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Optimization and Characteristics Analysis of High Torque Density 12/8 Switched Reluctance Motor Using Metaheuristic Gray Wolf Optimization Algorithm

Author

Listed:
  • Md Sydur Rahman

    (Department of Mechatronics Engineering, Kyungsung University, Busan 48434, Korea)

  • Grace Firsta Lukman

    (Department of Mechatronics Engineering, Kyungsung University, Busan 48434, Korea)

  • Pham Trung Hieu

    (Department of Mechatronics Engineering, Kyungsung University, Busan 48434, Korea)

  • Kwang-Il Jeong

    (Department of Mechatronics Engineering, Kyungsung University, Busan 48434, Korea)

  • Jin-Woo Ahn

    (Department of Mechatronics Engineering, Kyungsung University, Busan 48434, Korea)

Abstract

In this paper, the optimization and characteristics analysis of a three-phase 12/8 switched reluctance motor (SRM) based on a Grey Wolf Optimizer (GWO) for electric vehicles (EVs) application is presented. This research aims to enhance the output torque density of the proposed SRM. Finite element method (FEM) was used to analyze the characteristics and optimization process of the proposed motor. The proposed metaheuristic GWO combines numerous objective functions and design constraints with different weight factors. Maximum flux density, current density, and motor volume are selected as the optimization constraints, which play a significant role in the optimization process. GWO performs optimization for each iteration and sends it to FEM software to analyze the performance before starting another iteration until the optimized value is found. Simulations are employed to understand the characteristics of the proposed motor. Finally, the optimized prototype motor is manufactured and performance is verified by experiment. It is shown that the torque can be increased by 120% for the same outer volume, by using the proposed method.

Suggested Citation

  • Md Sydur Rahman & Grace Firsta Lukman & Pham Trung Hieu & Kwang-Il Jeong & Jin-Woo Ahn, 2021. "Optimization and Characteristics Analysis of High Torque Density 12/8 Switched Reluctance Motor Using Metaheuristic Gray Wolf Optimization Algorithm," Energies, MDPI, vol. 14(7), pages 1-17, April.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:7:p:2013-:d:530515
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    References listed on IDEAS

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    1. Gang Lei & Jianguo Zhu & Youguang Guo & Chengcheng Liu & Bo Ma, 2017. "A Review of Design Optimization Methods for Electrical Machines," Energies, MDPI, vol. 10(12), pages 1-31, November.
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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. Vijina Abhijith & M. J. Hossain & Gang Lei & Premlal Ajikumar Sreelekha & Tibinmon Pulimoottil Monichan & Sree Venkateswara Rao, 2022. "Hybrid Switched Reluctance Motors for Electric Vehicle Applications with High Torque Capability without Permanent Magnet," Energies, MDPI, vol. 15(21), pages 1-16, October.
    3. Mlungisi Ntombela & Kabeya Musasa, 2023. "Load Profile and Load Flow Analysis for a Grid System with Electric Vehicles Using a Hybrid Optimization Algorithm," Sustainability, MDPI, vol. 15(12), pages 1-23, June.
    4. Zheng Li & Xiaopeng Wei & Jinsong Wang & Libo Liu & Shenhui Du & Xiaoqiang Guo & Hexu Sun, 2022. "Design of a Deflection Switched Reluctance Motor Control System Based on a Flexible Neural Network," Energies, MDPI, vol. 15(11), pages 1-16, June.

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