Multi-objective optimization of a flux switching wound field machine using a response surface-based multi-level design approach
This study introduces a novel multilevel design optimization approach for enhancing the performance of brushless flux-switching wound-field machines (FSWFMs) in electric vehicles (EVs) and industrial drives. The proposed methodology targets key performance metrics namely, high torque, efficiency, po...
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| Published in: | Results in engineering Vol. 25; p. 103988 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01.03.2025
Elsevier |
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| ISSN: | 2590-1230, 2590-1230 |
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| Abstract | This study introduces a novel multilevel design optimization approach for enhancing the performance of brushless flux-switching wound-field machines (FSWFMs) in electric vehicles (EVs) and industrial drives. The proposed methodology targets key performance metrics namely, high torque, efficiency, power factor, and low torque ripple through a structured sensitivity analysis categorized into non-sensitive, mild-sensitive, and strong-sensitive levels. Using the Response Surface Method (RSM), Min-Max Search, and Multi-Objective Genetic Algorithms (MOGA), the Response Surface Multi-Level Optimization (RSMLO) method effectively harmonizes these competing objectives. The optimization process resulted in an 11% increase in average torque and a 69.06% reduction in torque ripple, demonstrating significant performance gains. These results underscore the potential of the RSMLO method as a robust tool for the advanced design of electric machines, offering substantial improvements in both performance and efficiency, and positioning it as a critical framework for future EV and industrial drive applications. |
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| AbstractList | This study introduces a novel multilevel design optimization approach for enhancing the performance of brushless flux-switching wound-field machines (FSWFMs) in electric vehicles (EVs) and industrial drives. The proposed methodology targets key performance metrics namely, high torque, efficiency, power factor, and low torque ripple through a structured sensitivity analysis categorized into non-sensitive, mild-sensitive, and strong-sensitive levels. Using the Response Surface Method (RSM), Min-Max Search, and Multi-Objective Genetic Algorithms (MOGA), the Response Surface Multi-Level Optimization (RSMLO) method effectively harmonizes these competing objectives. The optimization process resulted in an 11% increase in average torque and a 69.06% reduction in torque ripple, demonstrating significant performance gains. These results underscore the potential of the RSMLO method as a robust tool for the advanced design of electric machines, offering substantial improvements in both performance and efficiency, and positioning it as a critical framework for future EV and industrial drive applications. |
| ArticleNumber | 103988 |
| Author | Dowlatshahi, Milad Far, Aliakbar Jamshidi Aphale, Sumeet S. Okoro, Ogbonnaya I. Abunike, Chiweta E. |
| Author_xml | – sequence: 1 givenname: Chiweta E. orcidid: 0000-0003-4681-0845 surname: Abunike fullname: Abunike, Chiweta E. organization: Artificial Intelligence, Robotics and Mechatronic Systems Group (ARMS), School of Engineering, University of Aberdeen, Aberdeen, AB24 3UE, United Kingdom – sequence: 2 givenname: Milad surname: Dowlatshahi fullname: Dowlatshahi, Milad organization: Artificial Intelligence, Robotics and Mechatronic Systems Group (ARMS), School of Engineering, University of Aberdeen, Aberdeen, AB24 3UE, United Kingdom – sequence: 3 givenname: Aliakbar Jamshidi surname: Far fullname: Far, Aliakbar Jamshidi organization: Artificial Intelligence, Robotics and Mechatronic Systems Group (ARMS), School of Engineering, University of Aberdeen, Aberdeen, AB24 3UE, United Kingdom – sequence: 4 givenname: Ogbonnaya I. orcidid: 0000-0002-0104-561X surname: Okoro fullname: Okoro, Ogbonnaya I. organization: Department of Electrical/Electronic Engineering, Michael Okpara University of Agriculture, Umudike 440101, Abia State, Nigeria – sequence: 5 givenname: Sumeet S. surname: Aphale fullname: Aphale, Sumeet S. email: s.aphale@abdn.ac.uk organization: Artificial Intelligence, Robotics and Mechatronic Systems Group (ARMS), School of Engineering, University of Aberdeen, Aberdeen, AB24 3UE, United Kingdom |
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| Cites_doi | 10.1109/TIE.2018.2829679 10.1007/s42835-022-01358-y 10.1109/TIE.2019.2912779 10.1109/TIE.2020.3009578 10.1016/j.rineng.2024.102241 10.1109/TIE.2017.2721928 10.3390/electronics9111748 10.1109/TIA.2021.3080668 10.30941/CESTEMS.2022.00047 10.3390/wevj13050093 10.1109/TIA.2015.2406658 10.1109/ACCESS.2023.3321862 10.1109/TEC.2010.2050591 10.1109/TMAG.2021.3095615 10.3390/math11163596 10.1109/TIA.2022.3143777 10.1016/j.rineng.2023.101213 10.1109/TIE.2021.3063955 10.1109/TEC.2019.2938161 10.1109/TIE.2021.3068684 10.1109/TMAG.2012.2196706 10.1109/TEC.2020.3003050 10.1016/j.rineng.2024.102223 10.2174/1874110X00802010039 10.1016/j.enconman.2023.117515 |
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| Keywords | Flux switching wound field machine Sensitivity analysis method Response surface Torque ripple Torque capability Multilevel design optimization |
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| Snippet | This study introduces a novel multilevel design optimization approach for enhancing the performance of brushless flux-switching wound-field machines (FSWFMs)... |
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| SubjectTerms | Flux switching wound field machine Multilevel design optimization Response surface Sensitivity analysis method Torque capability Torque ripple |
| Title | Multi-objective optimization of a flux switching wound field machine using a response surface-based multi-level design approach |
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