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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Wenkang Wan & Jingan Feng & Bao Song & Xinxin Li, 2021. "Huber-Based Robust Unscented Kalman Filter Distributed Drive Electric Vehicle State Observation," Energies, MDPI, vol. 14(3), pages 1-15, February.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:3:p:750-:d:490680
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    References listed on IDEAS

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    1. Zepeng Gao & Sizhong Chen & Yuzhuang Zhao & Jinrui Nan, 2018. "Height Adjustment of Vehicles Based on a Static Equilibrium Position State Observation Algorithm," Energies, MDPI, vol. 11(2), pages 1-26, February.
    2. Taimoor Zahid & Weimin Li, 2016. "A Comparative Study Based on the Least Square Parameter Identification Method for State of Charge Estimation of a LiFePO 4 Battery Pack Using Three Model-Based Algorithms for Electric Vehicles," Energies, MDPI, vol. 9(9), pages 1-16, September.
    3. Jing Hou & He He & Yan Yang & Tian Gao & Yifan Zhang, 2019. "A Variational Bayesian and Huber-Based Robust Square Root Cubature Kalman Filter for Lithium-Ion Battery State of Charge Estimation," Energies, MDPI, vol. 12(9), pages 1-23, May.
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