Multi-Objective Design Optimization of Synchronous Reluctance Machines Based on the Analytical Model and the Evolutionary Algorithms

Abstract

This paper proposes a fast and generalized multiobjective design optimization method for the synchronous reluctance machines (SynRMs). The novel analytical model, based on the Maxwell's equations and assisted by a magnetic equivalent circuit (MEC), is adopted in order to calculate the essential performance indices (PIs) including the average torque, torque density and efficiency. The proposed model prevails over the prevalent finite element analysis (FEA) in terms of calculation speed, while maintains the accuracy of the calculation results. The multi-objective particle swarm optimization (PSO) and the differential evolution (DE) algorithms are both applied to find the Pareto front. Influences on the Pareto front caused by the parameters in the algorithms are also discussed. Two optimal designs are chosen from the Pareto front for further validation through FEA simulation. The proposed optimal design method is capable of finding the optimized SynRM designs subject to various design requirements and is able to accelerate the entire optimization process.