热滥用条件下锂离子电池的失效预警与温度预测

李杰; 张少坤; 郭凌云; 魏潇飞

doi:null

您当前的位置：

首页 >

文章列表页 >

热滥用条件下锂离子电池的失效预警与温度预测

更新时间：2026-10-13

- 热滥用条件下锂离子电池的失效预警与温度预测
- Failure Early Warning and Temperature Prediction of Lithium-Ion Batteries under Thermal Abuse Conditions
- 西安交通大学学报 2026年
- 作者机构：
  
  1.长安大学能源与电气工程学院，710064，陕西西安
  2.中国航空工业集团公司西安航空计算技术研究所，710076，陕西西安
  3.嵌入式共性基础软件技术创新中心，710076，陕西西安
- 作者简介：
- 基金信息：
  
  航空科学基金(2025L0670Q6001)
- DOI：
  中图分类号： TM912
- 收稿：2026-05-07，
  
  修回：2026-08-08，
  
  录用：2026-10-13，
- 稿件说明：
移动端阅览
李杰, 张少坤, 郭凌云, 等. 热滥用条件下锂离子电池的失效预警与温度预测[J/OL]. 西安交通大学学报,2026-10-13. http://zkxb.xjtu.edu.cn/zh/article/173927200/.

LI Jie, ZHANG Shaokun, GUO lingyun, et al. Failure Early Warning and Temperature Prediction of Lithium-Ion Batteries under Thermal Abuse Conditions[J/OL]. JOURNAL OF XI’AN JIAOTONG UNIVERSITY,2026-10-13. http://zkxb.xjtu.edu.cn/zh/article/173927200/.
李杰, 张少坤, 郭凌云, 等. 热滥用条件下锂离子电池的失效预警与温度预测[J/OL]. 西安交通大学学报,2026-10-13. http://zkxb.xjtu.edu.cn/zh/article/173927200/. DOI：

LI Jie, ZHANG Shaokun, GUO lingyun, et al. Failure Early Warning and Temperature Prediction of Lithium-Ion Batteries under Thermal Abuse Conditions[J/OL]. JOURNAL OF XI’AN JIAOTONG UNIVERSITY,2026-10-13. http://zkxb.xjtu.edu.cn/zh/article/173927200/. DOI：

摘要

针对热滥用条件下18650锂离子电池温升速率快、失效阶段难以提前识别以及单一温度或电压信号预测稳定性不足的问题，本文通过实际热滥用实验获得了不同SOC (State of Charge) 和加热功率作用下电池电压衰减、温度上升及失效时间的演化规律，揭示了SOC升高和外部热激励增强会导致失效提前、温升速率加快及热安全风险增加的特征。在此基础上，进一步考虑电压-温度耦合响应与时间序列变化特征，构建SVM-Res2Net-Transformer联合模型，实现热滥用过程中的失效预警与温度多步预测。首先，搭建单体电池热滥用实验平台，获取不同加热功率和荷电状态下的电压-温度数据；其次，基于COMSOL Multiphysics建立电-热耦合集总参数模型，并采用未参与参数标定的SOC和加热功率工况验证模型精度。在此基础上，构建SVM-Res2Net-Transformer联合模型，实现基于电压窗口特征的失效风险识别和温度多步预测。结果表明，SVM能够有效区分正常状态与失效风险状态；Res2Net-Transformer在多数组合工况下预测R

高于0.98，100% SOC工况下R

为0.965。与五种对比模型相比，所提模型综合性能最优，R

、RMSE和MAPE分别为0.998、4.401和0.016。研究结果可为锂离子电池热安全风险识别与预警提供参考。

Abstract

To address the rapid temperature rise of 18650 lithium-ion batteries under thermal abuse

the difficulty of early identification of failure stages

and the insufficient prediction stability when relying on a single temperature or voltage signal

this study conducts thermal abuse experiments to obtain the evolution patterns of battery voltage decay

temperature rise

and failure time under different SOC (State of Charge) and heating powers. The results reveal that increased SOC and enhanced external thermal excitation lead to earlier failure

faster temperature rise

and higher thermal safety risks. On this basis

considering the coupled voltage-temperature response and time-series variation characteristics

an SVM-Res2Net-Transformer hybrid model is developed to realize failure warning and multi-step temperature prediction during thermal abuse. First

a single-cell th

ermal abuse experimental platform is built to collect voltage-temperature data under different heating powers and states of charge.Secondly

