Abstract
Bearingless motors are electric machines capable of simultaneously creating both torque and suspension forces. Optimization algorithms are generally required to fully realize the performance capabilities of these machines due to trade-offs between force and torque performance. This paper focuses on developing design guidelines which can be used prior to employing an optimization algorithm to streamline the bearingless machine design process. As part of this effort, analytic tools are developed to evaluate the suspension performance of bearingless machines. To validate the model, observations made with the aid of the equations are cross-verified against FEA and optimization results. The paper goes on to show that by employing the proposed tools, engineers can gain physical insight into bearingless machine design and identify topologies best suited for their application early in the design process.
Original language | English (US) |
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Title of host publication | 2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 4554-4561 |
Number of pages | 8 |
ISBN (Electronic) | 9781728151359 |
DOIs | |
State | Published - 2021 |
Externally published | Yes |
Event | 13th IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Virtual, Online, Canada Duration: Oct 10 2021 → Oct 14 2021 |
Publication series
Name | 2021 IEEE Energy Conversion Congress and Exposition, ECCE 2021 - Proceedings |
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Conference
Conference | 13th IEEE Energy Conversion Congress and Exposition, ECCE 2021 |
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Country/Territory | Canada |
City | Virtual, Online |
Period | 10/10/21 → 10/14/21 |
Bibliographical note
Publisher Copyright:© 2021 IEEE.
Keywords
- analytical modeling
- bearingless
- machine design
- self-bearing