Multi-objective Optimization of Air-Cooled Lithium-Ion Battery Module Shape Using Artificial Neural Networks and Genetic Algorithms

Abstract

Lithium-ion batteries are used as a power source for electric vehicles or as an energy storage source for energy storage systems. Because battery life and durability are directly related to temperature rise, a technology that can uniformly and efficiently cool the battery pack is very important. In this study, the forced convection cooling method through air was selected to uniformly cool the battery cells, and the cooling performance was compared by adjusting the shape of the channel and the spacing between the battery cells. The sensitivity analysis was performed to analyze the effect of shape change on battery cell cooling. Based on the results of the sensitivity analysis, the shape of the channel with the lowest maximum temperature and the lowest temperature deviation between battery cells was designed.