Welcome to Journal of Automotive Safety and Energy,

Journal of Automotive Safety and Energy ›› 2025, Vol. 16 ›› Issue (3): 414-424.DOI: 10.3969/j.issn.1674-8484.2025.03.007

• Automotive Safety • Previous Articles     Next Articles

Vehicle state UKF estimation considering noise and initial state uncertainties

ZHANG Zhiyong(), DU Chenzhou, YI Sheng, YU Hui   

  1. College of Mechanical and Vehicle Engineering, Changsha University of Science and Technology, Changsha 410114, China
  • Received:2024-11-19 Revised:2024-12-26 Online:2025-06-30 Published:2025-07-01

Abstract:

An improved unscented Kalman filter (UKF) vehicle state estimation method was proposed to improve the estimation accuracy of vehicle states in the presence of noise covariance matrix and initial state uncertainties. This method introduced a windowing process based on the maximum a posteriori (MAP) estimation strategy to achieve dynamic estimation of the noise covariance matrix, while also integrating a static particle filter (SPF) algorithm to estimate the initial vehicle states. The improved UKF's estimation accuracy was verified using a co-simulation platform with CarSim and MATLAB/Simulink. The results show that, when measurement noise deviates from the true value, the windowed MAP dynamic estimation method for the noise covariance matrix improves the estimation accuracy of longitudinal and lateral speeds by 90% and 80%, respectively, compared to the standard UKF. In comparison to the UKF with adaptive noise covariance matrix adjustment, the estimation accuracy increases by 75% and 56%, respectively. Under initial state uncertainty, the SPF method improves the estimation accuracy of longitudinal and lateral vehicle speeds by 94% and 90%, respectively. Therefore, the proposed improved UKF estimation method significantly enhances estimation accuracy and robustness in the presence of noise covariance matrix and initial state uncertainties.

Key words: electric vehicle, vehicle state estimation, unscented Kalman filter (UKF), maximum a posteriori (MAP), static particle filter (SPF)

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