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.

电池动态特性的准确模拟对于提高荷电状态 (SoC) 估计性能非常重要。然而，电池等效电路模型在SoC估计过程中往往会改变电池动态特性的仿真精度。尽管自适应高度容积卡尔曼滤波器（AHCKF）对电池模型模拟的动态特性的估计比自适应容积卡尔曼滤波器（ACKF）更准确，但AHCKF对电池真实动态特性的估计精度可能较低。因此，在 SoC 估计期间的每个时间步长上，AHCKF 并不总是优于 ACKF。

为了提高SoC估计精度，本文提出了一种基于ACKF和AHCKF概率融合的估计方法。此外，还分析了滤波器初始权重对融合方法精度的影响。在动态压力测试下，当ACKF的初始权重为0.5时，基于所提方法的SoC估计的平均绝对误差和均方根误差与ACKF相比分别下降了22.35%和6.36%，分别下降了7.04%和7.04%。与 AHCKF 相比分别为 4.63%。结果表明，当滤波器的初始权重设置适当时，所提出的方法可以提高SoC估计的准确性。