An Accurate Multiobjective Optimization Strategy for Surface-Mounted Permanent-Magnet Machines Based on Nonlinear Finite-Permeability Subdomain Model

This article presents an accurate multiobjective optimization strategy for surface-mounted permanent-magnet machines (SMPMMs) by combining a nonlinear finite-permeability subdomain model (FPSM) with the nondominated sorting genetic algorithm II (NSGA-II). The nonlinear FPSM is developed by introducing a nonlinear iterative algorithm (NIA) to consider the magnetic saturation of soft magnetic materials and applied to SMPMMs. For the NIA, two iterative solving methods (ISMs), namely bisection and relaxation methods (RMs), are compared for the convergence speeds through statistical analysis of 500 designs. This analysis shows that the RM is the best in terms of computation time. When the relaxation coefficient equals to 1, the number of iterations comes down to 2 for this specific machine with the given allowable error. The electromagnetic performances of one optimal case are validated by finite-element models (FEMs) to demonstrate the effectiveness of the presented optimization strategy. The strategy proposed in this article can help designers to find the optimal designs for SMPMMs.