Lithium-ion batteries (LIBs) are critical for various applications, and effective thermal management is important for their safety, performance, and lifespan. Traditional physics-based modeling of battery thermal behavior is computationally complex and requires detailed parameters. Using data-driven modeling to predict thermal characteristics of batteries offers a promising alternative. This review comprehensively examines the utilization of data-driven methods in predicting LIB thermal behavior and designing battery thermal management systems. It explores commonly used data-driven techniques and focuses on their applications in predicting heat generation, temperature distribution, and cooling performance. Specific data-driven models for battery thermal prediction are presented, with a comparative analysis of their strengths and weaknesses. The review concludes that data-driven models can effectively predict battery thermal behavior, offering computational efficiency compared to physics-based simulations. Future research directions include hybrid data-driven/physical modeling, ensemble modeling, and incorporating explainable artificial intelligence techniques to enhance model interpretability. These advancements will lead to more accurate and interpretable models, contributing to the safe and efficient applications of LIB systems.

As a key energy storage component, the rapid detection of battery status and safety and reliability evaluation of the lithium battery have attracted more attention. Among them, the expansion behavior evaluation technology of the lithium battery, as a non-destructive rapid detection method, is an effective means to evaluate the health status of the battery and realize early safety warning. This paper reviews the causes of the current expansion phenomenon of lithium battery, the development status of expansion evaluation technology and the evaluation method of expansion test results. The existing expansion behavior evaluation technology under constant pressure and constant displacement conditions are classified and discussed. By accurately obtaining changes in the internal state of the battery, it can accurately predict and evaluate performance, life and safety. By obtaining the critical conditions of battery expansion, the design and optimization of the module structure are guided. On this basis, the limitations of lithium battery expansion test under different boundary conditions are further discussed. The future development of battery expansion test methods and battery expansion control and solutions are proposed. This paper aims to optimize the evaluation technology of lithium battery expansion behavior and improve the safety and reliability of the battery.