Title :
Design Optimization of ALA Rotor SynRM Drives Using T-AI-EM Environment
Author :
Arkadan, A.A. ; Hanbali, A.A. ; Al-Aawar, N.
Author_Institution :
Hariri Canadian Univ., Mechref
fDate :
4/1/2007 12:00:00 AM
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
Keywords :
artificial intelligence; design engineering; electric machine CAD; electromagnetic devices; fuzzy logic; hybrid electric vehicles; laminates; magnetic cores; reluctance motor drives; rotors; torque; traction motors; 100 kW; SynRM drives; axially laminated anisotropic rotors; core loss; design optimization; fuzzy logic optimization model; machine design optimization; machine optimal design; ohmic loss; synchronous reluctance motors; team artificial-intelligence electromagnetic environments; torque ripple; torque-speed condition; traction applications; Anisotropic magnetoresistance; Core loss; Couplings; Design optimization; Fuzzy logic; Predictive models; Prototypes; Reluctance motors; Rotors; Torque; Artificial intelligence; design optimization; hybrid electric vehicles; synchronous reluctance motors;
Journal_Title :
Magnetics, IEEE Transactions on
DOI :
10.1109/TMAG.2007.892493
