Improved Compound Correction-Electrical Equivalent Circuit Modeling and Double Transform-Unscented Kalman Filtering for the High-Accuracy Closed-Circuit voltage and State-of-Charge Co-Estimation of Whole-Life-Cycle Lithium-Ion batteries

For complex energy storage conditions, it is necessary to monitor the state-of-charge (SOC) and closed-circuit voltage (CCV) status accurately for the reliable power supply application of lithium-ion batteries. Herein, an improved compound correction-electrical equivalent circuit modeling (CC-EECM) method is proposed by considering the influencing effects of ambient temperature and charge–discharge current rate variations to estimate the CCV. Then, an improved adaptive double transform-unscented Kalman filtering (ADT-UKF) method is constructed with recursive sampling data correction to estimate the nonlinear SOC. A dynamic window function filtering strategy is constructed to obtain the new sigma point set for the online weighting coefficient correction. For a temperature range of 5–45 °C, the CCV for the improved CC-EECM responds well with a maximum error of 0.008608 V, and the maximum SOC estimation error is 6.317%. The proposed ADT-UKF method improves the CCV and SOC estimation reliability and adaptability to the time-varying current rate, temperature, and aging factors.