锂电池热失控早期典型气体精准检测方法

doi:10.19799/j.cnki.2095-4239.2024.0101

摘要/Abstract

摘要：

锂电池储能是消纳新能源发电，达成国家“双碳”目标的重要途径，是地面储能及工商业储能的核心装备，锂电池热失控消防安全是储能大规模应用的前提保障。锂电池热失控早期典型气体检测是储能消防预警的主要手段，但是在混合气体场景下各气体传感器数据存在交叉干扰，导致检测失准而造成预警延迟或误报警，进而导致消防隐患。针对上述问题，围绕典型气体H₂和CO的浓度精准检测，提出一种混合气体场景下传感器数据的解耦方法。通过建立各传感器在不同单一气体环境下的响应模型，建立不同气体对传感器的交叉耦合关系；进而推导混合气体场景下各传感器信号与气体组分与浓度的构成关系，建立方程组得出各气体的精确浓度数据，实现各传感器数据解耦。最后，搭建H₂和CO混合气体场景，用于模拟不同化学体系、不同SOC的锂电池热失控早期气体环境，进行实验测试，结果显示在0~1000 mL/m³浓度范围内的检测误差小于50 mL/m³，检测精度最大提升了15%，验证了本文所提方法的有效性。

关键词: 锂电池储能, 热失控, 气体检测, 交叉干扰, 数据交叉解耦算法

Abstract:

Lithium battery energy storage plays a crucial role in harnessing new energy for power generation and achieving the national "dual-carbon" objectives. This technology is indispensable for ground-based, industrial, and commercial energy-storage applications. A critical aspect of its widespread adoption is ensuring the safety of lithium batteries against thermal runaway and fire hazards. To this end, early detection of gases typically released during the thermal runaway phase of a lithium battery is essential for fire warning in energy storage. However, accurately identifying these gases poses challenges in environments with mixed gases owing to cross-interference from data collected by various gas sensors. Such interferences often lead to inaccurate detections, delayed or false alarms, and potential fire hazards. To address these issues, we propose a decoupling method designed to enhance the precision of detecting typical gases, namely H₂ and CO concentrations, in mixed gas environments. This method involves establishing response models for each sensor in different single-gas environments and understanding the cross-coupling relationships between different gases and sensors. By deriving the relationship between the sensor signals and the gas components and concentrations in mixed-gas scenarios, we can establish an equation system. This system is crucial for obtaining precise concentration data for each gas, thus facilitating the decoupling of sensor data. In experimental testing with a mixed gas scenario of H₂ and CO, which was built to simulate the early gas environment of lithium battery thermal runaway across different chemical systems and different states of charge. The results demonstrated a detection error of less than 50 mL/m³ within the concentration range of 0—1000 mL/m³. The maximum improvement in detection accuracy reached 15%, validating the effectiveness of the proposed method outlined in this study.

Key words: lithium battery energy storage, thermal runaway, gas detection, cross interference, data cross decoupling algorithm

中图分类号:

TM 912

刘宝泉, 曹小雨. 锂电池热失控早期典型气体精准检测方法[J]. 储能科学与技术, 2024, 13(6): 1995-2009.

Baoquan LIU, Xiaoyu CAO. Accurate typical gas detection of lithium battery in early thermal runaway period[J]. Energy Storage Science and Technology, 2024, 13(6): 1995-2009.

