Title: Estimation of vehicle state and road surface adhesion coefficient based on double square root cubature Kalman filter

Authors: Wei Gao; Wenfei Liu; Zhaowen Deng; Baohua Wang; Youqun Zhao

Addresses: Hubei Key Laboratory of Automotive Power Transmission and Electronic Control, College of Automotive Engineering, Hubei University of Automotive Technology, Shiyan 442002, China; College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210007, China ' Hubei Key Laboratory of Automotive Power Transmission and Electronic Control, College of Automotive Engineering, Hubei University of Automotive Technology, Shiyan 442002, China ' Institute of Automotive Engineers, Hubei University of Automotive Technology, Shiyan, 442002, China; College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210007, China ' Hubei Key Laboratory of Automotive Power Transmission and Electronic Control, College of Automotive Engineering, Hubei University of Automotive Technology, Shiyan 442002, China ' College of Energy and Power Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210007, China

Abstract: With a focus on the problem of estimating the vehicle state and road surface attachment coefficient while in motion, and using distributed drive electric vehicles as the research subject, a double-square-root volumetric Kalman filtering vehicle traveling state and road surface attachment coefficient estimator (DSRCKF) is designed. It is based on the idea of square-root filtering, which introduces the square-root factor of the covariance matrix and updates the process iteratively. Two linked sub-filters that are updated in real-time are present in the estimator. To achieve accurate estimation of the vehicle state and roadway attachment coefficients, a nonlinear seven-degree-of-freedom vehicle model based on the dugoff tyre model is first established. Next, a vehicle simulation platform for distributed-drive electric vehicles is built using the CarSim-Simulink software, and the double-square-root-volume Kalman filtering algorithm is deduced. The DSRCKF estimation method outperforms the DCKF and DUKF algorithms after the created algorithm has been simulated and tested under typical operating conditions.

Keywords: double square root cubature Kalman filter; vehicle state estimation; road surface adhesion coefficient estimation.

DOI: 10.1504/IJVP.2024.142103

International Journal of Vehicle Performance, 2024 Vol.10 No.4, pp.427 - 446

Received: 12 Jan 2024
Accepted: 18 Jun 2024
Published online: 07 Oct 2024 *

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