Chemical Reaction Neural Networks for Fitting Accelerating Rate Calorimetry Data

TL;DR

This paper introduces Chemical Reaction Neural Networks (CRNNs) to more accurately fit thermal runaway kinetic models to calorimetry data, improving safety analysis for lithium-ion batteries.

Contribution

The paper presents a novel CRNN approach for fitting Arrhenius ODE models to ARC data, surpassing traditional methods in accuracy and flexibility.

Findings

CRNNs outperform conventional fitting methods.

Models successfully simulate large-scale thermal runaway scenarios.

Flexible multi-equation models demonstrate robustness.

Abstract

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. Accelerating 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.