Joint Estimation of State of Charge and State of Temperature for Solid‐State Batteries Based on Dual‐Extended Kalman Filter

Due to significant electrothermal coupling, solid‐state lithium‐ion batteries pose challenges for accurately estimating battery state in battery management systems. This paper proposes a Dual‐Extended Kalman Filter (DEKF) framework for joint estimation of state of charge (SOC) and state of temperature (SOT). A second‐order equivalent circuit model and a two‐state thermal model are established, with parameters identified via particle swarm optimization and recursive least squares. An electrothermal coupling model is then built, and a DEKF‐based estimator is designed to simultaneously estimate SOC, surface temperature, and core temperature. Validation under four typical dynamic driving cycles shows that for SOC estimation, the mean absolute error (MAE), and root mean square error (RMSE) remain below 0.75% and 0.85%, respectively. For temperature estimation, the MAE for surface and core temperatures was kept within 0.5924 and 0.6293 °C, respectively, while the RMSE was kept within 0.7076 and 0.7611 °C, respectively. This study proposes a feasible method for state estimation in solid‐state batteries.