Mechatronic System Optimization based on Surrogate Models - Application to an Electric Vehicle

Moncef Hammadi, Jean-Yves Choley, Olivia Penas, Alain Riviere

Abstract

Preliminary optimization of mechatronic systems is an extremely important step in the development process of multi-disciplinary products. However, long computing time in optimization based on multi-domain modelling tools need to be reduced. Surrogate model technique comes up as a solution for decreasing time computing in multi-disciplinary optimization. In this paper, an electric vehicle has been optimized by combining Modelica modelling language with surrogate model technique. Modelica has been used to model the electric vehicle and surrogate model technique has been used to optimize the electric motor and the transmission gear ratio. Results show that combining surrogate model technique with Modelica reduces significantly computing time without much decrease in accuracy.

References

  1. Blanning, R. (1975). The construction and implementation of metamodels. Simulation, 24(6):177184.
  2. Box, G. E. P. and Wilson, K. B. (1951). On the experimental attainment of optimum conditions (with discussion). Journal of the Royal Statistical Society, Series B, 13:1-45.
  3. Craig, K. (2009). Mechatronic system design. Technical report, ASME Newsletter.
  4. Elmqvist, H., Mattsson, S., and Otter, M. (1998). Modelica : The new object-oriented modeling language. In The 12th European Simulation Multiconference, Manchester, UK.
  5. Hardy, R. L. (1971). Multiquadratic equations of topology and other irregular surfaces. Journal of Geophysical Research, 76:1905-1915.
  6. Hopfield, J. (1982). Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci., 79:2554-2558.
  7. Krige, D. (1951). A statistical approach to some basic mine valuation problems on the witwatersrand. Journal of the Chemical, Metallurgical and Mining Society, 52:119-139.
  8. Larminie, J. and Lowry, J. (2003). Electric Vehicle Technology Explained. John Wiley & Sons.
  9. Mackay, M. D., Beckman, R. J., and Conover, W. J. (1979). A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics, 21:239-245.
  10. Powel, M. (1985). Radial basis functions for multi-variable interpolation: A review. In IMA Conference on Algorithms for the Approximation of Functions and Data.
  11. Schittkowski, K. (1985). Nlpql: a fortran subroutine solving constrained nonlinear programming problems. Annals of Operations Research, 5 (1-4):485-500.
Download


Paper Citation


in Harvard Style

Hammadi M., Choley J., Penas O. and Riviere A. (2012). Mechatronic System Optimization based on Surrogate Models - Application to an Electric Vehicle . In Proceedings of the 2nd International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH, ISBN 978-989-8565-20-4, pages 11-16. DOI: 10.5220/0004011900110016


in Bibtex Style

@conference{simultech12,
author={Moncef Hammadi and Jean-Yves Choley and Olivia Penas and Alain Riviere},
title={Mechatronic System Optimization based on Surrogate Models - Application to an Electric Vehicle},
booktitle={Proceedings of the 2nd International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH,},
year={2012},
pages={11-16},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004011900110016},
isbn={978-989-8565-20-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Simulation and Modeling Methodologies, Technologies and Applications - Volume 1: SIMULTECH,
TI - Mechatronic System Optimization based on Surrogate Models - Application to an Electric Vehicle
SN - 978-989-8565-20-4
AU - Hammadi M.
AU - Choley J.
AU - Penas O.
AU - Riviere A.
PY - 2012
SP - 11
EP - 16
DO - 10.5220/0004011900110016