Co‐Estimation of State‐of‐Charge and State‐of‐Health Using Dual Fractional‐Order Adaptive Unscented Kalman Filter Under Different Temperature Levels

Accurate estimation of the state of charge (SOC) and state of health (SOH) is critical for ensuring the safety and reliability of lithium‐ion batteries, especially under varying temperature conditions. Temperature significantly impacts battery performance, introducing non‐linear effects that complicate state estimation. This paper proposes a novel multi‐scale co‐estimation strategy that integrates a temperature‐dependent fractional‐order model (FOM) with a dual fractional‐order adaptive unscented Kalman filter (DFOAUKF). The FOM is developed based on electrochemical impedance spectroscopy and the temperature‐dependent open‐circuit voltage, capturing intrinsic electrochemical characteristics of the battery. Parameters are identified using a combined offline and online method. The DFOAUKF algorithm leverages the time‐scale differences between SOC and SOH, enabling micro‐time SOC estimation in seconds and macro‐time SOH estimation over test cycles, while adaptively updating noise covariance matrices. Experimental validation under dynamic stress test (DST) and urban dynamometer driving schedule conditions at 10, 25 and 40 demonstrates the robustness and accuracy of the proposed method. Results show that the root mean square errors of terminal voltage remains within 0.039 V, while SOC and SOH estimation errors are less than 1.25% and 0.64%, respectively, across all temperature levels and aging degrees. The findings highlight the effectiveness of the proposed co‐estimation strategy in improving battery state monitoring and lifespan.