Fuzzy reasoning-based evaluation of the thermal diffusion probability of lithium-ion battery modules for energy storage systems

doi:10.19799/j.cnki.2095-4239.2025.0072

Abstract

Abstract:

Lithium-ion battery modules (LIBMs) are currently the most widely used battery components in energy storage systems. Thermal runaway events can significantly compromise the reliable operation of an energy storage system. Existing models for the qualitative analysis of thermal diffusion cannot be directly used to evaluate the thermal diffusion probability of LIBMs quantitatively under time-varying operation conditions. To overcome this problem, a fuzzy reasoning-based method for evaluating the thermal diffusion probability of LIBMs is proposed in this work. The study first built a thermal diffusion simulation model of LIBMs on the COMSOL platform. This model was used to analyze the effects of various heating modes, LIBM arrangement configurations, and state of charge (SOC) on LIBMs and to investigate the mechanisms of thermal diffusion in LIBMs. Subsequently, a fuzzy reasoning system was constructed based on the simulation test data. The cell temperature, inter-cell distance, and ambient temperature of the lithium-ion battery were taken as inputs, and the LIBM thermal runaway probability was the output. To improve the accuracy of the evaluation results, the improved dung beetle optimizer (IDBO) was used to optimize the membership function parameters in the fuzzy reasoning system. The results revealed that reducing the contact area between cells in an LIBM effectively mitigated thermal diffusion; additionally, slow heating of the LIBM resulted in a higher thermal runaway temperature for the last cell in the module to experience thermal runaway. The Pearson correlation coefficient of the thermal diffusion probability evaluation results obtained by the proposed method was higher compared with that of the traditional dung beetle algorithm, particle swarm algorithm, and sparrow search algorithm by 0.076, 0.041, and 0.047 respectively. The high coefficient provides a more reasonable reference basis for the risk warning of LIBM thermal diffusion in energy storage systems in engineering practice.

Key words: energy storage system, lithium-ion battery module, thermal diffusion probability, fuzzy reasoning, improved dung beetle optimizer (IDBO)

CLC Number:

TM 911

Liyue HU, Wei HUANG, Yun ZHOU, Yingqiang ZHOU, Changzheng SHAO, Ke WANG. Fuzzy reasoning-based evaluation of the thermal diffusion probability of lithium-ion battery modules for energy storage systems[J]. Energy Storage Science and Technology, 2025, 14(7): 2662-2674.

Figures/Tables 26

Fig. 1

Fig. 2

Table 1

Thermal property parameters of Li-ion battery"

组件	$ρ / (k g / m 3)$	$C p / [J / (g / K)]$	$k b a t / [W / (m ∙ K)]$
电芯	1367	2867	$k x = k y = 13.8$ ， $k z = 1.38$
正极极耳	2700	900	160
负极极耳	8960	385	146

Table 1

Table 2

Thermal diffusion of LIBM in different arrangement modes"

编号	方式a		方式b		方式c
编号	$T m a x / ℃$	$t i / s$	$T m a x / ℃$	$t i / s$	$T m a x / ℃$	$t i / s$
1	944	1199	952	1247	947	1573
2	970	1332	965	1589	978	1720
3	977	1502	965	1586	974	1791
4	992	1672	$999$	1720	993	2017

Table 2

Table 3

Thermal diffusion of LIBM under different heating modes"

编号	恒温加热		阶梯式升温加热		线性加热
编号	$T m a x / ℃$	$t i / s$	$T m a x / ℃$	$t i / s$	$T m a x / ℃$	$t i / s$
1	944	1199	916	2654	886	3141
2	970	1332	972	2762	979	3241
3	977	1502	975	2914	981	3387
4	992	1672	1009	3079	1016	3546

Table 3

Table 4

Thermal diffusion of LIBM under different SOC"

编号	SOC=90%		SOC=75%		SOC=60%
编号	$T m a x / ℃$	$t i / s$	$T m a x / ℃$	$t i / s$	$T m a x / ℃$	$t i / s$
$1$	918	1213	861	1234	808	1279
$2$	900	1354	884	1395	829	1445
$3$	938	1542	886	1590	830	1661
4	985	1721	920	1785	864	1874

