Surrogate-Based Multi-Objective Optimization of Flux-Focusing Halbach Coaxial Magnetic Gear

Aran Shoaei, Farnam Farshbaf-Roomi and Qingsong Wang ()
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Aran Shoaei: Département de Génie Électrique, École de Technologie Supérieure, Montréal, QC H3C 1K3, Canada
Farnam Farshbaf-Roomi: Département de Génie Électrique, École de Technologie Supérieure, Montréal, QC H3C 1K3, Canada
Qingsong Wang: Département de Génie Électrique, École de Technologie Supérieure, Montréal, QC H3C 1K3, Canada

Energies, 2024, vol. 17, issue 3, 1-18

Abstract: Due to their contact-free and low-maintenance features, magnetic gears (MGs) have been increasingly investigated to amplify the torque of electric motors in electric vehicles (EVs). In order to meet the requirements of propelling EVs, it is essential to design an MG with a high torque density. In this paper, a novel flux-focusing Halbach coaxial MG (FFH-CMG) is proposed, which combines the advantages of flux focusing and Halbach permanent magnet (PM) arrays. The proposed structure has a higher torque performance and greater efficiency than conventional structures. A multi-objective design optimization based on a surrogate model is implemented to achieve the maximum volumetric torque density (VTD), torque-per-PM volume (TPMV), and efficiency, as well as the minimum torque ripple, in the proposed FFH-CMG. The employed optimization approach has a higher accuracy and is less time-consuming compared to the conventional optimization methods based on direct finite-element analysis (FEA). The performance of the proposed FFH-CMG is then investigated through 2D-FEA. According to the simulation results, the optimized FFH-CMG can achieve a VTD of 411 kNm/m 3 , and a TPMV of 830 kNm/m 3 , which are significantly larger than those of the existing MGs and make the proposed FFH-CMG very suitable for EV applications.

Keywords: flux-focusing; Halbach; magnetic gear; multi-objective; surrogate model (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: 2024
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