State of charge estimation of Lithium-ion batteries based on the probabilistic fusion of two kinds of cubature Kalman filters

Abstract

The accurate simulation of batteries’ dynamic characteristics is important for improving the State of Charge (SoC) estimation performance. However, an equivalent circuit model of batteries tends to have changing simulation accuracy of the battery’s dynamic characteristics during SoC estimation. Although the adaptive high-degree cubature Kalman filter (AHCKF) has a more accurate estimation of the dynamic characteristics simulated by the battery model than the adaptive cubature Kalman filter (ACKF) does, AHCKF may have lower estimation accuracy of the battery’s real dynamic characteristics. Therefore, AHCKF does not always outperform ACKF at each time step during SoC estimation.To improve SoC estimation accuracy, this paper proposes an estimation method based on the probabilistic fusion of ACKF and AHCKF. Furthermore, the impact of the filters’ initial weights on the accuracy of the fusion method is analyzed. Under the dynamic stress test, when ACKF’s initial weight is 0.5, the mean absolute error and the root mean square error of SoC estimation based on the proposed method decrease by 22.35% and 6.36% compared with ACKF, respectively, as well as 7.04% and 4.63% compared with AHCKF, respectively. The result shows that the proposed method can improve the accuracy of SoC estimation when the filters’ initial weights are appropriately set.