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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/8314

Title: Identifying tyre models directly from vehicle test data using an extended Kalman filter
Authors: Best, Matt C.
Keywords: Tyre modelling
System identification
Kalman filter
Road friction estimation
Issue Date: 2010
Publisher: © Taylor and Francis
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.
Abstract: 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.
Description: 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
Version: Accepted for publication
DOI: 10.1080/00423110802684221
URI: https://dspace.lboro.ac.uk/2134/8314
Publisher Link: http://dx.doi.org/10.1080/00423110802684221
ISSN: 0042-3114
1744-5159
Appears in Collections:Published Articles (Aeronautical and Automotive Engineering)

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