Abstract
The transition to electric vehicles (EVs) has received global support as initiatives and legis-lation are introduced in support of a zero-emissions future envisaged for transportation. Integrated on-board battery chargers (OBCs), which exploit the EV drivetrain elements into the charging process, are considered an elegant solution to achieve this widespread adoption of EVs. Surface-mounted permanent-magnet (SPM) machines have emerged as plausible candidates for EV traction due to their nonsalient characteristics and ease of manufacturing. From an electric machine design perspec-tive, parasitic torque ripple and core losses need to be minimized in integrated OBCs during both propulsion and charging modes. The optimal design of EV propulsion motors has been extensively presented in the literature; however, the performance of the optimal traction machine under the charging mode of operation for integrated OBCs has not received much attention in the literature thus far. This paper investigates the optimal design of a six-phase SPM machine employed in an integrated OBC with two possible winding layouts, namely, dual three-phase or asymmetrical six-phase winding arrangements. First, the sizing equation and optimized geometrical parameters of a six-phase 12-slot/10-pole fractional slot concentrated winding (FSCW)-based SPM machine are introduced. Then, variations in the output average torque, parasitic torque ripple, and parasitic core losses with the slot opening width and the PM width-to-pole pitch ratio are further investigated for the two proposed winding layouts under various operation modes. Eventually, the optimally designed machine is simulated using analytical magnetic equivalent circuit (MEC) models. The obtained results are validated using 2D finite element (FE) analysis.
Original language | English (US) |
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Pages (from-to) | 1848 |
Journal | Energies |
Volume | 14 |
Issue number | 7 |
DOIs | |
State | Published - Mar 26 2021 |
Bibliographical note
KAUST Repository Item: Exported on 2021-06-09Acknowledgements: This work was achieved by the financial support of ITIDAs ITAC collaborative funded project under the category type of advanced research projects (ARP) and grant number ARP2020.R29.7.
ASJC Scopus subject areas
- General Computer Science