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

Zhifu Wang, Wei Luo, Song Xu, Yuan Yan, Limin Huang (), Jingkai Wang, Wenmei Hao and Zhongyi Yang
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Zhifu Wang: School of Mechanical and Vehicle Engineering, Beijing Institute of Technology, Beijing 100081, China
Wei Luo: School of Automation, Guangxi University of Science and Technology, Liuzhou 545000, China
Song Xu: School of Mechanical and Vehicle Engineering, Beijing Institute of Technology, Beijing 100081, China
Yuan Yan: School of Mechanical and Vehicle Engineering, Beijing Institute of Technology, Beijing 100081, China
Limin Huang: School of Mechanical Engineering, ChengDu University, Chengdu 610106, China
Jingkai Wang: School of Mechanical and Vehicle Engineering, Beijing Institute of Technology, Beijing 100081, China
Wenmei Hao: School of Mechanical and Vehicle Engineering, Beijing Institute of Technology, Beijing 100081, China
Zhongyi Yang: School of Automation, Guangxi University of Science and Technology, Liuzhou 545000, China

Sustainability, 2023, vol. 15, issue 2, 1-18

Abstract: 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.

Keywords: lithium-ion battery; electric vehicle; real-world vehicle data; fault diagnosis; data-driven; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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