图/表 25

图1

图2

图3

图4

图5

表1

图6

图7

图8

图9

图10

图11

图12

图13

图14

图15

图16

图17

图18

图19

图20

图21

图22

附表1

参考文献 31

1	陈银, 肖如, 崔怡琳, 等. 储能电站锂离子电池火灾早期预警与抑制技术研究综述[J]. 电气工程学报, 2022, 17(4): 72-87.
	CHEN Y, XIAO R, CUI Y L, et al. Research review on early warning and suppression technology of lithium-ion battery fire in energy storage power station[J]. Journal of Electrical Engineering, 2022, 17(4): 72-87.
2	劳力. 高比能锂离子动力电池系统充电策略及热失控安全研究[D]. 合肥: 中国科学技术大学, 2020.
	LAO L. Study on charging strategy and thermal runaway safety of high specific energy lithium ion power battery system[D]. Hefei: University of Science and Technology of China, 2020.
3	马敬轩, 宋宇航, 石爽, 等. 基于气压信号突变探测的液冷型磷酸铁锂电池模组热失控预警研究[J]. 储能科学与技术, 2023, 12(7): 2246-2255.
	MA J X, SONG Y H, SHI S, et al. Early warning of the thermal runaway of liquid-cooled LiFePO₄ battery module based on the sudden change of air-pressure signal detection[J]. Energy Storage Science and Technology, 2023, 12(7): 2246-2255.
4	HE D R, SUN J L, LI Y, et al. Thermal runaway warning based on safety management system of lithium iron phosphate battery for energy storage[C]// 2020 IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS). IEEE, 2020: 577-582.
5	谭则杰, 周晓燕, 徐振恒, 等. 锂离子电池热失控监测与预警的气敏技术研究进展[J]. 储能科学与技术, 2023, 12(11): 3456-3470.
	TAN Z J, ZHOU X Y, XU Z H, et al. Research progress of gas-sensing technologies for the monitoring and early warning of thermal runaway in lithium-ion batteries[J]. Energy Storage Science and Technology, 2023, 12(11): 3456-3470.
6	董明, 刘王泽宇, 李晓枫, 等. 基于电化学阻抗谱的锂电池过充电阻抗特性与检测方法研究[J]. 中国电机工程学报, 2024, 44(9): 3388-3399.
	DONG M, LIU W Z Y, LI X F, et al. Study on overcharge impedance characteristics and detection methods of lithium batteries based on electrochemical impedance spectroscop[J]. Proceedings of the CSEE, 2024, 44(9): 3388-3399.
7	靳欣, 张建茹, 王其钰, 等. 混合固液锂离子电池的热失控行为研究[J]. 储能科学与技术, 2024, 13(1): 48-56.
	JIN X, ZHANG J R, WANG Q Y, et al. Study on thermal runaway of hybrid solid-liquid batteries[J]. Energy Storage Science and Technology, 2024, 13(1): 48-56.
8	于天剑, 冯恩来, 伍珣. 基于数据驱动的动车组镍镉电池记忆效应消除策略研究[J/OL]. 铁道科学与工程学报: 1-14[2023-10-11]. https://doi.org/10.19713/j.cnki.43-1423/u.T20231236.
	YU T J, FENG E L, WU X. Research on data-driven strategies for eliminating the memory effect of nickel cadmium batteries in high-speed trains[J/OL]. Journal of Railway Science and Engineering: 1-14[2023-10-11]. https://doi.org/10.19713/j.cnki.43-1423/u.T20231236.
9	SONG Y H, LYU N W, SHI S, et al. Safety warning for lithium-ion batteries by module-space air-pressure variation under thermal runaway conditions[J]. Journal of Energy Storage, 2022, 2022(56): 105911.
10	李奎杰, 楼平, 管敏渊, 等. 锂离子电池热失控多维信号演化及耦合机制研究综述[J]. 储能科学与技术, 2023, 12(3): 899-912.
	LI K J, LOU P, GUAN M Y, et al. A review of multi-dimensional signal evolution and coupling mechanism of lithium-ion battery thermal runaway[J]. Energy Storage Science and Technology, 2023, 12(3): 899-912.
11	徐成善, 鲁博瑞, 张梦启, 等. 储能锂离子电池预制舱热失控烟气流动研究[J]. 储能科学与技术, 2022, 11(8): 2418-2431.
	XU C S, LU B R, ZHANG M Q, et al. Study on thermal runaway gas evolution in the lithium-ion battery energy storage cabin[J]. Energy Storage Science and Technology, 2022, 11(8): 2418-2431.
12	张斌, 吴楠, 赵希强, 等. 基于红外热成像技术的动力电池组热失控监测系统[J]. 电池工业, 2019, 23(4): 171-175, 185.
	ZHANG B, WU N, ZHAO X Q, et al. Thermal out-of-control monitoring system for power batteries based on infrared thermal imaging technology[J]. Chinese Battery Industry, 2019, 23(4): 171-175, 185.
13	苏同伦. 基于声信号的锂电池储能舱安全预警及故障定位方法研究[D]. 郑州: 郑州大学, 2021.
	SU T L. Research on safety warning and fault location of lithium battery energy storage cabin based on acoustic signal[D]. Zhengzhou: Zhengzhou University, 2021.
