Estimation of lithium-ion battery SOC based on GWO-optimized extreme learning machine

doi:10.19799/j.cnki.2095-4239.2020.0389

Abstract

Abstract:

Accurate estimation of battery state of charge (SOC) is the basic premise for the normal operation of electric vehicles. An extreme learning machine (ELM) based on the gray wolf optimizer (GWO) algorithm was proposed to estimate the SOC of lithium-ion batteries, improve the estimation accuracy, and shorten the estimation time. The traditional ELM generates model parameters randomly. This method runs fast and has good generalization performance. However, the ELM must determine the optimal hidden layer neuron parameters to achieve high accuracy. Therefore, the GWO algorithm was adopted to further optimize the model parameters. The appropriate activation function was selected to make up for the shortcomings of the traditional ELM. Finally, the advantages of this method in battery SOC estimation are verified under different working conditions by comparison with the particle swarm optimization-feedforward neural network algorithm (BPNN-PSO) and the ELM. The result shows that the ELM based on the GWO algorithm has high accuracy, short estimation time, and good robustness, which is better than those of the traditional SOC estimation method. This research helps promote the development and application of new energy vehicle battery management systems and provide support for the research and development of reliable battery management systems.

Key words: lithium-ion battery, state of charge, extreme learning machine, gray wolf optimizer

CLC Number:

TM 911

Qiao WANG, Meng WEI, Min YE, Jiabo LI, Xinxin XU. Estimation of lithium-ion battery SOC based on GWO-optimized extreme learning machine[J]. Energy Storage Science and Technology, 2021, 10(2): 744-751.

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工况	RMSE/%	MAE/%	运行时间/s
CCCV	1.49	1.06	4.054
DST	1.14	0.79	4.064

方法	工况	RMSE/%	MAE/%	运行时间/s
ELM-GWO	CCCV	1.49	1.06	4.054
ELM-GWO	DST	1.14	0.79	4.064
BPNN-PSO	CCCV	2.41	1.44	183.570
BPNN-PSO	DST	2.48	1.64	165.138
ELM	CCCV	4.74	3.69	0.584
ELM	DST	2.14	1.36	0.141

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