In-wheel motor electric vehicle state estimation by using unscented particle filter

Vehicle state parameters are essential for active safety stability control and very valuable in chassis design evaluation. In this paper, a method for vehicle state parameters estimation is developed for in-wheel motor (IWM) electric vehicle (EV). The observer is based on information fusion combining standard sensor suite in today's typical vehicle and feedback signals from IWM. This paper utilise unscented particle filter (UPF) for tyre lateral force, longitudinal velocity, lateral velocity and yaw rate estimation, which is based on a numerically efficient nonlinear stochastic estimation technique. Planar vehicle model and dynamic tyre model are developed to describe behaviour of IWM EV. Detailed simulation verifies the validation and robustness of proposed estimation algorithm.