An Optimization Method of Motor Topology Based on Analysis of Coefficient of Prognosis

This paper presents a method for optimizing motor topology based on the analysis of the Coefficient of Prognosis (CoP). The CoP is evaluated by scanning the space of input variables and corresponding responses, i.e., the relevant dimensions of the motor topology, based on Latin Hypercube Sampling (LHS). The metamodel of the motor can be derived by establishing corresponding variable space for each approximate response model by the Moving Least Squares (MLS) approximation. Therefore the multi-objective optimization problem with constrains is conducted by using the approximate metamodel. By means of the analysis of the CoP, one can improve the sampling efficiency and minimize the the sampling point correlation, which makes process in optimization more reasonable.