Cooling-Guide Diffusion Model for Battery Cell Arrangement
Nicholas Sung Liu Zheng Pingfeng Wang Faez Ahmed
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.