Online cell-by-cell SOC/SOH estimation method for battery module employing extended Kalman filter algorithm with aging effect consideration

Abstract

As the number of series connections of battery cells increases, individual cells are operating in diferent temperature profles, and the aging patterns of the cells become dissimilar from each other. Thenceforth, individual state-cell-characteristics should be tracked online for higher safety. Although Kalman-flter-based battery state estimation is one of the most popular methods, it is sensitive to the accuracy of the battery model parameters and difcult to be applied to every cell. This work proposes an online cell-by-cell state-of-charge (SOC)/state-of-health (SOH) estimation method to mitigate this limitation. The aging patterns of the individual cells are predicted by introducing a combination of a switch-matrix fying capacitor and electrochemical impedance spectroscopy (EIS) model parameter scanning techniques. Accordingly, the accuracy of the SOC estimation for individual cells is enhanced. The proposed method is verifed by a real-time simulation platform, where the SOC and SOH levels of the cells are individually estimated within a 1.24% error.