Comparative parametric study of metaheuristics based on impedance modeling for lithium-ion batteries

doi:10.19799/j.cnki.2095-4239.2024.0658

Abstract

Abstract:

Fast and accurate identification of electrochemical parameters is crucial for mechanistic modeling of lithium-ion batteries. Traditional parameter identification methods mostly use direct fitting, which makes it difficult to accurately reflect the internal state of a battery. To solve this problem, in this study, a modified simplified impedance model mapped with an electrochemical model was constructed based on the Faradaic process of electrochemical reactions, the non-Faradaic process of the double-layer capacitance dispersion effect, and the conduction process in the solid and liquid phases. The model was applied to a 37 Ah ternary battery. The model's inputs are the three-electrode electrochemical impedance spectra (EIS) under different states of charge (SOC), unlike the P2D model, which are used as inputs to the three-electrode EIS under the different SOCs. The corresponding working conditions of the electrochemical parameters were obtained by fitting the EIS to achieve accurate parameter identification of the battery model. By fitting the impedance spectra, 16 highly sensitive electrochemical parameters were identified: 7 for the positive and 9 for the negative electrodes. Further, we compared the performance of 66 metaheuristic algorithms in lithium-ion battery electrochemical parameter identification and analyzed them multidimensionally in terms of identification accuracy, computational efficiency, and robustness. The results showed that the adaptive differential evolutionary algorithm has the best overall effect in parameter identification, with its average absolute percentage error of less than 3% and the number of non-repeating function calculations of less than 35000, indicating that it achieves maximum accuracy with low arithmetic and that the proposed identification method not only better reflects the physical significance of the parameters, but it also provides strong support for simplified computation and on-line identification of the electrochemical model.

Key words: lithium-ion battery, simplified impedance model, metaheuristic algorithm, EIS

CLC Number:

TM 912

Bingxiang SUN, Xin YANG, Xingzhen ZHOU, Shichang MA, Zhihao WANG, Weige ZHANG. Comparative parametric study of metaheuristics based on impedance modeling for lithium-ion batteries[J]. Energy Storage Science and Technology, 2024, 13(9): 2952-2962.

Figures/Tables 11

Fig. 1

Table 1

Parameters involved in identification"

类别	参数	单位	简要描述/定义	上下边界
传递参数	$D s +$	$m 2 / s$	阴极固体扩散系数	$10 - 14 ~ 10 - 13$
	$D s -$	$m 2 / s$	阳极固体扩散系数	$10 - 14 ~ 10 - 13$
	$- ∂ U + ∂ c s$	$V · m 3 / m o l$	阴极平衡电位对表面锂离子浓度的导数	$10 - 6 ~ 10 - 3$
	$- ∂ U - ∂ c s$	$V · m 3 / m o l$	阳极平衡电位对表面锂离子浓度的导数	$10 - 6 ~ 10 - 3$

动力学参数	$k +$	$m 2.5 / (m o l 0.5 · s)$	阴极反应速率系数	$10 - 11 ~ 10 - 10$
	$k -$	$m 2.5 / (m o l 0.5 · s)$	阳极反应速率系数	$10 - 11 ~ 2 × 10 - 10$
	$R f i l m$	$Ω · m 2$	阳极SEI膜电阻	$10 - 3 ~ 10 - 2$

双电层电容	$C a p +$	$F / m 2$	阳极双电层电容	$0.1 ~ 0.9$
	$C a p -$	$F / m 2$	阴极双电层电容	$0.1 ~ 0.9$
	$C a p s e i$	$F / m 2$	SEI膜双电层电容	$0.1 ~ 5$
	$v +$	$1$	阴极弥散效应	$0 ~ 1$
	$v -$	$1$	阳极弥散效应	$0 ~ 1$

Table 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Table 2

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工况	MAE/mV	RMSE/mV
JADE	7.8	10.1
Original SARO	8.5	10.7
Dev GSKA	9.7	11.9

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