Electric Vehicle Lithium-Ion Battery Fault Diagnosis Based on Multi-Method Fusion of Big Data

被引:19
|
作者
Wang, Zhifu [1 ,2 ]
Luo, Wei [2 ]
Xu, Song [1 ]
Yan, Yuan [1 ]
Huang, Limin [3 ]
Wang, Jingkai [1 ]
Hao, Wenmei [1 ]
Yang, Zhongyi [2 ]
机构
[1] Beijing Inst Technol, Sch Mech & Vehicle Engn, Beijing 100081, Peoples R China
[2] Guangxi Univ Sci & Technol, Sch Automat, Liuzhou 545000, Peoples R China
[3] ChengDu Univ, Sch Mech Engn, Chengdu 610106, Peoples R China
基金
中国国家自然科学基金;
关键词
lithium-ion battery; electric vehicle; real-world vehicle data; fault diagnosis; data-driven; machine learning;
D O I
10.3390/su15021120
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Power batteries are the core of electric vehicles, but minor faults can easily cause accidents; therefore, fault diagnosis of the batteries is very important. In order to improve the practicality of battery fault diagnosis methods, a fault diagnosis method for lithium-ion batteries in electric vehicles based on multi-method fusion of big data is proposed. Firstly, the anomalies are removed and early fault analysis is performed by t-distribution random neighborhood embedding (t-Sne) and wavelet transform denoising. Then, different features of the vehicle that have a large influence on the battery fault are identified by factor analysis, and the faulty features are extracted by a two-way long and short-term memory network method with convolutional neural network. Finally a self-learning Bayesian network is used to diagnose the battery fault. The results show that the method can improve the accuracy of fault diagnosis by about 12% when verified with data from different vehicles, and after comparing with other methods, the method not only has higher fault diagnosis accuracy, but also reduces the response time of fault diagnosis, and shows superiority compared to graded faults, which is more in line with the practical application of engineering.
引用
收藏
页数:18
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