Optimized Jet Impingement Cooling for High-Power SiC Modules in Electric Vehicles Using Genetic Algorithm

This paper presents an optimized design, developed using a genetic algorithm (GA), to enhance the performance of a jet impingement cooling system for a high-power silicon carbide (SiC) power module in an electric vehicle (EV) application. First, a baseline simulation of the cooling system is conducted, providing standard performance results prior to optimization. The GA is then applied to identify the ideal nozzle configuration, jet velocity, and nozzle arrangement for improved thermal performance. The baseline and optimized designs are then compared with a conventional cold plate cooling method. This study reduces the reliance on iterative design by using GA to find the optimal configuration. The target of the optimized design is to keep a balance between cooling performance and pressure drop. The optimized design results in an optimal junction temperature of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$121{}^{\circ} \mathrm{C}$</tex>, with a pressure drop of 6 kPa, achieving a lower pressure drop than the baseline design while maintaining the same pressure as the conventional cooling method, with improved cooling performance.