基于无迹卡尔曼滤波的轮毂电机驱动车辆状态观测

doi:10.13229/j.cnki.jdxbgxb201602001

吉林大学学报(工学版) ›› 2016, Vol. 46 ›› Issue (2): 333-339.doi: 10.13229/j.cnki.jdxbgxb201602001

• 论文 • 下一篇

基于无迹卡尔曼滤波的轮毂电机驱动车辆状态观测

宋传学¹, 肖峰¹, 刘思含², 李少坤¹, 段亮¹, 彭思仑¹

1.吉林大学汽车仿真与控制国家重点实验室,长春 130022;
2.吉林大学机械科学与工程学院,长春 130022

收稿日期:2014-09-22 出版日期:2016-02-20 发布日期:2016-02-20
通讯作者: 彭思仑(1987-),男,工程师.研究方向:汽车系统动力学.E-mail:pengsilun@126.com E-mail:songchx@126.com
作者简介:宋传学(1959-),男,教授,博士生导师.研究方向:汽车系统动力学.E-mail:songchx@126.com
基金资助:
科技部国际合作计划项目(2010DFB83650)

State estimation of electric vehicle with in-wheel motors based on UKF

SONG Chuan-xue¹, XIAO Feng¹, LIU Si-han², LI Shao-kun¹, DUAN Liang¹, PENG Si-lun¹

1.State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China;
2.College of Mechanical Science and Engineering, Jilin University, Changchun 130022, China

Received:2014-09-22 Online:2016-02-20 Published:2016-02-20

摘要/Abstract

摘要： 针对车辆质心(CG)侧偏角和轮胎附着力等参数无法用传感器直接测量得到的情况,本文设计了一种基于无迹卡尔曼滤波的车辆状态观测器.该方法考虑了车辆运行工况变化时的非线性因素,引入了非线性动态轮胎模型来提高轮胎侧向力的估算精度.与动力学软件AMESim建立的参考模型进行了对比仿真,仿真结果表明:本文建立的观测器能够准确估算出轮毂电机驱动汽车的状态参数.

关键词: 车辆工程, 轮毂电机驱动汽车, 状态观测, 无迹卡尔曼滤波

Abstract: For four-wheel independently drive in-wheel motors electric vehicle, the dynamic control systems, such as direct yaw moment control, can be easily achieved. Accurate estimation of vehicle state variables and uncertain parameters can improve the robustness of the vehicle dynamic control system. Various sensors are generally equipped to acquire the vehicle dynamics. For both technical and economic reasons, some fundamental vehicle parameters, such as the sideslip angle and tire-road forces can hardly be obtained directly by sensors. Therefore, a state observer is developed to estimate these parameters based on Unscented Kalman Filter (UKF). In addition, a nonlinear dynamics tire model is utilized to improve the accuracy of tire lateral force estimation. Compared to the reference model built in AMEsim, the simulation results show that the proposed observer can provide the precision values of the vehicle state.

Key words: vehicle engineering, in-wheel-motor vehicle, state estimation, unscented kalman filter

中图分类号:

U461

宋传学, 肖峰, 刘思含, 李少坤, 段亮, 彭思仑. 基于无迹卡尔曼滤波的轮毂电机驱动车辆状态观测[J]. 吉林大学学报(工学版), 2016, 46(2): 333-339.

SONG Chuan-xue, XIAO Feng, LIU Si-han, LI Shao-kun, DUAN Liang, PENG Si-lun. State estimation of electric vehicle with in-wheel motors based on UKF[J]. 吉林大学学报(工学版), 2016, 46(2): 333-339.

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基于无迹卡尔曼滤波的轮毂电机驱动车辆状态观测

State estimation of electric vehicle with in-wheel motors based on UKF

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