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M. S. Mirazimi,A. Kiyoumarsi,S. M. Madani 대한전기학회 2017 Journal of Electrical Engineering & Technology Vol.12 No.5
A study on the multi-objective optimization of Interior Permanent-Magnet Synchronous Motors (IPMSMs) with 2, 3, 4 and 5 flux barriers per magnetic pole, based on Genetic Algorithm (GA) is presented by considering the aspect of irreversible demagnetization. Applying the 2004 Toyota Prius single-layer IPMSM as the reference machine, the asymmetrical two-, three-, four- and five-layer rotor models with the same amount of Permanent-Magnets (PMs) is presented to improve the torque characteristics, i.e., reducing the torque pulsation and increasing the average torque. A reduction of the torque pulsations is achieved by adopting different and asymmetrical flux barrier geometries in each magnetic pole of the rotor topology. The demagnetization performance in the PMs is considered as well as the motor performance; and analyzed by using finite element method (FEM) for verification of the optimal solutions.
Mirazimi, M.S.,Kiyoumarsi, A.,Madani, Sayed M. The Korean Institute of Electrical Engineers 2017 Journal of Electrical Engineering & Technology Vol.12 No.5
A study on the multi-objective optimization of Interior Permanent-Magnet Synchronous Motors (IPMSMs) with 2, 3, 4 and 5 flux barriers per magnetic pole, based on Genetic Algorithm (GA) is presented by considering the aspect of irreversible demagnetization. Applying the 2004 Toyota Prius single-layer IPMSM as the reference machine, the asymmetrical two-, three-, four- and five-layer rotor models with the same amount of Permanent-Magnets (PMs) is presented to improve the torque characteristics, i.e., reducing the torque pulsation and increasing the average torque. A reduction of the torque pulsations is achieved by adopting different and asymmetrical flux barrier geometries in each magnetic pole of the rotor topology. The demagnetization performance in the PMs is considered as well as the motor performance; and analyzed by using finite element method (FEM) for verification of the optimal solutions.