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Cogging torque reduction of permanent magnet synchronous motor using multi-objective optimization

Author

Listed:
  • Ilka, Reza
  • Alinejad-Beromi, Yousef
  • Yaghobi, Hamid

Abstract

Cogging torque is an important issue in design of permanent magnet motors, especially in certain high accuracy applications. Most of the methods utilized for cogging torque reduction lead to motor structure complexity, increasing manufacturing cost and also influencing the output torque. This research tries to find an optimal solution set of the PMSM with the aim of reducing the cogging torque while the output torque is not affected. For this purpose, multi-objective optimization is a proper and reliable approach which can provide the solution set involving conflicting functions simultaneously. Multi-objective optimization determines the logical range of cogging torque reduction with respect to the output torque. In this paper, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), which is a powerful and well-known multi-objective optimization method, is applied to find the optimal design of a surface-mounted Permanent Magnet Synchronous Motor (PMSM). Simulation results show efficacy of the NSGA-II. In the suggested design solutions that are selected from the Pareto-optimal set, cogging torque is reduced considerably while the output torque has experienced a slight decrease with respect to the nominal value. At last, time-stepping Finite-element Analysis (FEA) is used to validate the multi-objective optimization.

Suggested Citation

  • Ilka, Reza & Alinejad-Beromi, Yousef & Yaghobi, Hamid, 2018. "Cogging torque reduction of permanent magnet synchronous motor using multi-objective optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 153(C), pages 83-95.
    • Handle: RePEc:eee:matcom:v:153:y:2018:i:c:p:83-95
    DOI: 10.1016/j.matcom.2018.05.018
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    Cited by:

    1. Chiweta Emmanuel Abunike & Ogbonnaya Inya Okoro & Sumeet S. Aphale, 2022. "Intelligent Optimization of Switched Reluctance Motor Using Genetic Aggregation Response Surface and Multi-Objective Genetic Algorithm for Improved Performance," Energies, MDPI, vol. 15(16), pages 1-23, August.
    2. Jean-Michel Grenier & Ramón Pérez & Mathieu Picard & Jérôme Cros, 2021. "Magnetic FEA Direct Optimization of High-Power Density, Halbach Array Permanent Magnet Electric Motors," Energies, MDPI, vol. 14(18), pages 1-19, September.

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