Research on the detection algorithm for internal short circuits in lithium-ion batteries and its application to real operating data

doi:10.19799/j.cnki.2095-4239.2022.0452

Abstract

Abstract:

Lithium-ion batteries are commonly used in electric vehicles and energy storage. Internal short circuits in a lithium-ion battery could result in thermal runaway of the battery, which could be dangerous. To identify the incidence of internal short circuits, this work suggests a lithium-ion battery internal short circuit detection technique based on long-term operation data. This method takes into account the voltage and temperature inconsistency, the self-discharge effect, and the abnormal temperature rise effect induced by internal short circuits. Features are collected, and a clustering method is used to precisely locate the battery with internal short circuit. Graded fault alarms are provided with the use of normalized indicators. The algorithm's effectiveness is evaluated using long-term operational data from a number of battery packs. The analytical findings demonstrate that the algorithm proposed in this study has a high detection rate and a low false alarm rate.

Key words: lithium-ion battery, clustering algorithm, parameter normalization, internal short circuit detection

CLC Number:

TM 911.3

Yue PAN, Xuebing HAN, Minggao OUYANG, Huahua REN, Wei LIU, Yuejun YAN. Research on the detection algorithm for internal short circuits in lithium-ion batteries and its application to real operating data[J]. Energy Storage Science and Technology, 2023, 12(1): 198-208.

Figures/Tables 13

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数据项		采样点个数		精度
时间		1		10 s
电压		90		1 mV
电流		1		100 mA
温度		18		1 ℃
充放电标志位		1		/
数据组编号	内短路故障标签	总帧数	数据组编号	内短路故障标签	总帧数
1	1	213万帧	6	0	37万帧
2	1	140万帧	7	0	98万帧
3	0	389万帧	8	0	85万帧
4	0	30万帧	9	0	18万帧
5	0	225万帧	10	0	10万帧

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