Research on lithium-ion battery thermal runaway early warning method based on prediction error

doi:10.19799/j.cnki.2095-4239.2024.0539

Abstract

Abstract:

Lithium-ion batteries play a crucial role in promoting the widespread use of renewable energy and ensuring the stable operation of power grids. However, thermal runaway-induced thermal spread in these batteries can result in significant losses. Therefore, thermal runaway warning technology for lithium-ion batteries is essential for preventing safety issues. This study develops a lithium-ion battery thermal runaway test platform, collecting data on surface temperature, output voltage, and battery expansion pressure during normal cycles and overcharge-induced thermal runaway events. A total of 66895 samples were obtained, including 245 samples exhibiting overcharge thermal runaway. The dataset captures the continuous transition from normal operation to overcharge and subsequent thermal runaway in a time series. The internal volume changes of the battery are effectively represented by measuring the expansion pressure signals. A regression prediction algorithm based on SE-Res-LSTM is developed to predict battery expansion pressure, utilizing the prediction error to detect overcharge-induced thermal runaway in real-time. In terms of timeliness, early detection of overcharge thermal runaway is achieved 12 s after the onset of overcharging, which is 233 s earlier than the conventional temperature-based method that triggers a warning when the battery surface reaches 60 ℃. This significantly enhances the accuracy and responsiveness of early warning systems.

Key words: lithium-ion battery, thermal runaway, dataset, prediction error, early warning

CLC Number:

TM 912

Jianjie JIANG, Ping LOU, Guohua XU, Jun LAI, Yao WANG, Zhicheng CAO, Weixin ZHANG, Yuancheng CAO. Research on lithium-ion battery thermal runaway early warning method based on prediction error[J]. Energy Storage Science and Technology, 2024, 13(11): 4187-4197.

