Design Optimization of SynRM Drives for HEV Power Train Applications

Abstract

This work investigates the feasibility of utilizing a team artificial intelligence-electromagnetic, TAI-EM, environment for the characterization and design optimization of synchronous reluctance motors, SynRM, with axially laminated anisotropic, ALA, rotor configurations. 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 TAI-EM environment resulted in an optimized machine design. The results are verified by comparing major performance indices of the predicted optimized design to those obtained from the prototype measurements.