Improved performance of single sided axial flux for reduction in cogging torque (IMPACT)
•Cogging torque, specifically in permanent magnet machine design, is a concern due to the addition of undesirable harmonics to the pulsating torque.•A proposed solution is to use a single-sided axial flux permanent magnet machine with arc-shaped skew magnets to minimize cogging torque.•To achieve hi...
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Veröffentlicht in: | Results in engineering 2025-03, Vol.25, p.103778, Article 103778 |
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Sprache: | eng |
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Zusammenfassung: | •Cogging torque, specifically in permanent magnet machine design, is a concern due to the addition of undesirable harmonics to the pulsating torque.•A proposed solution is to use a single-sided axial flux permanent magnet machine with arc-shaped skew magnets to minimize cogging torque.•To achieve high efficiency, the stator-sided iron must remain unsaturated, and torque ripples should be less.•The research focuses on designing and optimizing a single sided slotless (AFPM) machine to reduce the cogging torque by arc skew magnet shape.•A finite eliminate method and genetic algorithm is employed to simulate the machine's performance.
Pulsating torque contains two components, namely torque ripples and cogging torque. The decrease in pulsating torque is sought by the electrical machine designers. In PM machine design cogging torque is a concern since it adds undesirable harmonics to pulsating torque. Hence, in this paper a single-sided axial flux permanent magnet (AFPM) machine is proposed having an arc shaped skew magnets to reduce the cogging torque of a suggested machine. For high efficiency, stator sided iron must be unsaturated and torque ripples are nearby zero. Research aims to design and optimize Single Sided Slotless AFPM machines. Mathematical calculations and sizing equations are used to design the machine, and the simulation tools Finite Eliminate Method (FEM) is used to simulate the machine. Latin Hypercube Sampling (LHS) is used to create samples, kriging method is applied to approximate the model and optimized model is obtained by using Genetic Algorithm (GA). |
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ISSN: | 2590-1230 2590-1230 |
DOI: | 10.1016/j.rineng.2024.103778 |