State of Charge Estimation of Lithium-Ion Batteries Using the Window Attention Sinks Transformer

Lithium-ion batteries are the core energy storage devices for electric vehicles, and accurate state of charge (SOC) estimation is critical to ensuring their safe and reliable operation. Most existing SOC estimation methods are only suitable for constant-temperature scenarios and cannot adapt to the dynamic temperature variations in actual charging and discharging processes. To address the issue of insufficient estimation accuracy under complex conditions such as high and low temperatures, this study proposes a Window Attention Sinks Transformer (WASFormer) model. Based on the PatchTST framework, the model integrates Rotary Positional Encoding (RoPE) and Window Attention Sinks (WAS) mechanisms, and combines Huber Loss with Reversible Instance Normalization (RevIN) to establish a full-chain robustness enhancement scheme from feature preprocessing to loss optimization, which effectively suppresses the interference of noise and distribution shift on estimation stability. Comparative experiments, generalization tests, and ablation studies under various temperatures and working conditions show that the proposed model achieves higher estimation accuracy, stronger generalization ability, and robustness. It provides an effective and stable new approach for high-precision SOC estimation of lithium-ion batteries over a wide temperature range and under complex operating conditions.