Online Prediction of Electric Vehicle Battery Failure Using LSTM Network-Reference-Cited by-同舟云学术

Online Prediction of Electric Vehicle Battery Failure Using LSTM Network

Published:2023-06-15 Issue:12 Volume:16 Page:4733
ISSN:1996-1073
Container-title:Energies
language:en
Short-container-title:Energies

Author:

Li Xuemei¹,Chang Hao²¹,Wei Ruichao²,Huang Shenshi³,Chen Shaozhang¹,He Zhiwei²¹,Ouyang Dongxu⁴^ORCID

Affiliation:

1. School of Mechanic and Electronic Engineering, Guilin University of Electronic Technology, Guilin 541000, China

2. School of Automobile and Transportation, Shenzhen Polytechnic, Shenzhen 518000, China

3. School of Architectural Engineering, Shenzhen Polytechnic, Shenzhen 518055, China

4. College of Safety Science and Engineering, Nanjing Tech University, Nanjing 211816, China

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.

Funder

National Natural Science Foundation of China

Scientific Research Startup Fund for Shenzhen High-Caliber Personnel of SZPT

Special Project in the Key Areas of General Universities in Guangdong Province

Postdoctoral Later-stage Foundation Project of Shenzhen Polytechnic

Publisher

MDPI AG

Subject

Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction

Link

https://www.mdpi.com/1996-1073/16/12/4733/pdf

Reference30 articles.

1. New energy vehicle sales exceed 6.8 million units in 2022;Yao;Auto Rev.,2023

2. Zhang, H., Li, S., Chen, F., Pan, X., Feng, H., and Sun, Y. (2022). Battery voltage fault diagnosis for electric vehicles considering driving condition variation. IET Intell. Transp. Syst., 1–17.

3. Long Short-Term Memory;Hochreiter;Neural Comput.,1997

4. GERS F (2001). Long Short-Term Memory in Recurrent Neural Networks, Universität Hannover.

5. Liwicki, M., Graves, A., Fernandez, S., Bunke, H., and Schmidhuber, J. (2007, January 23–26). A novel approach to on-line handwriting recognition based on bidirectional long short-term memory networks. Proceedings of the 9th International Conference on Document Analysis and Recognition, ICDAR 2007, Curitiba, Brazil.

Cited by 7 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Butterfly valve erosion prediction based on LSTM network;Flow Measurement and Instrumentation;2024-09

2. An Early Warning Model for Turbine Intermediate-Stage Flux Failure Based on an Improved HEOA Algorithm Optimizing DMSE-GRU Model;Energies;2024-07-24

3. Optimizing EV Battery Management: Advanced Hybrid Reinforcement Learning Models for Efficient Charging and Discharging;Energies;2024-06-12

4. State of health prediction of lithium-ion batteries based on SSA optimized hybrid neural network model;Electrochimica Acta;2024-05

5. Advanced Battery Management for Electric Vehicles: A Deep Dive into Estimation Techniques Based on Deep Learning for the State of Health and State of Charge of Lithium-Ion Batteries;2024 IEEE Open Conference of Electrical, Electronic and Information Sciences (eStream);2024-04-25