基于预测误差的锂离子电池热失控预警方法研究

doi:10.19799/j.cnki.2095-4239.2024.0539

储能科学与技术 ›› 2024, Vol. 13 ›› Issue (11): 4187-4197.doi: 10.19799/j.cnki.2095-4239.2024.0539

基于预测误差的锂离子电池热失控预警方法研究

蒋建杰¹(), 楼平², 徐国华², 来骏², 王瑶¹, 曹志成³, 张炜鑫³(), 曹元成³

^1.湖州电力设计院有限公司，浙江湖州 313098
^2.国网浙江省湖州供电公司，浙江湖州 313099
^3.华中科技大学电气与电子工程学院，湖北武汉 430074

收稿日期:2024-06-17 修回日期:2024-08-14 出版日期:2024-11-28 发布日期:2024-11-27
通讯作者: 张炜鑫 E-mail:3455709348@qq.com;weixinzhang@hust.edu.cn
作者简介:蒋建杰（1978—），男，硕士，高级工程师，研究方向为电力系统及其自动化，E-mail：3455709348@qq.com；
基金资助:
浙江泰仑集团有限责任公司科技项目(HZJTKJ2022-04)

Research on lithium-ion battery thermal runaway early warning method based on prediction error

Jianjie JIANG¹(), Ping LOU², Guohua XU², Jun LAI², Yao WANG¹, Zhicheng CAO³, Weixin ZHANG³(), Yuancheng CAO³

^1.Huzhou Electric Power Design Institute Co. , Ltd, Huzhou 313098, Zhejiang, China
^2.State Grid Huzhou Electric Power Supply Company, Huzhou 313099, Zhejiang, China
^3.School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, Hubei, China

Received:2024-06-17 Revised:2024-08-14 Online:2024-11-28 Published:2024-11-27
Contact: Weixin ZHANG E-mail:3455709348@qq.com;weixinzhang@hust.edu.cn

摘要/Abstract

摘要：

锂离子电池对于推动可再生能源的广泛利用、实现电网的平稳运行等具有举足轻重的意义，但其热失控形成的热蔓延会造成巨大损失。锂离子电池热失控预警技术已经成为预防电池安全问题的有效手段。以280 Ah方形铝壳磷酸铁锂(LFP)电池为例，本工作搭建了锂离子电池热失控试验平台，采用正常循环与过充诱发热失控的方式，采集了锂电池表面温度、输出电压及电池膨胀压力信号等数据集，样本总量为66895个，其中过充热失控样本245个。该数据集在时间序列上描述了电池从正常循环到过充再到热失控的连续过程，通过测量电池膨胀压力信号，有效映射了电池内部体积变化。进一步在数据集的基础上，设计了一种基于注意力机制-残差结构-长短时记忆网络(SE-Res-LSTM)的电池膨胀压力回归预测算法，并利用预测误差对电池过充热失控进行实时检测。在时效性上，在电池发生过充12 s后，即能检出早期过充热失控风险，相比于以电池表面达到60 ℃的温度划分方法提早233 s，显著提高了预警时效性和准确性。

关键词: 锂离子电池, 热失控, 数据集, 预测误差, 早期预警

Abstract:

Lithium-ion batteries play a crucial role in promoting the widespread use of renewable energy and ensuring the stable operation of power grids. However, thermal runaway-induced thermal spread in these batteries can result in significant losses. Therefore, thermal runaway warning technology for lithium-ion batteries is essential for preventing safety issues. This study develops a lithium-ion battery thermal runaway test platform, collecting data on surface temperature, output voltage, and battery expansion pressure during normal cycles and overcharge-induced thermal runaway events. A total of 66895 samples were obtained, including 245 samples exhibiting overcharge thermal runaway. The dataset captures the continuous transition from normal operation to overcharge and subsequent thermal runaway in a time series. The internal volume changes of the battery are effectively represented by measuring the expansion pressure signals. A regression prediction algorithm based on SE-Res-LSTM is developed to predict battery expansion pressure, utilizing the prediction error to detect overcharge-induced thermal runaway in real-time. In terms of timeliness, early detection of overcharge thermal runaway is achieved 12 s after the onset of overcharging, which is 233 s earlier than the conventional temperature-based method that triggers a warning when the battery surface reaches 60 ℃. This significantly enhances the accuracy and responsiveness of early warning systems.

Key words: lithium-ion battery, thermal runaway, dataset, prediction error, early warning

中图分类号:

TM 912

蒋建杰, 楼平, 徐国华, 来骏, 王瑶, 曹志成, 张炜鑫, 曹元成. 基于预测误差的锂离子电池热失控预警方法研究[J]. 储能科学与技术, 2024, 13(11): 4187-4197.

Jianjie JIANG, Ping LOU, Guohua XU, Jun LAI, Yao WANG, Zhicheng CAO, Weixin ZHANG, Yuancheng CAO. Research on lithium-ion battery thermal runaway early warning method based on prediction error[J]. Energy Storage Science and Technology, 2024, 13(11): 4187-4197.

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实验设备	型号
电池测试系统	新威CT-8008
防爆箱	东莞贝尔BE-101-1200L
无纸化数据采集仪	日置LR8450
轮辐式压力传感器	Z673
热电偶	OMEGA GG-K-30

工步	工作模式	限制类型	限制条件
1	搁置	时间	1 h
2	恒流放电100 A	电压	≤2 V
3	搁置	时间	2 h
4	恒流充电100 A	电压	3.65 V
5	恒压充电3.67 V	电流	2 A
6	搁置	时间、次数	1 h，3次
7	循环	次数	4次
8	恒流充电100 A	时间	5 h
9	搁置	时间	1 h

混淆矩阵		预测结果
混淆矩阵		正类	负类
真实结果	正类	TP	FN
真实结果	负类	FP	TN

网络结构	MAPE/%	RMSE	R²
LSTM	2.23	345.32	0.9201
CNN-LSTM	2.0906	341.42	0.92892
Res-LSTM	1.8385	318.29	0.93214
SE-LSTM	1.6517	291.53	0.94299
SE-Res-LSTM	1.1887	202.32	0.97258

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基于预测误差的锂离子电池热失控预警方法研究

Research on lithium-ion battery thermal runaway early warning method based on prediction error

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网络层	卷积核数量	卷积核大小	步长	节点数量
1-D卷积层1	32	2	1	—
1-D卷积层2	64	2	1	—
1-D卷积层3	64	1	1	—
全局平均池化层	—	3	—	—
全连接层1	—	—	—	16
全连接层2	—	—	—	64
LSTM	—	—	—	6
全连接层3	—	—	—	1