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Research on fault prediction and management of charging station combined with deep learning model

Author

Listed:
  • Ye Ji
  • Liuting Gu
  • Hao Huang
  • Wendi Wang
  • Weiya Zhang

Abstract

In this paper, a fault prediction method combining particle swarm optimization (PSO) and Bidirectional long short-term memory (Bi-LSTM) is proposed, and the Bi-LSTM model is optimized by using PSO, which can effectively capture the time characteristics of equipment failures in charging stations and improve the efficiency of model parameter optimization. Experimental results show that the PSO-Bi-LSTM outperforms other methods in terms of recall rate, precision rate, and F1 score. Our model achieves 0.951 in precision, 0.963 in recall, and 0.957 in F1-score. This validates the effectiveness and superiority of this method in fault prediction for charging stations.

Suggested Citation

  • Ye Ji & Liuting Gu & Hao Huang & Wendi Wang & Weiya Zhang, 2025. "Research on fault prediction and management of charging station combined with deep learning model," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 20, pages 848-854.
    • Handle: RePEc:oup:ijlctc:v:20:y:2025:i::p:848-854.
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    File URL: http://hdl.handle.net/10.1093/ijlct/ctaf045
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