Title
A comprehensive analytical sizing methodology for transverse and radial flux machinesAuthor (from another institution)
xmlui.dri2xhtml.METS-1.0.item-contributorOtherinstitution
https://ror.org/012a91z28Version
http://purl.org/coar/version/c_970fb48d4fbd8a85
Rights
© 2023 The AuthorsAccess
http://purl.org/coar/access_right/c_abf2Publisher’s version
https://doi.org/10.1109/ACCESS.2023.3318527Published at
IEEE Access Vol. 11. Pp. 106063-106082Publisher
IEEEKeywords
Analytical sizing equations
electric vehicles
finite element method
multiobjective genetic algorithm ... [+]
electric vehicles
finite element method
multiobjective genetic algorithm ... [+]
Analytical sizing equations
electric vehicles
finite element method
multiobjective genetic algorithm
permanent magnet synchronous machines
transverse flux machines [-]
electric vehicles
finite element method
multiobjective genetic algorithm
permanent magnet synchronous machines
transverse flux machines [-]
Abstract
Transverse flux machines have the potential to offer high torque density in direct-drive vehicle traction applications. Besides, sizing equations are a wide-spread technique for transverse flux machin ... [+]
Transverse flux machines have the potential to offer high torque density in direct-drive vehicle traction applications. Besides, sizing equations are a wide-spread technique for transverse flux machines design, as their computational cost is much lower than the finite element method. In this paper a novel analytical sizing methodology for transverse and radial flux machines is presented, focusing on the current load and the pole length factor as the main design parameters. The motor specifications are intended for a light-duty electric vehicle application. As transverse flux machines have a single, hoop-shaped coil per phase that embraces the flux of all the pole pairs, their principle of operation and therefore their sizing equations differ from radial flux machines. The proposed analytical method allows to compare transverse and radial flux machines easily through a similarity analysis and a parametric study. Furthermore, the discrepancies between the analytical model and the finite element method are quantified and then included in previous equations. Then the analytical model is optimized with a multiobjective genetic algorithm in the final stage. According to the sizing methodology presented here, transverse flux machines have a superior performance than radial flux machines in terms of torque density and efficiency. [-]
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