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