Multi-objective optimization of nanofluid thermal performance in battery cold plates using computational fluid dynamics

Abstract

Inspired by the tree root structure, this paper uses commercial CFD software to simulate and analyze the cold plate of three-dimensional battery, and focuses on the heat transfer problem of tree root sine pipe to improve the heat dissipation performance of LiFePO₄ battery. Utilizing nanofluids as working fluids, the research explores the impact of channel geometry, discharge power, nanofluid type, volume fraction, initial temperature, and velocity on heat transfer characteristics. The results show that the heat transfer effect of sinusoidal pipeline is obviously improved compared with traditional pipeline. Among the tested nanofluids, a 5 % Cu-water mixture exhibited the highest heat transfer efficiency. Furthermore, higher initial nanofluid velocity and lower initial temperature led to reduced average cold plate temperatures. Response surface methodology and genetic algorithm were utilized to optimize the thermophysical properties and initial operating conditions of the nanofluid. The influencing factors, including initial temperature (288 K–298 K), initial velocity (0.05 m/s–0.25 m/s), and volume fraction (1 %–5 %), were analyzed with the objective of establishing a relationship between the average battery temperature and pressure drop. And the optimized solution identified an ideal combination of inlet velocity, temperature, and nanofluid volume fraction of 0.14 m/s, 288.00 K, and 4.41 %, respectively, maximizing heat dissipation while minimizing pressure loss.