## 1 Introduction  
With the extensive adoption of Li-ion batteries in both commercial aviation and Battery Electric Vehicles, the safety of them has garnered significant attention [1]. Although considerable advancements have been made in Li-ion battery technology, the design of cells and modules to enhance safety remains essential [2, 3]. Therefore, it is important to develop the model that can swiftly and accurately predict the temperature change of battery cells including thermal runaway, in order to accelerate the development cycle [1, 3]. In addition to numerous experimental approaches [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], a wide range of simulations have been conducted in order to evaluate battery cell safety under various conditions, including external heating [13, 14], overcharging [15, 16], internal short-circuiting [17, 18], and mechanical deformation [19, 20]. Typically, the modeling process begins with obtaining the thermal decomposition reaction profile of the electrode via differential scanning calorimetry (DSC) or similar methods, which is then used to develop a heat generation reaction model based on the relationship between temperature and heat generation. The reaction rate of heat generation is often modeled using either the Arrhenius [21] or modified Arrhenius equation [22]. Subsequently, the time-dependent temperature of the battery cell is predicted based on the energy equation incorporating Joule heating from short circuits and the thermal decomposition reactions of the electrodes as source terms. So far, various models have been proposed, ranging from simplified zero-dimensional geometries [21; 22] to detailed three-dimensional representations [14; 15; 16; 17; 18; 19; 20]. Recently, there has been growing interest in modeling the coupling of gas generation and fluid dynamics resulting from electrolyte vaporization [23; 24; 25], as well as in applying them for battery modules and packs [26; 27; 28; 29]. One of the primary challenges with these simulations is reducing computational demands, with studies reporting on simplification or replacement with machine learning models [30; 31; 32; 33; 34]. Nonetheless, due to the fundamentally nonlinear nature of the differential equations over time, obtaining an analytical solution is generally difficult, necessitating numerical solutions through methods such as the finite element method (FEM) [35] or finite difference method (FDM) [36]. More recently, the quality and accuracy of commercial software, as well as the elucidation of detailed mechanisms through experimental research, have been rapidly advancing, leading to improved accuracy of simulation models. In thermal decomposition reactions, studies detailing models for both the positive and negative electrodes [37; 38], peak separation techniques combined with machine learning algorithms [39], and ARC-based thermal decomposition reaction models considering the \"crosstalk\" between electrodes [40] have been conducted. Furthermore, calculations considering the detailed three-dimensional shape of cells for gas venting [41; 42], numerical simulation models assuming the ejection of solid particles in addition to gas [43], and models including the influence of ambient pressure [44] have been proposed. Notably, the application of these models to battery modules [45] and packs [30] is also progressing. Theoretical studies on battery thermal runaway have also been reported [23; 46; 47; 48; 49; 50; 51; 52; 53; 54]. One of the simplest approaches is stability analysis focused on the balance of heat generation and dissipation, as represented by methods developed by [PERSON] [46], [PERSON] [47], and [PERSON] [48]. [PERSON] et al. [23] measured the critical temperature of 18650-type LCM/graphite systems under various conditions and analyzed their relationship with thermal conductivity. Additionally, [PERSON] et al. [49] introduced a novel temperature parameter based on [PERSON]'s theory to analyze thermal stability in high-capacity cells.