基于简化阻抗模型和比较元启发式算法的锂离子电池参数辨识方法

doi:10.19799/j.cnki.2095-4239.2024.0658

摘要/Abstract

摘要：

快速准确地辨识电化学参数对锂离子电池机理建模至关重要。而传统的参数辨识方法多采用直接拟合，难以精确反映电池的内部状态。为解决这一问题，本工作以37 Ah三元电池为研究对象，基于电化学反应中的法拉第过程、双层电容的弥散效应的非法拉第过程以及固相与液相的传导过程，构建了一个与电化学模型映射的修正简化阻抗模型，与伪二维(P2D)模型不同，该模型输入为不同荷电状态(SOC)下的三电极电化学阻抗谱(EIS)，通过拟合EIS得到对应工况电化学参数，实现对电池模型准确的参数识别。通过拟合阻抗谱，辨识得到了16个高敏感度的电化学参数，其中正极7个、负极9个。我们进一步比较了66种元启发式算法在锂离子电池电化学参数识别中的性能表现，从识别精度、计算效率和鲁棒性等方面对其进行多维分析。研究结果表明，自适应差分进化算法在参数识别中综合效果最佳，其平均绝对百分比误差小于3%，非重复函数计算次数小于35000次，表明其达到最大准确度的同时运算量较低，提出的辨识方法不仅更好地反映了参数的物理意义，还为电化学模型的简化计算和在线辨识提供了有力支持。

关键词: 锂离子电池, 简化阻抗模型, 元启发式算法, 电化学阻抗谱

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

中图分类号:

TM 912

孙丙香, 杨鑫, 周兴振, 马仕昌, 王志豪, 张维戈. 基于简化阻抗模型和比较元启发式算法的锂离子电池参数辨识方法[J]. 储能科学与技术, 2024, 13(9): 2952-2962.

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.

图/表 11

图1

表1

参与辨识的参数"

