Fault diagnosis method of high power charging equipment based on Neural Network

被引：0
|
作者：
Gao, De-xin [1 ]
Lv, Yi-wei [1 ]
Wang, Kai [1 ]
Wang, Yi [1 ]
Yang, Qing [2 ]
机构：
[1] Qingdao Univ Sci & Technol, Coll Automat & Elect Engineer, Qingdao 266061, Peoples R China
[2] Qingdao Univ Sci & Technol, Coll Informat Sci & Technol, Qingdao 266061, Peoples R China
来源：
PROCEEDINGS OF THE 33RD CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2021) | 2021年
关键词：
Electric vehicles; High power charging equipment; fault diagnosis; BP neural network;
D O I：
10.1109/CCDC52312.2021.9601632
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
High power charging equipment is the necessary supporting facilities in the electric vehicle industry, but failure will inevitably occur in the process of equipment use. Through the research on the structure and principle of charging equipment, the influencing factors and causes of equipment failure are analyzed. In this paper, BP neural network algorithm is used to establish the nonlinear relationship between the fault influencing factors and fault causes of the charging equipment with the input and output of the neural network, and the fault diagnosis is carried out for the high-power charging equipment of electric vehicles. Through the simulation, the BP neural network fault diagnosis model has higher fault diagnosis accuracy. The model is applied to the condition monitoring and fault diagnosis system of high-power charging equipment of electric vehicles, and good diagnosis results are obtained, which has practical application value.
引用
收藏
页码：4542 / 4547
页数：6
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