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Advanced Design Optimization Technique for Torque Profile Improvement in Six-Phase PMSM Using Supervised Machine Learning for Direct-Drive EV
- Source :
- IEEE Transactions on Energy Conversion. 34:2041-2051
- Publication Year :
- 2019
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2019.
-
Abstract
- Few of the challenges with development of a single on–board motor for direct–drive electric vehicles include high torque density and low torque ripple. Therefore, in this paper, a 36–slot, 34–pole consequent pole six–phase permanent magnet synchronous machine (PMSM) has been optimized to address the aforementioned challenges for direct–drive application. Existing literature on optimization processes that rely solely on finite element models are restricted to three–phase machines only and also take longer computation time. Therefore, this paper proposes a novel optimization approach based on supervised machine learning for six–phase PMSM. In this approach, a non–conventional extended dual dq– frame model that accounts for higher order space harmonics in inductances and flux linkages has been developed and used for accurate computation of average torque and torque ripple of six–phase PMSM. Using the performance characteristics obtained from the extended dual dq –frame model for a set of initial design candidates, support vector regression algorithm is employed for supervised machine learning and increasing solutions in the design space. Furthermore, pareto front is used for selecting optimal machine models with maximum torque density and reduced torque ripple. Multi–objective trade–offs and comparison of initial and optimized designs based on average torque, torque ripple, efficiency and cost are performed.
- Subjects :
- business.industry
Computation
020208 electrical & electronic engineering
Frame (networking)
Energy Engineering and Power Technology
02 engineering and technology
Machine learning
computer.software_genre
Multi-objective optimization
Support vector machine
Harmonic analysis
Harmonics
0202 electrical engineering, electronic engineering, information engineering
Torque
Torque ripple
Artificial intelligence
Electrical and Electronic Engineering
business
computer
Subjects
Details
- ISSN :
- 15580059 and 08858969
- Volume :
- 34
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Energy Conversion
- Accession number :
- edsair.doi...........51ac85866c8c26fa5f5b32905d2af92a