Table 4

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Table 5

Fuzzy domains of fuzzy language values and input and output variables"

变量	模糊语言值	模糊域
$T s e l f$	$S T s e l f$ , $M T s e l f$ , $B T s e l f$	$0,1$
$T e n v$	$S T e n v$ , $M T e n v$ , $B T e n v$	$0,1$
$D N$	VN,SN,MN,BN	$0,3$
$P t r$	VP,SP,MP,RP,BP	$0,1$

Table 5

Fig. 7

Table 6

Fuzzy rules"

序号	$T e n v$	$T s e l f$	$D N$	$P t r$	序号	$T e n v$	$T s e l f$	$D N$	$P t r$	序号	$T e n v$	$T s e l f$	$D N$	$P t r$
1	$S T e n v$	S $T s e l f$	VN	MP	13	$M T e n v$	S $T s e l f$	VN	MP	25	$B T e n v$	S $T s e l f$	VN	RP
2			SN	SP	14			SN	SP	26			SN	MP
3			MN	VP	15			MN	SP	27			MN	SP
4			BN	VP	16			BN	VP	28			BN	SP

5		M $T s e l f$	VN	RP	17		M $T s e l f$	VN	RP	29		M $T s e l f$	VN	BP
6			SN	MP	18			SN	RP	30			SN	RP
7			MN	SP	19			MN	MP	31			MN	MP
8			BN	VP	20			BN	SP	32			BN	SP

9		B $T s e l f$	VN	BP	21		B $T s e l f$	VN	BP	33		B $T s e l f$	VN	BP
10			SN	RP	22			SN	BP	34			SN	BP
11			MN	RP	23			MN	RP	35			MN	RP
12			BN	MP	24			BN	MP	36			BN	RP

Table 6

Fig. 8

Fig. 9

Fig. 10

Table 7

Figure.11

Table 8

Membership function parameters after applying IDBO optimization"

参数	取值	参数	取值
$μ 1$	0.66	$μ 5$	0.29
$μ 2$	0.34	$μ 6$	0.62
$μ 3$	2.72	$μ 7$	0.77
$μ 4$	0.87

Table 8

Fig. 12

Fig. 13

Table 9

Table 10

Fig. 14

Table 11

The probability of thermal runaway of different battery cells in LIBM under experimental conditions a"

