Multi-Objective Optimization Design of High-Power-Density BLDC Motors Based on Surrogate Models

Abstract

Aiming at insufficient rated torque and excessive magnet thermal loss of vehicle high-power-density BLDC motors, a multi-objective optimization framework integrating a surrogate model and NSGA-II is proposed. An eight-pole 48-slot finite-element model is built to analyze initial electromagnetic defects. Taguchi experiments conduct parameter sensitivity screening to reduce simulation cost. Two surrogate models (RSM, BP neural network) are quantitatively compared via R2 and MSE; the BP network achieves R2 = 0.995 for magnet loss with overall error below 5%. Combined with NSGA-II, Pareto-optimal structural parameters are obtained. Simulation results show that optimized motor rated torque rises by 9.01% and peak magnet loss drops by 16.5%, while torque ripple and cogging torque meet engineering standards. This method features high efficiency and precision, providing references for automotive BLDC optimal design.