Remaining Useful Life Prediction Model Based on Bidirectional Long Short-Term Memory Network and Attention Mechanism for Lithium Batteries

doi:10.3969/j.issn.1008-0198.2025.05.001

Abstract

Abstract: To enhance the reliability and accuracy of predictions, a lithium battery RUL prediction model based on Bidirectional Long Short-Term Memory Network(BiLSTM) and Attention mechanism is constructed. Firstly, the model takes the key performance parameters during the charge-discharge process of lithium batteries as the inputs, and utilizes the bidirectional memory characteristics of the BiLSTM network to fully capture the long-term and short-term temporal dependencies in the battery performance degradation process and effectively excavates the degradation trend features hidden in the data. Secondly, the Attention mechanism is introduced to assign higher weights to the key time-step features that affect RUL prediction, strengthening the model's ability to focus on important information and thereby outputting accurate RUL prediction values. Validation and analysis on four sets of public CALCE lithium battery datasets show that the R² values of the BiLSTM-Attention model reach 98.77%, 99.64%, 99.50%, and 98.47% respectively. Compared with other prediction methods, this method exhibits superior prediction performance.

Key words: lithium battery, remaining useful life (RUL) prediction, BiLSTM, attention mechanism

CLC Number:

TM911

ZHU Guangming, HU Jinhao, XU Song, WAN Dai. Remaining Useful Life Prediction Model Based on Bidirectional Long Short-Term Memory Network and Attention Mechanism for Lithium Batteries[J]. Hunan Electric Power, 2025, 45(5): 1-7.

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