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

Authors: Wenbo Chu; Yugong Luo; Yifan Dai; Keqiang Li

Addresses: State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China ' State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China ' State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China ' State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China

Abstract: 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.

Keywords: IWM; in-wheel motors; electric vehicles; state estimation; UPF; unscented particle filter; active safety; stability control; chassis design evaluation; information fusion; tyre lateral force; longitudinal velocity; lateral velocity; yaw rate estimation; planar vehicle modelling; dynamic modelling; tyre modelling; simulation; vehicle design.

DOI: 10.1504/IJVD.2015.068134

International Journal of Vehicle Design, 2015 Vol.67 No.2, pp.115 - 136

Available online: 15 Mar 2015 *

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