State of health estimation method for lithium-ion batteries based on principal component analysis and whale optimization algorithm-Elman model

doi:10.19799/j.cnki.2095-4239.2022.0384

Abstract

Abstract:

Accurate estimation state of health (SOH) of lithium-ion batteries is the key to ensuring the efficient, safe, and sustainable operation of electric vehicles. The accuracy of SOH estimation can be improved with a data-driven method. However, the accuracy of SOH estimation in this method is highly dependent on the selected feature and estimation model. The redundancy between features and the lack of generalization of the estimation model will affect the accurate estimation of battery SOH. According to principal component analysis and whale optimization algorithm (WOA)-Elman, a new SOH estimation method is proposed to reduce the redundancy of input features, increase the model's generalization, and improve the accuracy of SOH estimation. Firstly, the features highly related to the aging of lithium-ion batteries were extracted and selected from the charging process curve. Principal component analysis was used to decrease the dimension of features and reduce the redundancy between features. Then, the WOA method was used to optimize the initial weights and thresholds of the Elman model to establish the WOA-Elman model. The B01 battery was used to train the model while B02 and B03 batteries were used to verify the model. Simultaneously, comparing the commonly used long short-term memory neural network, support vector regression, extreme learning machine, and the unoptimized Elman model. The results show that the root-mean-square error of the WOA-Elman estimation model is 1.2113%. Finally, the SOH of the remaining two groups of batteries was estimated and verified by alternating test data of three groups of batteries as training sets, and the maximum root-mean-square deviation of the estimated results was only 0.1771%. Therefore, the proposed method can estimate battery SOH more accurately and perform better generalization.

Key words: lithium-ion battery, state of health estimation, principal component analysis, elman neural network, whale optimization algorithm

CLC Number:

TM 912

Xudong LI, Xiangwen ZHANG. State of health estimation method for lithium-ion batteries based on principal component analysis and whale optimization algorithm-Elman model[J]. Energy Storage Science and Technology, 2022, 11(12): 4010-4021.

Figures/Tables 24

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Table 9

Error quantization results of B02 battery"

SOH估计模型	误差量化标准/%
SOH估计模型	$R M S E$	$M A P E$	$M A E$
WOA-Elman	1.2113	1.1591	0.9968
Elman	1.6132	1.5463	1.3182
LSTM	1.6226	1.3329	1.1573
SVR	1.6562	1.5332	1.3007
ELM	1.6477	1.5840	1.3497

Table 9

Table 10

Error quantization results of B03 battery"

SOH估计模型	误差量化标准/%
SOH估计模型	$R M S E$	$M A P E$	$M A E$
WOA-Elman	0.9057	0.6949	0.6032
Elman	0.9921	0.8418	0.7257
LSTM	1.4986	1.0121	0.9024
SVR	1.0157	0.8724	0.7530
ELM	1.0109	0.8674	0.7503

Table 10

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阶段	特征
CC阶段	T_CC、V_CC、T_DVF
CV阶段	T_CV、I_CV、T_DIF

特征	皮尔森相关系数
特征	B01	B02	B03
T_CC	0.9777	0.9804	0.9832
V_CC	-0.9622	-0.9680	-0.9696
T_DVF	0.9747	0.9778	0.9806
T_CV	-0.9568	-0.9607	-0.9690
I_CV	0.9577	0.9649	0.9663
T_DIF	-0.9600	-0.9670	-0.9686

特征	VIF
特征	B01	B02	B03
T_CC	517	587	740
V_CC	142	184	154
T_DVF	998	1146	1213
T_CV	69	50	57
I_CV	25	23	22
T_DIF	49	41	40

主成分	贡献率/%
主成分	B01	B02	B03
主成分1	97.958	97.890	98.086
主成分2	1.196	1.202	1.056
主成分3	0.468	0.497	0.463
主成分4	0.231	0.257	0.251
主成分5	0.136	0.145	0.134
主成分6	0.001	0.009	0.008

电池编号	皮尔森相关系数
B01	-0.9749
B02	-0.9802
B03	-0.9823