Vehicle tyre and handling model identification using an extended Kalman filter
conference contributionposted on 27.04.2011 by Matt Best, Andrew P. Newton
Any type of content contributed to an academic conference, such as papers, presentations, lectures or proceedings.
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.
- Aeronautical, Automotive, Chemical and Materials Engineering
- Aeronautical and Automotive Engineering