This paper discusses the multi-objective optimization of axial flux permanent magnet (AFPM) machines with ferrite spoke-type magnets, utilizing 3D finite element models. Three-dimensional finite element analysis is computationally expensive, and furthermore, substantial computation time is expended by optimization algorithms in evaluating low performing designs whose performance is far from the optimum if the search space is not specified correctly. In this regard, this work proposes two new methods for identifying the search space. The search is limited to ranges of input geometric variables where high performing designs are likely to be found. The optimization algorithm utilized is based on surrogate models and differential evolution. It is found that the combined use of these approaches drastically reduces the solution time.
|Title of host publication||2019 IEEE International Electric Machines and Drives Conference, IEMDC 2019|
|Number of pages||6|
|State||Published - May 2019|
|Event||11th IEEE International Electric Machines and Drives Conference, IEMDC 2019 - San Diego, United States|
Duration: May 12 2019 → May 15 2019
|Name||2019 IEEE International Electric Machines and Drives Conference, IEMDC 2019|
|Conference||11th IEEE International Electric Machines and Drives Conference, IEMDC 2019|
|Period||5/12/19 → 5/15/19|
Bibliographical noteFunding Information:
ACKNOWLEDGMENT The support of Regal Beloit Corporation, University of Kentucky, the L. Stanley Pigman endowment and the SPARK program, and ANSYS Inc. is gratefully acknowledged.
© 2019 IEEE.
- Axial flux
- Search space
- Sensitivity analysis
- Surrogate kriging model
ASJC Scopus subject areas
- Energy Engineering and Power Technology
- Electrical and Electronic Engineering
- Mechanical Engineering