Early warning method for fire safety of containerized lithium-ion battery energy storage systems

doi:10.19799/j.cnki.2095-4239.2023.0950

Abstract

Abstract:

To mitigate the risk of fires in containerized lithium-ion battery energy storage systems, we propose an early warning method for fire safety. This method involves analyzing the heat generation process in such systems and establishing the correlation between temperature, voltage, and the fire safety of the storage system. By collecting operational data, including voltage, current, temperature, and acoustic signals, we assess the system's surface health and predict its operational trends based on changes in the operational data and surface health status. Considering various influencing factors, we establish early warning levels for the system's fire safety and implement a comprehensive early warning system. Performance testing demonstrates that, compared to conventional warning methods, our optimized approach significantly reduces the rates of false positives and negatives by more than 0.90%. Furthermore, the method exhibited only one warning error across different warning levels, and achieved a warning accuracy of 99.7% across various surface damage locations, proving its effectiveness and reliability.

Key words: container type batteries, lithium-ion batteries, energy storage system, fire safety, early warning

CLC Number:

TM 912

Runyuan LI, Fu'ao GUO, Gangchao ZHAO. Early warning method for fire safety of containerized lithium-ion battery energy storage systems[J]. Energy Storage Science and Technology, 2024, 13(5): 1595-1602.

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预警等级	温度/℃	表面破损面积/mm²	声信号强度/dB
Ⅰ	[80,200]	[50,200]	[60,150]
Ⅱ	[60,80]	[30,50]	[50,60]
Ⅲ	[50,60]	[20,30]	[40,50]
Ⅳ	[40,50]	[10,20]	[30,40]
Ⅴ	[30,40]	[0,10]	[0,30]

电池样品编号	电压/V	电流/A	温度/℃	表面破损面积/mm²	声信号强度/dB	预期预警等级
01	230	2.5	69	31	55	Ⅱ
02	250	3.8	125	121	71	Ⅰ
03	228	2.4	56	23	46	Ⅲ
04	226	1.8	52	21	42	Ⅲ
05	238	2.7	73	45	58	Ⅱ
06	231	2.4	42	13	34	Ⅳ
07	229	2.3	35	7	21	Ⅴ

实验次数	预期预警样品数量/个	基于数据-模型混合驱动的电池储能系统早期预警方法		基于气液逸出物图像识别的电池储能系统早期预警方法		优化设计集装箱式锂离子电池储能系统消防安全早期预警方法
实验次数	预期预警样品数量/个	预警错误样品数量/个	实际预警样品数量/个	预警错误样品数量/个	实际预警样品数量/个	预警错误样品数量/个	实际预警样品数量/个
1	297	3	292	5	292	0	297
2	293	2	290	1	291	0	293
3	299	3	296	7	292	1	298
4	295	3	292	2	291	0	295
5	294	4	289	5	288	0	294

预警等级	预期预警样品数量/个	预警正确数量/个
预警等级	预期预警样品数量/个	基于数据-模型混合驱动的电池储能系统早期预警方法	基于气液逸出物图像识别的电池储能系统早期预警方法	优化设计集装箱式锂离子电池储能系统消防安全早期预警方法
Ⅰ	60	58(2个全部误判为Ⅱ)	59(误判为Ⅱ)	60
Ⅱ	60	58(1个误判为Ⅰ，1个误判为Ⅲ)	58(2个全部误判为Ⅲ)	60
Ⅲ	60	59(误判为Ⅱ)	58(2个全部误判为Ⅳ)	60
Ⅳ	60	60	58(2个全部误判为Ⅴ)	59(误判为Ⅲ)
Ⅴ	60	59(误判为无需预警)	58(2个全部误判为无需预警)	60

样本数量/个	预警准确率/%
样本数量/个	基于数据-模型混合驱动的电池储能系统早期预警方法	基于气液逸出物图像识别的电池储能系统早期预警方法	优化设计集装箱式锂离子电池储能系统消防安全早期预警方法
50	100.0	100.0	100.0
100	99.0	98.0	100.0
150	98.7	98.0	100.0
200	98.5	97.5	100.0
250	98.4	96.8	100.0