volume | PIER Journals

Abstract

This paper proposes a surrogate model-based multi-objective optimization framework to improve the comprehensive electromagnetic performance of outer rotor permanent magnet brushless DC motors. Targeting key performance metrics including efficiency, torque ripple, cogging torque, core loss and total harmonic distortion (THD) of back-electromotive force (EMF), a parametric finite element model is established to identify key parameters and performance indicators. Dimensionality reduction is carried out through statistical correlation and cluster analysis. An Elliptical Basis Functions (EBF) neural network surrogate model is thereafter constructed to substitute the computationally expensive finite-element model. Different from conventional Response Surface Methodology (RSM) and Radial Basis Function (RBF) models, EBF adopts Mahalanobis distance to mitigate prediction deviation induced by parameter coupling and achieves superior fitting accuracy for strongly-nonlinear motor responses under limited sample size. The Pareto solution set is obtained through the Non-dominated Sorting Genetic Algorithm (NSGA-II). Then, the GRA-TOPSIS multi-criteria decision-making approach is employed to objectively select the global optimal solution from the Pareto solution set and avoid subjective solution selection. Finite element analysis results confirm that the motor performance has been significantly improved: torque ripple, peak cogging torque, core loss, and eddy-current loss are reduced by 18.8%, 18.7%, 6.97%, and 7.12%, respectively, and the effectiveness of constructing an efficient optimization framework has been verified.

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Dr. Xiaoting Ye

Dr. Xianhai Yu

Dr. Zirui Chen

Dr. Tao Zhang