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A Method for Predicting the Remaining Life of Lithium-Ion Batteries Based on an Improved Dempster–Shafer Evidence Theory Framework

Author

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  • Tongrui Zhang

    (Schulich School of Engineering, University of Calgary, Calgary, AB T2N1N4, Canada)

  • Hao Sun

    (College of Artificial Intelligence, Nankai University, Tianjin 300110, China)

Abstract

Lithium-ion batteries (LIBs) are widely used in consumer electronics, electric vehicles, and renewable energy systems, but their performance decays with their lifespan, which poses safety risks. Therefore, it is crucial to develop remaining useful life (RUL) prediction technology. This paper proposes a RUL prediction method for lithium-ion batteries based on an improved Dempster–Shafer (D-S) evidence theory framework, which aims to improve the accuracy and robustness of prediction by integrating the advantages of a wavelet packet decomposition convolutional neural network (WPD-CNN) and an extended Kalman filter (EKF). The results show that the improved D-S theory overcomes the limitations of the classical D-S theory, improves the accuracy and robustness of diagnosis and prediction, and can effectively integrate multi-source information. Experimental verification shows that the fused model is significantly better than a single model in terms of prediction accuracy and robustness, providing an efficient and reliable solution for fault diagnosis and health management of lithium-ion batteries.

Suggested Citation

  • Tongrui Zhang & Hao Sun, 2025. "A Method for Predicting the Remaining Life of Lithium-Ion Batteries Based on an Improved Dempster–Shafer Evidence Theory Framework," Energies, MDPI, vol. 18(13), pages 1-13, June.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:13:p:3370-:d:1688544
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    References listed on IDEAS

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    1. Ding, Pan & Liu, Xiaojuan & Li, Huiqin & Huang, Zequan & Zhang, Ke & Shao, Long & Abedinia, Oveis, 2021. "Useful life prediction based on wavelet packet decomposition and two-dimensional convolutional neural network for lithium-ion batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
    2. Li, Penghua & Zhang, Zijian & Grosu, Radu & Deng, Zhongwei & Hou, Jie & Rong, Yujun & Wu, Rui, 2022. "An end-to-end neural network framework for state-of-health estimation and remaining useful life prediction of electric vehicle lithium batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
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