an electro-thermal coupled lumped-parameter model was developed in COMSOL Multiphysics

and its accuracy was validated using uncalibrated SOC and heating power operating conditions.Based on this

the SVM-Res2Net-Transformer hybrid model is constructed to identify failure risks using voltage-window features and to perform multi-step temperature prediction. The results show that SVM can effectively distinguish between normal states and failure-risk states. The Res2Net-Transformer achieves prediction R

values above 0.98 under most combined operating conditions

with an R

of 0.975 under 100% SOC. Compared with five benchmark models

the proposed model demonstrates the best overall performance

with R

RMSE

and MAPE values of 0.998

4.401

and 0.016

respectively. The findings provide a reference for thermal safety risk identification and early warning of lithium-ion batteries.

关键词

Keywords

references

HUANG Y , WANG T , XU W , et al . Study on thermal runaway characteristics of lithium batteries under high-rate charge/discharge and development of a deep learning-based early warning model [J ] . Energy , 2025 , 334 : 137676 .

PENG R , KONG D , PING P , et al . Thermal runaway modeling of lithium-ion batteries at different scales: Recent advances and perspectives [J ] . Energy Storage Materials , 2024 , 69 : 103417 .

WANG Z , SADEGHI H , HUANG X , et al . Thermal runaway and flame propagation in battery packs: numerical simulation and deep learning prediction [J ] . Engineering Applications of Computational Fluid Mechanics , 2025 , 19 ( 1 ): 2445160 .

吴忠强 , 吴江浩 . 基于PyConv-Transformer的锂离子电池剩余寿命预测 [J ] . 计量学报 , 2026 , 47 ( 1 ): 102 - 110 .

WU Z Q , WU J H . Remaining useful life prediction of lithium-ion batteries based on PyConv-Transformer [J ] . Acta Metrologica Sinica , 2026 , 47 ( 1 ): 102 - 110 .

山彤欣 , 王震坡 , 洪吉超 , 等 . 新能源汽车动力电池“机械滥用-热失控”及其安全防控技术综述 [J ] . 机械工程学报 , 2022 , 58 ( 14 ): 252 - 275 .

SHAN T , WANG Z , HONG J , et al . Review of mechanical abuse-thermal runaway and safety prevention and control technologies for power batteries of new energy vehicles [J ] . Journal of Mechanical Engineering , 2022 , 58 ( 14 ): 252 - 275 .

杨世春 , 王潇 , 陈飞 , 等 . 锂离子电池故障预测及健康管理技术进展 [J ] . 北京航空航天大学学报 , 2025 , 51 ( 5 ): 1490 - 1502 .

YANG S , WANG X , CHEN F , et al . Progress in fault prediction and health management technology of lithium-ion batteries [J ] . Journal of Beijing University of Aeronautics and Astronautics , 2025 , 51 ( 5 ): 1490 - 1502 .

KONG D , LV H , PING P , et al . A review of early warning methods of thermal runaway of lithium-ion batteries [J ] . Journal of Energy Storage , 2023 , 64 : 107073 .

YU K , LIU P , XU B , et al . Warning lithium-ion battery thermal runaway with 4-min relaxation voltage [J ] . Applied Energy , 2025 , 377 : 124466 .

SONG Y , JIANG X , LYU N , et al . Early warning of lithium-ion battery thermal runaway based on gas sensors [J ] . eTransportation , 2025 , 26 : 100502 .

张青松 , 刘添添 , 郝朝龙 , 等 . 锂离子电池热失控气体快速检测及危险性分析方法 [J ] . 北京航空航天大学学报 , 2023 , 49 ( 9 ): 2227 - 2233 .

ZHANG Qing Song S , LIU Tian Tian , HAO Chao Long , et al . Rapid detection and risk analysis method for gases generated by thermal runaway of lithium-ion batteries [J ] . Journal of Beijing University of Aeronautics and Astronautics , 2023 , 49 ( 9 ): 2227 - 2233 .

HU D , HUANG S , WEN Z , et al . A review on thermal runaway warning technology for lithium-ion batteries [J ] . Renewable and Sustainable Energy Reviews , 2024 , 206 : 114882 .

JEONG J , KWAK E , KIM J H , et al . Prediction of thermal runaway for a lithium-ion battery through multiphysics-informed DeepONet with virtual data [J ] . eTransportation , 2024 , 21 : 100337 .

