Gain-Scheduled Fault Diagnosis of In-Wheel Motor Electric Vehicles

被引：0
|
作者：
Yuan, Jingxin [1 ]
Wang, Yulei [1 ]
Yu, Shuyou [1 ]
Chen, Hong [1 ]
机构：
[1] Jilin Univ, Dept Control Sci & Engn, State Key Lab Automot Simulat & Control, Changchun 130025, Jilin, Peoples R China
来源：
2017 29TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC) | 2017年
基金：
中国国家自然科学基金; 中国博士后科学基金;
关键词：
Electric Vehicle; Fault Diagnosis; Gain-Scheduled; SYSTEMS;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents a gain-scheduled fault diagnosis approach for in-wheel motor (IWM) driven electric vehicles. The dynamics of vehicle slip ratio, which is usually used for vehicle stability and torque allocation, is modeled to monitor the motor torque and evaluate the loss of effectiveness. A gain-scheduled extended state observer (GS-ESO) is investigated to estimate the fault in each of in-wheel motors. With the aid of gain-scheduled techniques, the formulation of fault diagnosis for IWM electric vehicles is transformed to an output feedback gain-scheduled robust control problem. Simulation results using a high-fidelity full-vehicle model in VeDYNA validate the effectiveness of the proposed approach.
引用
收藏
页码：4093 / 4098
页数：6
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