A novel collaborative multiscale weighting factor‐adaptive Kalman filtering method for the time‐varying whole‐life‐cycle state of charge estimation of lithium‐ion batteries. (23rd January 2022) - Wang, Shunli; Fan, Yongcun; Yu, Chunmei; Jin, Siyu; Takyi‐Aninakwa, Paul; Fernandez, Carlos; Stroe, Daniel‐Ioan
Summary: Accurate state of charge (SOC) estimation is essential for the whole‐life‐cycle safety guarantee and protection of lithium‐ion batteries, which is quite difficult to realize. In this study, a novel weighting factor‐adaptive Kalman filtering (WF‐AKF) method is proposed for the accurate estimation of SOC with a collaborative model for parameter identification. An improved bipartite electrical equivalent circuit (BEEC) model is constructed to describe the dynamic characteristics combined with the mathematical correction of the time‐varying factors. The model parameters are identified online, corresponding to various SOC levels and temperature conditions. Considering the internal resistances, ambient temperature, and complex current rate variations, an adaptive multi‐time scale iterative calculation model is constructed and combined with the real‐time estimation and correction strategies. The maximum closed‐circuit voltage (CCV) traction error is 0.36% and 0.24% for the main pulse‐current charging and discharging processes, respectively. The proposed WF‐AKF algorithm stabilizes the large initial SOC estimation error by tracking the actual value with a maximum error of 0.46% under the complex working condition. The SOC estimation is accurate and robust to the time‐varying characteristics and working conditions even when the initial error is large, providing a safety protection theory for lithium‐ion batteries. Abstract : A novel weighting factor‐adaptive Kalman filtering (WF‐AKF) method is proposed for the whole‐life‐cycle state of charge (SOC) estimation of lithium‐ion batteries. The improved collaborative bipartite electrical equivalent circuit (BEEC) model is constructed by considering the time‐varying factors. An adaptive multi‐time scale model is built with real‐time estimation and correction strategies. Multiple influencing factors are taken into consideration, including internal parameters, ambient temperature, and current rate variation. The proposed WF‐AKF algorithm stabilizes the large initial SOC estimation error by tracking the actual value with a maximum error of 0.46% under time‐varying ambient temperatures, current rates, and complex working condition.