A Multi-Objective Finite-Element Method Optimization That Reduces Computation Resources Through Subdomain Model Assistance, for Surface-Mounted Permanent-Magnet Machines Used in Motion Systems

Abstract

Surface-mounted permanent-magnet (PM) motors are widely used in motion control systems because of their high peak torque-to-inertia ratio. These motors exhibit high magnetic saturation to produce high peak torque. A precise finite-element model (FEM) is needed to optimize these motors. It requires high computation power, especially for multi-objective optimizations. On the contrary, subdomain models require low computing power but are inaccurate when magnetic saturation occurs. To solve this problem, we use a subdomain-assisted FEM optimization method (SAFEMOM) that combines subdomain models for preoptimization and FEM for refined optimization. We provide the quantitative measurement methods to compare SAFEMOM with FEM-only optimization. If the constraints in the magnetic fluxes in the subdomain part of SAFEMOM are based on the actual saturation value of the materials, then the advantage of SAFEMOM is not significant. The machines in this study use non-linear material with a knee point at 1.3 T and hence show heavy magnetic saturation above 1.5 T. In that case, contrary to what could be intuitively thought, we need to increase the magnetic flux density limit to 2.6 T - 3.0 T in SAFEMOM to have a significant advantage. SAFEMOM reduces about 80% of computing time to obtain a slightly better convergence than the one using FEM only. Also, if limited computing resource is allowed, SAFEMOM gives an error reduced by a factor of nearly eight compared to the optimization error using FEM only. Those results are validated on a family of surface-mounted permanent-magnet machines with a wide range of design parameters.