Huber-Based Robust Unscented Kalman Filter Distributed Drive Electric Vehicle State Observation

Wenkang Wan, Jingan Feng, Bao Song and Xinxin Li
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Wenkang Wan: School of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China
Jingan Feng: School of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832000, China
Bao Song: School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430000, China
Xinxin Li: Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130000, China

Energies, 2021, vol. 14, issue 3, 1-15

Abstract: Accurate and real-time acquisition of vehicle state parameters is key to improving the performance of vehicle control systems. To improve the accuracy of state parameter estimation for distributed drive electric vehicles, an unscented Kalman filter (UKF) algorithm combined with the Huber method is proposed. In this paper, we introduce the nonlinear modified Dugoff tire model, build a nonlinear three-degrees-of-freedom time-varying parametric vehicle dynamics model, and extend the vehicle mass, the height of the center of gravity, and the yaw moment of inertia, which are significantly influenced by the driving state, into the vehicle state vector. The vehicle state parameter observer was designed using an unscented Kalman filter framework. The Huber cost function was introduced to correct the measured noise and state covariance in real-time to improve the robustness of the observer. The simulation verification of a double-lane change and straight-line driving conditions at constant speed was carried out using the Simulink/Carsim platform. The results show that observation using the Huber-based robust unscented Kalman filter (HRUKF) more realistically reflects the vehicle state in real-time, effectively suppresses the influence of abnormal error and noise, and obtains high observation accuracy.

Keywords: distributed drive; Huber method; unscented Kalman filter; state estimate (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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