Driving cycle-centric design optimization and experimental validation of high torque density outer rotor 8/18 MTSRM for an E-Bike
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Date
2025
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier Ltd
Abstract
This paper presents an innovative methodology for optimizing the design parameters of a 500 W low-speed outer rotor switched reluctance motor (OR-SRM) for an electric bicycle (E-bike) in accordance with a driving cycle. Design optimization of SRMs based on driving cycles has been minimally explored in the literature, with all existing research focusing exclusively on high-speed electric vehicle (EV) applications. These studies utilized computationally intensive dynamic current analysis methods to account for the significant dynamic effects incurred. Given the E-bike's low-speed characteristics, the present study mitigates the computational load of design optimization through static current analysis. A high torque density 8/18 OR-multi-teeth (MT) SRM topology has been proposed. The benefits of this topology, such as mass, cost, torque ripple reductions, and improved torque density, have been highlighted through a comparison with a conventional 6/10 OR-SRM topology. The reliability of the finite element analysis models used in this study is validated through experiments conducted on an 8/18 OR-MTSRM prototype. The multi-objective design optimization aims to maximize starting torque and minimize torque ripple and electromagnetic losses throughout the driving cycle. The efficacy of the optimization is confirmed by the enhancement in the performance parameters of the optimal design compared to the preliminary design. © 2025 Elsevier Ltd
Description
Keywords
Convergence of numerical methods, Electric bikes, Integrated circuit design, Magnetic levitation vehicles, Reluctance motors, Structural dynamics, Design optimization, Driving cycle, Electric bicycle (E-bike), Electric bicycles, Finite element analyse, Finite element analyze, K-means++ clustering, Outer rotor, Outer rotor multi-tooth SRM, Prototype machine, K-means clustering
Citation
Computers and Electrical Engineering, 2025, 123, , pp. -
