Surrogate-Assisted Multi-Objective Optimization of Interior Permanent Magnet Synchronous Motors with a Limited Sample Size

Interior permanent magnet synchronous motors (IPMSMs) are critical for electric vehicle traction and industrial systems, yet optimizing their performance under high-dimensional design spaces remains computationally challenging. This study proposes a surrogate-assisted multi-objective optimization framework tailored for limited sample sizes. The methodology integrates random forest (RF) and analysis of variance (ANOVA) for variable importance analysis to reduce model complexity, followed by a Generalized Regression Neural Network (GRNN) to establish an efficient surrogate model. A multi-objective particle swarm optimization (MOPSO) algorithm generates Pareto-optimal solutions, while an entropy-weighted distance metric objectively selects the final design. Experimental results demonstrate that the optimized IPMSM achieves a 4.62% increase in average output torque, a 0.15% improvement in efficiency, and a 10.48% reduction in torque ripple compared to the prototype. Finite element analysis validates the consistency between predicted and simulated outcomes, with relative errors below 2.92%. The framework effectively balances computational efficiency and accuracy, offering a data-driven approach for motor optimization under constrained experimental resources.