Adaptive self-attention LSTM for RUL prediction of lithium-ion batteries

To achieve an accurate remaining useful life (RUL) prediction for lithium-ion batteries (LIBs), this study proposes an adaptive self-attention long short-term memory (SA-LSTM) prediction model. The innovations of the designed prediction model include the following. (1) It features an opti-mized local tangent space alignment algorithm, which allows the extraction of an indirect health indicator (HI) that can precisely describe battery degeneration from charge data. The extracted HI exhibits a high correlation with the standard capacity, thus facilitating RUL estimation. (2) By introducing a masked multi-head self-attention module into the time-series prediction model based on LSTM, critical information in the sequences is captured and the prediction performance is improved. (3) An online self-tuning mechanism for the weights and biases of neural networks is designed to correct cumulative estimation errors in long-term predictions and reduce the effects of local fluctuations and regeneration. The proposed prediction model enables the HI values in future cycles to be iteratively estimated using the one-step-ahead method, and the RUL can be forecast once the predicted signal falls. Experimental results indicate the effectiveness and su-periority of the proposed prediction method.