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.