Multi-objective optimization of a sandwich rectangular-channel liquid cooling plate battery thermal management system: A deep-learning approach

Abstract

Maintaining the battery's ideal temperature not only does boost its performance but also lengthens its life span. In this regard, a sandwich cold plate design was proposed and successfully optimized for a battery pack containing 96 cylindrical 4680 battery cells. To make the multi-objective optimization of the battery thermal management system computationally viable, a reduced-cost optimization strategy was proposed. Four design variables, i.e., channel height, number of channels, the distance between adjacent walls of two channels, and inlet velocity system, were considered in this study to reduce the temperature difference in the battery pack, reduce the system's power consumption, and increase its promptitude. The finite element method was used for the three-dimensional simulation of heat transfer and fluid dynamics of the system. The proposed combined steady-transient optimization approach employs neural networks to reduce the cost of repetitive computational fluid dynamics simulations for numerous cases considered by the genetic algorithm during the optimization process. According to the findings, utilizing this approach leads to a dramatic drop of about 95% in computational cost. The optimum design having two cold plates located on the top and bottom of the battery pack was found to have sixteen 1 cm-tall and 0.945 cm-wide channels (per plate) with an inlet velocity equal to 0.45 m/s. Consequently, the objectives faced roughly 20% reduction, 30% reduction, and 10% increase, respectively, as compared to the base design.