Coupled physics-informed neural networks for thermal assessment of a phase change material-based battery pack under high discharge rates
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Overview
Randomized trial evaluates thermal dynamics in phase change material batteries, suggesting effective modeling techniques.
Key Points
- This research aims to enhance thermal analysis of phase change material-based battery packs using a novel neural network approach.
- Developed a coupled PINN ensemble for analyzing battery thermal behavior.
- Embedded an enthalpy-based partial differential equation to model latent heat.
- Utilized Fourier features to address sharp thermal gradients at cell surfaces.
- Achieved less than 0.5 °C root mean square error (RMSE) using sparse experimental data.
- Generated continuous 4D/3D/2D thermal and phase change visualizations.
Cite This Study
Ganji et al. (2026) studied this question.