State Estimation of Distributed Drive Electric Vehicle Based on Adaptive Extended Kalman Filter

被引：9

作者：

Zhang Z. ^{[1
,2
]}

Zhang S. ^{[2
]}

Huang C. ^{[3
]}

Zhang L. ^{[2
]}

Li B. ^{[2
]}

机构：

[1] Key Laboratory of Lightweight and Reliability Technology for Engineering Vehicle, Changsha

[2] College of Automobile and Mechanical Engineering, Changsha University of Science and Technology, Changsha

[3] College of Mechanical and vehicle Engineering, Hunan university, Changsha

来源：

Jixie Gongcheng Xuebao/Journal of Mechanical Engineering | 2019年 / 55卷 / 06期

关键词：

Adaptive control; Distributed drive; Electric vehicle; Extended Kalman filter; States estimation;

D O I：

10.3901/JME.2019.06.156

中图分类号：

学科分类号：

摘要：

Longitudinal velocity and sideslip angle are the key referent state signals for vehicle active safety control system, and are usually estimated by Kalman filtering algorithm. The uncertainties of the statistical characteristics of system noise and measurement noise may cause filter to deviate or even diverge. Using the characteristics of the torques and speeds of four wheels can measurement directly in a distributed drive electric vehicle, an adaptive extended Kalman filtering method for vehicle state estimation is proposed. With normalized innovation square, the validity of vehicle state estimation is detected, and an adaptive adjustment rule of sliding window length is designed. An adaptive adjustment strategy of the gain of Kalman filter and the covariance matrix of state estimation error are proposed based on the statistical characteristics of innovation. The determination principle of adaptive parameters based on the steady-state error of vehicle state estimation and the dynamic response speed is determined. The numerical simulation and experiment can prove that the proposed algorithm of vehicle state estimation not only can improve estimation accuracy, but also has advantages of high real-time and easy to implement. © 2019 Journal of Mechanical Engineering.

引用

页码：156 / 165

页数：9

共 18 条

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