Comparative Analysis to Determine the State of Charge of a Lithium‐Ion Cell

Accurate state of charge (SOC) estimation remains a challenge in lithium‐ion battery management systems (BMSs) due to the cells’ complex, nonlinear internal dynamics, and their high sensitivity to operating conditions. While most existing data‐driven studies rely on a core input configuration of voltage ( V ), current ( I ), and temperature ( T ), these models often struggle with temperature‐induced variations, which typically require complex architectures. This study addresses the challenge of thermal dependency through an innovative feature engineering approach designed to maintain a low computational cost suitable for real‐time applications. Recognizing that internal resistance ( R ) inherently reflects both the cell’s temperature and aging state, we propose replacing T with R as the third input feature, resulting in an [I, V, R] configuration. We systematically compared the performance of four estimation models: DE‐long short‐term memory (LSTM), DE‐gated recurrent unit (GRU), LSTM‐unscented Kalman filter (UKF), and GRU‐UKF, using both the traditional [ I , V , T ] and the proposed [ I , V , R ] input sets. The results validate the superiority of the R ‐based configuration, which led to significant reductions in mean absolute error (MAE) by 46.02%, 43.31%, 14.02%, and 35.85%, and in root mean square error (RMSE) by 48.95%, 39.87%, 21.90%, and 23.95% for the DE‐LSTM, DE‐GRU, LSTM‐UKF, and GRU‐UKF models, respectively. This confirms that substituting T with R captures nonlinear temperature and aging effects more effectively while maintaining low computational complexity.