State-of-charge estimation of commercial electric vehicles by using battery-aware transformer network algorithms

This study proposes a battery-aware transformer network (BATNet) for accurate and robust state-of-charge estimation in lithium-ion batteries for electric vehicles (EVs). BATNet combines feedforward neural networks with an encoder-only transformer enhanced by local attention to focus on recent signal trends and an adaptive forgetting mechanism to suppress transient anomalies. Simulations were conducted on a high-fidelity Hyundai KONA EV model in MATLAB Simulink, encompassing four driving cycles and five ambient temperatures. Results show that BATNet significantly outperforms traditional models, including gated recurrent units, recurrent neural networks, and long short-term memory networks, achieving a root mean square error as low as 0.0436%. An ablation study further verifies the contributions of the proposed architectural components. BATNet effectively captures nonlinear battery dynamics and remains stable under varying thermal and load conditions, making it well-suited for integration into real-time battery management systems in EV applications.