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	$0 ~ 1$

表1

图2

图3

图4

图5

图6

图7

图8

图9

表2

参考文献 22

1	安富强, 赵洪量, 程志, 等. 纯电动车用锂离子电池发展现状与研究进展[J]. 工程科学学报, 2019, 41(1): 22-42. DOI: 10.13374/j.issn2095-9389.2019.01.003.
	AN F Q, ZHAO H L, CHENG Z, et al. Development status and research progress of power battery for pure electric vehicles[J]. Chinese Journal of Engineering, 2019, 41(1): 22-42. DOI: 10.13374/j.issn2095-9389.2019.01.003.
2	陈天雨, 高尚, 冯旭宁, 等. 锂离子电池热失控蔓延研究进展[J]. 储能科学与技术, 2018, 7(6): 1030-1039. DOI: 10.12028/j.issn.2095-4239.2018.0167.
	CHEN T Y, GAO S, FENG X N, et al. Recent progress on thermal runaway propagation of lithium-ion battery[J]. Energy Storage Science and Technology, 2018, 7(6): 1030-1039. DOI: 10.12028/j.issn.2095-4239.2018.0167.
3	王莉, 谢乐琼, 张干, 等. 锂离子电池一致性筛选研究进展[J]. 储能科学与技术, 2018, 7(2): 194-202. DOI: 10.19799/j.cnki.2095-4239.2020.0345.
	WANG L, XIE L Q, ZHANG G, et al. Research progress in the consistency screening of Li-ion batteries[J]. Energy Storage Science and Technology, 2018, 7(2): 194-202. DOI: 10.19799/j.cnki.2095-4239.2020.0345.
4	KARIMI G, DEHGHAN A R. Thermal analysis of high-power lithium-ion battery packs using flow network approach[J]. International Journal of Energy Research, 2014, 38(14): 1793-1811. DOI: 10.1002/er.3173.
5	JIANG Z Y, QU Z G, ZHANG J F, et al. Rapid prediction method for thermal runaway propagation in battery pack based on lumped thermal resistance network and electric circuit analogy[J]. Applied Energy, 2020, 268: 115007. DOI: 10.1016/j.apenergy.2020.115007.
6	ZOU C F, ZHANG L, HU X S, et al. A review of fractional-order techniques applied to lithium-ion batteries, lead-acid batteries, and supercapacitors[J]. Journal of Power Sources, 2018, 390: 286-296. DOI: 10.1016/j.jpowsour.2018.04.033.
7	WANG Z L, FENG G J, ZHEN D, et al. A review on online state of charge and state of health estimation for lithium-ion batteries in electric vehicles[J]. Energy Reports, 2021, 7: 5141-5161. DOI: 10.1016/j.egyr.2021.08.113.
8	ZHOU Y, WANG B C, LI H X, et al. A surrogate-assisted teaching-learning-based optimization for parameter identification of the battery model[J]. IEEE Transactions on Industrial Informatics, 2021, 17(9): 5909-5918. DOI: 10.1109/TII.2020.3038949.
9	LI Y M, LIU G X, DENG W, et al. Comparative study on parameter identification of an electrochemical model for lithium-ion batteries via meta-heuristic methods[J]. Applied Energy, 2024, 367: 123437. DOI: 10.1016/j.apenergy.2024.123437.
10	LI W H, CAO D C, JÖST D, et al. Parameter sensitivity analysis of electrochemical model-based battery management systems for lithium-ion batteries[J]. Applied Energy, 2020, 269: 115104. DOI: 10.1016/j.apenergy.2020.115104.
11	SANTHANAGOPALAN S, GUO Q Z, WHITE R E. Parameter estimation and model discrimination for a lithium-ion cell[J]. Journal of the Electrochemical Society, 2007, 154(3): A198. DOI: 10.1149/1.2422896.
12	BOOVARAGAVAN V, HARINIPRIYA S, SUBRAMANIAN V R. Towards real-time (milliseconds) parameter estimation of lithium-ion batteries using reformulated physics-based models[J]. Journal of Power Sources, 2008, 183(1): 361-365. DOI: 10.1016/j.jpowsour.2008.04.077.
13	SHUI Z Y, LI X H, FENG Y, et al. Combining reduced-order model with data-driven model for parameter estimation of lithium-ion battery[J]. IEEE Transactions on Industrial Electronics, 2023, 70(2): 1521-1531. DOI: 10.1109/TIE.2022.3157980.
14	FAN G D. Systematic parameter identification of a control-oriented electrochemical battery model and its application for state of charge estimation at various operating conditions[J]. Journal of Power Sources, 2020, 470: 228153. DOI: 10.1016/j.jpowsour.2020.228153.
15	VAN THIEU N, MIRJALILI S. MEALPY: An open-source library for latest meta-heuristic algorithms in Python[J]. Journal of Systems Architecture, 2023, 139: 102871. DOI: 10.1016/j.sysarc.2023. 102871.
16	ONG I J, NEWMAN J. Double-layer capacitance in a dual lithium ion insertion cell[J]. Journal of the Electrochemical Society, 1999, 146(12): 4360-4365. DOI: 10.1149/1.1392643.
17	SUTHAR B, NORTHROP P W C, RIFE D, et al. Effect of porosity, thickness and tortuosity on capacity fade of anode[J]. Journal of the Electrochemical Society, 2015, 162(9): A1708-A1717. DOI: 10.1149/2.0061509jes.
18	HUANG J, GE H, LI Z, et al. An agglomerate model for the impedance of secondary particle in lithium-ion battery electrode[J]. Journal of the Electrochemical Society, 2014, 161(8): E3202-E3215. DOI: 10.1149/2.027408jes.
19	HUANG J, LI Z, ZHANG J B, et al. An analytical three-scale impedance model for porous electrode with agglomerates in lithium-ion batteries[J]. Journal of the Electrochemical Society, 2015, 162(4): A585-A595. DOI: 10.1149/2.0241504jes.
20	ZHOU X Z, WANG Z H, ZHANG W G, et al. Construction of simplified impedance model based on electrochemical mechanism and identification of mechanism parameters[J]. Journal of Energy Storage, 2024, 76: 109673. DOI: 10.1016/j.est.2023.109673.
21	LANDESFEIND J, GASTEIGER H A. Temperature and concentration dependence of the ionic transport properties of lithium-ion battery electrolytes[J]. Journal of the Electrochemical Society, 2019, 166(14): A3079-A3097. DOI: 10.1149/2.0571912jes.
22	LI W H, DEMIR I, CAO D C, et al. Data-driven systematic parameter identification of an electrochemical model for lithium-ion batteries with artificial intelligence[J]. Energy Storage Materials, 2022, 44: 557-570. DOI: 10.1016/j.ensm.2021.10.023.

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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