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

Tongrui Zhang () and Hao Sun ()
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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

Energies, 2025, vol. 18, issue 13, 1-13

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.

Keywords: lithium-ion battery; Dempster–Shafer Theory; remaining useful life (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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