基于GA-ELM的锂离子电池RUL间接预测方法

doi:10.3969/j.issn.1000-1158.2020.06.17

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Abstract Aiming at the problem that it is difficult to directly measure the capacity of the lithium-ion battery when the remaining useful life(RUL)of the battery is predicted and the prediction result is inaccurate, an indirect prediction method was proposed. First, after fully analyzing the parameters of battery life sates, the equivalent voltage drop discharge time was chosen as the indirect health index of the battery. Secondly, genetic algorithm was introduced to optimize the extreme learning machine parameters to establish an indirect RUL prediction model for lithium-ion battery. Finally, the correctness of the GA-ELM method was verified based on NASA data and independent experimental data. The results showed, compared with the ELM method and Gaussian process regression method, this new method is accurate and effective, with high predicting speed, and its output is stable.

Key words： metrology Lithium-ion battery remaining useful life indirect health index genetic algorithm extreme learning machine

Received: 26 July 2018 Published: 08 June 2020

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TB971

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	Articles by authors
	CHEN Ze-wang
	LI Fu-sheng
	LIN Ya
	YANG Ke
	WANG You-ren

Cite this article:

CHEN Ze-wang,LI Fu-sheng,LIN Ya, et al. Indirect Prediction Method of RUL for Lithium-ion Battery Based on GA-ELM[J]. Acta Metrologica Sinica, 2020, 41(6): 735-742.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2020.06.17 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2020/V41/I6/735

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