14	FU Y Y, LU S, LI K Y, et al. An experimental study on burning behaviors of 18650 lithium ion batteries using a cone calorimeter[J]. Journal of Power Sources, 2015, 273: 216-222.
15	YUAN Q F, ZHAO F G, WANG W D, et al. Overcharge failure investigation of lithium-ion batteries[J]. Electrochimica Acta, 2015, 178: 682-688.
16	朱艳丽, 徐艺博, 王聪杰, 等. 不同荷电状态磷酸铁锂电池热失控温度与产气特性分析[J]. 安全与环境学报, 2024, 24(1): 143-151.
	ZHU Y L, XU Y B, WANG C J, et al. Analysis of thermal runaway temperature and gas production characteristics of lithium iron phosphate batteries with different states of charge[J]. Journal of Safety and Environment, 2024, 24(1): 143-151.
17	WANG L, MA Y, GAO Y, et al. Study of gas evolution behavior of lithium-ion battery via in situ FT-IR spectroscopy[J]. Journal of Electroanalytical Chemistry, 2023, 16(7): 109-112.
18	JIN Y, ZHENG Z K, WEI D H, et al. Detection of micro-scale Li dendrite via H₂ gas capture for early safety warning[J]. Joule, 2020, 8(4): 1714-1729.
19	YUAN L M, DUBANIEWICZ T, ZLOCHOWER I, et al. Experimental study on thermal runaway and vented gases of lithium-ion cells[J]. Process Safety and Environmental Protection, 2020, 144: 186-192.
20	CAI T, STEFANOPOULOU A G, SIEGEL J B. Early detection for Li-ion batteries thermal runaway based on gas sensing[J]. ECS Transactions, 2019, 89(1): 85-97.
21	吴静云, 郭鹏宇, 张淼, 等. 基于气体检测的锂电池热失控预警研究进展[J]. 消防科学与技术, 2022, 41(2): 161-164.
	WU J Y, GUO P Y, ZHANG M, et al. Research progress on the warning of the thermal runaway of lithium-ion battery based on the gas detection[J]. Fire Science and Technology, 2022, 41(2): 161-164.
22	王铭民, 孙磊, 郭鹏宇, 等. 基于气体在线监测的磷酸铁锂储能电池模组过充热失控特性[J]. 高电压技术, 2021, 47(1): 279-286.
	WANG M M, SUN L, GUO P Y, et al. Overcharge and thermal runaway characteristics of lithium iron phosphate energy storage battery modules based on gas online monitoring[J]. High Voltage Engineering, 2021, 47(1): 279-286.
23	孙宇峰, 黄行九, 刘伟, 等. 电化学CO气体传感器及其敏感特性[J]. 传感器技术, 2004, 23(7): 14-17.
	SUN Y F, HUANG X J, LIU W, et al. CO electrochemical gas sensor and it's sensitive character[J]. Journal of Transducer Technology, 2004, 23(7): 14-17.
24	陆熊. 基于电化学传感器的气体检测仪的设计[D]. 上海: 上海交通大学, 2015.
	LU X. Design of A gas detection system based on electrochemical sensors[D]. Shanghai: Shanghai Jiao Tong University, 2015.
25	刘辰旸. 用于呼气检测的光激发半导体气体传感器[D]. 大连: 大连理工大学, 2022.
	LIU C Y. UV-activated semiconductor gas sensor for exhaled breath detection[D]. Dalian: Dalian University of Technology, 2022.
26	曹利峰, 贾博文, 蔡宏忱, 等. 一种电化学传感器交叉干扰的补偿方法与实践[J]. 中国环保产业, 2021(7): 64-72.
	CAO L F, JIA B W, CAI H C, et al. Compensation method and practice for cross interference of electrochemical gas sensors[J]. China Environmental Protection Industry, 2021(7): 64-72.
27	蒋学悟, 刘海韬, 魏海明. 电化学气体传感器测量干扰排除的探讨[C]//中国土木工程学会城市燃气分会应用专业委员会2010年年会论文集. 中国土木工程学会, 2010: 182-185.
28	GOLUBKOV A W, FUCHS D, WAGNER J, et al. Thermal-runaway experiments on consumer Li-ion batteries with metal-oxide and olivin-type cathodes[J]. RSC Advances, 2014, 4(7): 3633-3642.
29	FERNANDES Y, BRY A, DE PERSIS S. Identification and quantification of gases emitted during abuse tests by overcharge of a commercial Li-ion battery[J]. Journal of Power Sources, 2018, 389: 106-119.
30	赵春朋. 受限空间三元锂离子电池热失控燃爆危险性研究[D]. 合肥: 中国科学技术大学, 2021.
	ZHAO C P. Study on the risk of thermal runaway explosion of ternary lithium ion battery in confined space[D]. Hefei: University of Science and Technology of China, 2021.
31	秦鹏. 磷酸铁锂电池热失控产热产气规律及火焰主控机制研究[D]. 合肥: 中国科学技术大学, 2022.
	QIN P. The investigation on the heat-gas regularity and flame controlling mechanism of lithium iron phosphate battery thermal runaway[D]. Hefei: University of Science and Technology of China, 2022.

