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Identifying tyre models directly from vehicle test data using an extended Kalman filter

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posted on 2011-04-21, 09:19 authored by Matt BestMatt Best
Individual tyre models are traditionally derived from component tests, with their parameters matched to force and slip measurements. They are imported into vehicle models which should, but do not always properly provide suspension geometry interaction. Recent advances in Global Positioning System (GPS)/inertia vehicle instrumentation now make full state measurement viable in test vehicles, so tyre slip behaviour is directly measurable. This paper uses an extended Kalman filter for system identification, to derive individual load-dependent tyre models directly from these test vehicle state measurements. The resulting model therefore implicitly compensates for suspension geometry and compliance. The paper looks at two variants of the tyre model, and also considers real-time adaptation of the model to road surface friction variations. Test vehicle results are used exclusively, and the results show successful tyre model identification, improved vehicle model state prediction – particularly in lateral velocity reproduction – and an effective real-time solution for road friction estimation.

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Citation

BEST, M.C., 2010. Identifying tyre models directly from vehicle test data using an extended Kalman filter. Vehicle System Dynamics: International Journal of Vehicle Mechanics and Mobility, 48 (2), pp. 171-187.

Publisher

© Taylor and Francis

Version

  • AM (Accepted Manuscript)

Publication date

2010

Notes

This is an electronic version of an article that was accepted for publication in the journal, Vehicle Systems Dynamics: International Journal of Vehicle Mechanics and Mobility [© Taylor and Francis] and the definitive version is available at: http://dx.doi.org/10.1080/00423110802684221

ISSN

0042-3114;1744-5159

Language

  • en

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