Jinghua Sun

Accurate state-of-health (SOH) estimation is an essential prerequisite for a battery management system (BMS) to improve battery utilization efficiency. The impedance information can be utilized to reflect the SOH. However, the traditional electrochemical impedance spectroscopy (EIS) method suffers from time-consuming measurements and specialized equipment. This study aims to establish a connection between EIS and the current interrupt method, which reduces the difficulty of obtaining impedance information through its utilization. This method can be applied in real time during charging, enabling it to be incorporated into a BMS. A genetic algorithm optimized back propagation neural network (GA-BPNN) is developed to estimate the SOH based on the impedance information obtained in the current interrupt method as inputs. The genetic algorithm improves the weights and thresholds of the neural network, which solves the parameter calibration problem. In this study, besides utilizing measurement data from different aging conditions as training set data, hybrid tests comparable to the actual usage environment are employed as validation set data. The experimental results show that a combination of the proposed current interrupt method and GA-BPNN can estimate SOH accurately with the root mean square error (RMSE) as low as 0.77 % in a complex hybrid test environment.