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Optimal Design of PMSM Based on Automated Finite Element Analysis and Metamodeling

Author

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  • Yong-Min You

    (Department of Automotive Engineering, Honam University, Gwangju 62399, Korea)

Abstract

To obtain accurate optimal design results in electric machines, the finite element analysis (FEA) technique should be used; however, it is time-consuming. In addition, when the design of experiments (DOE) is conducted in the optimal design process, mechanical design, analysis, and post process must be performed for each design point, which requires a significant amount of design cost and time. This study proposes an automated DOE procedure through linkage between an FEA program and optimal design program to perform DOE easily and accurately. Parametric modeling was developed for the FEA model for automation, the files required for automation were generated using the macro function, and the interface between the FEA and optimal design program was established. Shape optimization was performed on permanent magnet synchronous motors (PMSMs) for small electric vehicles to maximize torque while maintaining efficiency, torque ripple, and total harmonic distortion of the back EMF using the built-in automation program. Fifty FEAs were performed for the experimental points selected by optimal Latin hypercube design and their results were analyzed by screening. Eleven metamodels were created for each output variable using the DOE results and root mean squared error tests were conducted to evaluate the predictive performance of the metamodels. The optimization design based on metamodels was conducted using the hybrid metaheuristic algorithm to determine the global optimum. The optimum design results showed that the average torque was improved by 2.5% in comparison to the initial model, while satisfying all constraints. Finally, the optimal design results were verified by FEA. Consequently, it was found that the proposed optimal design method can be useful for improving the performance of PMSM as well as reducing design cost and time.

Suggested Citation

  • Yong-Min You, 2019. "Optimal Design of PMSM Based on Automated Finite Element Analysis and Metamodeling," Energies, MDPI, vol. 12(24), pages 1-18, December.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:24:p:4673-:d:295677
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    References listed on IDEAS

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    4. Wenping Chai & Thomas A. Lipo & Byung-il Kwon, 2018. "Design and Optimization of a Novel Wound Field Synchronous Machine for Torque Performance Enhancement," Energies, MDPI, vol. 11(8), pages 1-15, August.
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    Cited by:

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    2. Hanaa Elsherbiny & Laszlo Szamel & Mohamed Kamal Ahmed & Mahmoud A. Elwany, 2022. "High Accuracy Modeling of Permanent Magnet Synchronous Motors Using Finite Element Analysis," Mathematics, MDPI, vol. 10(20), pages 1-20, October.
    3. Catalin Petrea Ion & Marius Daniel Calin & Ioan Peter, 2023. "Design of a 3 kW PMSM with Super Premium Efficiency," Energies, MDPI, vol. 16(1), pages 1-11, January.
    4. Jean-Michel Grenier & Ramón Pérez & Mathieu Picard & Jérôme Cros, 2021. "Magnetic FEA Direct Optimization of High-Power Density, Halbach Array Permanent Magnet Electric Motors," Energies, MDPI, vol. 14(18), pages 1-19, September.
    5. Sunghun Kim & Youngjin Park & Seungbeom Yoo & Sejun Lee & Uttam Kumar Chanda & Wonjun Cho & Ocktaeck Lim, 2023. "Optimization of the Uniformity Index Performance in the Selective Catalytic Reduction System Using a Metamodel," Sustainability, MDPI, vol. 15(18), pages 1-16, September.
    6. Lukáš Veg & Jan Kaska & Martin Skalický & Roman Pechánek, 2021. "A Complex Study of Stator Tooth-Coil Winding Thermal Models for PM Synchronous Motors Used in Electric Vehicle Applications," Energies, MDPI, vol. 14(9), pages 1-16, April.
    7. Armin Dietz & Antonino Oscar Di Tommaso & Fabrizio Marignetti & Rosario Miceli & Claudio Nevoloso, 2020. "Enhanced Flexible Algorithm for the Optimization of Slot Filling Factors in Electrical Machines," Energies, MDPI, vol. 13(5), pages 1-21, February.
    8. Duc Tan Vu & Ngac Ky Nguyen & Eric Semail & Hailong Wu, 2021. "Adaline-Based Control Schemes for Non-Sinusoidal Multiphase Drives—Part II: Torque Optimization for Faulty Mode," Energies, MDPI, vol. 15(1), pages 1-21, December.
    9. Kan Wang & Zhong Wu & Zhongyi Chu, 2020. "DC-Link Current Control with Inverter Nonlinearity Compensation for Permanent Magnet Synchronous Motor Drives," Energies, MDPI, vol. 13(3), pages 1-16, January.
    10. Sunghun Kim & Youngjin Park & Seungbeom Yoo & Ocktaeck Lim & Bernike Febriana Samosir, 2023. "Development of Machine Learning Algorithms for Application in Major Performance Enhancement in the Selective Catalytic Reduction (SCR) System," Sustainability, MDPI, vol. 15(9), pages 1-20, April.

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