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

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.

Automated summary

In this study, a novel feedback correction-adaptive Kalman filtering method was proposed for the online prediction of battery state. Experimental results show that the proposed method can accurately predict the state of charge and has fast convergence and error reduction in closed-circuit voltage prediction.