A Transformer-Based Model for State of Charge Estimation of Electric Vehicle Batteries

Batteries play a critical role in Electric Vehicle (EV) systems devices. The safety and performance of these applications rely on accurate Battery Management Systems (BMS) to monitor and optimize battery performance. Traditional BMS systems face challenges in charge prediction processes due to complex chemical processes and battery aging, leading to faults. The absence of a perfect sensor highlights limitations in measurement issues arising from external factors, especially sensor noise. Therefore, there is a need for algorithms that can solve the real-world battery charge prediction problem. This study compares an innovative solution, the Transformer model, with traditional Long Short-Term Memory (LSTM), Bi-directional LSTM (Bi-LSTM) models, and Support Vector Regression (SVR). This research aims to provide new perspectives on Battery State of Charge (SoC) predictions using NASA, BMW i3, Stanford University Battery Datasets, and real-world battery data obtained from L5 EV of the Musoshi brand collected for this work. This research’s primary objective is to apply the Transformer model to real-world battery data, evaluating it as a significant step in EV range optimization and battery management. The Transformer model in the study achieved the best result with Root Mean Square Error (RMSE) value closest to 1 (~0.99).