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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Results in engineering Jg. 25; S. 103988
Hauptverfasser: Abunike, Chiweta E., Dowlatshahi, Milad, Far, Aliakbar Jamshidi, Okoro, Ogbonnaya I., Aphale, Sumeet S.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.03.2025
Elsevier
Schlagworte:
Flux switching wound field machine
Multilevel design optimization
Response surface
Sensitivity analysis method
Torque capability
Torque ripple
Flux switching wound field machine
Sensitivity analysis method
Response surface
Torque ripple
Torque capability
Multilevel design optimization
ISSN:2590-1230, 2590-1230
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
ISSN:2590-1230
2590-1230
DOI:10.1016/j.rineng.2025.103988