| 吴忠强,胡晓宇.基于 STPF 的 SOC 估计及在多锂电池均衡中的应用[J].电子测量与仪器学报,2022,36(2):235-244 |
| 基于 STPF 的 SOC 估计及在多锂电池均衡中的应用 |
| SOC estimation based on STPF and its application inmulti-lithium battery equalization |
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| DOI： |
| 中文关键词: 系统工程 SOC 估计 强跟踪粒子滤波 锂电池 主动均衡 离散度 |
| 英文关键词:systems engineering SOC estimation strong tracking particle filtering lithium battery active equalization dispersion |
| 基金项目:河北省自然科学基金(F2020203014)项目资助 |
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| 中文摘要: |
| 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。 |
| 英文摘要: |
| 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. |
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