基于IALO算法的蓄电池参数辨识

吴忠强; 王国勇; 谢宗奎; 卢雪琴; 何怡林

doi:10.3969/j.issn.1000-1158.2021.09.14

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计量学报 ›› 2021, Vol. 42 ›› Issue (9) : 1206-1213. DOI: 10.3969/j.issn.1000-1158.2021.09.14

电磁学计量

基于IALO算法的蓄电池参数辨识

吴忠强,王国勇,谢宗奎,卢雪琴,何怡林

作者信息 +

Parameter Identification of Battery Based on IALO Algorithm

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

Author information +

文章历史 +

摘要

合理的等效电路模型及准确的模型参数对蓄电池荷电状态(SOC)的准确估计具有重要影响。针对蓄电池三阶Thevenin等效电路模型,基于改进蚁狮优化算法,提出了一种模型参数辨识方法。引入混沌Logistic映射初始化,使初始化群体遍及解空间,有利于寻找全局最优解;引入自适应惯性权重加随机柯西变异策略,有效提高了算法收敛速度;引入精英反向学习策略,有效提高了群体的多样性,避免算法陷入局部最优解。5个测试函数的测试结果表明:相比于蚁狮优化算法、粒子群算法与樽海鞘优化算法,改进蚁狮优化算法收敛速度更快,精度更高。对蓄电池三阶Thevenin等效电路模型进行参数辨识,结果表明:改进蚁狮优化算法相比蚁狮优化算法具有更高的辨识精度。

Abstract

A reasonable equivalent circuit model and accurate model parameters have an important impact on the accurate estimation of the battery SOC. Aiming at the third-order Thevenin equivalent circuit model of battery, a parameter identification method based on ant lion optimization algorithm was proposed. The introduction of chaotic logistic map initialization could make the initialization population spread over the solution space, which was beneficial to find the global optimal solution. The introduction of adaptive inertia weight and random Cauchy mutation strategy could effectively improve the convergence speed of the algorithm. Elite reverse learning strategy was introduced to effectively improve the diversity of population and avoid the algorithm trapping into local optimal solution. The test results of five test functions showed that compared with ant lion optimization algorithm, particle swarm optimization algorithm and salp optimization algorithm, the improved ant lion optimization algorithm had the faster convergence speed and higher accuracy. The parameter identification of third-order Thevenin equivalent circuit model of battery showed that the improved ant lion optimization algorithm had the higher identification accuracy than ant lion optimization algorithm.

导出引用

吴忠强,王国勇,谢宗奎,卢雪琴,何怡林. 基于IALO算法的蓄电池参数辨识[J]. 计量学报. 2021, 42(9): 1206-1213 https://doi.org/10.3969/j.issn.1000-1158.2021.09.14

WU Zhong-qiang,WANG Guo-yong,XIE Zong-kui,LU Xue-qin,HE Yi-lin. Parameter Identification of Battery Based on IALO Algorithm[J]. Acta Metrologica Sinica. 2021, 42(9): 1206-1213 https://doi.org/10.3969/j.issn.1000-1158.2021.09.14

中图分类号： TB971

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