Chemical Reaction Neural Networks for Fitting Accelerating Rate Calorimetry Data

Thermal runaway in battery packs is a major safety concern for commercial applications such as electric vehicles, potentially leading to catastrophic outcomes like battery pack fires. This phenomenon occurs due to thermal abuse conditions that lead to exothermic degradation reactions of battery components, such as anode decomposition, cathode conversion, SEI decomposition, and electrolyte breakdown[1, 2]. Typical thermal abuse failure modes include, but are not limited to, physical damage, internal short circuits, overcharging, or overheating (e.g., extreme temperature exposure)[1]. The heat released under such conditions, when a cell or group of cells fails, can lead to a chain reaction where adjacent cells enter a self-heating state and undergo thermal runaway[3]. This propagation can consume an entire battery module or pack. These safety concerns are even more pressing in today's electrification environment, particularly as the industry moves towards higher power and energy density cells[1, 4]. To address these concerns, cell and pack manufacturers must adhere to strict safety protocols to avoid catastrophic outcomes. Simulation-driven design offers a platform to optimize designs and aid in the prevention and mitigation of thermal runaway. For example, thermal analysis of novel heat shield materials can be conducted efficiently to understand their effectiveness at mitigating propagation.