Battery Open Access Index 2026 08 04

This study explores a novel layered swarm optimization approach for enhancing the fitting process of thermal runaway models associated with batteries, specifically gaging their behavior under accelerated rate calorimetry conditions. The objective of this research is to develop a robust modeling technique that accurately predicts the thermal runaway characteristics of lithium-ion batteries, which is critical for ensuring their safe application in various energy storage systems. We employed a combination of swarm intelligence algorithms to optimize parameters influencing battery stability. Experimental data were collected through accelerated rate calorimetry tests designed to simulate thermal runaway scenarios. The results demonstrated that our proposed optimization method significantly improves the fitting accuracy of thermal runaway models when benchmarked against standard modeling techniques. Moreover, the model developed through this optimized process exhibited enhanced predictive capabilities regarding thermal events in lithium-ion batteries. These findings highlight the potential of our layered swarm optimization methodology as a valuable tool for researchers and engineers working on battery safety and thermal management solutions. This research paves the way for improved safety protocols in battery usage and storage applications.