Incorporating nickel foam with nano-encapsulated phase change material and water emulsion for battery thermal management: Coupling CFD and machine learning

Abstract

In recent years, the rise of machine learning (ML) has prompted researchers to expand the datasets required for optimizing and designing thermal systems. Also, the development and widespread use of electric vehicles (EVs) have surged significantly. However, one of the major challenges associated with EVs is the efficient cooling of Lithium-ion batteries (LIBs). Therefore, the exploration of innovative cooling methods can contribute greatly to the rapidly growing electric vehicle industry. This study focused on investigating the impact of embedding a nickel porous medium around a single 38,120 LiFeO4 cell. To conduct the study, the LIB, along with the nickel porous medium, was placed inside a duct that received a flow of water and Nano-encapsulated phase change materials (NEPCMs). The results obtained from the study indicate that embedding nickel porous media around the LIB led to a significant decrease in the maximum temperature of LIB, more than 40 C, and a remarkable increase in pressure drop more than 100 times. Additionally, it was observed that the decrease in porosity from 1 to 0.97 had a more pronounced effect on pressure drop and the maximum temperature of the LIB's surface, compared to the decrease from 0.97 to 0.95.

Keywords

• Battery thermal management system
• Nickel foam
• NEPCM
• Lithium-ion batteries
• CFD simulation
• Machine learning