Online Prediction of Electric Vehicle Battery Failure Using LSTM Network
Xuemei Li,
Hao Chang,
Ruichao Wei (rcwei@mail.ustc.edu.cn),
Shenshi Huang,
Shaozhang Chen,
Zhiwei He and
Dongxu Ouyang (ouyang11@mail.ustc.edu.cn)
Additional contact information
Xuemei Li: School of Mechanic and Electronic Engineering, Guilin University of Electronic Technology, Guilin 541000, China
Hao Chang: School of Automobile and Transportation, Shenzhen Polytechnic, Shenzhen 518000, China
Ruichao Wei: School of Automobile and Transportation, Shenzhen Polytechnic, Shenzhen 518000, China
Shenshi Huang: School of Architectural Engineering, Shenzhen Polytechnic, Shenzhen 518055, China
Shaozhang Chen: School of Mechanic and Electronic Engineering, Guilin University of Electronic Technology, Guilin 541000, China
Zhiwei He: School of Automobile and Transportation, Shenzhen Polytechnic, Shenzhen 518000, China
Dongxu Ouyang: College of Safety Science and Engineering, Nanjing Tech University, Nanjing 211816, China
Energies, 2023, vol. 16, issue 12, 1-14
Abstract:
The electric vehicle industry is developing rapidly as part of the global energy structure transformation, which has increased the importance of overcoming power battery safety issues. In this paper, first, we study the relationship between different types of vehicle faults and battery data based on the actual vehicle operation data in the big data supervisory platform of new energy vehicles. Second, we propose a method to realize the online prediction of electric vehicle battery faults, based on a Long Short-Term Memory (LSTM). Third, we carry out prediction research for two kinds of faults: low State of Charge (SOC) alarm and insulation alarm. Last, we show via experimental results that the model based on the LSTM network can effectively predict battery faults with an accuracy of more than 85%. Through this research, it is possible to complete online pre-processing of vehicle operation data and fault prediction of power batteries, improve vehicle monitoring capabilities and ensure the safety of electric vehicle use.
Keywords: electric vehicle; power battery; LSTM network; failure prediction; real-time supervision (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (4)
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:12:p:4733-:d:1172019
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