A Layered Swarm Optimization Method for Fitting Battery Thermal Runaway Models to Accelerating Rate Calorimetry Data

TL;DR

This paper presents a layered swarm optimization approach that efficiently fits complex Arrhenius-based models to calorimetry data, improving accuracy and reducing computational costs for thermal runaway modeling in batteries.

Contribution

It introduces a divide-and-conquer PSO method for fitting multi-equation Arrhenius models, enhancing accuracy and efficiency over brute-force approaches.

Findings

More accurate parameter fitting than brute-force methods

Maintains low computational costs

Models validated with experimental data and simulations

Abstract

Thermal runaway in lithium-ion batteries is a critical safety concern for the battery industry due to its potential to cause uncontrolled temperature rises and subsequent fires that can engulf the battery pack and its surroundings. Modeling and simulation offer cost-effective tools for designing strategies to mitigate thermal runaway. Accurately simulating the chemical kinetics of thermal runaway, commonly represented by systems of Arrhenius-based Ordinary Differential Equations (ODEs), requires fitting kinetic parameters to experimental calorimetry data, such as Accelerating Rate Calorimetry (ARC) measurements. However, existing fitting methods often rely on empirical assumptions and simplifications that compromise generality or require manual tuning during the fitting process. Particle Swarm Optimization (PSO) offers a promising approach for directly fitting kinetic parameters to experimental data. Yet, for systems created by multiple Arrhenius ODEs, the computational cost of fitting using a brute-force approach that searches the entire parameter space simultaneously can become prohibitive. This work introduces a divide-and-conquer approach based on PSO to fit N-equation Arrhenius ODE models to ARC data. The proposed method achieves more accurate parameter fitting compared to the brute-force method while maintaining low computational costs. The method is analyzed using two distinct ARC datasets, and the resulting models are further validated through simulations of 3D ARC and oven tests, showing excellent agreement with experimental data and alignment with expected trends.