# Cooling-Guide Diffusion Model for Battery Cell Arrangement

arXiv: https://arxiv.org/abs/2403.10566 · HTML: https://mlsanity.com/paper/2403.10566/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Diffusion foundations and sampling, one of 14 directions in Diffusion models.
- Builds on 5 of its 18 references that are in the map.
- First posted on arXiv 14 Mar 2024.

## Placed in

- Diffusion models / [Diffusion foundations and sampling](https://mlsanity.com/diffusion/diffusion-foundations-and-sampling/)

## Builds on (most cited first, 5 of 5)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [TabDDPM: Modelling Tabular Data with Diffusion Models](https://mlsanity.com/paper/2209.15421/) (2022)
- [ShipGen: A Diffusion Model for Parametric Ship Hull Generation with Multiple Objectives and Constraints](https://mlsanity.com/paper/2311.06315/) (2023)

## Built on by (most cited first, 3 of 3)

- [Physics-informed Machine Learning for Battery Pack Thermal Management](https://mlsanity.com/paper/2411.09915/) (2024)
- [BlendedNet++: A dataset and benchmark for field-resolved aerodynamics and inverse design of blended wing body aircraft](https://mlsanity.com/paper/2512.03280/) (2025)
- [Generative Design for Direct-to-Chip Liquid Cooling for Data Centers](https://mlsanity.com/paper/2604.10941/) (2026)

## Abstract (arXiv)

Our study introduces a Generative AI method that employs a cooling-guided diffusion model to optimize the layout of battery cells, a crucial step for enhancing the cooling performance and efficiency of battery thermal management systems. Traditional design processes, which rely heavily on iterative optimization and extensive guesswork, are notoriously slow and inefficient, often leading to suboptimal solutions. In contrast, our innovative method uses a parametric denoising diffusion probabilistic model (DDPM) with classifier and cooling guidance to generate optimized cell layouts with enhanced cooling paths, significantly lowering the maximum temperature of the cells. By incorporating position-based classifier guidance, we ensure the feasibility of generated layouts. Meanwhile, cooling guidance directly optimizes cooling-efficiency, making our approach uniquely effective. When compared to two advanced models, the Tabular Denoising Diffusion Probabilistic Model (TabDDPM) and the Conditional Tabular GAN (CTGAN), our cooling-guided diffusion model notably outperforms both. It is five times more effective than TabDDPM and sixty-six times better than CTGAN across key metrics such as feasibility, diversity, and cooling efficiency. This research marks a significant leap forward in the field, aiming to optimize battery cell layouts for superior cooling efficiency, thus setting the stage for the development of more effective and dependable battery thermal management systems.