电池种类	荷电状态	注入气体浓度	加算法前误差最大值	加算法前误差最小值	加算法后误差最大值	加算法后误差最小值	提升精度平均值
LFP	100% SOC	100 mL/m³ H₂ 30 mL/m³ CO	120	25	29	5	5.37%
		100 mL/m³ H₂ 30 mL/m³ CO	46	13	37	4	2.7%
		150 mL/m³ H₂ 45 mL/m³ CO	162	31	46	9	9.46%
		150 mL/m³ H₂ 45 mL/m³ CO	52	18	27	10	4.38%
	50% SOC	120 mL/m³ H₂ 20 mL/m³ CO	156	17	34	4	8.4%
		120 mL/m³ H₂ 20 mL/m³ CO	58	23	42	14	6.56%
		180 mL/m³ H₂ 30 mL/m³ CO	145	44	29	8	8.92%
		180 mL/m³ H₂ 30 mL/m³ CO	41	8	23	1	6.75%

NMC	100% SOC	40 mL/m³ H₂ 70 mL/m³ CO	127	25	46	5	10.04%
		40 mL/m³ H₂ 70 mL/m³ CO	84	11	37	3	2.62%
		80 mL/m³ H₂ 140 mL/m³ CO	112	35	45	15	9.36%
		80 mL/m³ H₂ 140 mL/m³ CO	87	31	42	15	3.46%
	50% SOC	50 mL/m³ H₂ 100 mL/m³ CO	106	15	41	5	9%
		50 mL/m³ H₂ 100 mL/m³ CO	104	19	42	8	3.4%
		75 mL/m³ H₂ 150 mL/m³ CO	115	30	48	10	9.8%
		75 mL/m³ H₂ 150 mL/m³ CO	91	16	39	9	3.17%

气体种类	气体浓度	传感器信号	交叉干扰系数
硫化氢	100 mL/m³	0 mL/m³	0%
乙烯	100 mL/m³	80 mL/m³	80%
一氧化氮	35 mL/m³	6 mL/m³	17.14%
二氧化氮	5 mL/m³	0 mL/m³	0%
乙醇	100 mL/m³	0 mL/m³	0%
氯气	10 mL/m³	1 mL/m³	10%
二氧化硫	20 mL/m³	0.6 mL/m³	3%
氢气	500 mL/m³	43 mL/m³	8.6%
氨气	50 mL/m³	1 mL/m³	2%
一氯甲烷	5 mL/m³	0 mL/m³	0%
环氧乙烷	10 mL/m³	0 mL/m³	0%
苯	100 mL/m³	1.5 mL/m³	1.5%
丙酮	100 mL/m³	3.5 mL/m³	3.5%
甲醇	200 mL/m³	0 mL/m³	0%

