State of Health Estimation for Lithium-Ion Batteries Using Separable LogSparse Self-Attention Transformer

This is the authors' abstract. We don't add key points for this paper.

The state of health (SOH) of lithium-ion batteries (LIBs) is an important indicator for evaluating the working condition of batteries and a crucial factor for the reliable functioning of battery management systems. The unfavorable factors of battery use and potential safety hazards can be reduced by accurately estimating the SOH. In this study, a separable LogSparse self-attention transformer (SLATrans) method is proposed, which incorporates a multichannel fusion adaptive embedding module and utilizes an encoder based on a multihead separable LogSparse self-attention module as well as an improved decoder for enhancing the estimation of SOH. The accuracy of long-sequence forecasting is improved while significantly reducing the computational complexity. To assess the efficacy of the SLATrans method, a comprehensive evaluation is carried out using the NASA and Center for Advanced Life Cycle Engineering (CALCE) datasets, focusing on mean absolute percentage error (MAPE), mean absolute error (MAE), and root-mean-square error (RMSE). The three types of minimum errors for SLATrans method on the two datasets are 1.31%, 0.97%, and 1.36% and 2.05%, 1.57%, and 2.50%, respectively. The findings indicate that the SLATrans method outperforms alternative network models in terms of estimation accuracy and dependability.