An induction machine design with parameter optimization for a 120-kW electric vehicle

Zhao, Nan and Schofield, Nigel (2020) An induction machine design with parameter optimization for a 120-kW electric vehicle. IEEE Transactions on Transportation Electrification, 6 (2): 9090215. pp. 592-601.

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Abstract

Electric traction machines have been applied to both industrial variable-speed applications and electric vehicles. The induction machine (IM) is a potential candidate for traction machines due to the established industrial base. However, most of the existing studies on traction IM design rely on optimization algorithms or iterative calculation programs that subsequently give no clear understanding of the design requirements for field-weakening or extended speed operation. Hence, published procedures to guide IM design for traction applications are somewhat ad hoc to date. This article identifies the key IM design parameters required to achieve a traction characteristic within power supply constraints. As an example, a 120-kW electric vehicle traction machine is studied as a benchmark machine and then the proposed design procedure is employed to redesign the machine to improve its extended speed performance. Improvements in terms of efficiency and field-weakening capability are presented. The experimental results from the benchmark machine are used for validation of the subsequent simulation studies reported in this article. Finally, the identified parameters are shown to be similar with brushless permanent magnet machines. Thus, a generalized machine design philosophy can be concluded.

Item Type:
Journal Article
Journal or Publication Title:
IEEE Transactions on Transportation Electrification
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2200/2203
Subjects:
?? induction motorsmachine designpower electronics (pe)traction motor drivesautomotive engineeringtransportationenergy engineering and power technologyelectrical and electronic engineering ??
ID Code:
184854
Deposited By:
Deposited On:
02 Feb 2023 09:35
Refereed?:
Yes
Published?:
Published
Last Modified:
19 Sep 2024 10:33