Vehicle tyre and handling model identification using an extended Kalman filter Matt Best Andrew P. Newton 2134/8320 https://repository.lboro.ac.uk/articles/conference_contribution/Vehicle_tyre_and_handling_model_identification_using_an_extended_Kalman_filter/9221600 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. 2011-04-27 11:15:44 Modelling and simulation technology Tyre property Vehicle control Engineering not elsewhere classified