Modified strong tracking cubature Kalman filter for LiFePO4 storage system

Abstract

Lithium Iron Phosphate (LiFePO4) batteries have obtained extensive interests for the high energy density, little contamination, and ready availability. To enhance the compatibility of the batteries in electrical systems, the accurate estimation of the state of charge (SOC) is remarkably significant. Conventionally, Kalman filter algorithm and its derivations can be utilized for SOC estimation. To obtain SOC values more precisely and faster, this paper proposes a modified strong tracking cubature Kalman filter (MSTCKF). The estimation algorithm robustness is strengthened with the adoption of eigenvalue decomposition, which ensures the positive definiteness and symmetry of the priori error covariance matrix. Corresponding simulations are provided to compare the SOC estimation accuracy of MSTCKF, strong tracking cubature Kalman filter (STCKF), cubature Kalman filter (CKF) and extended Kalman filter (EKF). By setting different initial SOC errors, the results show that MSTCKF averagely performs more accurate than STCKF, CKF, and EKF by 19.82%, 26.74%, and 46.63% respectively.