基于WOA-UKF算法的锂电池容量与SOC联合估计

吴忠强; 王国勇; 谢宗奎; 何怡林; 卢雪琴

doi:10.3969/j.issn.1000-1158.2022.05.14

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计量学报 ›› 2022, Vol. 43 ›› Issue (5) : 649-656. DOI: 10.3969/j.issn.1000-1158.2022.05.14

电磁学计量

基于WOA-UKF算法的锂电池容量与SOC联合估计

吴忠强,王国勇,谢宗奎,何怡林,卢雪琴

作者信息 +

Joint Estimation of the Capacity and SOC of Lithium Battery Based on WOA-UKF Algorithm

WU Zhong-qiang,WANG Guo-yong,XIE Zong-kui,HE Yi-lin,LU Xue-qin

Author information +

文章历史 +

摘要

锂电池的荷电状态(SOC)和有效容量是表征电池当前剩余电量和电池寿命的重要参数,提出一种锂离子电池有效容量和SOC的联合估计方法。在电池全寿命周期内,给出一种开路电压与SOC和电池有效容量非线性模型的两变量多项式描述;当电池循环使用次数超过预设值,采用鲸鱼优化算法估计当前电池容量与电池模型参数,根据模型参数与容量值采用无迹卡尔曼滤波器估计电池SOC;在SOC估计过程中,采用鲸鱼优化算法更新无迹卡尔曼滤波器的观测噪声方差和过程噪声方差,实现噪声方差的自适应调节,进而提高估计精度。实验结果验证了该方法的有效性和联合估计方案的可行性。

Abstract

The state of charge (SOC) and effective capacity of lithium batteries are important parameters to characterize the current remaining capacity and life of the battery. A joint estimation method for the effective capacity and SOC of lithium-ion batteries is proposed. During the battery life cycle, a two-variable polynomial description for the non-linear model of open circuit voltage to SOC and battery effective capacity is given; when the number of battery cycles exceeds a preset value, whale optimization algorithm is used to estimate the current battery capacity and battery model parameters, and then an unscented Kalman filter is used to estimate the SOC of battery according to the model parameters and capacity values. In the estimation process of SOC, the whale optimization algorithm is used to update the noise variance of unscented Kalman filter, furthermore, the estimation accuracy is improved. Experimental results test the effectiveness of the method and the feasibility of the joint estimation scheme.

导出引用

吴忠强,王国勇,谢宗奎,何怡林,卢雪琴. 基于WOA-UKF算法的锂电池容量与SOC联合估计[J]. 计量学报. 2022, 43(5): 649-656 https://doi.org/10.3969/j.issn.1000-1158.2022.05.14

WU Zhong-qiang,WANG Guo-yong,XIE Zong-kui,HE Yi-lin,LU Xue-qin. Joint Estimation of the Capacity and SOC of Lithium Battery Based on WOA-UKF Algorithm[J]. Acta Metrologica Sinica. 2022, 43(5): 649-656 https://doi.org/10.3969/j.issn.1000-1158.2022.05.14

中图分类号： TB971

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