Semi-empirical degradation model of lithium-ion battery with high energy density

doi:10.19799/j.cnki.2095-4239.2021.0725

Abstract

Abstract:

The capacity of the battery will continue to fall as the cycle ages, and the internal resistance will progressively grow, affecting the battery's service life and performance. To establish the semi-empirical model of capacity decline and internal resistance growth and shorten the time required to investigate the battery aging characteristics, the 21700 lithium-ion battery with high energy density is adopted to conduct the cycle aging experiment of the battery under six working conditions at 0 ℃, 23 ℃, and 40 ℃ combined with 1 C and 2 C discharge rates. The effect of temperature and other environmental conditions on battery capacity reduction and internal resistance increase is investigated.

When the semi-empirical model of battery capacity decline is created by combining the Arrhenius equation, an additional power function and a constant term about the number of cycles are introduced, which can broaden the model's applicability and make it suitable for different capacity decline trends at different temperatures. A semi-empirical model of battery internal resistance growth is developed using the double exponential function multiplication formula, which can effectively predict the law of internal resistance growth under various working conditions. The cross-validation approach is used to demonstrate the accuracy of semi-empirical models of capacity decrease and internal resistance increase, which may be used to forecast the aging law of batteries at various temperatures. Finally, the semi-empirical model of battery aging is used to predict the capacity and internal resistance of the battery at 15 ℃, 30 ℃, and 45 ℃, allowing for a better understanding of the battery's aging characteristics while avoiding a large number of repeated experiments and increasing research efficiency.

Key words: lithium-ion battery, capacity decline, internal resistance growth, semi-empirical degradation model

CLC Number:

TM 911

Qingwei ZHU, Xiaoli YU, Qichao WU, Yidan XU, Fenfang CHEN, Rui HUANG. Semi-empirical degradation model of lithium-ion battery with high energy density[J]. Energy Storage Science and Technology, 2022, 11(7): 2324-2331.

Figures/Tables 15

Table 1

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Table 2

Fig. 7

Fig. 8

Table 3

Fig. 9

Fig. 10

Table 4

Fig. 11

References 11

1	林名强, 吴登高, 郑耿峰, 等. 基于表面温度和增量容量的锂电池健康状态估计[J]. 汽车工程, 2021, 43(9): 1285-1290, 1284.
	LIN M Q, WU D G, ZHENG G F, et al. Estimation method of state of health of lithium battery based on surface temperature and incremental capacity[J]. Automotive Engineering, 2021, 43(9): 1285-1290, 1284.
2	WEI J W, DONG G Z, CHEN Z H. Remaining useful life prediction and state of health diagnosis for lithium-ion batteries using particle filter and support vector regression[J]. IEEE Transactions on Industrial Electronics, 2018, 65(7): 5634-5643.
3	任璞, 王顺利, 何明芳, 等. 基于内阻增加和容量衰减双重标定的锂电池健康状态评估[J]. 储能科学与技术, 2021, 10(2): 738-743.
	REN P, WANG S L, HE M F, et al. State of health estimation of Li-ion battery based on dual calibration of internal resistance increasing and capacity fading[J]. Energy Storage Science and Technology, 2021, 10(2): 738-743.
4	WALDMANN T, WILKA M, KASPER M, et al. Temperature dependent ageing mechanisms in Lithium-ion batteries - A Post-Mortem study[J]. Journal of Power Sources, 2014, 262: 129-135.
5	WANG J, LIU P, HICKS-GARNER J, et al. Cycle-life model for graphite-LiFePO₄ cells[J]. Journal of Power Sources, 2011, 196(8): 3942-3948.
6	LI Z, LU L G, OUYANG M G, et al. Modeling the capacity degradation of LiFePO₄/graphite batteries based on stress coupling analysis[J]. Journal of Power Sources, 2011, 196(22): 9757-9766.
7	LEWERENZ M, MÜNNIX J, SCHMALSTIEG J, et al. Systematic aging of commercial LiFePO₄ Graphite cylindrical cells including a theory explaining rise of capacity during aging[J]. Journal of Power Sources, 2017, 345: 254-263.
8	GUAN T, ZUO P J, SUN S, et al. Degradation mechanism of LiCoO₂/mesocarbon microbeads battery based on accelerated aging tests[J]. Journal of Power Sources, 2014, 268: 816-823.
9	GAO Y, JIANG J C, ZHANG C P, et al. Lithium-ion battery aging mechanisms and life model under different charging stresses[J]. Journal of Power Sources, 2017, 356: 103-114.
10	张金, 魏影, 韩裕生, 等. 一种锂离子电池容量退化经验模型[J]. 电源技术, 2016, 40(6): 1176-1179.
	ZHANG J, WEI Y, HAN Y S, et al. Empirical capacity degradation model of lithium-ion battery[J]. Chinese Journal of Power Sources, 2016, 40(6): 1176-1179.
11	刘建, 李占锋, 屈薇薇. 不同应力作用下钴酸锂电池老化特性分析[J]. 电子元件与材料, 2019, 38(12): 38-42.
	LIU J, LI Z F, QU W W. Aging analysis of lithium cobalt oxide batteries under different charge conditions[J]. Electronic Components and Materials, 2019, 38(12): 38-42.

