Advancing thermal runaway modeling in lithium-ion batteries: A review of heat source models, thermodynamic frameworks, and microkinetic approaches

Heat sources models for modeling exothermic chemical reactions during thermal abuse in lithium-ion batteries (LIBs) have become widely used to assess the safety of LIBs by predicting rises in temperature and heat transfer, as well as pressure accumulation and ignition behavior. These Conventional models have progressed significantly in terms of forecasting the initial stages, limitations, and spread of the thermal runaway (TR). However, these models have some drawbacks. Originally, these models were material-specific, assumed breakdown products a priori and failed to forecast rapid heat release above ~450 K. Additionally, the accuracy of sub-models of thermal abuse, such as venting and combustion models, is dependent on decomposition models' ability to predict temperature and gas evolution. This review provides valuable guidance for researchers involved in the development of TR models for LIBs. By highlighting the limitations of existing models and presenting innovative approaches, it offers a roadmap for advancing the state-of-the-art in this field. This review discusses upgraded traditional models and novel approaches that refine and extend prior models for exothermic reactions during TR. In general, there are two primary approaches: legacy modeling employing empirical equations and innovative approaches including thermodynamic and microkinetic approaches, for modeling thermal abuse often known as exothermic reactions. Micro-kinetic modeling enhances modeling skills by predicting flammable gas generation while Thermodynamic models can be employed to quantitatively quantify heat production under a variety of failure conditions, including Li-ion batteries, and solid-state batteries. Emphasizing the integration of micro-kinetic and thermodynamic modeling techniques is vital to enhance predictive accuracy and achieve a more comprehensive understanding of failure mechanisms particularly at large scale applications.