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Magnetic FEA Direct Optimization of High-Power Density, Halbach Array Permanent Magnet Electric Motors

Author

Listed:
  • Jean-Michel Grenier

    (LEEPCI, Department of Electrical and Computer Engineering, Laval University, 1065, Avenue de la Médecine, Quebec, QC G1V 0A6, Canada)

  • Ramón Pérez

    (LEEPCI, Department of Electrical and Computer Engineering, Laval University, 1065, Avenue de la Médecine, Quebec, QC G1V 0A6, Canada)

  • Mathieu Picard

    (CAMUS Laboratory, Université de Sherbrooke, 2500 Bld University, Sherbrooke, QC J1K 2R1, Canada)

  • Jérôme Cros

    (LEEPCI, Department of Electrical and Computer Engineering, Laval University, 1065, Avenue de la Médecine, Quebec, QC G1V 0A6, Canada)

Abstract

Hybrid electric aero-propulsion requires high power-density electric motors. The use of a constrained optimization method with the finite element analysis (FEA) is the best way to design these motors and to find the best solutions which maximize the power density. This makes it possible to take into account all the details of the geometry as well as the non-linear characteristics of magnetic materials, the conductive material and the current control strategy. Simulations were performed with a time stepping magnetodynamic solver while taking account the rotor movement and the stator winding was connected by an external electrical circuit. This study describes the magnetic FEA direct optimization approach for the design of Halbach array permanent magnet synchronous motors (PMSMs) and its advantages. An acceptable compromise between precision and computation time to estimate the electromagnetic torque, iron losses and eddy current losses was found. The finite element simulation was paired with analytical models to compute stress on the retaining sleeve, aerodynamic losses, and copper losses. This type of design procedure can be used to find the best machine configurations and establish design rules based on the specifications and materials selected. As an example, optimization results of PM motors minimizing total losses for a 150-kW application are presented for given speeds in the 2000 rpm to 50,000 rpm range. We compare different numbers of poles and power density between 5 kW/kg and 30 kW/kg. The choice of the number of poles is discussed in the function of the motor nominal speed and targeted power density as well as the compromise between iron losses and copper losses. In addition, the interest of having the current-control strategy as an optimization variable to generate a small amount of flux weakening is clearly shown.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:18:p:5939-:d:638692
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    References listed on IDEAS

    as
    1. 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.
    2. Yu-Xi Liu & Li-Yi Li & Ji-Wei Cao & Qin-He Gao & Zhi-Yin Sun & Jiang-Peng Zhang, 2018. "The Optimization Design of Short-Term High-Overload Permanent Magnet Motors Considering the Nonlinear Saturation," Energies, MDPI, vol. 11(12), pages 1-20, November.
    3. Luigi Pio Di Noia & Luigi Piegari & Renato Rizzo, 2020. "Optimization Methodology of PMSM Cooled by External Convection in Aircraft Propulsion," Energies, MDPI, vol. 13(15), pages 1-22, August.
    4. Wenlong Wei & Jinping Zhang & Jin Yao & Siqi Tang & Shiyou Zhang, 2020. "Performance Analysis and Optimization of Power Density Enhanced PMSM with Magnetic Stripe on Rotor," Energies, MDPI, vol. 13(17), pages 1-14, August.
    5. Ilka, Reza & Alinejad-Beromi, Yousef & Yaghobi, Hamid, 2018. "Cogging torque reduction of permanent magnet synchronous motor using multi-objective optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 153(C), pages 83-95.
    6. Gang Lei & Jianguo Zhu & Youguang Guo & Chengcheng Liu & Bo Ma, 2017. "A Review of Design Optimization Methods for Electrical Machines," Energies, MDPI, vol. 10(12), pages 1-31, November.
    7. Keun-Young Yoon & Soo-Whang Baek, 2019. "Robust Design Optimization with Penalty Function for Electric Oil Pumps with BLDC Motors," Energies, MDPI, vol. 12(1), pages 1-14, January.
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    Cited by:

    1. Changchuang Huang & Baoquan Kou & Xiaokun Zhao & Xu Niu & Lu Zhang, 2022. "Multi-Objective Optimization Design of a Stator Coreless Multidisc Axial Flux Permanent Magnet Motor," Energies, MDPI, vol. 15(13), pages 1-13, June.
    2. Antonino Di Gerlando & Claudio Ricca, 2023. "Analytical Modeling of Magnetic Field Distribution at No Load for Surface Mounted Permanent Magnet Machines," Energies, MDPI, vol. 16(7), pages 1-19, April.
    3. Xiaoquan Lu & Xinyi He & Ping Jin & Qifeng Huang & Shihai Yang & Mingming Chen, 2021. "General 3D Analytical Method for Eddy-Current Coupling with Halbach Magnet Arrays Based on Magnetic Scalar Potential and H-Functions," Energies, MDPI, vol. 14(24), pages 1-15, December.

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