主管：中华人民共和国应急管理部
主办：应急管理部天津消防研究所
ISSN 1009-0029  CN 12-1311/TU
消防科学与技术

消防科学与技术 ›› 2026, Vol. 45 ›› Issue (6): 1-7.DOI: 10. 20168/j. 1009-0029. 2026. 06. 0001. 07

• •    下一篇

声发射技术在锂离子电池热失控监测预警中的应用研究

刘冰汐1,3,4,5, 马畅1,3,4, 张晋1,3,4, 卓萍1,3,4,5, 李伏民2, 毕晓阳2   

  1. （1.应急管理部天津消防研究所，天津 300381； 2.河北工业大学 机械工程学院，天津 300401； 3.电化学能源消防安全联合创新应急管理部重点实验室，北京 102000； 4.天津市消防安全技术重点实验室，天津 300381； 5.工业与公共建筑火灾防控技术应急管理部重点实验室，天津 300381）
  • 收稿日期:2025-04-09 修回日期:2025-07-28 出版日期:2026-06-15 发布日期:2026-06-15
  • 作者简介:刘冰汐，应急管理部天津消防研究所，助理研究员，主要从事锂离子电池火灾防控预警方面的研究，天津市南开区卫津南路110号，300381，liubingxi@tfri.com.cn。
  • 基金资助:
    国家重点研发计划项目（2022YFE0207400）；天津市自然科学基金联合基金项目（多元投入）（24JCYBJC00190，24JCQNJC00300）；工业与公共建筑火灾防控技术应急管理部重点实验室开放课题（2024KLIB03）；应急管理部天津消防研究所基科费项目（2023SJ02）

Research on the application of acoustic emission technology in monitoring and early warning of thermal runaway of lithium⁃ion batteries

Liu Bingxi1,3,4,5, Ma Chang1,3,4, Zhang Jin1,3,4, Zhuo Ping1,3,4,5, Li Fumin2, Bi Xiaoyang2   

  1. (1. Tianjin Fire Science and Technology Research Institute of MEM, Tianjin 300381, China; 2. School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, China; 3. Key Laboratory of Electrochemical Energy and Fire Safety Joint Innovation, Ministry of Emergency Management, Beijing 102000, China; 4. Tianjin Key Laboratory of Fire Safety Technology, Tianjin 300381, China; 5. Key Laboratory of Fire Protection Technology for Industry and Public Building, Ministry of Emergency Management, Tianjin 300381, China)
  • Received:2025-04-09 Revised:2025-07-28 Online:2026-06-15 Published:2026-06-15

摘要： 针对声发射检测技术在锂离子电池热失控孕育阶段超早期识别的可行性开展研究，搭建了一套包含充放电设备、声发射系统、电池电压采集系统及电池温度采集系统等多种监测设备的试验平台。通过获取锂离子电池在正常充放电及过充诱发热失控过程中内部产生的弹性波特征，构建神经网络模型进行训练与识别。结果表明，正常充放电时，声发射信号幅值分布在±0.04 V之间，存在220 kHz的主频，并在60、100、180 kHz处出现特征频率；发生过充时，声发射信号幅值存在多个超出该范围的特异点，并在20 kHz处产生新的特征频率。采用卷积神经网络对频谱信息的灰度图进行图像处理与训练，该模型能够以99.87%的准确率完成对声发射信号分类的任务，且在交叉验证中保持了较高的准确率。该声发射监测方法能够对锂离子电池热失控孕育过程的“早、中、后”各时期进行识别与划分，实现对锂离子电池热失控的监测预警。

关键词: 锂离子电池, 热失控, 声发射, 卷积神经网络

Abstract: This paper investigates the feasibility of acoustic emission detection technology for ultra-early identification of the incubation stage of thermal runaway in lithium-ion batteries. An experimental platform was established, integrating various monitoring devices including charge-discharge equipment, an acoustic emission system, a battery voltage acquisition system, and a battery temperature acquisition system. By acquiring the elastic wave characteristics generated inside lithium-ion batteries during normal charge-discharge cycles and during thermal runaway induced by overcharging, a neural network model was constructed for training and identification. The results show that， during normal charge-discharge, the acoustic emission signal amplitude is distributed within ±0.04 V, with a dominant frequency of 220 kHz and characteristic frequencies at 60， 100, 180 kHz; during overcharging, multiple outlier points exceeding this range appear in the signal amplitude, and a new characteristic frequency emerges at 20 kHz. Using a convolutional neural network to process and train the grayscale images of spectral information, the model achieves a classification accuracy of 99.87% for acoustic emission signals and maintains a high accuracy in cross-validation. This acoustic emission monitoring method can identify and differentiate the early, middle, and late stages of the thermal runaway incubation process in lithium-ion batteries, thereby enabling monitoring and early warning of thermal runaway.

Key words: lithium-ion battery, thermal runaway, acoustic emission, convolutional neural network