参数	数值
能量密度/(Wh/kg)	260.9
重量/g	69.5
额定容量/Ah	4.9
额定电压/V	3.63
最大充电电流/A	4.9
最大持续放电电流/A	9.8
充电截止电压/V	4.2
放电截止电压/V	2.5
循环充放电温度范围/℃	0～45

参数	0+23 ℃-40 ℃	0+40 ℃-23 ℃	23+40 ℃-0 ℃
D	0.013	0.0119	0.0115
E	3.94	3.968	3.899
F	-75.53	-76.46	-65.49
G	11380	11750	8310
H	166.1	169.2	141.4
I	0.432	0.4305	0.422
J	-0.9555	-0.9545	-0.6476
R-square	0.9840	0.9824	0.9833
RMSE	1.0757	1.0663	1.1012

工况	K	L	M	P	R-square
0 ℃-C1D1	9.89E6	-975.3	-35.52	35.49	0.9400
0 ℃-C1D2	73.09	-1128	-35.58	35.59	0.9408
23 ℃-C1D1	57.36	-886.4	-35.69	35.74	0.9921
23 ℃-C1D2	1.40E7	-1053	-35.38	35.38	0.9866
40 ℃-C1D1	49.52	-709	-35.04	35.1	0.9892
40 ℃-C1D2	56.77	-928.4	-35.72	35.66	0.9947

参数	0+23 ℃-40 ℃	0+40 ℃-23 ℃	23+40 ℃-0 ℃
S	28.02	28	27.97
U	30.24	30.59	30.97
V	2.691	2.703	2.716
W	-908.6	-915	-922.3
Z	35.34	35.38	35.42
R-square	0.9894	0.9894	0.9893
RMSE	0.5497	0.5348	0.5371

[1]	Fan YANG, Jiarui HE, Ming LU, Lingxia LU, Miao YU. SOC estimation of lithium-ion batteries based on BP-UKF algorithm [J]. Energy Storage Science and Technology, 2023, 12(2): 552-559.
[2]	Wenkai ZHU, Xing ZHOU, Yajie LIU, Tao ZHANG, Yuanming SONG. Real time state of charge estimation method of lithium-ion battery based on recursive gated recurrent unit neural network [J]. Energy Storage Science and Technology, 2023, 12(2): 570-578.
[3]	Zhifu WANG, Wei LUO, Yuan YAN, Song XU, Wenmei HAO, Conglin ZHOU. Fault diagnosis of lithium-ion battery sensors using GAPSO-FNN [J]. Energy Storage Science and Technology, 2023, 12(2): 602-608.
[4]	Deliu ZHANG, Yan ZHANG, Hai WANG, Jiadong WANG, Xuanwen GAO, Chaomeng LIU, Dongrun YANG, Wenbin LUO. Optimization of high nickel cathode materials for lithium ion batteries by magnesium doped heterogeneous aluminum oxide coating [J]. Energy Storage Science and Technology, 2023, 12(2): 339-348.
[5]	Yue PAN, Xuebing HAN, Minggao OUYANG, Huahua REN, Wei LIU, Yuejun YAN. Research on the detection algorithm for internal short circuits in lithium-ion batteries and its application to real operating data [J]. Energy Storage Science and Technology, 2023, 12(1): 198-208.
[6]	Xiaolong HE, Xiaolong SHI, Ziyang WANG, Luhao HAN, Bin YAO. Experimental study on thermal runaway characteristics of vehicle NCM lithium-ion batteries under overcharge， overheating， and their combined effects [J]. Energy Storage Science and Technology, 2023, 12(1): 218-226.
[7]	Linwang DENG, Tianyu FENG, Shiwei SHU, Bin GUO, Zifeng ZHANG. Nondestructive lithium plating online detection for lithium-ion batteries： A review [J]. Energy Storage Science and Technology, 2023, 12(1): 263-277.
[8]	Linwang DENG, Tianyu FENG, Shiwei SHU, Zifeng ZHANG, Bin GUO. Review of a fast-charging strategy and technology for lithium-ion batteries [J]. Energy Storage Science and Technology, 2022, 11(9): 2879-2890.
[9]	Zhizhan LI, Jinlei QIN, Jianing LIANG, Zhengrong LI, Rui WANG, Deli WANG. High-nickel ternary layered cathode materials for lithium-ion batteries： Research progress， challenges and improvement strategies [J]. Energy Storage Science and Technology, 2022, 11(9): 2900-2920.
[10]	Xiaoyu CHEN, Mengmeng GENG, Qiankun WANG, Jiani SHEN, Yijun HE, Zifeng MA. Electrochemical impedance feature selection and gaussian process regression based on the state-of-health estimation method for lithium-ion batteries [J]. Energy Storage Science and Technology, 2022, 11(9): 2995-3002.
[11]	Yong MA, Xiaohan LI, Lei SUN, Dongliang GUO, Jinggang YANG, Jianjun LIU, Peng XIAO, Guangjun QIAN. Parameter design of lithium-ion batteries based on a three-dimensional electrochemical thermal coupling lithium precipitation model [J]. Energy Storage Science and Technology, 2022, 11(8): 2600-2611.
[12]	Liang TANG, Xiaobo YIN, Houfu WU, Pengjie LIU, Qingsong WANG. Demand for safety standards in the development of the electrochemical energy storage industry [J]. Energy Storage Science and Technology, 2022, 11(8): 2645-2652.
[13]	Liping HUO, Weiling LUAN, Zixian ZHUANG. Development trend of lithium-ion battery safety technology for energy storage [J]. Energy Storage Science and Technology, 2022, 11(8): 2671-2680.
[14]	Zhicheng CAO, Kaiyun ZHOU, Jiali ZHU, Gaoming LIU, Min YAN, Shun TANG, Yuancheng CAO, Shijie CHENG, Weixin ZHANG. Patent analysis of fire-protection technology of lithium-ion energy storage system [J]. Energy Storage Science and Technology, 2022, 11(8): 2664-2670.
[15]	Yue ZHANG, Depeng KONG, Ping PING. Performance and design optimization of a cold plate for inhibiting thermal runaway propagation of lithium-ion battery packs [J]. Energy Storage Science and Technology, 2022, 11(8): 2432-2441.