Research Progress on Deep Learning-Based Thermal Runaway Early Warning Technology for Lithium-Ion Batteries

ZHONG Zhidong; NI Ming; PENG Jiyu; WANG Jiajing; ZHAO Zhangfeng; JIANG Jiandong; YING Guangyao; MA Heng; XU Jinglei

doi:10.19799/j.cnki.2095-4239.2026.0145

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Research Progress on Deep Learning-Based Thermal Runaway Early Warning Technology for Lithium-Ion Batteries

更新时间：2026-05-15

- Research Progress on Deep Learning-Based Thermal Runaway Early Warning Technology for Lithium-Ion Batteries
- Energy Storage Science and Technology Pages: 1-13(2026)
- 作者机构：
  
  1.浙江华电器材检测研究院有限公司，浙江省杭州市 310000
  2.浙江工业大学机械工程学院，浙江省杭州市 310023
- 作者简介：
- 基金信息：
- DOI：10.19799/j.cnki.2095-4239.2026.0145
  CLC： TM 912
- Received：09 February 2026，
  
  Revised：2026-05-02，
  
  Online First：15 May 2026，
- 稿件说明：
移动端阅览
钟智栋, 倪明, 彭继宇, 等. 基于深度学习的锂电池热失控预警技术研究进展[J]. 储能科学与技术, XXXX, XX(XX): 1-13.

ZHONG Zhidong, NI Ming, PENG Jiyu, et al. Research Progress on Deep Learning-Based Thermal Runaway Early Warning Technology for Lithium-Ion Batteries[J]. Energy Storage Science and Technology, XXXX, XX(XX): 1-13.
钟智栋, 倪明, 彭继宇, 等. 基于深度学习的锂电池热失控预警技术研究进展[J]. 储能科学与技术, XXXX, XX(XX): 1-13. DOI： 10.19799/j.cnki.2095-4239.2026.0145.

ZHONG Zhidong, NI Ming, PENG Jiyu, et al. Research Progress on Deep Learning-Based Thermal Runaway Early Warning Technology for Lithium-Ion Batteries[J]. Energy Storage Science and Technology, XXXX, XX(XX): 1-13. DOI： 10.19799/j.cnki.2095-4239.2026.0145.

摘要

锂电池型号多样、老化程度差异显著，且运行工况复杂，使热失控过程具有较强的不确定性，导致传统预警手段面临误报率高、时效性低等固有局限。深度学习方法具备挖掘复杂热失控机理和特征演化规律的能力，可为锂电池热失控早期预警提供有效技术手段。本文围绕“基于深度学习的锂电池热失控预警技术”展开系统性综述，首先梳理了锂电池热失控关键预警特征、常用数据预处理方法及典型深度学习方法；其次，介绍了深度学习方法在锂电池热失控关键参数预测中的应用进展，并分析了其与物理约束、多物理场耦合模型融合的研究现状；随后，总结了基于热失控参数的多级预警策略与多源特征融合技术的实践路径；最后，针对锂电池热失控预警面临的故障数据稀缺、模型可解释性不足和模型轻量化部署等挑战开展系统讨论，并对未来改进方向进行展望。研究表明，深度学习技术能够有效缓解传统预警手段误报率高、时效性低等问题，对提升锂电池热失控预警的准确性和及时性具有积极作用。本文可为理解深度学习技术在锂电池热失控预警中的作用机制提供理论参考，并为推动其在锂电池安全管理系统中的工程应用、完善锂电池安全管理体系提供支撑。

Abstract

Lithium-ion batteries feature diverse models

significant variations in aging degrees over their service life

and operation under complex

variable working conditions

including dynamic charge-discharge rates

ambient temperature fluctuations

and mechanical vibrations. These factors collectively endow the thermal runaway process with strong uncertainty. Traditional early warning methods

relying primarily on fixed threshold judgment and simple empirical analysis

struggle to adapt to such diversity and complexity. As a result

they suffer from inherent limitations: high false alarm rates

potential missed alarms

and inadequate timeliness

failing to capture the subtle early precursor signals of thermal runaway. In contrast

deep learning methods boast strong nonlinear fitting and autonomous feature mining capabilities

enabling them to deeply explore the complex internal reaction mechanisms of thermal runaway and implicitly capture the dynamic evolution of characteristic parameters throughout its initiation and propagation. This breaks through the bottlenecks of conventional monitoring approaches

offering an effective technical solution for high-precision

advance early warning of lithium-ion battery thermal runaway. This paper conducts a systematic review centered on the theme of "Research Progress on Deep Learning-Based Thermal Runaway Early Warning Technology for Lithium-Ion Batteries". Firstly

it systematically sorts out the core early warning characteristics of thermal runaway

mainstream data preprocessing methods (including cleaning

denoising

normalization

and dataset partitioning)

and the structural features and application scenarios of typical deep learning models. Secondly

it elaborates on the application progress of deep learning algorithms in predicting key thermal runaway parameters (e.g.

voltage

temperature

gas concentration

state of charge

and state of health)

and analyzes the current research status of fusion frameworks integrating deep learning with physical constraints and multi-physics field coupling models

addressing the lack of physical interpretability in pure data-driven models. Subsequently

it summarizes the practical implementation paths of multi-level early warning strategies (aligned with thermal runaway's staged evolution) and multi-source feature fusion technologies that integrate electrical

thermal

gas

and mechanical monitoring data. Finally

the paper systematically discusses core challenges hindering the technology's development and engineering application: the scarcity of real thermal runaway fault data

insufficient interpretability of black-box deep learning models

and difficulties in lightweight deployment on embedded hardware with limited computing power. Corresponding future research priorities and improvement directions are also proposed. Research findings indicate that deep learning effectively mitigates the high false alarm rates and poor timeliness of traditional methods

significantly enhancing the accuracy and timeliness of thermal runaway early warning. This paper provides theoretical references for understanding the mechanism of deep learning in this field

supports its engineering application in lithium-ion battery safety management systems

and contributes to optimizing the full-lifecycle safety assurance system for lithium-ion batteries.

关键词

Keywords

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