A General SVM-Based Multi-Objective Optimization Methodology for Axial Flux Motor Design: YASA Motor of an Electric Vehicle as a Case Study
| Autor: | Aiyu Gu, Bo Ruan, Wenyao Cao, Qikai Yuan, Yingzhan Lian, Huanyao Zhang |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | IEEE Access, Vol 7, Pp 180251-180257 (2019) |
| Druh dokumentu: | article |
| ISSN: | 2169-3536 14185644 |
| DOI: | 10.1109/ACCESS.2019.2958088 |
| Popis: | Axial flux motor design is normally depended on a designer's experience to adjust design parameters, which is vague and complex, for example, torque density and torque ripple are two key factors of a motor to restrain its development, since torque density dominates a motor's volume and weight, while torque ripple determines its stability. Therefore, a general optimizations methodology is required in its design process. To realize this purpose, this paper proposes a general multi-objective optimization methodology for practical motor design. In detail, this methodology is based on Support Vector Machine-Chaotic Cultural Differential Evolution(SVM-CCDE) algorithm for both maximizing the torque density and minimizing the torque ripple, and Yokeless And Segmented Armature(YASA) motor of an electric vehicle(EV) considering practical constraints is presented as a typical example since it is a special topology of axial flux motor(AFM). A comparative analysis is presented in the paper to demonstrate the proposed method's advanced features. Finally, the effectiveness of the optimization method is verified by finite element analysis (FEA) via Ansys software, the results well agree the analyses and further validate the proposed method. The proposed method is a potential feasible solution to improve the EV's motor design in the coming future. |
| Databáze: | Directory of Open Access Journals |
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