LI J , ZHANG Y , YUAN B , et al . A data-driven thermal runaway warning method for lithium-ion batteries under mechanical abuse [J ] . IEEE Transactions on Transportation Electrification , 2025 , 11 ( 2 ): 5169 - 5179 .

KIM S W , KWAK E , KIM J H , et al . Modeling and prediction of lithium-ion battery thermal runaway via multiphysics-informed neural network [J ] . Journal of Energy Storage , 2023 , 60 : 106654 .

MASALKOVAITĖ K , GASPER P , FINEGAN D P , et al . Predicting the heat release variability of li-ion cells under thermal runaway with few or no calorimetry data [J ] . Nature Communications , 2024 , 15 ( 1 ): 8399 .

QIAN W , FANG W , TIAN Y , et al . Data-Driven Prediction of Li-Ion Battery Thermal Behavior: Advances and Applications in Thermal Management [J ] . Processes , 2025 , 13 ( 9 ): 2769 .

LANUBILE A , BOSONI P , POZZATO G , et al . Domain knowledge-guided machine learning framework for state of health estimation in lithium-ion batteries [J ] . Communications Engineering , 2024 , 3 ( 1 ): 168 .

SHE C , LIU C , LI Y , et al . A novel early warning method for thermal runaway of lithium-ion batteries based on mechanical stress signal [J ] . Process Safety and Environmental Protection , 2025 , 201 : 107626 .

LI Y , JIANG L , ZHANG N , et al . Early warning method for thermal runaway of lithium-ion batteries under thermal abuse condition based on online electrochemical impedance monitoring [J ] . Journal of Energy Chemistry , 2024 , 92 : 74 - 86 .

JIA T , ZHANG Y , MA C , et al . The early warning for overcharge thermal runaway of lithium-ion batteries based on a composite parameter [J ] . Journal of Power Sources , 2023 , 555 : 232393 .

何俊贤 , 谢松 , 陈现涛 . 气压及加热功率对锂离子电池热安全的影响机制 [J ] . 北京航空航天大学学报 , 2023 , 49 ( 11 ): 3197 - 3206 .

HE J , XIE S , CHEN X . Influence mechanism of air pressure and heating power on thermal safety of lithium-ion battery [J ] . Journal of Beijing University of Aeronautics and Astronautics , 2023 , 49 ( 11 ): 3197 - 3206 .

李杰 , 程乐 , 赵鸿祥 , 等 . 电气连接方式和排列形状对圆柱锂电池模组热失控传播影响 [J/OL ] . 电源学报 , 2024-11-27 .

LI J , CHENG L , ZHAO H , et al . Influence of electrical connection mode and arrangement shape on thermal runaway propagation of cylindrical lithium-ion battery modules [J/OL ] . Journal of Power Supply , 2024-11-27 .

EKSTRÖM H , FRIDHOLM B , LINDBERGH G . Comparison of lumped diffusion models for voltage prediction of a lithium-ion battery cell during dynamic loads [J ] . Journal of Power Sources , 2018 , 402 : 296 - 300 .

CHEN Y , EVANS J W . Three-dimensional thermal modeling of lithium-polymer batteries under galvanostatic discharge and dynamic power profile [J ] . Journal of The Electrochemical Society , 1994 , 141 ( 11 ): 2947 - 2955 .

DAS GOSWAMI B R , MASTROGIORGIO M , RAGONE M , et al . A combined multiphysics modeling and deep learning framework to predict thermal runaway in cylindrical Li-ion batteries [J ] . Journal of Power Sources , 2024 , 595 : 234065 .

LEI G , GUO Y G , ZHU J G , et al . Sequential Subspace Optimization Method for Electromagnetic Devices Design With Orthogonal Design Technique [J ] . IEEE Transactions on Magnetics , 2012 , 48 ( 2 ): 479 - 482 .

ZHANG T , LI R , ZHOU Y . Battery Fault Diagnosis Method Based on Online Least Squares Support Vector Machine [J ] . Energies , 2023 , 16 ( 21 ): 7273 .

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

多负载赝厄米无线电能传输系统稳态传输性能分析

考虑复杂控制切换特性的高压直流输电稳态建模及系统平衡点求解

小型高速氖气离心压缩机设计与性能优化研究

碳酸氢钠体系中基于pH特征的聚球藻生长模型构建与验证

超声波激励下倾斜表面液滴逆重力迁移的润湿特性研究