Optimization of bionic liquid cooling plate of SiC MOSFETs based on Taguchi method and grey relational analysis

Abstract SiC MOSFETs of electric vehicles under high-load operating conditions suffer from severe thermal management challenges. In this study, different structural parameters were combined by the Taguchi method, selecting the average junction temperature of the SiC MOSFETs (Tavg), the coefficient of temperature variation (CVt) and pressure drop (ΔP) as optimization objectives. The influence degree of different structural parameters on optimization objectives was analyzed by signal-to-noise ratio (SNR). Owing to its foundation in a single-objective framework, the Taguchi method is constrained in addressing multi-objective optimization. This study transformed three optimization objectives into grey relational grade (GRG) for thermal management performance optimization through grey relational analysis. The first optimization revealed the number of channels contributed most significantly to Tavg, the channel angle most strongly influenced CVt and the channel height was the primary determinant of pressure drop. Furthermore, through the second optimization, the width ratio was determined to be the greatest effect on Tavg and CVt, while the contraction ratio with the greatest effect on pressure drop. The optimized bionic liquid cooling plate reduced Tavg by up to 15%, CVt by up to 10% and pressure drop by up to 37.6%. This research provides a prospective insight for the thermal management of SiC MOSFETs under high-load operating conditions.