Title: Estimation of road adhesion coefficient based on Sage-Husa adaptive filtering improved square root cubature Kalman filter algorithm

Authors: Quanwei Wang; Xiaobin Fan; Shuwen He; Zipeng Huang; Mingxin Chen

Addresses: School of Mechanical and Power Engineering, Henan Polytechnic University, Jiaozuo 454000, China; Guangxi Key Laboratory of Automobile Components and Vehicle Technology, Guangxi University of Science and Technology, Liuzhou, 545006, China ' School of Mechanical and Power Engineering, Henan Polytechnic University, Jiaozuo 454000, China; Guangxi Key Laboratory of Automobile Components and Vehicle Technology, Guangxi University of Science and Technology, Liuzhou, 545006, China ' School of Mechanical and Power Engineering, Henan Polytechnic University, Jiaozuo 454000, China; Guangxi Key Laboratory of Automobile Components and Vehicle Technology, Guangxi University of Science and Technology, Liuzhou, 545006, China ' School of Mechanical and Power Engineering, Henan Polytechnic University, Jiaozuo 454000, China; Guangxi Key Laboratory of Automobile Components and Vehicle Technology, Guangxi University of Science and Technology, Liuzhou, 545006, China ' School of Mechanical and Power Engineering, Henan Polytechnic University, Jiaozuo 454000, China; Guangxi Key Laboratory of Automobile Components and Vehicle Technology, Guangxi University of Science and Technology, Liuzhou, 545006, China

Abstract: Due to the influence of various factors, real-time and stability analysis of road adhesion coefficient is extremely complex. This paper proposes a road adhesion coefficient estimation algorithm based on Sage-Husa adaptive filtering improved square root cubature Kalman filter based on the experimental vehicle driven by in-wheel motor. Based on the Dugoff tyre model, an eight-degree-of-freedom vehicle dynamics model is established. Then, using the vehicle state parameters, the Sage-Husa adaptive filter combined with the square root cubature Kalman filter algorithm is used to estimate the road adhesion coefficient. Through the simulation comparison with traditional unscented Kalman filter algorithm, combined with real vehicle road experiment, the results show that the estimation accuracy of the in-wheel motor drive electric vehicle based on improved square root cubature Kalman filter algorithm based on the Sage-Husa adaptive filter is significantly improved. The algorithm can meet estimation accuracy and real-time requirements of road adhesion coefficient.

Keywords: road adhesion coefficient; square root cubature Kalman filter; Sage-Husa adaptive filtering; 8-DOF vehicle; four-wheel hub motor drive vehicle.

DOI: 10.1504/IJHVS.2024.141430

International Journal of Heavy Vehicle Systems, 2024 Vol.31 No.5, pp.641 - 664

Received: 07 May 2023
Accepted: 01 Aug 2023
Published online: 12 Sep 2024 *

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