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Design and Optimization of Permanent Magnet Brushless Machines for Electric Vehicle Applications

Author

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  • Weiwei Gu

    (School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Xiaoyong Zhu

    (School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Li Quan

    (School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Yi Du

    (School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China)

Abstract

In this paper, by considering and establishing the relationship between the maximum operating speed and d -axis inductance, a new design and optimization method is proposed. Thus, a more extended constant power speed range, as well as reduced losses and increased efficiency, especially in the high-speed region, can be obtained, which is essential for electric vehicles (EVs). In the first step, the initial permanent magnet (PM) brushless machine is designed based on the consideration of the maximum speed and performance specifications in the entire operation region. Then, on the basis of increasing d -axis inductance, and meanwhile maintaining constant permanent magnet flux linkage, the PM brushless machine is optimized. The corresponding performance of the initial and optimal PM brushless machines are analyzed and compared by the finite-element method (FEM). Several tests are carried out in an EV simulation model based on the urban dynamometer driving schedule (UDDS) for evaluation. Both theoretical analysis and simulation results verify the validity of the proposed design and optimization method.

Suggested Citation

  • Weiwei Gu & Xiaoyong Zhu & Li Quan & Yi Du, 2015. "Design and Optimization of Permanent Magnet Brushless Machines for Electric Vehicle Applications," Energies, MDPI, vol. 8(12), pages 1-13, December.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:12:p:12410-14008:d:60343
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    References listed on IDEAS

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    1. Ping Zheng & Fan Wu & Yu Lei & Yi Sui & Bin Yu, 2013. "Investigation of a Novel 24-Slot/14-Pole Six-Phase Fault-Tolerant Modular Permanent-Magnet In-Wheel Motor for Electric Vehicles," Energies, MDPI, vol. 6(10), pages 1-23, September.
    2. Ming Cheng & Le Sun & Giuseppe Buja & Lihua Song, 2015. "Advanced Electrical Machines and Machine-Based Systems for Electric and Hybrid Vehicles," Energies, MDPI, vol. 8(9), pages 1-24, September.
    3. Qingsong Wang & Shuangxia Niu, 2015. "Electromagnetic Design and Analysis of a Novel Fault-Tolerant Flux-Modulated Memory Machine," Energies, MDPI, vol. 8(8), pages 1-17, August.
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    Cited by:

    1. Peixin Liang & Yulong Pei & Feng Chai & Kui Zhao, 2016. "Analytical Calculation of D - and Q -axis Inductance for Interior Permanent Magnet Motors Based on Winding Function Theory," Energies, MDPI, vol. 9(8), pages 1-11, July.
    2. Emad Roshandel & Amin Mahmoudi & Solmaz Kahourzade & Amirmehdi Yazdani & GM Shafiullah, 2021. "Losses in Efficiency Maps of Electric Vehicles: An Overview," Energies, MDPI, vol. 14(22), pages 1-27, November.
    3. Guoyu Chu & Rukmi Dutta & Alireza Pouramin & Muhammed Fazlur Rahman, 2020. "Analysis of Torque Ripple of a Spoke-Type Interior Permanent Magnet Machine," Energies, MDPI, vol. 13(11), pages 1-16, June.
    4. Hui Zhang & Oskar Wallmark, 2017. "Limitations and Constraints of Eddy-Current Loss Models for Interior Permanent-Magnet Motors with Fractional-Slot Concentrated Windings," Energies, MDPI, vol. 10(3), pages 1-19, March.
    5. Jyun-You Chen & Shih-Chin Yang & Kai-Hsiang Tu, 2018. "Comparative Evaluation of a Permanent Magnet Machine Saliency-Based Drive with Sine-Wave and Square-Wave Voltage Injection," Energies, MDPI, vol. 11(9), pages 1-15, August.
    6. Damian Caballero & Borja Prieto & Gurutz Artetxe & Ibon Elosegui & Miguel Martinez-Iturralde, 2018. "Node Mapping Criterion for Highly Saturated Interior PMSMs Using Magnetic Reluctance Network," Energies, MDPI, vol. 11(9), pages 1-19, August.
    7. Oğuz Mısır & Mehmet Akar, 2022. "Efficiency and Core Loss Map Estimation with Machine Learning Based Multivariate Polynomial Regression Model," Mathematics, MDPI, vol. 10(19), pages 1-18, October.
    8. Thanh Anh Huynh & Min-Fu Hsieh, 2018. "Performance Analysis of Permanent Magnet Motors for Electric Vehicles (EV) Traction Considering Driving Cycles," Energies, MDPI, vol. 11(6), pages 1-24, May.
    9. Zhengming Shu & Xiaoyong Zhu & Li Quan & Yi Du & Chang Liu, 2017. "Electromagnetic Performance Evaluation of an Outer-Rotor Flux-Switching Permanent Magnet Motor Based on Electrical-Thermal Two-Way Coupling Method," Energies, MDPI, vol. 10(5), pages 1-16, May.
    10. Yuqing Yao & Chunhua Liu & Christopher H.T. Lee, 2018. "Quantitative Comparisons of Six-Phase Outer-Rotor Permanent-Magnet Brushless Machines for Electric Vehicles," Energies, MDPI, vol. 11(8), pages 1-18, August.
    11. Konstantina Bitsi & Sjoerd G. Bosga & Oskar Wallmark, 2022. "Design Aspects and Performance Evaluation of Pole-Phase Changing Induction Machines," Energies, MDPI, vol. 15(19), pages 1-18, September.
    12. Chengming Zhang & Qingbo Guo & Liyi Li & Mingyi Wang & Tiecheng Wang, 2017. "System Efficiency Improvement for Electric Vehicles Adopting a Permanent Magnet Synchronous Motor Direct Drive System," Energies, MDPI, vol. 10(12), pages 1-27, December.
    13. Kai Zhou & Min Ai & Dongyang Sun & Ningzhi Jin & Xiaogang Wu, 2019. "Field Weakening Operation Control Strategies of PMSM Based on Feedback Linearization," Energies, MDPI, vol. 12(23), pages 1-18, November.
    14. Yi Li & Feng Chai & Zaixin Song & Zongyang Li, 2017. "Analysis of Vibrations in Interior Permanent Magnet Synchronous Motors Considering Air-Gap Deformation," Energies, MDPI, vol. 10(9), pages 1-18, August.

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