基于 STPF 的 SOC 估计及在多锂电池均衡中的应用

提出一种能跟踪突变状态的锂电池荷电状态(SOC)估计方法，并应用于多锂电池组的SOC均衡中。在粒子滤波算法中引入强跟踪滤波，将当前的采样结果融入到预测误差更新中，得到新的校正项，然后利用该校正项对粒子滤波算法的粒子集进行校正，从而使粒子快速推向高似然区域，抑制粒子退化；渐消因子的引入能实时调整误差协方差矩阵，使粒子滤波算法兼具强跟踪滤波的强鲁棒性和对突变状态的跟踪能力，有效克服模型的不确定性，进一步提高SOC的估计精度。将所提方法应用于多电池主动均衡中，提出一种基于SOC一致性的均衡策略，率先均衡容量差距较大的相邻电池组，再控制能量实时双向传递，提高了整体均衡速度。实验结果表明，改进算法的平均估计误差在0.13%以内，标准差为0.12%;相比传统的粒子滤波算法、扩展卡尔曼滤波算法和强跟踪算法，精度分别提升约64%、85%和75%,并且稳定性也得到了进一步加强。在多电池主动均衡中的应用表明，有效减小了电池组容量在充放电过程中的不一致性，电池组离散度被控制在1%以内，有利于提高电池容量的利用率与使用寿命。

SOC estimation based on STPF and its application in multi-lithium battery equalization

A SOC estimation method for a lithium battery that can track the abrupt state is proposed and applied to the SOC equalization of the multi-lithium battery pack. The strong tracking filter is introduced into the particle filter algorithm, and the current sampling results are incorporated into the prediction error update to get a new correction term, and then the correction term is used to correct the particle set of the particle filter, so as to quickly push the particles to the high likelihood region and restrain the particle degradation. The introduction of the fading factor can adjust the error covariance matrix in real-time so that the particle filter algorithm has both the strong robustness of the strong tracking filter and the tracking ability of the mutation state, which can effectively overcome the uncertainty of the model and further improve the estimation accuracy of the SOC. The proposed method was applied to much cell-active balance. The equilibrium strategy was designed based on the consistency criterion of battery SOC, and the adjacent cells with larger capacity gap were equilibrium first, then energy real-time bidirectional transmission is controlled, so the overall balancing speed was improved. Experimental results show that the average estimation error of the improved algorithm is within 0. 13%, and the standard deviation is 0. 12%. The improved algorithm is about 64%, 85%, and 75% higher than that of the traditional particle filter algorithm, the extended Kalman algorithm and the strong tracking algorithm, and the stability is further enhanced. The application in multi-battery active equalization can effectively reduce the inconsistency of battery pack capacity in the process of charge and discharge, and control the dispersion of the battery pack within 1%, which is beneficial to improve the utilization rate and service life of battery capacity.