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Remaining Useful Life Prediction of Lithium-Ion Batteries by Using a Denoising Transformer-Based Neural Network

Author

Listed:
  • Yunlong Han

    (Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia)

  • Conghui Li

    (Zibo Vocational Institute, Zibo 255314, China)

  • Linfeng Zheng

    (Institute of Rail Transportation, Jinan University, Zhuhai 510632, China)

  • Gang Lei

    (Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia)

  • Li Li

    (Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia)

Abstract

In this study, we introduce a novel denoising transformer-based neural network (DTNN) model for predicting the remaining useful life (RUL) of lithium-ion batteries. The proposed DTNN model significantly outperforms traditional machine learning models and other deep learning architectures in terms of accuracy and reliability. Specifically, the DTNN achieved an R 2 value of 0.991, a mean absolute percentage error (MAPE) of 0.632%, and an absolute RUL error of 3.2, which are superior to other models such as Random Forest (RF), Decision Trees (DT), Multilayer Perceptron (MLP), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Dual-LSTM, and DeTransformer. These results highlight the efficacy of the DTNN model in providing precise and reliable predictions for battery RUL, making it a promising tool for battery management systems in various applications.

Suggested Citation

  • Yunlong Han & Conghui Li & Linfeng Zheng & Gang Lei & Li Li, 2023. "Remaining Useful Life Prediction of Lithium-Ion Batteries by Using a Denoising Transformer-Based Neural Network," Energies, MDPI, vol. 16(17), pages 1-16, August.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:17:p:6328-:d:1230056
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    References listed on IDEAS

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