach can reproduce the physical behavior of the power train dynamics adequately. Thereby especially rotational irregularities of the power train causing the low frequency boom noise are considered. Based on the example “tire model”, different model improvements are investigated by means of sensitivity analysis. Efficient measures are chosen to validate the quality of the results in comparison to test results.
A manual approach by a stepwise analysis of each parameter is time consuming and often does not lead to accurate results due to nonlinear model behavior. Furthermore, the interdependence of an increasing number of parameters can hardly be managed manually. Therefore, the second part of the paper focuses on parameter identification methods through nonlinear optimization based on full vehicle measurements. The whole optimization process - from the formulation of the objective function to the analysis of the results - is discussed.
The model improvements as well as the parameter optimization are based on vehicle tests of an all-wheel driven passenger car. The combination of model refinement and updating with non linear optimization methods demonstrates an effective approach to fine-tune simulation models for an optimum support in the product development process.