锂离子电池内部温度检测与估计研究进展

doi:10.3901/JME.260061

摘要/Abstract

摘要： 随着新能源发电规模的持续扩大，储能需求显著增长。以锂离子电池为代表的电化学储能技术，凭借其高能量密度和长循环寿命等优势，已成为电化学储能领域的主导技术。然而，近年来大规模锂离子电池储能电站的安全问题日益突出，热失控及火灾事故频发。锂离子电池内部温度是反映其工作状态的关键指标，可为早期热失控提供精准预警。目前，电池管理系统(Battery management system,BMS)主要通过布置在电池表面的温度传感器进行温度监测，但该方法存在测量误差和响应滞后等问题。首先对锂离子电池内部温度升高时的微观结构变化和热失控机制进行了介绍，然后从嵌入温度传感器内部测温、基于电化学阻抗谱(Electrochemical impedance spectroscopy,EIS)内部温度估计以及基于机器学习算法的内部温度预测三个方面展开分析与综述，通过比较三种方法的优缺点，系统梳理了其研究进展，并展望了未来发展方向。研究结果为BMS实现锂离子电池温度精准预测提供了理论参考，同时也为锂离子电池热失控预警研究提供了新的思路和基础。

关键词: 锂离子电池, 内部温度, 温度传感器, 电化学阻抗谱, 机器学习算法

Abstract: With the continuous expansion of new energy power generation, the demand for energy storage is significantly increasing. Electrochemical energy storage technology, represented by lithium-ion batteries, has become the dominant technology in this field due to its high energy density and long cycle life. However, safety issues in large-scale lithium-ion battery energy storage power stations have become increasingly prominent in recent years, with frequent thermal runaway and fire accidents. The internal temperature of a lithium-ion battery is regarded as a key indicator of its operational status, providing precise early warning for thermal runaway. Currently, the Battery Management System(BMS) primarily monitors temperature using sensors placed on the battery surface, but this method is associated with measurement errors and response delays. First, the microstructural changes and thermal runaway mechanisms during internal temperature rise in lithium-ion batteries are introduced. Then, an analysis and review are conducted from three aspects: internal temperature measurement using embedded sensors, internal temperature estimation based on Electrochemical Impedance Spectroscopy(EIS), and internal temperature prediction using machine learning algorithms. By comparing the advantages and disadvantages of these three methods, research progress is systematically summarized, and future development directions are prospected. The research results provide a theoretical reference for the accurate prediction of lithium-ion battery temperature in BMS and offers new ideas and a foundation for research on thermal runaway early warning.

Key words: lithium-ion batteries, internal temperature, temperature sensor, electrochemical impedance spectroscopy, machine learning algorithm

中图分类号:

TG156

陈瑞, 董明, 任明, 张崇兴, 王若谷. 锂离子电池内部温度检测与估计研究进展[J]. 机械工程学报, 2026, 62(2): 367-384.

CHEN Rui, DONG Ming, REN Ming, ZHANG Chongxing, WANG Ruogu. Research Progress on Internal Temperature Detection and Estimation of Lithium-ion Batteries[J]. Journal of Mechanical Engineering, 2026, 62(2): 367-384.

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