Modeling Techniques and Parameter Estimation for the Simulation of Complex Vehicle Structures

  • T. Butz
  • O. vonStryk
  • C. Chucholowski
  • St. Truskawa
  • T. M Wolter
Part of the Lecture Notes in Computational Science and Engineering book series (LNCSE, volume 21)


The numerical simulation of complex vehicle structures requires dynamic models for passenger cars as well as for trucks and vehicles with trailers. Tailored numerical modeling and integration techniques must be employed to achieve real-time capability of the considered vehicle dynamics program which is vital for its use within hardware-in-the-loop test-benches. To efficiently calibrate the vehicle model a parameter estimation tool was developed which relies on observations obtained from driving tests. Combining robust nonlinear optimization algorithms and careful numerical differentiation it is well suited for low-cost parallel computing platforms, such as heterogeneous PC clusters, which are usually available for automotive suppliers and industries employing vehicle dynamics simulations


Vehicle Model Vehicle Dynamic Wheel Speed Rear Wheel Driving Test 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    T. Butz, O. von Stryk, M. Vögel, T.-M. Wolter, C. Chucholowski:Parallel parameter estimation in full motor vehicle dynamicsSIAM News 33, 4 (2000)Google Scholar
  2. 2.
    T. Butz, O. von Stryk, T.-M. Wolter:A parallel optimization scheme for parameter estimation in motor vehicle dynamicsIn: A. Bode et al. (ads.): Euro-Par 2000 - Parallel Processing. Lecture Notes in Computer Science 1900, Springer, Berlin (2000) 829–834Google Scholar
  3. 3.
    C. Chucholowski, M. Vögel, O. von Stryk, T.-M. Wolter:Real time simulation and online control for virtual test drives of carsIn: H.-J. Bungartz et al. (eds.): High Performance Scientific and Engineering Computing. Lecture Notes in Computational Science and Engineering 8, Springer, Berlin (1999) 157–166Google Scholar
  4. 4.
    M. Gergeleit:ONC RPC for Windows NT HomepageWorld Wide Web (1996)Google Scholar
  5. 5.
    P. E. Gill, W. Murray, M. H. Wright:Practical OptimizationAcademic Press, London New York (1981)zbMATHGoogle Scholar
  6. 6.
    P. Gilmore:IFFCO: Implicit Filtering for Constrained Optimization User’s GuideTechnical Report CRSC-TR93–7, Center for Research in Scientific Computation, North Carolina State University, Raleigh (1993)Google Scholar
  7. 7.
    J. J. Moré:The Levenberg-Marquardt Algorithm: Implementation and TheoryIn: A. Dold, B. Eckmann (eds.): Numerical Analysis. Lecture Notes in Mathematics 630. Springer, Berlin Heidelberg (1978) 105–116Google Scholar
  8. 8.
    I. Rechenberg: Evolutionsstrategie. Frommann-Holzboog, Stuttgart (1994)Google Scholar
  9. 9.
    G. Rill:Simulation von KraftfahrzeugenVieweg, Braunschweig (1994)Google Scholar
  10. 10.
    TESISDYNAware: veDYNA User0027s GuideM00FCnchen (1997)Google Scholar
  11. 11.
    The MathWorks Inc.:Optimization Toolbox User0027s GuideNattick (1999)Google Scholar
  12. 12.
    The MathWorks Inc.:Using MATLABNattick (1999)Google Scholar
  13. 13.
    The MathWorks Inc.:Using SimulinkNattick (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • T. Butz
    • 1
    • 2
  • O. vonStryk
    • 2
  • C. Chucholowski
    • 1
  • St. Truskawa
    • 1
  • T. M Wolter
    • 1
  1. 1.TESIS DYNAware GmbHImplerstraße 26München
  2. 2.Technische Universität DarmstadtFG Simulation und SystemoptimierungDarmstadt

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