编号	1号	2号	3号	4号
概率	0.984	0.25	0.44	0.19
$T m a x / ℃$	945	56	87	32

Table 11

Fig. 15

References 25

[1]	孙玉树, 杨敏, 师长立, 等. 储能的应用现状和发展趋势分析[J]. 高电压技术, 2020, 46(1): 80-89. DOI: 10.13336/j.1003-6520.hve. 20191227008.
	SUN Y S, YANG M, SHI C L, et al. Analysis of application status and development trend of energy storage[J]. High Voltage Engineering, 2020, 46(1): 80-89. DOI: 10.13336/j.1003-6520.hve. 20191227008.
[2]	展浚哲, 史泽中, 汪涛. 多元电化学储能技术综述[J]. 电力科技与环保, 2024, 40(3): 237-249. DOI: 10.19944/j.eptep.1674-8069. 2024.03.003.
	ZHAN J Z, SHI Z Z, WANG T. Overview of multivariate electrochemical energy storage technologies[J]. Electric Power Technology and Environmental Protection, 2024, 40(3): 237-249. DOI: 10.19944/j.eptep.1674-8069.2024.03.003.
[3]	杨续来, 袁帅帅, 杨文静, 等. 锂离子动力电池能量密度特性研究进展[J]. 机械工程学报, 2023, 59(6): 239-254.
	YANG X L, YUAN S S, YANG W J, et al. Research progress on energy density of Li-ion batteries for EVs[J]. Journal of Mechanical Engineering, 2023, 59(6): 239-254.
[4]	曹东学. 锂离子电池负极材料技术现状和发展趋势[J]. 炼油技术与工程, 2024, 54(9): 1-7. DOI: 10.20138/j.cnki.issn1002-106X. 2024. 09.001.
	CAO D X. Technology status and development trend of anode materials for lithium-ion batteries[J]. Petroleum Refinery Engineering, 2024, 54(9): 1-7. DOI: 10.20138/j.cnki.issn1002-106X.2024.09.001.
[5]	中国化学与物理电源行业协会. 2024新型储能产业发展发展白皮书[R]. 2024-03.
	China Chemical and Physical Power Supply Industry Association. 2024 White paper on the development of new energy storage industry [R]. 2024-03.
[6]	曹文炅, 雷博, 史尤杰, 等. 韩国锂离子电池储能电站安全事故的分析及思考[J]. 储能科学与技术, 2020, 9(5): 1539-1547. DOI: 10. 19799/j.cnki.2095-4239.2020.0127.
	CAO W J, LEI B, SHI Y J, et al. Ponderation over the recent safety accidents of lithium-ion battery energy storage stations in South Korea[J]. Energy Storage Science and Technology, 2020, 9(5): 1539-1547. DOI: 10.19799/j.cnki.2095-4239.2020.0127.
[7]	曹志成, 管敏渊, 楼平, 等. 锂离子电池热失控早期特征及预警方法综述[J]. 电源技术, 2024, 48(5): 771-780. DOI: 10.3969/j.issn. 1002-087X.2024.05.002.
	CAO Z C, GUAN M Y, LOU P, et al. Review on early thermal runaway characteristic signal and warning method of lithium ion battery[J]. Chinese Journal of Power Sources, 2024, 48(5): 771-780. DOI: 10.3969/j.issn.1002-087X.2024.05.002.
[8]	秦睿, 陈思思, 匡卫洪, 等. 锂离子电池储能电站安全问题与解决策略分析[J]. 电工技术, 2024(9): 159-161. DOI: 10.19768/j.cnki.dgjs. 2024.09.045.
	QIN R, CHEN S S, KUANG W H, et al. Analysis of safety issues and solutions for lithium-ion battery energy storage power stations[J]. Electric Engineering, 2024(9): 159-161. DOI: 10.19768/j.cnki.dgjs.2024.09.045.
[9]	周志钻, 王博轩, 宋露露, 等. 锂离子电池热失控行为及火灾危险性研究综述[J]. 消防科学与技术, 2024, 43(5): 605-612. DOI: 10. 20168/j.1009-0029.2024.05.605.08.
	ZHOU Z Z, WANG B X, SONG L L, et al. Review on thermal runaway behaviors and fire hazards of lithium-ion batteries[J]. Fire Science and Technology, 2024, 43(5): 605-612. DOI: 10. 20168/j.1009-0029.2024.05.605.08.
[10]	刘宇, 张玉魁, 王荣, 等. 锂离子电池储能设备安全风险分析及管控措施[J]. 内蒙古电力技术, 2024, 42(3): 1-7. DOI: 10.19929/j.cnki.nmgdljs.2024.0033.
	LIU Y, ZHANG Y K, WANG R, et al. Analysis and control measures for safety risks of lithium-ion battery energy storage equipments[J]. Inner Mongolia Electric Power, 2024, 42(3): 1-7. DOI: 10.19929/j.cnki.nmgdljs.2024.0033.
[11]	JIA Y K, UDDIN M, LI Y X, et al. Thermal runaway propagation behavior within 18, 650 lithium-ion battery packs: A modeling study[J]. Journal of Energy Storage, 2020, 31: 101668. DOI: 10.1016/j.est.2020.101668.
