Optimal Design of a Spoke-type Permanent Magnet Motor with Phase-group Concentrated-coil Windings to Minimize Torque Pulsations

This paper proposes a novel multistep design and optimization strategy to minimize torque pulsations in a spoke-type permanent magnet motor with phase-group concentrated-coil windings. Different from the conventional techniques that work on the motor itself, in the first step, an auxiliary inner sta...

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Bibliographic Details
Published in:IEEE transactions on magnetics 2017-06, Vol.53 (6), p.1-4
Main Authors: Wenliang Zhao, Jung-woo Kwon, Xiuhe Wang, Lipo, Thomas A., Byung-il Kwon
Format: Article
Language:English
Subjects:
Algorithms
Auxiliary inner stator
Back electromotive force
Cogging
cogging torque
Coils (windings)
Design analysis
Design optimization
Electric potential
Finite element method
finite-element method (FEM)
Forging
Fourier analysis
genetic algorithm
Harmonic analysis
Kriging interpolation
Magnetism
Mathematical analysis
Mathematical models
Motors
Optimization
Permanent magnet motors
spoke-type permanent magnet motor
Stator windings
Torque
torque ripple
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Summary:This paper proposes a novel multistep design and optimization strategy to minimize torque pulsations in a spoke-type permanent magnet motor with phase-group concentrated-coil windings. Different from the conventional techniques that work on the motor itself, in the first step, an auxiliary inner stator is designed to suppress torque pulsations with the aid of a finite-element method (FEM). In the second step, the optimization by using the Kriging method and genetic algorithm, is then applied to the auxiliary inner stator to further reduce torque pulsations. In the third step, the harmonic injected current is utilized to suppress torque ripple by maintaining the required power, while the optimal input current waveform is optimized by the aforementioned algorithms based on the harmonic analysis of back electromotive force. Through the analysis results by the FEM, it shows that the cogging torque and torque ripple of the final model are significantly reduced by 95.7% and 70.0%, respectively, when compared with those of the initial model.
ISSN:0018-9464
1941-0069
DOI:10.1109/TMAG.2017.2664075