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Systematic multi-level optimization design and dynamic control of less-rare-earth hybrid permanent magnet motor for all-climatic electric vehicles

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  • Zhu, Xiaoyong
  • Fan, Deyang
  • Xiang, Zixuan
  • Quan, Li
  • Hua, Wei
  • Cheng, Ming

Abstract

Motors that convert electrical energy to mechanical energy, have been considered as one of the most crucial components of all-climatic electric vehicles (EVs). In order to realize high-quality energy conversion, motor optimization has attracted considerable attention in the research field of motors. In this paper, systematic multi-level optimization design and dynamic control strategy is proposed for a less-rare-earth hybrid permanent magnet (LRE-HPM) motor. In the proposed systematic optimization strategy, the design requirements of the motor design level and control level are considered comprehensively. In the motor design level, the comprehensive sensitivity analysis is adopted to stratify design parameters, and the response surface method and multi-objective genetic algorithm are implemented respectively. In the motor control level, the resonance compensation strategy is established to further suppress torque ripple and speed vibration. Finally, a prototype motor is built and tested. Both simulation and experimental results not only verify the effectiveness of the proposed systematic optimization and dynamic control strategy, but also offer a research orientation for realizing high-quality energy conversion of all-climatic EVs.

Suggested Citation

  • Zhu, Xiaoyong & Fan, Deyang & Xiang, Zixuan & Quan, Li & Hua, Wei & Cheng, Ming, 2019. "Systematic multi-level optimization design and dynamic control of less-rare-earth hybrid permanent magnet motor for all-climatic electric vehicles," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    • Handle: RePEc:eee:appene:v:253:y:2019:i:c:55
    DOI: 10.1016/j.apenergy.2019.113549
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    References listed on IDEAS

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    1. Guo, Qingbo & Zhang, Chengming & Li, Liyi & Gerada, David & Zhang, Jiangpeng & Wang, Mingyi, 2017. "Design and implementation of a loss optimization control for electric vehicle in-wheel permanent-magnet synchronous motor direct drive system," Applied Energy, Elsevier, vol. 204(C), pages 1317-1332.
    2. He, Hongwen & Guo, Xiaoguang, 2018. "Multi-objective optimization research on the start condition for a parallel hybrid electric vehicle," Applied Energy, Elsevier, vol. 227(C), pages 294-303.
    3. He, Hongwen & Zhou, Nana & Guo, Jinquan & Zhang, Zheng & Lu, Bing & Sun, Chao, 2018. "Tolerance analysis of electrified vehicles on the motor demagnetization fault: From an energy perspective," Applied Energy, Elsevier, vol. 227(C), pages 239-248.
    4. Hung, Yi-Hsuan & Wu, Chien-Hsun, 2015. "A combined optimal sizing and energy management approach for hybrid in-wheel motors of EVs," Applied Energy, Elsevier, vol. 139(C), pages 260-271.
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    Cited by:

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    2. Vamsi Krishna Reddy, Aala Kalananda & Venkata Lakshmi Narayana, Komanapalli, 2022. "Meta-heuristics optimization in electric vehicles -an extensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    3. Jiaxing Lei & Chaofan Wei & Hui Yang & Hao Zheng & Wenjia Wang & Shuang Feng, 2019. "Design Considerations of Switched Flux Memory Machine with Partitioned Stators," Energies, MDPI, vol. 12(20), pages 1-15, October.
    4. Wenye Wu & Qingzhang Chen & Xiaoyong Zhu & Fuzhou Zhao & Zixuan Xiang, 2020. "Electromagnetic–Mechanical Coupling Optimization of an IPM Synchronous Machine with Multi Flux Barriers," Energies, MDPI, vol. 13(7), pages 1-13, April.

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