[12]	HOELLE S, ZIMMERMANN S, HINRICHSEN O. 3D thermal simulation of thermal runaway propagation in lithium-ion battery cell stack: Review and comparison of modeling approaches[J]. Journal of the Electrochemical Society, 2023, 170(6): 060516. DOI: 10.1149/1945-7111/acd966.
[13]	胡力月, 姚行艳. 基于正交试验的锂离子电池热失控仿真[J]. 储能科学与技术, 2023, 12(4): 1268-1277. DOI: 10.19799/j.cnki.2095-4239.2022.0701.
	HU L Y, YAO X Y. Thermal runaway of lithium-ion batteries based on orthogonal test[J]. Energy Storage Science and Technology, 2023, 12(4): 1268-1277. DOI: 10.19799/j.cnki.2095-4239.2022. 0701.
[14]	齐创, 邝男男, 张亚军, 等. 高比能锂离子电池模组热扩散行为仿真研究[J]. 高电压技术, 2021, 47(7): 2633-2643. DOI: 10.13336/j. 1003-6520.hve.20201049.
	QI C, KUANG N N, ZHANG Y J, et al. Simulation study on the thermal propagation behavior of high energy density lithium-ion battery module[J]. High Voltage Engineering, 2021, 47(7): 2633-2643. DOI: 10.13336/j.1003-6520.hve.20201049.
[15]	陈龙, 张振东, 盛雷, 等. 锂离子电池模组热失控传播实验研究[J]. 上海理工大学学报, 2024, 46(5): 525-532. DOI: 10.13255/j.cnki.jusst. 20230427006.
	CHEN L, ZHANG Z D, SHENG L, et al. Experimental study on thermal runaway propagation of lithium-ion battery modules[J]. Journal of University of Shanghai for Science and Technology, 2024, 46(5): 525-532. DOI: 10.13255/j.cnki.jusst.20230427006.
[16]	孙延先, 姜兆华. 锂离子电池模组过充热失控扩散仿真[J]. 电池, 2019, 49(6): 481-484. DOI: 10.19535/j.1001-1579.2019.06.007.
	SUN Y X, JIANG Z H. Simulation of thermal runaway diffusion in overcharging of Li-ion battery module[J]. Battery Bimonthly, 2019, 49(6): 481-484. DOI: 10.19535/j.1001-1579.2019.06.007.
[17]	李校磊, 高健, 周伟东, 等. COMSOL Multiphysics在锂离子电池中的应用[J]. 储能科学与技术, 2024, 13(2): 546-567. DOI: 10.19799/j.cnki.2095-4239.2023.0577.
	LI X L, GAO J, ZHOU W D, et al. Application of COMSOL multiphysics in lithium-ion batteries[J]. Energy Storage Science and Technology, 2024, 13(2): 546-567. DOI: 10.19799/j.cnki. 2095-4239.2023.0577.
[18]	JOKAR A, RAJABLOO B, DÉSILETS M, et al. Review of simplified pseudo-two-dimensional models of lithium-ion batteries[J]. Journal of Power Sources, 2016, 327: 44-55. DOI: 10.1016/j.jpowsour.2016.07.036.
[19]	NEWMAN J, THOMAS-ALYEA K E. Electroche-mical systems[M]. American: John Wiley and Sons, 2012.
[20]	BIZERAY A M, ZHAO S, DUNCAN S R, et al. Lithium-ion battery thermal-electrochemical model-based state estimation using orthogonal collocation and a modified extended Kalman filter[J]. Journal of Power Sources, 2015, 296: 400-412. DOI: 10.1016/j.jpowsour.2015.07.019.
[21]	王兵. 车用锂离子动力电池及模组热失控的实验与仿真研究[D]. 北京: 北京工业大学, 2018.
	WANG B. Study on thermal runaway of lithium-ion power battery or module for electric vehicle through experiment and simulation[D]. Beijing: Beijing University of Technology, 2018.
[22]	冯旭宁. 车用锂离子动力电池热失控诱发与扩展机理、建模与防控[D]. 北京: 清华大学, 2016.
	FENG X N. Thermal runaway initiation and propagation of lithium-ion traction battery for electric vehicle: Test, modeling and prevention[D]. Beijing: Tsinghua University, 2016.
[23]	OUYANG N, ZHANG W C, YIN X X, et al. A data-driven method for predicting thermal runaway propagation of battery modules considering uncertain conditions[J]. Energy, 2023, 273: 127168. DOI: 10.1016/j.energy.2023.127168.
[24]	Cohen J, Cohen P, West S. G, et al. Applied multiple regression/correlation analysis for the behavioral sciences[M]. American:Lawrence Erlbaum Associates,2003.
[25]	XUE J K, SHEN B. Dung beetle optimizer: A new meta-heuristic algorithm for global optimization[J]. The Journal of Supercomputing, 2023, 79(7): 7305-7336. DOI: 10.1007/s11227-022-04959-6.