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A Power Battery Fault Diagnosis Method Based on Long-Short Term Memory-Back Propagation

by Yuheng Yin, Jiahao Song*, Minghui Yang

School of Automation, Harbin University of Science and Technology, Harbin, 150080, China

* Corresponding Author: Jiahao Song. Email: email

(This article belongs to the Special Issue: Advanced Modelling, Operation, Management and Diagnosis of Lithium Batteries)

Energy Engineering 2025, 122(2), 709-731. https://doi.org/10.32604/ee.2024.059021

Abstract

The lithium battery is an essential component of electric cars; prompt and accurate problem detection is vital in guaranteeing electric cars’ safe and dependable functioning and addressing the limitations of Back Propagation (BP) neural networks in terms of vanishing gradients and inability to effectively capture dependencies in time series, and the limitations of Long-Short Term Memory (LSTM) neural network models in terms of risk of overfitting. A method based on LSTM-BP is put forward for power battery fault diagnosis to improve the accuracy of lithium battery fault diagnosis. First, a lithium battery model is constructed based on the second-order RC equivalent circuit and the electro-thermal coupling model, and various lithium battery failures are simulated to examine the fault characteristics. Then, the lithium battery charging and discharging experiments collect, clean, and process the battery data. By constructing a neural network LSTM-BP model, we verified the superiority and accuracy of the LSTM-BP neural network model by comparing the LSTM model and BP model vertically and by comparing the Recurrent Neural Network (RNN) model, the Gated Recurrent Unit (GRU) model, and the Residual Neural Network (ResNet) model of a more advanced architecture horizontally. Finally, the lithium battery fault diagnosis process is summarized through the threshold quantitative criteria, and different faults are diagnosed and analyzed. The results show that the LSTM-BP neural network not only overcomes the limitations of the LSTM neural network and BP neural network but also improves the ability to process sequence data and reduces the risk of overfitting.

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APA Style
Yin, Y., Song, J., Yang, M. (2025). A power battery fault diagnosis method based on long-short term memory-back propagation. Energy Engineering, 122(2), 709–731. https://doi.org/10.32604/ee.2024.059021
Vancouver Style
Yin Y, Song J, Yang M. A power battery fault diagnosis method based on long-short term memory-back propagation. Energ Eng. 2025;122(2):709–731. https://doi.org/10.32604/ee.2024.059021
IEEE Style
Y. Yin, J. Song, and M. Yang, “A Power Battery Fault Diagnosis Method Based on Long-Short Term Memory-Back Propagation,” Energ. Eng., vol. 122, no. 2, pp. 709–731, 2025. https://doi.org/10.32604/ee.2024.059021



cc Copyright © 2025 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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