基于自适应协同引导的电池组性能衰退参数辨识

doi:10.19799/j.cnki.2095-4239.2022.0118

储能科学与技术 ›› 2022, Vol. 11 ›› Issue (10): 3345-3353.doi: 10.19799/j.cnki.2095-4239.2022.0118

基于自适应协同引导的电池组性能衰退参数辨识

尹建光¹(), 崔相宇¹, 李方伟¹, 臧玉魏¹, 彭飞²()

^1.国网山东省电力公司电力科学研究院，济南山东 250002
^2.青岛大学电气工程学院，青岛山东 266071

收稿日期:2022-03-07 修回日期:2022-05-23 出版日期:2022-10-05 发布日期:2022-10-10
通讯作者: 彭飞 E-mail:yinjianbingjiuzhen@163.com;kilmer_pf@126.com
作者简介:尹建光（1987—），男，工程师，主要研究方向为新能源动力系统，Email：yinjianbingjiuzhen@163.com；
基金资助:
山东电力研究院自主科研项目(ZY-2021-09)

Research on the parameter identification of battery performance degradation based on self-adaptive synergistic guiding

Jianguang YIN¹(), Xiangyu CUI¹, Fangwei LI¹, Yuwei ZANG¹, Fei PENG²()

^1.State Grid Shandong Electric Power Research Institute, Jinan 250002, Shandong, China
^2.School of Electrical Engineering, Qingdao University, Qingdao 266071, Shandong, China

Received:2022-03-07 Revised:2022-05-23 Online:2022-10-05 Published:2022-10-10
Contact: Fei PENG E-mail:yinjianbingjiuzhen@163.com;kilmer_pf@126.com

摘要/Abstract

摘要：

老化导致的电池组性能衰退与电池组电荷吞吐能力密切相关，对电池组性能衰退参数的快速精确辨识对提高电池组的服役寿命预测有效性至关重要。然而，既有的电池组性能衰退参数辨识方法仍然存在对大种群规模和高迭代次数的显著依赖，不利于提高电池组性能衰退模型的在线辨识更新适用性。针对此，本文提出了一种基于自适应协同引导的电池组性能衰退参数辨识方法。该方法首先基于自适应协同策略，综合考虑种群差异度和种群适应度的折中，实现种群个体对参数搜索空间的初期全局分布；在此基础上，基于精英引导策略，使种群中的个体在全局精英个体周围局部搜索，实现后期快速收敛至全局最优解。基于实测数据验证的统计结果表明，本文提出方法针对半经验容量衰退模型和内阻增量模型，在小种群规模下的参数辨识效率和精度均得到显著提升，分别在0.6 s和1.1 s内达到0.237%和0.37%的适应度终值，相对于蚁狮算法在辨识效率提高81.35%的同时适应度均值降低了3.8%，相对于灰狼算法在辨识效率提高17.14%的同时最终适应度均值降低了22.11%。

关键词: 动力电池, 性能衰退, 参数辨识, 自适应协同, 精英引导

Abstract:

Performance degradation due to battery aging highly depends on the charge handling capacity. It is essential to identify battery performance degradation parameters efficiently and accurately such that the prediction performance of battery service life can be improved. However, due to the large population size and more expected iterations, the current parameter identification approaches for the battery performance degradation model are still severely constrained. As a result, it is not conducive to improving the applicability of online parameter identification and update. Aiming at solving this problem, a parameter identification method based on self-adaptive synergistic guidance is proposed for the battery performance degradation model in this paper. To achieve the initial-stage global distribution of population individuals in the parameter searching space, a complete compromise between population variety and population fitness is first considered based on the adaptive synergistic method. Based on this, the population individuals search locally around the global elite individuals during the elite guiding, aiming to quickly converge to the global optimal solution in the later stage. The verification results based on the measured datasets show that the parameter identification efficiency and accuracy for the battery pack performance degradation model can be obviously improved by the proposed method in the case of small population size. For the capacity fade model and power fade model, 0.237% and 0.37% fitness values within 0.6 s and 1.1 s can be achieved, respectively. In fact, the identification efficiency is improved by 81.35% and the mean fitness is reduced by 3.8% compared with Ant Lion Optimizer, while the identification efficiency is improved by 17.14% and the mean fitness is reduced by 22.11% compared with Grey Wolf Optimizer.

Key words: power battery, performance degradation, parameter identification, self-adaptive synergistic, elite guiding

中图分类号:

TM 911

尹建光, 崔相宇, 李方伟, 臧玉魏, 彭飞. 基于自适应协同引导的电池组性能衰退参数辨识[J]. 储能科学与技术, 2022, 11(10): 3345-3353.

Jianguang YIN, Xiangyu CUI, Fangwei LI, Yuwei ZANG, Fei PENG. Research on the parameter identification of battery performance degradation based on self-adaptive synergistic guiding[J]. Energy Storage Science and Technology, 2022, 11(10): 3345-3353.

图/表 9

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组别	Ratio	SOC_min/%	充电倍率	温度/℃
1	1	45	1 C/3	30
2		35	1 C/3	30
3		25	1 C/3	30
4		45	1.5 C	30
5		35	1.5 C	30
6		25	1.5 C	30
7		45	5 C	30
8		35	5 C	30
9		25	5 C	30
10	0	45	—	30
11		35	—	30
12		25	—	30
13	0.5	35	1.5 C	10
14	0.5	35	1.5 C	30
15	0.25	35	1.5 C	30
16	0.5	35	1.5 C	45

模型		算法	α_c	β_c	γ_c	b	SOC₀	c	z	E_ac
容量损失模型		SSG	0.6772	2.092	81.84	0.3538	0.2154	3.678	0.5391	22093.27
		ALO	1.167	3.665	93.84	0.3738	0.2072	3.463	0.5187	23000
		GWO	1.979	4.666	99.68	0.396	0.2557	4.793	0.5191	23776.57
模型	算法	α_R	β_R	γ_R	d	e	SOC₀	CR₀	C_R	E_aR
内阻增量模型	SSG	1620.94	1.203e7	23.17	1.008	1.835	0.2470	4.337	5.828	50074.18
	ALO	1567.16	1.285e7	12.99	0.9940	1.801	0.2419	4.949	5.986	50000
	GWO	2235.48	1.007e7	20.85	0.9479	2.518	0.2291	5.018	5.768	51022.14

模型	算法	Fitness_avg	Fitness_max	Fitness_min	Fitness_std	迭代时间/s
容量损失模型	SSG	0.002375453	0.002413694	0.002367423	9.18389E-06	0.58
	GWO	0.003241283	0.003666964	0.002511781	0.000346998	0.7
	ALO	0.002488313	0.002602306	0.002383872	6.99888E-05	6.97
内阻增量模型	SSG	0.003705269	0.004011711	0.003617721	7.81364E-05	1.06
	GWO	0.004606979	0.007191518	0.003881936	0.000668846	1.7
	ALO	0.003976416	0.004684231	0.003627127	0.000293146	8.38

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基于自适应协同引导的电池组性能衰退参数辨识

Research on the parameter identification of battery performance degradation based on self-adaptive synergistic guiding

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