An Application of Latin Hypercube Sampling Strategy for Cogging Torque Reduction of Large-Scale Permanent Magnet Motor

An adaptive response surface method with Latin hypercube sampling strategy is employed to optimize a magnet pole shape of large-scale brushless direct current (BLDC) motor to minimize the cogging torque. The proposed algorithm consists of the multi-objective Pareto optimization and (1 + lambda) evolution strategy to find the global optimal points with relatively fewer sampling data. In the adaptive response surface method (RSM), an adaptive sampling point insertion method is developed utilizing the design sensitivities computed by using finite-element method to get a reasonable response surface with a relatively small number of sampling points. The developed algorithm is applied to the shape optimization of PM poles for 6 MW BLDC motor, and the cogging torque is reduced to 19% of the initial one.