Optimal Design of Experiments for Estimating Parameters of a Vehicle Dynamics Simulation Model

The calibration of complex simulation models for vehicle component and controller development usually relies on numerical methods. In this contribution, a two-level optimization scheme for estimating unknown model parameters in a commercial real-time capable vehicle dynamics program is proposed. In order to increase the reliability of the model coefficients estimated from reference data, the measuring test is improved by methods for the optimal design of experiments. Specifically, the control variables of the experimental setup are adjusted in such a way as to maximize the sensitivity of the parameters in demand with respect to the objective function. The numerical results show that this two-level optimization scheme is capable of estimating the parameters of a multibody suspension model.

[1]  Torsten Butz,et al.  Real-time capable vehicle–trailer coupling by algorithms for differential-algebraic equations , 2007 .

[2]  Henry P. Wynn,et al.  Screening, predicting, and computer experiments , 1992 .

[3]  Michael James Sasena,et al.  Flexibility and efficiency enhancements for constrained global design optimization with kriging approximations. , 2002 .

[4]  Peter Eberhard,et al.  OPTIMIZATION OF DAMPING CHARACTERISTICS IN VEHICLE DYNAMICS , 1999 .

[5]  Donald R. Jones,et al.  Efficient Global Optimization of Expensive Black-Box Functions , 1998, J. Glob. Optim..

[6]  Carl Tim Kelley,et al.  Iterative methods for optimization , 1999, Frontiers in applied mathematics.

[7]  Thomas Hemker,et al.  Derivative Free Surrogate Optimization for Mixed-Integer Nonlinear Black Box Problems in Engineering , 2010 .

[8]  Torsten Butz,et al.  Modeling Techniques and Parameter Estimation for the Simulation of Complex Vehicle Structures , 2002 .

[9]  Michael A. Saunders,et al.  USER’S GUIDE FOR SNOPT 5.3: A FORTRAN PACKAGE FOR LARGE-SCALE NONLINEAR PROGRAMMING , 2002 .

[10]  P. Gill,et al.  User's Guide for SOL/NPSOL: A Fortran Package for Nonlinear Programming. , 1983 .

[11]  F. Pukelsheim.  Optimal Design of Experiments , 1993 .

[12]  P. Gill,et al.  Some theoretical properties of an augmented lagrangian merit function , 1986 .

[13]  O. V. Stryk,et al.  DERIVATIVE-FREE OPTIMIZATION METHODS FOR HANDLING FIXED COSTS IN OPTIMAL GROUNDWATER REMEDIATION DESIGN , 2006 .

[14]  Stefan Körkel,et al.  Numerische Methoden für Optimale Versuchsplanungsprobleme bei nichtlinearen DAE-Modellen , 2002 .

[15]  Hans Bock,et al.  Numerical methods for optimum experimental design in DAE systems , 2000 .

[16]  J. Douglas Faires,et al.  Numerical Analysis , 1981 .

[17]  Jorge J. Moré,et al.  The Levenberg-Marquardt algo-rithm: Implementation and theory , 1977 .