基于IALO算法的蓄电池参数辨识

doi:10.3969/j.issn.1000-1158.2021.09.14

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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.

Key words： metrology battery parameter identification IALO adaptive weight random Cauchy variation elite reverse learning

Received: 27 August 2019 Published: 24 September 2021

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TB971

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

Cite this article:

WU Zhong-qiang,WANG Guo-yong,XIE Zong-kui, et al. Parameter Identification of Battery Based on IALO Algorithm[J]. Acta Metrologica Sinica, 2021, 42(9): 1206-1213.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2021.09.14 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2021/V42/I9/1206

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