A Computational Fluid Dynamics Coupled Multi-Objective Optimization Framework for Thermal System Design for Li-Ion Batteries With Metal Separators

Abstract

Abstract Battery thermal management system (BTMS) has significant impacts on the performance of electric vehicles (EVs). In this research, a computational fluid dynamics (CFD) coupled multi-objective optimization framework is proposed to improve the thermal performance of the battery pack having metal separators. CFD is utilized to study the thermal and fluid dynamics performance of the designed battery pack. Input parameters include inlet air temperature, thermal conductivity of coolant, thermal conductivity of metal separator, and diameter of heat dissipation hole. Five vital output parameters are maximum temperature, average temperature, temperature standard deviation (TSD), maximum pressure, and volume of the pack. The support vector machine (SVM) model is used to replace the real output parameters of the battery pack. Sensitivity analysis results indicate that the diameter of heat dissipation hole is the main factor affecting the volume of the structure and the pressure drop, while the inlet air temperature has significant influence on the battery pack thermal behavior. The cooling efficiency and the uniformity of temperature distribution are mainly determined by the inlet air temperature. The decrease of inlet air temperature could lead to a rise of temperature standard deviation. The nondominated sorting genetic algorithm-II (NSGA-II) is taken to acquire the optimum set of input parameters. The obtained optimal scheme of battery pack can improve the cooling efficiency as well as reducing the volume cost and the energy consumption of the cooling system while such design may result in a higher level of nonuniformity of the temperature and pressure distribution.