Accurate typical gas detection of lithium battery in early thermal runaway period

doi:10.19799/j.cnki.2095-4239.2024.0101

Abstract

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

CLC Number:

TM 912

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.

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Appendix

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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%

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