This paper presents the optimal study of a verniertype axial-flux permanent-magnet (AFPM) machine, which has a high-polarity spoke-type PM rotor, a wound stator with a low number of coils, and a profiled stator. Both stators have profiled teeth to enhance the magnetic interaction between the rotor PM array and stator windings for torque production. Compared to the topology with two wound stators, the studied one has a smaller total axial length and is expected more suitable for applications where the space is limited in axial direction. Both topologies are optimized through 3-dimensional (3D) finite element analysis (FEA) by the combined design of experiments (DOE) based sensitivity analysis and surrogate-assisted multiobjective differential evolution (DE) algorithm. Key factors affecting the two objectives, i.e., total active material cost and total electromagnetic loss, are identified. The optimization results are presented and compared, providing practical guidelines for the optimal design and operation of such machines. The manufacturing aspects and their impacts on the electromagnetic performance are also discussed.
|Title of host publication||ECCE 2020 - IEEE Energy Conversion Congress and Exposition|
|Number of pages||4|
|State||Published - Oct 11 2020|
|Event||12th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2020 - Virtual, Detroit, United States|
Duration: Oct 11 2020 → Oct 15 2020
|Name||ECCE 2020 - IEEE Energy Conversion Congress and Exposition|
|Conference||12th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2020|
|Period||10/11/20 → 10/15/20|
Bibliographical noteFunding Information:
ACKNOWLEDGMENT The support of the National Science Foundation, NSF Grant #1809876, of University of Kentucky, the L. Stanley Pigman endowment and ANSYS Inc., is gratefully acknowledged.
© 2020 IEEE.
- Electric machine
- axial-flux permanent magnet machines
- high-torque density
- verniertype machine
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Mechanical Engineering
- Control and Optimization
- Energy Engineering and Power Technology