Robust State of Charge Estimation of a Lithium Iron Phosphate Battery Module Using a Dual‐UKF Under Various Operational Temperatures

This paper proposes a robust framework to estimate the state of charge (SOC) of a lithium iron phosphate (LFP) battery module operating under diverse temperature conditions. The proposed framework integrates an enhanced equivalent circuit model (EECM) with a dual unscented Kalman filter (dual‐UKF) to address SOC inconsistency and temperature‐dependent nonlinearity in LFP modules. Bayesian optimization (BO) is employed to identify both model parameters and filter hyperparameters systematically, ensuring high fidelity and adaptability without manual tuning. The dual‐loop UKF architecture simultaneously estimates SOC and updates time‐varying parameters, effectively compensating for initialization errors of up to 10%. The proposed framework maintains an SOC root‐mean‐square error (RMSE) below 1% across a wide temperature range from −15 to 45°C, with a faster convergence observed at low temperatures caused by enhanced observability from increased potential variations. Comprehensive experimental validation using standard dynamic driving cycles and real‐world stochastic battery energy storage system (BESS) data confirms the accuracy, robustness, and applicability of the proposed framework. Thus, the proposed framework offers a practical solution for battery management systems (BMSs) in LFP‐based battery applications, as it provides accurate monitoring capabilities under diverse environmental and operational uncertainties.