Lithium-ion battery RUL prediction using CNN-BiLSTM-Attention
TL;DR
A hybrid prediction framework integrating convolutional neural networks (CNN), bidirectional long short-term memory networks (BiLSTM), attention mechanisms, and ensemble learning strategies is developed to improve prediction accuracy and robustness in lithium-ion battery remaining useful life prediction.
Abstract
Lithium-ion battery remaining useful life (RUL) prediction is strongly affected by nonlinear degradation behavior and complex temporal dependence under practical operating environments. To improve prediction accuracy and robustness, this study develops a hybrid prediction framework integrating convolutional neural networks (CNN), bidirectional long short-term memory networks (BiLSTM), attention mechanisms, and ensemble learning strategies. First, multidimensional battery operation signals, including voltage, current, temperature, charging capacity, and discharge energy, are normalized and processed to extract representative degradation information. An attention mechanism is further introduced to adaptively emphasize important degradation stages and suppress irrelevant temporal information. In addition, a stacked ensemble structure combining support vector regression (SVR) and LightGBM is designed to enhance model generalization and reduce prediction variance. Experiments conducted on the CALCE lithium-ion battery dataset demonstrate that the proposed framework achieves excellent predictive performance, with an R² value of 0.9995, RMSE of 0.0504, and MAE of 0.0476. Compared with conventional CNN, LSTM, BiLSTM, and hybrid deep learning approaches, the proposed method exhibits higher prediction stability and stronger adaptability under complicated operating conditions. The developed model provides an effective technical solution for intelligent battery health management and remaining useful life estimation.