Abstract
In this paper, an oriented auto-tuning niching algorithm (OANA) is proposed for the design optimization of a permanent magnet assisted synchronous reluctance motor (PMa-SynRM) for a pedal-assist system electric bicycle (PAS-EB). The OANA is a fast and accurate optimization algorithm for finding the optimal points in multi-modal functions. Specifically, the OANA remedies the shortcomings of the conventional auto-tuning niching genetic algorithm by adopting a probability method that is based on the navigational pathways. The OANA showed excellent performance by utilizing mathematical multi-modal test functions. Then, the OANA was applied to cogging torque optimization for a PMa-SynRM design of the PAS-EB and found several optimal points. Finally, the optimum design is derived by comparing the average torque and torque ripple of the obtained design points.
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Acknowledgements
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (Ministry of Science and ICT) (No. 2019R1F1A1061132).
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Lee, TH., Kang, YR., Son, JC. et al. Optimized Design of Permanent Magnet Assisted Synchronous Reluctance Motor Using Oriented Auto-tuning Niching Algorithm. J. Electr. Eng. Technol. 16, 1495–1503 (2021). https://doi.org/10.1007/s42835-021-00694-9
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DOI: https://doi.org/10.1007/s42835-021-00694-9