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References 21

1	喻航, 张英, 徐超航, 等. 锂电储能系统热失控防控技术研究进展[J]. 储能科学与技术, 2022, 11(8): 2653-2663. DOI: 10.19799/j.cnki. 2095-4239.2022.0116.
	YU H, ZHANG Y, XU C H, et al. Research progress of thermal runaway prevention and control technology for lithium battery energy storage systems[J]. Energy Storage Science and Technology, 2022, 11(8): 2653-2663. DOI: 10.19799/j.cnki.2095-4239.2022.0116.
2	管敏渊, 沈建良, 徐国华, 等. 锂离子电池储能系统靶向消防装备设计与性能[J]. 储能科学与技术, 2023, 12(4): 1131-1138. DOI: 10.19799/j.cnki.2095-4239.2022.0719.
	GUAN M Y, SHEN J L, XU G H, et al. Design and performance research of targeted-fire fighting equipment for lithium-ion battery energy storage system[J]. Energy Storage Science and Technology, 2023, 12(4): 1131-1138. DOI: 10.19799/j.cnki.2095-4239.2022.0719.
3	FENG X N, ZHENG S Q, REN D S, et al. Investigating the thermal runaway mechanisms of lithium-ion batteries based on thermal analysis database[J]. Applied Energy, 2019, 246: 53-64. DOI: 10.1016/j.apenergy.2019.04.009.
4	周剑文. 锂离子电池热失控建模与热蔓延抑制研究[D]. 济南: 山东大学, 2022. DOI: 10.27272/d.cnki.gshdu.2022.004758.
	ZHOU J W. Thermal runaway modeling and thermal spread suppression of lithium-ion batteries[D]. Jinan: Shandong University, 2022. DOI: 10.27272/d.cnki.gshdu.2022.004758.
5	李钊, 陈才星, 牛慧昌, 等. 锂离子电池热失控早期预警特征参数分析[J]. 消防科学与技术, 2020, 39(2): 146-149. DOI: 10.3969/j.issn.1009-0029.2020.02.002.
	LI Z, CHEN C X, NIU H C, et al. Characteristic parameter analysis of thermal\r runaway early warning of lithium-ion battery[J]. Fire Science and Technology, 2020, 39(2): 146-149. DOI: 10.3969/j.issn.1009-0029.2020.02.002.
6	OMAER FARUQ GONI M, MONDAL M N I, RIAZUL ISLAM S M, et al. Diagnosis of malaria using double hidden layer extreme learning machine algorithm with CNN feature extraction and parasite inflator[J]. IEEE Access, 2023, 11: 4117-4130. DOI: 10.1109/ACCESS.2023.3234279.
7	ZHOU Z X, HUBER N R, INOUE A, et al. Multislice input for 2D and 3D residual convolutional neural network noise reduction in CT[J]. Journal of Medical Imaging, 2023, 10(1): 014003. DOI: 10.1117/1.JMI.10.1.014003.
8	YAO Y, LIU D. Short-term wind power forecasting based on attention mechanism of CNN-LSTM[J]. Modern Electric Power, 2022, 39(2): 212-218. DOI: 10.19725/j.cnki.1007-2322. 2021. 0108.
9	CHANG S Z, DU B, ZHANG L P. A subspace selection-based discriminative forest method for hyperspectral anomaly detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(6): 4033-4046. DOI: 10.1109/TGRS.2019.2960391.
10	HUANG S, DU Z J, ZHOU Q, et al. In situ measurement of temperature distributions in a Li-ion cell during internal short circuit and thermal runaway[J]. Journal of the Electrochemical Society, 2021, 168(9): 090510. DOI: 10.1149/1945-7111/ac1d7b.
11	周炜航, 叶青, 叶蕾, 等. 锂离子电池内温度场健康状态分布式光纤原位监测技术研究[J]. 中国激光, 2020, 47(12): 154-163. DOI: 10.3788/CJL202047.1204002.
	ZHOU W H, YE Q, YE L, et al. Distributed optical fiber in situ monitoring technology for a healthy temperature field in lithium ion batteries[J]. Chinese Journal of Lasers, 2020, 47(12): 154-163. DOI: 10.3788/CJL202047.1204002.
12	SONG X Y, LIU Y T, XUE L, et al. Time-series well performance prediction based on Long Short-Term Memory (LSTM) neural network model[J]. Journal of Petroleum Science and Engineering, 2020, 186: 106682. DOI: 10.1016/j.petrol. 2019. 106682.
13	OJO O, LANG H X, KIM Y, et al. A neural network based method for thermal fault detection in lithium-ion batteries[J]. IEEE Transactions on Industrial Electronics, 2021, 68(5): 4068-4078. DOI: 10.1109/TIE.2020.2984980.
14	杨启帆, 马宏忠, 段大卫, 等. 基于气体特性的锂离子电池热失控在线预警方法[J]. 高电压技术, 2022, 48(3): 1202-1211. DOI: 10.13336/j.1003-6520.hve.20210261.
	YANG Q F, MA H Z, DUAN D W, et al. Thermal runaway online warning method for lithium-ion battery based on gas characteristics[J]. High Voltage Engineering, 2022, 48(3): 1202-1211. DOI: 10.13336/j.1003-6520.hve.20210261.
15	邓原冰. 锂离子动力电池热失控及其预警机制的试验与仿真研究[D]. 武汉: 华中科技大学, 2017.DENG Y B. Experimental and simulation study on thermal runaway of lithium-ion power battery and its early warning mechanism[D]. Wuhan: Huazhong University of Science and Technology, 2017.
16	LI D, LIU P, ZHANG Z S, et al. Battery thermal runaway fault prognosis in electric vehicles based on abnormal heat generation and deep learning algorithms[J]. IEEE Transactions on Power Electronics, 2022, 37(7): 8513-8525. DOI: 10.1109/TPEL. 2022. 3150026.
17	王娜, 李强. 大数据分析管理系统在新能源汽车事故分析中的应用[J]. 时代汽车, 2024(2): 192-194. DOI: 10.3969/j.issn.1672-9668.2024.02.061.
	WANG N, LI Q. Application of big data analysis management system in new energy vehicle accident analysis[J]. Auto Time, 2024(2): 192-194. DOI: 10.3969/j.issn.1672-9668.2024.02.061.
18	HONG J C, WANG Z P, CHEN W, et al. Synchronous multi-parameter prediction of battery systems on electric vehicles using long short-term memory networks[J]. Applied Energy, 2019, 254: 113648. DOI: 10.1016/j.apenergy.2019.113648.
19	HONG J C, WANG Z P, YAO Y T. Fault prognosis of battery system based on accurate voltage abnormity prognosis using long short-term memory neural networks[J]. Applied Energy, 2019, 251: 113381. DOI: 10.1016/j.apenergy.2019.113381.
20	HU J, TANG X J, ZHU X L, et al. Suppression of thermal runaway induced by thermal abuse in large-capacity lithium-ion batteries with water mist[J]. Energy, 2024, 286: 129669. DOI: 10.1016/j.energy.2023.129669.
21	NORTHWOOD K, PEARSON E, ARNAUTOVSKA U, et al. Optimising plasma clozapine levels to improve treatment response: An individual patient data meta-analysis and receiver operating characteristic curve analysis[J]. British Journal of Psychiatry, 2023, 222(6): 241-245. DOI: 10.1192/bjp.2023.27.

实验设备	型号
电池测试系统	新威CT-8008
防爆箱	东莞贝尔BE-101-1200L
无纸化数据采集仪	日置LR8450
轮辐式压力传感器	Z673
热电偶	OMEGA GG-K-30

工步	工作模式	限制类型	限制条件
1	搁置	时间	1 h
2	恒流放电100 A	电压	≤2 V
3	搁置	时间	2 h
4	恒流充电100 A	电压	3.65 V
5	恒压充电3.67 V	电流	2 A
6	搁置	时间、次数	1 h，3次
7	循环	次数	4次
8	恒流充电100 A	时间	5 h
9	搁置	时间	1 h

网络层	卷积核数量	卷积核大小	步长	节点数量
1-D卷积层1	32	2	1	—
1-D卷积层2	64	2	1	—
1-D卷积层3	64	1	1	—
全局平均池化层	—	3	—	—
全连接层1	—	—	—	16
全连接层2	—	—	—	64
LSTM	—	—	—	6
全连接层3	—	—	—	1

混淆矩阵		预测结果
混淆矩阵		正类	负类
真实结果	正类	TP	FN
真实结果	负类	FP	TN

网络结构	MAPE/%	RMSE	R²
LSTM	2.23	345.32	0.9201
CNN-LSTM	2.0906	341.42	0.92892
Res-LSTM	1.8385	318.29	0.93214
SE-LSTM	1.6517	291.53	0.94299
SE-Res-LSTM	1.1887	202.32	0.97258