Failure Early Warning and Temperature Prediction of Lithium-Ion Batteries under Thermal Abuse Conditions

LI Jie; ZHANG Shaokun; GUO lingyun; WEI Xiaofei

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Failure Early Warning and Temperature Prediction of Lithium-Ion Batteries under Thermal Abuse Conditions

更新时间：2026-10-13

- Failure Early Warning and Temperature Prediction of Lithium-Ion Batteries under Thermal Abuse Conditions
- JOURNAL OF XI’AN JIAOTONG UNIVERSITY (2026)
- 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：
- DOI：
  CLC： TM912
- Received：07 May 2026，
  
  Revised：2026-08-08，
  
  Accepted：13 October 2026，
- 稿件说明：
移动端阅览
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/en/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/en/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.

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Keywords

references

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