Design Optimization of an Axial Flux Magnetic Gear by Using Reluctance Network Modeling and Genetic Algorithm

Gerardo Ruiz-Ponce (), Marco A. Arjona, Concepcion Hernandez and Rafael Escarela-Perez
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Gerardo Ruiz-Ponce: La Laguna Institute of Technology, TNM, Torreon 27000, Mexico
Marco A. Arjona: La Laguna Institute of Technology, TNM, Torreon 27000, Mexico
Concepcion Hernandez: La Laguna Institute of Technology, TNM, Torreon 27000, Mexico
Rafael Escarela-Perez: Energy Department, Metropolitan Autonomous University, Azcapotzalco, Ciudad de Mexico 02128, Mexico

Energies, 2023, vol. 16, issue 4, 1-15

Abstract: The use of a suitable modeling technique for the optimized design of a magnetic gear is essential to simulate its electromagnetic behavior and to predict its satisfactory performance. This paper presents the design optimization of an axial flux magnetic gear (AFMG) using a two-dimensional (2D) magnetic equivalent circuit model (MEC) and a Multi-objective Genetic Algorithm (MOGA). The proposed MEC model is configured as a meshed reluctance network (RN) with permanent magnet magnetomotive force sources. The non-linearity in the ferromagnetic materials is accounted for by the MEC. The MEC model based on reluctance networks (RN) is considered to be a good compromise between accuracy and computational effort. This new model will allow a faster analysis and design for the AFMG. A multi-objective optimization is carried out to achieve an optimal volume-focused design of the AFMG for future practical applications. The performance of the optimized model is then verified by establishing flux density comparisons with finite element simulations. This study shows that with the combination of an MEC-RN model and a GA for its optimization, a satisfactory accuracy can be achieved compared to that of the finite element analysis (FEA), but with only a fraction of the computational time.

Keywords: axial flux magnetic gear; magnetic equivalent circuit; reluctance network; finite element analysis; multi-objective optimization; genetic algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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