基于BSA-RELM的纯电动汽车锂离子电池SOC估计

doi:10.3969/j.issn.1000-1158.2019.04.24

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Abstract A method based on bird swarm algorithm optimizing robust extreme learning machine is proposed to estimate the charge state of the battery. Robust extreme learning machine overcomes the shortcomings that extreme learning machine can not deal with the abnormal value, so the prediction accuracy of the network is improved. The parameters such as the number of hidden nodes and the adjustment factors of robust extreme learning machine are optimized by bird swarm algorithm, so the problems that the parameters such as the number of hidden nodes and the adjustment factors are difficult to be determined can be solved, which can further improve the convergence speed of the network and help to find the global optimal value. Several key parameters including current, voltage, temperature and internal resistance, which affect the SOC characteristics of the battery, are collected to model and test by ADVISOR software. Simulation results show that compared with other algorithms such as BPNN, RBFNN and FNN, BSA-RELM has a smaller error and higher prediction accuracy.

Key words： metrology SOC Li-ion battery PEV BSA robust extreme learning machine

Received: 24 January 2018 Published: 10 June 2019

PACS:

TB971

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	Articles by authors
	WU Zhong-qiang
	SHANG Meng-yao
	SHEN Dan-dan
	QI Song-qi
	ZHU Xiang-dong

Cite this article:

WU Zhong-qiang,SHANG Meng-yao,SHEN Dan-dan, et al. Estimation of SOC of Li-ion Battery in Pure Electric Vehicle by BSA-RELM[J]. Acta Metrologica Sinica, 2019, 40(4): 693-699.

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http://jlxb.china-csm.org:81/Jwk_jlxb/EN/10.3969/j.issn.1000-1158.2019.04.24 OR http://jlxb.china-csm.org:81/Jwk_jlxb/EN/Y2019/V40/I4/693

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