Lithium-ion battery state prediction based on CEEMDAN and ISOA-ELM

doi:10.19799/j.cnki.2095-4239.2022.0708

Abstract

Abstract:

Lithium batteries have high energy density, high output voltage, and no memory effect, but their overcharge and over-discharge can cause accidents. In this regard, accurate prediction of the state of charge (SOC) of lithium batteries serves as the best condition. In this paper, a method based on an adaptive noise integrated Empirical Mode decomposition (CEEMDAN) and data-driven model is proposed to predict the state of charge of lithium-ion batteries. The original voltage data of lithium-ion batteries were modally decomposed to obtain the modal components of multiple sub-sequences. An improved Seagull algorithm (ISOA) based on the inertia weight and the Levy flight mechanism was proposed. The parameters of the extreme learning machine prediction model were optimized, and ISOA-ELM lithium battery prediction model was built. The SOC prediction results of the lithium batteries were obtained by training the model. The experimental results show that the model can better fit the actual SOC in practice and is more conducive to the lithium battery working in the best state.

Key words: lithium-ion batteries, state of charge, complete ensemble empirical mode decomposition with adaptive noise, extreme learning machine, seagull optimization algorithm

CLC Number:

TM 911

Feng LIU, Haizhong CHEN. Lithium-ion battery state prediction based on CEEMDAN and ISOA-ELM[J]. Energy Storage Science and Technology, 2023, 12(4): 1244-1256.

Figures/Tables 13

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工况	预测模型	平均绝对误差MAE	均方根误差RMSE	绝对相关系数R²	运行时间/s
DST	CEEMDAN-ISOA-ELM	0.0033653	0.0048175	0.9949	24.785
	PSO-ELM	0.0051031	0.0067167	0.9891	16.066
	SOA-ELM	0.0061713	0.0077381	0.9806	19.427
	LSTM	0.032081	0.036609	0.6476	70
	CNN-GRU	0.012798	0.014423	0.9650	99
FUDS	CEEMDAN-ISOA-ELM	0.0051382	0.0061629	0.9912	28.998
	PSO-ELM	0.0089028	0.011362	0.9763	19.474
	SOA-ELM	0.010233	0.014265	0.9672	20.597
	LSTM	0.024916	0.033485	0.5548	72
	CNN-GRU	0.030218	0.033868	0.6756	68
US06	CEEMDAN-ISOA-ELM	0.0062962	0.0076641	0.9863	23.761
	PSO-ELM	0.0072198	0.0084125	0.9837	19.894
	SOA-ELM	0.012482	0.015775	0.9597	19.532
	LSTM	0.043681	0.056403	0.0945	69
	CNN-GRU	0.032721	0.036823	0.6643	35
1 A循环测试	CEEMDAN-ISOA-ELM	0.011061	0.021227	0.9445	21.641
	PSO-ELM	0.037605	0.067052	0.7332	16.868
	SOA-ELM	0.027703	0.052061	0.8175	13.237
	LSTM	0.021475	0.036256	0.7320	91
	CNN-GRU	0.017286	0.021406	0.9235	65

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