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

. 2024 Feb 29 ; 10 (4) : e26369. [epub] 20240215

Status PubMed-not-MEDLINE Jazyk angličtina Země Anglie, Velká Británie Médium electronic-ecollection

Typ dokumentu časopisecké články

Perzistentní odkaz   https://www.medvik.cz/link/pmid38404848
Odkazy

PubMed 38404848
PubMed Central PMC10884493
DOI 10.1016/j.heliyon.2024.e26369
PII: S2405-8440(24)02400-9
Knihovny.cz E-zdroje

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.

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