Design of Gravity Energy Storage Switched Reluctance Machine Based on Artificial Intelligence Optimization Algorithm

Authors

  • Wenju Yan School of Electrical Engineering, China University of Mining and Technology Xuzhou 221116, China, Shenzhen Research Institute, China University of Mining and Technology Shenzhen 518057, China
  • Xinzhu Sun School of Electrical Engineering, China University of Mining and Technology Xuzhou 221116, China
  • Jun Xin School of Electrical Engineering, China University of Mining and Technology Xuzhou 221116, China
  • Hao Chen School of Electrical Engineering, China University of Mining and Technology Xuzhou 221116, China
  • Yang Wang School of Electrical Engineering, China University of Mining and Technology Xuzhou 221116, China
  • Vuong Dang Quoc School of Electrical Engineering, Hanoi University of Science and Technology Hanoi, Vietnam
  • Thanh Nguyen Vu School of Electrical Engineering, Hanoi University of Science and Technology Hanoi, Vietnam

DOI:

https://doi.org/10.13052/2024.ACES.J.400904

Keywords:

Gravity energy storage, motor and generator, multi-objective optimization, switched reluctance machine

Abstract

To address the operational characteristics of gravity energy storage systems, this paper proposes an optimized design method for a switched reluctance machine (SRM) suitable for a gravity energy storage system. A novel 12/8 salient stator tooth structure is introduced to enhance performance in both energy storage and power generation modes. Four key optimization objectives are defined: average torque and torque ripple in the energy storage state, as well as generation power and efficiency in the power generation state. The influence of structural parameters on these optimization objectives is systematically analyzed and a multi-objective optimization of the structural parameters is conducted by the Multi-Objective Grey Wolf Optimizer (MOGWO) algorithm. Finite element analysis (FEA) is performed to evaluate the electromagnetic characteristics of the optimized design. The results demonstrate that the proposed SRM achieves superior performance compared to the traditional 12/8 SRM, making it well-suited for gravity energy storage applications.

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Author Biographies

Wenju Yan, School of Electrical Engineering, China University of Mining and Technology Xuzhou 221116, China, Shenzhen Research Institute, China University of Mining and Technology Shenzhen 518057, China

Wenju Yan (M’19) received the B.S. degree in Electrical Engineering and Automation from the China University of Mining and Technology, Xuzhou, China, in 2013. He received the Ph.D. degree in electrical engineering from the China University of Mining and Technology, Xuzhou, China, in 2018. Since 2018, he has been with China University of Mining and Technology, where he is currently an associate professor in the School of Electrical Engineering. His current research interests include electric vehicles, electric traction, iron loss analysis, and motor design.

Xinzhu Sun, School of Electrical Engineering, China University of Mining and Technology Xuzhou 221116, China

Xinzhu Sun received the B.S degree from of Electrical Engineering, Sichuan University, Chengdu, China, in 2023, She is currently working towards the M.S. degree in electrical engineering from the China University of Mining and Technology, Xuzhou, China. Her research interests include integrated drive systems for electric vehicles and energy storage.

Jun Xin, School of Electrical Engineering, China University of Mining and Technology Xuzhou 221116, China

Jun Xin received the B.S. degree in Electrical Engineering and Automation from the China University of Mining and Technology, Xuzhou, China, in 2023. He is currently working toward the M.S. degree in electrical engineering from the China University of Mining and Technology, Xuzhou, China. His research interests include integrated drive systems for electric vehicles and double-stator switched reluctance motors.

Hao Chen, School of Electrical Engineering, China University of Mining and Technology Xuzhou 221116, China

Hao Chen received the B.S. and Ph.D. degrees in Electrical Engineering from the Department of Automatic Control, Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 1991 and 1996, respectively. In 1998, he became an Associate Professor with the School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou, China, where he has been a professor since 2001. From 2002 to 2003, he was a Visiting Professor at Kyungsung University, Busan, Korea. Since 2008, he is Adjunct Professor at the University of Western Australia, Perth, Australia. He is the author of one book and has authored more than 200 papers. His current research interests include motor control, linear launcher, electric vehicles, electric traction, servo drives, and wind power generator control. Chen was the recipient of both the Prize of Science and Technology of Chinese Youth and the Prize of the Fok Ying Tong Education Foundation for Youth Teachers in both 2004. He became the Chinese New Century National Hundred-Thousand Ten-Thousand Talents Engineering National Talent in 2007 and won the Government Especial Allowance of People’s Republic of China State Department in 2006.

Yang Wang, School of Electrical Engineering, China University of Mining and Technology Xuzhou 221116, China

Yang Wang received the B.S. degree in Electrical Engineering and Automation from the Hefei University of Technology, Hefei, China, in 2023. He is currently working toward the M.S. degree in electrical engineering from the China University of Mining and Technology, Xuzhou, China. His research interests include electric machine control.

Vuong Dang Quoc, School of Electrical Engineering, Hanoi University of Science and Technology Hanoi, Vietnam

Vuong Dang Quoc received the Ph.D. degree in electrical engineering in July 2013 at the Faculty of Applied Sciences at the University of Liège in Belgium. He moved to Hanoi University of Science and Technology in September 2013, where he is currently working as a deputy director of Training Center of Electrical and Electrical Engineering, School of Electrical Engineering (SEEE), Hanoi University of Science and Technology (HUST). He became an associate professor in 2020. Vuong’s research domain encompasses modeling of electromagnetic systems, electrical machines, optimization method, numerical methods and subproblem methods.

Thanh Nguyen Vu, School of Electrical Engineering, Hanoi University of Science and Technology Hanoi, Vietnam

Thanh Nguyen Vu received the Ph.D. degree in 2015 from the Department of Electrical Engineering, School of Electrical Engineering, Hanoi University of Science and Technology (HUST). He is currently working as a Head of Department of Electrical Engineering, School of Electrical and Electronic Engineering, HUST. His research domain encompasses modeling of electrical machines, optimization and numerical methods.

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Published

2025-09-30

How to Cite

[1]
W. . Yan, “Design of Gravity Energy Storage Switched Reluctance Machine Based on Artificial Intelligence Optimization Algorithm”, ACES Journal, vol. 40, no. 09, pp. 831–839, Sep. 2025.

Issue

2025: Vol 40 Iss 9

Section

Advances in Analysis, Design and Control of Switched Reluctance Machines