Design Optimization of ALA Rotor SynRM Drives Using T-AI-EM Environment

Abstract

An integrated team artificial-intelligence electromagnetic (T-AI-EM) environment is developed to accurately determine the performance of synchronous reluctance motors (SynRM) with axially laminated anisotropic (ALA) rotor configurations. This identifier coupled to a Fuzzy Logic optimization model is used to predict an optimal design of the machine for any given input torque. The main objective of this optimization is to minimize the torque ripple, as well as Ohmic and core losses at a given torque-speed condition. This environment is applied for the characterization and design optimization of a prototype 100-kW, 6000-rev/min ALA Rotor SynRM drive system for traction applications. The T-AI-EM environment resulted in an optimized machine design. The simulation results were compared to measured performance data for verification