Abstract
A novel automated design algorithm for application-based optimization of permanent magnet (PM) machines is presented in this paper. The proposed algorithm features precise performance evaluation of the potentially heavily saturated machines at high-energy-throughput operating zones using finite element (FE) techniques. First, the energy consumption function associated with the machine's operating cycle is efficiently modeled by a number of representative load points using a k-means clustering algorithm. Subsequently, a new approach is developed to assess the performance of the machine at each representative load point with proper control to conform to practical operational constraints imposed by voltage and current limits of the motor-drive system. The developed algorithm is applicable to the optimization of any configuration of PM and synchronous reluctance motors over any conceivable operating cycle. Its effectiveness is demonstrated by optimizing the well-established reference/benchmark design represented by the 2004 Toyota Prius IPM motor configuration over a compound operating cycle consisting of common US driving schedules.
Original language | English |
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Title of host publication | 2015 IEEE Energy Conversion Congress and Exposition, ECCE 2015 |
Pages | 4383-4390 |
Number of pages | 8 |
ISBN (Electronic) | 9781467371506 |
DOIs | |
State | Published - Oct 27 2015 |
Event | 7th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2015 - Montreal, Canada Duration: Sep 20 2015 → Sep 24 2015 |
Publication series
Name | 2015 IEEE Energy Conversion Congress and Exposition, ECCE 2015 |
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Conference
Conference | 7th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2015 |
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Country/Territory | Canada |
City | Montreal |
Period | 9/20/15 → 9/24/15 |
Bibliographical note
Funding Information:This work was supported by General Motors Corporation under Grant No. 75717.
Publisher Copyright:
© 2015 IEEE.
ASJC Scopus subject areas
- Energy Engineering and Power Technology
- Electrical and Electronic Engineering