Application of molecular dynamics in the optimization of thermal management systems for air-cooled Li-ion batteries using nanofluids and phase change materials

WJ He and ZC Yan and M Tong, THERMAL SCIENCE AND ENGINEERING PROGRESS, 65, 103913 (2025).

DOI: 10.1016/j.tsep.2025.103913

This study investigates the optimization of a cooling system (CLSM) for Li-ion battery packs using artificial intelligence. The system consists of cylindrical batteries surrounded by phase change nanomaterials (PCMs) within a closure. A nanofluid (NFs), composed of water and single-walled carbon nanotubes, is used as a coolant, with its thermal properties calculated through molecular dynamics (MD) simulations. The cooling system design incorporates airflow in a channel and nanofluid flow through a pipe. Key variables analyzed include air and nanofluid velocities, the volume fraction of nanoparticles (VOP) in the PCM, and the pipe diameter. The transient temperature distribution of the batteries is examined, and AI is employed to predict the temperature of the nanofluid, output air temperature, and pressure drop (PDP) in the cooling system. Simulations are conducted using both MD and finite element method (FEM) approaches. The results show that the lowest output air temperature occurs at the highest air and nanofluid velocities and the largest pipe diameter, while the highest temperatures are observed at the lowest velocities and smallest pipe diameters. Additionally, increases in air and nanofluid velocities reduce the outflow temperature of the nanofluid, while optimizing the pipe diameter and nanoparticle volume fraction can significantly decrease the pressure drop by 85%.

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