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

被引:21
|
作者
Wan, Wenkang [1 ]
Feng, Jingan [1 ]
Song, Bao [2 ]
Li, Xinxin [3 ]
机构
[1] Shihezi Univ, Sch Mech & Elect Engn, Shihezi 832000, Peoples R China
[2] Huazhong Univ Sci & Technol, Sch Mech Sci & Engn, Wuhan 430000, Peoples R China
[3] Chinese Acad Sci, Changchun Inst Opt Fine Mech & Phys, Changchun 130000, Peoples R China
基金
中国国家自然科学基金;
关键词
distributed drive; Huber method; unscented Kalman filter; state estimate;
D O I
10.3390/en14030750
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
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.
引用
收藏
页数:15
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