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Vehicle tyre and handling model identification using an extended Kalman filter

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conference contribution
posted on 27.04.2011, 11:15 by Matt Best, Andrew P. Newton
This paper uses an Extended Kalman filter in an unusual way to identify a vehicle handling model and its associated tyre model. The method can be applied as an off-line batch process, or in real-time; here we concentrate on batch analysis of data from a Jaguar XJ test vehicle. The Identifying Extended Kalman Filter (IEKF) uses the full state measurement that is available from combination GPS / inertia instrumentation packs. Previous IEKF studies have shown success in identifying a bicycle model with a tyre force function for each axle. This paper extends to identification of a single, load dependent tyre model which applies to all four wheelstations, identified within a yaw-roll-sideslip model structure. The resulting model provides impressive open-loop state replication, including accurate tyre slip prediction across the fully nonlinear slip range of the tyre.

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Citation

Best, M.C. and Newton, A.P., 2008. Vehicle tyre and handling model identification using an extended Kalman filter. IN: Proceedings of the 9th International Symposium on Advanced Vehicle Control (AVEC), Vol 1, Kobe, Japan, 6th-9th October, pp. 69–74.

Publisher

© Society of Automotive Engineers of Japan (JSAE)

Version

AM (Accepted Manuscript)

Publication date

2008

Notes

This is a conference paper.

ISBN

9784904056202

Language

en

Exports

Loughborough Publications

Exports