Design and multi-objective optimization of a 12-slot/10-pole integrated OBC using magnetic equivalent circuit approach
KAUST DepartmentElectrical and Computer Engineering Program
Ali I. Al-Naimi Petroleum Engineering Research Center (ANPERC)
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Permanent link to this recordhttp://hdl.handle.net/10754/674883
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AbstractPermanent magnet machines (PMs) equipped with fractional slot concentrated windings (FSCWs) have been preferably proposed for electric vehicle (EV) applications. Moreover, integrated on-board battery chargers (OBCs), which employ the powertrain elements in the charging process, promote the zero-emission future envisaged for transportation through the transition to EVs. Based on the available literature, the employed machine, as well as the adopted winding configuration, highly affects the performance of the integrated OBC. However, the optimal design of the FSCW-based PM machine in the charging mode of operation has not been conceived thus far. In this paper, the design and multi-objective optimization of an asymmetrical 12-slot/10-pole integrated OBC based on the efficient magnetic equivalent circuit (MEC) approach are presented, shedding light on machine performance during charging mode. An ‘initial’ surface-mounted PM (SPM) machine is first designed based on the magnetic equivalent circuit (MEC) model. Afterwards, a multi-objective genetic algorithm is utilized to define the optimal machine parameters. Finally, the optimal machine is compared to the ‘initial’ design using finite element (FE) simulations in order to validate the proposed optimization approach and to highlight the performance superiority of the optimal machine over its initial counterpart.
CitationMetwly, M. Y., Hemeida, A., Abdel-Khalik, A. S., Hamad, M. S., & Ahmed, S. (2021). Design and Multi-Objective Optimization of a 12-Slot/10-Pole Integrated OBC Using Magnetic Equivalent Circuit Approach. Machines, 9(12), 329. doi:10.3390/machines9120329
SponsorsThis 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.