State observation of distributed drive electric vehicle using square root cubature Kalman filter

被引：4
|
作者：
Jin X. [1 ,2 ]
Yin G. [1 ]
Chen N. [1 ]
Chen J. [1 ]
Zhang N. [1 ]
机构：
[1] School of Mechanical Engineering, Southeast University, Nanjing
[2] Department of Mechanical and Aerospace Engineering, The Ohio State University, Columbus
来源：
Yin, Guodong (ygd@seu.edu.cn) | 1600年 / Southeast University卷 / 46期
关键词：
Electric vehicles; Square root cubature Kalman filter; State observation; Vehicle dynamics;
D O I：
10.3969/j.issn.1001-0505.2016.05.016
中图分类号：
学科分类号：
摘要：
To deal with nonlinear challenges on vehicle dynamics state estimation, the eight-DOF(degree of freedom) nonlinear vehicle dynamics state estimation system, including longitudinal, lateral, yaw, and roll motions was constructed by introducing a nonlinear dynamics Dugoff tire model. Based on multi-sensor data fusion, the nonlinear observer with square root cubature Kalman filter was designed to estimate some key parameters, such as lateral tire-road forces and vehicle sideslip angle. Then the co-simulation platform with Simulink-Carsim for the estimated system of distributed drive electric vehicles was built in Matlab/Simulink environment. Simulations for double lane change manoeuvre were carried out to evaluate the feasibility and the effectiveness of the observer. The results show that the observed values with traditional extended Kalman filter state observer deviate from the real values of the vehicle running state when vehicles deliver high lateral acceleration, while the nonlinear observer with the proposed square root cubature Kalman filter has smooth results and reflects the real-time nonlinear vehicle dynamics state during double lane change manoeuvre. And it possesses smaller observer errors and higher observation precision. © 2016, Editorial Department of Journal of Southeast University. All right reserved.
引用
收藏
页码：992 / 996
页数：4
相关论文
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