A novel feedback correction-adaptive Kalman filtering method for the whole-life-cycle state of charge and closed-circuit voltage prediction of lithium-ion batteries based on the second-order electrical equivalent circuit model

Abstract

Accurate state of charge (SOC) and closed-circuit voltage (CCV) prediction is essential for lithium-ion batteries and their model performance. In this study, a novel feedback correction-adaptive Kalman filtering (FC-AKF) method is proposed for the online battery state co-prediction, which is adaptive to the whole-life-cycle of the lithium-ion battery based on the improved second-order equivalent circuit model (SO-ECM). For the feedback correction strategy, the optimized iterative state initialization is conducted using the uncertainty covariance matrix of the prior three-time points with the convergence of the updating process. The experimental results show that the SOC prediction error of the proposed FC-AKF method is 0.0099% and 0.975% compared with the ampere-hour integral method under the dynamic stress test (DST) and the Beijing bus dynamic stress test (BBDST) working conditions, respectively. Also, the CCV traction by the SO-ECM is 0.80 V and has fast initial convergence and quick prediction error reduction characteristics. The constructed iterative calculation model promotes the accurate SOC and CCV co-prediction effect, improving the safety and longevity of lithium-ion batteries with high precision and fast convergence advantages.