Early Prognostics of Remaining Useful Life in Lithium Ion Batteries Using Hybrid LSTM-Att-MLP Model with Fusing Aging Information

Predicting the remaining useful lifetime (RUL) stands as a crucial aspect of lithium-ion battery management systems, acting as a core component of their functioning. Accurately predicting the RUL is essential for ensuring safety, preventing failures, and averting catastrophic incidents, but it is challenging, due to capacity degradation and aging effects. To overcome this, a hybrid model termed LAM: LSTM with an attention mechanism and MLP, for early RUL prediction, leveraging fused aging information is proposed. LSTM adeptly captures the significance embedded within feature sequences, preserving essential long-term features while effectively filtering out less pertinent information. Embedding attention mechanism with LSTM, the model dynamically focuses on different parts of the input sequence by assigning varying importance levels to different aging information, enhancing the prediction performance. For capturing the dynamic and nonlinear degradation trend of batteries and predicting RUL by effectively learning intricate degradation patterns MLP is utilized. The proposed model’s efficacy is evaluated using a NASA dataset through leave-one-out evaluation, utilizing 50% of the training data from three batteries to predict the others, and with varying starting points. The results indicate that under conditions of limited historical samples, the LAM attains higher accuracy and achieves minimum Mean Squared Error of 3.9962 × 10 −5 .