Fault diagnosis of micro-internal short circuits in lithium-ion battery using the isolated forest algorithm

doi:10.19799/j.cnki.2095-4239.2024.0509

Abstract

Abstract:

Internal short-circuit (ISC) faults in lithium-ion battery (LIB) systems are major contributors to thermal runaway and fire incidents. Diagnosing ISC faults is crucial for early warning of potential accidents and ensuring the safe operation of LIB systems. The isolated forest algorithm, an unsupervised anomaly detection method, is widely utilized in identifying anomalous data. Leveraging the characteristic voltage deviation of ISC-affected LIBs within a series-connected LIB pack, this study proposes an ISC fault diagnosis method based on the isolated forest algorithm. To validate the proposed method, a series-connected LIB module was constructed to perform ISC experiments under various short-circuit resistance conditions. ISC experiments were also conducted in an echelon-utilized LIB energy storage system (ESS) under real-world operating conditions. The isolated forest algorithm was then applied to analyze the experimental data. Results indicate that, under cyclic charging and discharging conditions, the algorithm achieved an accuracy rate of over 74%, a recall rate exceeding 76%, and a precision rate above 91% for a 1000 Ω ISC fault. For dynamic driving conditions of electric vehicles, the algorithm demonstrated an accuracy rate above 86% and a recall rate over 95% for a 300 Ω ISC fault. In the ESS's actual operating conditions, the recall rate for detecting a 25 Ω ISC fault exceeded 98%. The experimental outcomes confirm that the isolated forest algorithm effectively detects micro-ISC faults in LIBs across various operational scenarios, with detected ISC resistance reaching the magnitude of thousands of ohms.

Key words: lithium-ion batteries, isolation forest algorithm, micro internal short circuit fault, energy storage system

CLC Number:

TM 912

Yu GUO, Yiwei WANG, Peng PENG, Yinfei WANG, Yishu QIU, Fangming JIANG. Fault diagnosis of micro-internal short circuits in lithium-ion battery using the isolated forest algorithm[J]. Energy Storage Science and Technology, 2024, 13(11): 4102-4112.

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实际数据情况	算法检测结果
实际数据情况	异常	正常
异常	TP	FN
正常	FP	TN

电池序号(在第2簇中)	R_isc/Ω	故障电池位置
77	25	第2簇7号模块5号电池
80	100	第2簇7号模块8号电池
89	500	第2簇8号模块5号电池

工况	短路阻值	精准率	召回率	准确率
循环充放电工况	R_isc=300 Ω	0.9583	0.9547	0.9855
	R_isc=510 Ω	0.9151	0.9151	0.9717
	R_isc=710 Ω	0.8634	0.8603	0.9540
	R_isc=1000 Ω	0.7490	0.7608	0.9176
	R_isc=2000 Ω	0.5676	0.5787	0.8563
	R_isc=3000 Ω	0.4205	0.4370	0.8058
	R_isc=4000 Ω	0.0996	0.1055	0.6921

DST工况	R_isc=100 Ω	1	1	1
DST工况	R_isc=300 Ω	0.8671	0.8671	0.9557

数据日期	召回率
数据日期	R_isc=25 Ω	R_isc=100 Ω
2021-12-23	0.9966	0.2075
2021-12-27	0.9898	0.6052

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