Saliency Ratio-Based Torque Enhancement of Switched Reluctance Motors for Electric Bikes

Abdul Ghani, Hassan Abdullah Khalid () and Habibur Rehman
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Abdul Ghani: US Pakistan Center for Advanced Studies in Energy, Department of Electrical Power Engineering, National University of Science and Technology, Islamabad 44000, Pakistan
Hassan Abdullah Khalid: US Pakistan Center for Advanced Studies in Energy, Department of Electrical Power Engineering, National University of Science and Technology, Islamabad 44000, Pakistan
Habibur Rehman: College of Engineering, American University of Sharjah, Sharjah P.O. Box 26666, United Arab Emirates

Energies, 2023, vol. 16, issue 21, 1-20

Abstract: Switched reluctance motors (SRMs) face challenges in achieving high performances in terms of the power factor, torque per ampere, and torque ripples. Although several parameters impact their performance, magnetic saliency has a significant impact. This paper presents a study on the influence of the saliency ratio on the performance of switched reluctance motors. A detailed design procedure for a mid-drive electric motor for a high-performance electric bike is presented. A systematic methodology is adopted to estimate the power rating for desired vehicle performance while considering the vehicle’s mechanical and aerodynamic parameters. Additionally, the paper discusses and analyzes the design of two SRM motor configurations with different rotor poles (6/4 and 6/10) while performing magnetostatic analysis for generated torque validation. A parametric study is performed on the 6/10 SRM design for further improvement in the torque profile while keeping the same fill factor and electrical loading. Furthermore, the transient analysis in Ansys Maxwell 2D for both SRMs confirms the designed performance. The results shows that the SRM with higher rotor poles (6/10) is a better choice.

Keywords: SRM electromagnetic design; saliency ratio; parametric variation analysis; magnetostatic analysis; steady state analysis; finite element analysis; Maxwell 2D; electric bike; EV power rating estimation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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