Early fault diagnosis of lithium-ion battery packs based on improved local outlier detection and standard deviation method

doi:10.19799/j.cnki.2095-4239.2023.0306

Abstract

Abstract:

Lithium-ion batteries are commonly used for their high energy density and long service life. This paper suggests a fault detection method for actual battery pack operation data to more accurately detect faulty batteries with safety hazards in the battery pack. First, considering the differences in battery pack consistency caused by faulty cells, the local outlier detection algorithm is used with sliding windows to diagnose faulty cells more accurately, while detecting the inconsistency of battery cells and capturing the evolutionary characteristics of cell inconsistencies. Cells only with inconsistencies and faulty cells with hidden dangers are distinguished based on evolution; meanwhile, the inconsistency degree of cells is graded. Second, the fault types of detected cells are diagnosed by an improved standard deviation algorithm. Features containing fault type information are extracted as input, and a malfunction coefficient is introduced to realize the amplification of fault features, distinguishing different fault types by combining judgment criteria and thresholds for different faults. Early internal short circuits can be diagnosed effectively. Finally, the proposed method is verified through real operating data of the battery pack. The analytical findings demonstrate the effectiveness and reliability of the algorithm proposed in this study.

Key words: lithium-ion battery, local outlier detection, standard deviation, fault detect

CLC Number:

TM 911.3

Jiwei LI, Ruihan LIU, Taolin LU, Long PAN, Changjun MA, Qingbo LI, Zhiyun ZHAO, Wen YANG, Jingying XIE. Early fault diagnosis of lithium-ion battery packs based on improved local outlier detection and standard deviation method[J]. Energy Storage Science and Technology, 2023, 12(9): 2917-2926.

Figures/Tables 12

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References 26

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月份	采样点个数	采样间隔
9月	12072	5 s
10月	12382	5 s
11月	12096	5 s
12月	11238	5 s

离群程度	电池序号	离群因子	一致性
3	195	39.54	非常差
2	9	5.14	很差
1	129	4.05	较差
1	143	3.71	较差
1	21	3.47	较差
1	101	3.11	较差
1	134	2.66	较差
1	33	2.63	较差
1	141	2.53	较差
1	185	2.34	较差

离群程度	单体序号	平均局部离群因子	一致性
3	195	47.02	非常差
2	9	5.87	很差
1	129	3.33	较差
1	143	3.28	较差
1	101	3.03	较差
1	21	2.83	较差
1	33	2.25	较差
1	185	2.21	较差
1	14、213	2.19	较差

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