Transformer and self-supervised learning-based state-of-charge prediction method for lithium-ion batteries

Abstract

The state-of-charge (SOC) estimation of lithium-ion batteries is essential for the performance, safety, and longevity of electric vehicles. While traditional physics-based models face difficulties in predicting SOC under complex conditions, data-driven methods demand large volumes of labeled data, limiting their practical applicability. This paper introduces a Transformer-based SOC estimation method that incorporates a self-supervised learning framework. By combining a masking recovery mechanism with a self-attention mechanism, the approach effectively captures SOC’s long-term dependencies and identifies global patterns under complex operating conditions. Experimental results on the Nissan Leaf and University of Maryland datasets demonstrate significant performance improvements. Compared to traditional deep learning methods, this approach delivers higher prediction accuracy and robustness, offering an innovative solution for efficient SOC estimation in complicated environments.