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

doi:10.19799/j.cnki.2095-4239.2022.0118

Abstract

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

CLC Number:

TM 911

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.

Figures/Tables 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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