Optimizing brushless direct current motor design: An application of the multi-objective generalized normal distribution optimization

Sundaram B Pandya; Pradeep Jangir; Miroslav Mahdal; Kanak Kalita; Jasgurpreet Singh Chohan; Laith Abualigah

doi:10.1016/j.heliyon.2024.e26369

Optimizing brushless direct current motor design: An application of the multi-objective generalized normal distribution optimization

Heliyon. 2024 Feb 15;10(4):e26369. doi: 10.1016/j.heliyon.2024.e26369. eCollection 2024 Feb 29.

Authors

Sundaram B Pandya¹, Pradeep Jangir², Miroslav Mahdal³, Kanak Kalita⁴, Jasgurpreet Singh Chohan⁵, Laith Abualigah^{6

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Affiliations

¹ Department of Electrical Engineering, Shri K.J. Polytechnic, Bharuch 392 001, India.
² Department of Biosciences, Saveetha School of Engineering. Saveetha Institute of Medical and Technical Sciences, Chennai 602105, India.
³ Department of Control Systems and Instrumentation, Faculty of Mechanical Engineering, VSB-Technical University of Ostrava, 17. Listopadu 2172/15, 70800 Ostrava, Czech Republic.
⁴ Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi 600 062, India.
⁵ Department of Mechanical Engineering and University Centre for Research & Development, Chandigarh University, Mohali, 140413, India.
⁶ Computer Science Department, Prince Hussein Bin Abdullah Faculty for Information Technology, Al al-Bayt University, Mafraq 25113, Jordan.
⁷ Hourani Center for Applied Scientific Research, Al-Ahliyya Amman University, Amman 19328, Jordan.
⁸ MEU Research Unit, Middle East University, Amman, Jordan.
⁹ School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang 11800, Malaysia.
¹⁰ Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13‑5053, Lebanon.
¹¹ School of Engineering and Technology, Sunway University Malaysia, 27500 Petaling Jaya, Malaysia.
¹² Artificial Intelligence and Sensing Technologies (AIST) Research Center, University of Tabuk, 71491 Tabuk, Saudi Arabia.
¹³ Applied Science Research Center, Applied Science Private University, Amman 11931, Jordan.

Abstract

In this study, we tackle the challenge of optimizing the design of a Brushless Direct Current (BLDC) motor. Utilizing an established analytical model, we introduced the Multi-Objective Generalized Normal Distribution Optimization (MOGNDO) method, a biomimetic approach based on Pareto optimality, dominance, and external archiving. We initially tested MOGNDO on standard multi-objective benchmark functions, where it showed strong performance. When applied to the BLDC motor design with the objectives of either maximizing operational efficiency or minimizing motor mass, the MOGNDO algorithm consistently outperformed other techniques like Ant Lion Optimizer (ALO), Ion Motion Optimization (IMO), and Sine Cosine Algorithm (SCA). Specifically, MOGNDO yielded the most optimal values across efficiency and mass metrics, providing practical solutions for real-world BLDC motor design. The MOGNDO source code is available at: https://github.com/kanak02/MOGNDO.

Keywords: BLDC motor; Electromagnetics; Metaheuristic; Non-dominated sorting generalized normal distribution optimization.