气体种类	气体浓度	传感器信号	交叉干扰系数
硫化氢	15 mL/m³	4 mL/m³	26.67%
二氧化硫	5 mL/m³	0 mL/m³	0%
一氧化碳	200 mL/m³	30 mL/m³	15%
一氧化氮	35 mL/m³	10 mL/m³	28.57%
二氧化氮	5 mL/m³	0.5 mL/m³	10%
氯气	10 mL/m³	0 mL/m³	0%
氯化氢	5 mL/m³	0 mL/m³	0%
乙烯	100 mL/m³	85 mL/m³	85%

[1]	莫子鸣, 饶宗昕, 杨建飞, 杨孟昊, 蔡黎明. 锂离子电池过充热失控气热模型构建及关键参数影响分析[J]. 储能科学与技术, 2025, 14(5): 1784-1796.
[2]	彭磊, 倪照鹏, 于越, 孙福鹏, 夏修龙, 张鹏, 孙思博. 过充导致三元锂电池电动汽车火灾的试验研究[J]. 储能科学与技术, 2025, 14(4): 1484-1495.
[3]	彭鹏, 王成东, 陈满, 王青松, 雷旗开, 金凯强. 某钛酸锂电池储能电站热失控致灾危害评价[J]. 储能科学与技术, 2025, 14(4): 1617-1630.
[4]	范文强, 史梓男, 杨代铭, 梁惠施, 陈烨. 不同冷却工质对电池热失控抑制的效果[J]. 储能科学与技术, 2025, 14(4): 1554-1563.
[5]	李勇琦, 李志远, 闻有为, 王成东, 段强领, 王青松. 大容量钠离子电池热失控特性实验研究[J]. 储能科学与技术, 2025, 14(4): 1657-1667.
[6]	张新宇, 罗声豪, 吴颖欣, 刘针莹, 张立志, 凌子夜. 复合相变材料用于锂离子电池热管理和热失控防护研究进展[J]. 储能科学与技术, 2025, 14(3): 1040-1053.
[7]	朱鹏杰, 李伟, 张楚, 宋浩, 李贝贝, 刘秀梅, 刘利利. 基于烟气扩散的储能柜内锂电池热失控预警研究[J]. 储能科学与技术, 2025, 14(2): 624-635.
[8]	叶锦昊, 侯军辉, 张正国, 凌子夜, 方晓明, 黄思林, 肖质文. 100 Ah磷酸铁锂软包电池的热失控特性及产气行为[J]. 储能科学与技术, 2025, 14(2): 636-647.
[9]	黄怀宇, 黄思林, 赵荣超, 肖质文, 侯军辉, 闫力玮. 磷酸铁锂电池铝塑膜壳体绝缘失效触发热失控特性实验研究[J]. 储能科学与技术, 2025, 14(2): 613-623.
[10]	李和雨, 洪小波, 陈子涵, 阮殿波. 多孔隔热板对锂离子电池模组热蔓延阻隔效果研究[J]. 储能科学与技术, 2025, 14(2): 479-487.
[11]	张文婧, 肖伟, 伊亚辉, 钱利勤. 锂离子电池安全改性策略研究进展[J]. 储能科学与技术, 2025, 14(1): 104-123.
[12]	陈晔, 李晋, 吴候福, 张少禹, 储玉喜, 卓萍. 大容量储能电池模组热失控传播行为与燃爆风险分析[J]. 储能科学与技术, 2024, 13(8): 2803-2812.
[13]	许利君, 许利红, 宋方宇轩. 电化学储能电站的系统故障监测与诊断分析[J]. 储能科学与技术, 2024, 13(8): 2788-2790.
[14]	陈国贺, 吕培召, 李孟涵, 饶中浩. 锂离子电池热失控传播特性及其抑制策略研究进展[J]. 储能科学与技术, 2024, 13(7): 2470-2482.
[15]	曹勇, 杨大鹏, 朱清, 梁坤峰, 周训, 常艳琴. 大容量磷酸铁锂电池模组热失控研究[J]. 储能科学与技术, 2024, 13(7): 2462-2469.