What does this research mean for the field?

A coupled physics-informed neural network (PINN) ensemble incorporating enthalpy-based PDEs and Fourier features accurately models the thermal and latent heat dynamics of phase change material-based battery packs under high discharge rates, achieving less than 0.5 °C RMSE against sparse experimental data. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This research aims to enhance thermal analysis of phase change material-based battery packs using a novel neural network approach.

May 31, 2026Applied Thermal EngineeringOpen Access

Coupled physics-informed neural networks for thermal assessment of a phase change material-based battery pack under high discharge rates

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Authors

MGMohammad J. Ganji MAMartin Agelin-Chaab MRMarc A. Rosen

Discussion

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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.

synapsesocial.com/papers/6a1bcfe15783ba022b6fbce6 https://doi.org/10.1016/j.applthermaleng.2026.131629

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May 31, 2026Applied Thermal EngineeringOpen Access

Coupled physics-informed neural networks for thermal assessment of a phase change material-based battery pack under high discharge rates

View Full Paper

Ask AI

Bookmark

Authors

MGMohammad J. Ganji MAMartin Agelin-Chaab MRMarc A. Rosen

Discussion

Member takes

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.

synapsesocial.com/papers/6a1bcfe15783ba022b6fbce6 https://doi.org/10.1016/j.applthermaleng.2026.131629

View Full Paper

Ask AI

Bookmark