As the demand for lithium-ion batteries rapidly increases there is a need to design these cells in a safe manner to mitigate thermal runaway. Thermal runaway in batteries leads to an uncontrollable temperature rise and potentially fires, which is a major safety concern. Typically, when modelling the chemical kinetics of thermal runaway calorimetry data ( e.g. Accelerated Rate Calorimetry (ARC)) is needed to determine the temperature-driven decomposition kinetics. Conventional methods of fitting Arrhenius Ordinary Differential Equation (ODE) thermal runaway models to Accelerated Rate Calorimetry (ARC) data make several assumptions that reduce the fidelity and generalizability of the obtained model. In this paper, Chemical Reaction Neural Networks (CRNNs) are trained to fit the kinetic parameters of N-equation Arrhenius ODEs to ARC data obtained from a Molicel 21700 P45B. The models are found to be better approximations of the experimental data. The flexibility of the method is demonstrated by experimenting with two-equation and four-equation models. Thermal runaway simulations are conducted in 3D using the obtained kinetic parameters, showing the applicability of the obtained thermal runaway models to large-scale simulations.

中文翻译：

---

用于拟合加速量热数据的化学反应神经网络


随着对锂离子电池的需求迅速增加，需要以安全的方式设计这些电池以减轻热失控。电池的热失控会导致无法控制的温度升高并可能引发火灾，这是一个主要的安全问题。通常，在对热失控量热法数据（例如加速量热法（ARC））的化学动力学进行建模时，需要确定温度驱动的分解动力学。将阿伦尼乌斯常微分方程 (ODE) 热失控模型与加速量热法 (ARC) 数据拟合的传统方法做出了一些假设，从而降低了所获得模型的保真度和通用性。在本文中，化学反应神经网络 (CRNN) 经过训练，可将 N 方程阿伦尼乌斯 ODE 的动力学参数拟合到从 Molicel 21700 P45B 获得的 ARC 数据。发现这些模型更接近实验数据。通过二方程和四方程模型的实验证明了该方法的灵活性。使用获得的动力学参数进行3D热失控模拟，显示了获得的热失控模型对大规模模拟的适用性。