Warning method for battery-overcharge thermal runaway based on a temporal pattern attention mechanism and an isolated forest algorithm

doi:10.19799/j.cnki.2095-4239.2025.0301

Abstract

Abstract:

Lithium-ion battery (LIB) thermal runaway proceeds rapidly and violently representing a significant challenging in the field of energy-storage safety, as it can initiate thermal propagation within energy storage systems and lead to losses. To meet the timeliness requirements for early warning of overcharge-induced thermal runaway in LIBs, an early warning method integrating the temporal pattern attention (TPA) mechanism, bidirectional long short-term memory (BiLSTM) network, and isolation forest algorithm is proposed. In this method, the changing patterns of battery-state characteristics across multiple time steps are first captured using TPA, which applies differentiated weighting to focus on the mostvaluable information. Subsequently, the bidirectional neural network structure of BiLSTM is used to extract bidirectional information from battery-characteristic data, thus enhancing the prediction accuracy of the model. Finally, by integrating the isolation forest algorithm, the real battery dataset is used to establish an isolation forest model, which calculates the anomaly scores of battery-state characteristics. Thereafter, the battery states are classified by selecting the optimal anomaly score threshold, enabling early warning of abnormal battery conditions. The experimental results reveal that the proposed method achieves an F1 score of 0.9509 and can provide early warning of abnormal battery conditions 7 s before overcharge-induced thermal runaway; this time is 252 s earlier than that of the temperature threshold method. Overall, the method demonstrated here offers insights into enhancing the accuracy and timeliness of early warning for overcharge-induced thermal runaway in LIBs.

Key words: lithium-ion battery, thermal runaway, warning methods, mechanism, isolation forest

CLC Number:

TM 912

Changhao LI, Zhicheng CAO, Shuping WANG, Heng XIE, Weixin ZHANG, Yuancheng CAO. Warning method for battery-overcharge thermal runaway based on a temporal pattern attention mechanism and an isolated forest algorithm[J]. Energy Storage Science and Technology, 2025, 14(10): 3742-3754.

Figures/Tables 20

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Table 1

Determination of abnormal score results"

$g (x)$	$f (x, n)$	结论
→ $C (n)$	→ $T$	无明显异常
→ $0$	→1	异常
→ $n - 1$	→0	正常

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名称	配置信息
操作系统	Ubuntu 22.04
开发语言	Python 3.8.0
框架	Pytorch 1.10.+cuda 11.1
CPU	I9-11900K 11^th
GPU	RTX 3090ti 12GB
内存	200GB

窗口大小	压力预测RMSE	温度预测RMSE
16	2.3358	0.0535
32	3.0370	0.0475
48	2.2097	0.0432
64	2.8472	0.0478

网络结构	MAPE/%	RMSE	MAE
LSTM^[21]	0.1873	0.0594	0.0435
CNN-LSTM^[25]	0.1530	0.0503	0.0415
TPA-LSTM	0.1237	0.0496	0.0367
Attention-BiLSTM	0.1385	0.0503	0.0398
TPA-BiLSTM	0.1144	0.0432	0.0341

网络结构	MAPE/%	RMSE	MAE
LSTM^[21]	0.1472	3.2776	14.227
CNN-LSTM^[25]	0.1582	3.4049	14.475
TPA-LSTM	0.1406	2.7023	13.609
Attention-BiLSTM	0.1234	2.6178	13.526
TPA-BiLSTM	0.1108	2.2097	10.343

预警方法	F1-score	Precision	提前报警时间/s
方法1	0.7443	1	214
方法2	0.8742	0.9276	306
方法3	0.9161	0.9816	306
本工作方法1	0.9688	1	300
本工作方法2	0.9509	1	319