Real-time abnormal-battery detection based on the local outlier factor algorithm and the spatiotemporal data features of battery cells

doi:10.19799/j.cnki.2095-4239.2025.0318

Abstract

Abstract:

Battery modules exhibit a complex status in energy storage systems, and the accurate identification of abnormal lithium-ion batteries is crucial for system safety and stability. To resolve the shortcomings of traditional anomaly detection methods, such as their insufficient real-time performance and strong dependence on abnormal samples, an unsupervised anomaly detection method that integrates the local outlier factor (LOF) algorithm with spatiotemporal features of battery-operation data is proposed. This method comprehensively explores inter-cell consistency and data variation to achieve efficient, real-time, accurate abnormal celldetection without the need for pre-training. Specifically, the method involves: designing a Cornish-Fisher expansion-based distribution correction approach to calculate thresholds; utilizing a sliding-window mechanism to segment the continuous data stream from energy storage stations into dynamic data slices, thereby improving responsiveness to sudden anomalies; and applying the LOF algorithm for the local-density analysis of time series data within each window to detect low-density outliers and enable unsupervised anomaly detection. Experimental results on the dataset from 3920 to 3960 reveal that the proposed method accurately detects abnormal cells No. 155 and No. 364. These results are fully consistent with manual labeling, with zero false positives or negatives. Furthermore, the proposed method achieves the shortest average detection time of 0.0106 seconds, outperforming traditional methods, such as K-means clustering, isolation forest, Shannon entropy, and autoencoders, thus underscoring its excellent generalizability and engineering adaptability.

Key words: lithium-ion batteries, anomaly detection, local outlier factor, sliding-window, real-time monitoring

CLC Number:

TM 911

Yiqing LIU, Hao WANG, Lingxia LU, Haozhan LI, Minrui YAN, Miao YU. Real-time abnormal-battery detection based on the local outlier factor algorithm and the spatiotemporal data features of battery cells[J]. Energy Storage Science and Technology, 2025, 14(10): 3968-3981.

Figures/Tables 13

Fig. 1

Fig. 2

Fig. 3

Table 1

Fig. 4

Fig. 5

Fig. 6

Table 2

Battery cluster running data"

时间	簇总电压(HV1-Vbatt)	簇电流	V1	V2	$⋯$	V364
2024/3/3 23:59	786.4	0	3.212	3.212	$⋯$	3.219
2024/3/3 23:59	786.4	0	3.212	3.212	$⋯$	3.219
$⋮$	$⋮$	$⋮$	$⋮$	$⋮$	$⋮$	$⋮$
2024/3/4 18:36	797.8	131.2	3.258	3.258	$⋯$	3.267
2024/3/4 18:36	787.6	131.3	3.227	3.225	$⋯$	3.23
$⋮$	$⋮$	$⋮$	$⋮$	$⋮$	$⋮$	$⋮$

Table 2

Fig. 7

Fig. 8

Table 3

Fig. 9

Fig. 10

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项目	KS统计值	p值
正态分布	0.034	0.189
拉普拉斯分布	0.058	0.002

方法		是否预训练	检测异常点	异常检出率	检测准确率	检测时间/s
本工作所提出的检测框架	K-means聚类	N	[364]	0.5	99.76%	0.0317
	隔离森林	N	[130][155][182][246] [386][387][388][389]	0.5	98.08%	0.1099
	香农熵	N	[155]	0.5	99.76%	0.0418
	余弦相似度	N	[155][364]	1.0	100%	0.5971
	自编码器	Y	[246][386][388][389]	0	98.56%	2.9411
	LOF	N	[155][364]	1.0	100%	0.0106

PCA+CUSUM		Y	[155][35][241]	0.5	99.28%	2.8286
TSF+DQN		Y	共检测到16个：异常分数前3[155][286] [338](无[364])	0.5	96.15%	5.0936
			共检测到16个：异常分数前3[155][286] [338](无[364])