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Vehicle tyre and handling model identification using an extended Kalman filter
conference contribution
posted on 2011-04-27, 11:15 authored by Matt BestMatt Best, Andrew P. NewtonThis 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
2008Notes
This is a conference paper.ISBN
9784904056202Publisher version
Language
- en