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Volume 18
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Article

Research on GRU-LSTM-Based Early Warning Method for Electric Vehicle Lithium-Ion Battery Voltage Fault Classification

by
Liang Zhang
1,
Qizhi Wu
1,*,
Longfei Wang
1,
Ling Lyu
1,
Linru Jiang
2 and
Yu Shi
3
1
School of Electrical Engineering, Northeast Electric Power University, Jilin 132012, China
2
China Electric Power Research Institute Limited, Haidian District, Beijing 100192, China
3
Changchun Electric Power Exploration & Design Institute, Changchun 130000, China
*
Author to whom correspondence should be addressed.
Energies 2025, 18(6), 1315; https://doi.org/10.3390/en18061315
Submission received: 7 February 2025 / Revised: 28 February 2025 / Accepted: 5 March 2025 / Published: 7 March 2025
(This article belongs to the Topic Cooperative Localization, Optimization and Control of Networked Autonomous Systems: Theories, Analysis Tools and Applications)
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Abstract

Battery cell voltage is an important evaluation index for electric vehicle condition estimation and one of the main monitoring parameters of the battery management system, and accurate voltage prediction is crucial for electric vehicle battery failure warning. Therefore, this paper proposes a novel hybrid gated recurrent unit and long short-term memory (GRU-LSTM) neural network to predict electric vehicle lithium-ion battery cell voltage. Firstly, Pearson coefficient correlation analysis is carried out to determine the input parameters of the neural network by analyzing the influence factors of the voltage parameters, and the hyperparameters of the neural network are determined through cross-validation to construct the lithium-ion battery single-unit voltage prediction model based on GRU-LSTM. Secondly, the voltage prediction accuracy and robustness of the GRU-LSTM model are verified by training the historical data of real vehicles in spring, summer, fall, and winter, combined with four different error indicators. Finally, the feasibility of the proposed method is verified by designing hierarchical warning rules based on the prediction data to realize the accurate warning of multiple voltage anomalies.
Keywords: electric vehicle; battery system; voltage prediction; GRU-LSTM; fault warning electric vehicle; battery system; voltage prediction; GRU-LSTM; fault warning

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MDPI and ACS Style

Zhang, L.; Wu, Q.; Wang, L.; Lyu, L.; Jiang, L.; Shi, Y. Research on GRU-LSTM-Based Early Warning Method for Electric Vehicle Lithium-Ion Battery Voltage Fault Classification. Energies 2025, 18, 1315. https://doi.org/10.3390/en18061315

AMA Style

Zhang L, Wu Q, Wang L, Lyu L, Jiang L, Shi Y. Research on GRU-LSTM-Based Early Warning Method for Electric Vehicle Lithium-Ion Battery Voltage Fault Classification. Energies. 2025; 18(6):1315. https://doi.org/10.3390/en18061315

Chicago/Turabian Style

Zhang, Liang, Qizhi Wu, Longfei Wang, Ling Lyu, Linru Jiang, and Yu Shi. 2025. "Research on GRU-LSTM-Based Early Warning Method for Electric Vehicle Lithium-Ion Battery Voltage Fault Classification" Energies 18, no. 6: 1315. https://doi.org/10.3390/en18061315

APA Style

Zhang, L., Wu, Q., Wang, L., Lyu, L., Jiang, L., & Shi, Y. (2025). Research on GRU-LSTM-Based Early Warning Method for Electric Vehicle Lithium-Ion Battery Voltage Fault Classification. Energies, 18(6), 1315. https://doi.org/10.3390/en18061315

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