A numerical investigation of a two-phase nanofluid flow with phase change materials in the thermal management of lithium batteries and use of machine learning in the optimization of the horizontal and vertical distances between batteries

Abstract

In this article, a battery pack cooling system having multiple lithium-ion (LIB) battery cells with a laminar nanofluid (NFD) flow and phase change materials (PCMs) was simulated using the finite element method (FEM). The cooling system’s walls were curved, and the NFD flow was simulated using the two-phase method. PCMs were positioned inside the elliptical enclosure and encompassed all the battery cells. The temperature of the battery cells, heat transfer coefficient (HTC), and phase change by PCMs were transiently investigated by the alteration of the vertical distance between batteries from 0.7 to 1.1, the horizontal distance between batteries from 0.5 to 1, and the NFD input size from 0.5 to 1.5. The data were optimized using the artificial intelligence (AI) technique to achieve the best results. The results showed that the maximum pressure drop occurs at the biggest NFD input, the highest horizontal distance, and the lowest vertical distance between batteries (583% difference). Similarly, the lowest maximum temperature (MXT) of batteries (TLIB) occurred at the lowest horizontal and vertical distances between batteries and the smallest NFD input dimensions (7.15° difference). The highest HTC (794.26 W/m2K) between the NFD and batteries occurred at the highest horizontal distance and the lowest vertical distance between batteries and the highest NFD input dimensions.

Keywords

• Lithium-ion battery
• Two-phase nanofluid
• Phase change material
• Artificial intelligence
• Optimization