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Hybrid electric vehicle electric motors for optimum energy efficiency: A computationally efficient design

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  • Wei, Dong
  • He, Hongwen
  • Cao, Jianfei

Abstract

This paper proposes a new optimal design approach of a permanent magnet synchronous motor (PMSM) in hybrid electric vehicles (HEVs). It aims to solve the key research problem of how to find a viable and computationally efficient solution to achieve the maximum energy efficiency of the motor over the driving cycles. A one-dimensional analytical model is, firstly, built and validated to design the geometric parameters and calculate motor efficiency, maintaining high fidelity calculation with low computational cost. Then, by analyzing the motor energy distribution of the driving cycle, the energy efficiency is characterized by representative points, which can dramatically reduce the computation time during the optimal design. Leveraging by these points, the approximation model is presented to replace the PMSM optimization model to further reduce the computational cost. Finally, a combinatorial optimization algorithm is developed to return and characterize the PMSM optimal design in the studied scenario benefiting in the energy-loss reduction. The performance of the approach has been illustrated and verified with a HEV dynamics model. The results show that the optimal design approach can reduce the motor energy losses by 18.35% and improve the HEV fuel economy by 3.2% over the driving cycle compared with the initial design.

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  • Wei, Dong & He, Hongwen & Cao, Jianfei, 2020. "Hybrid electric vehicle electric motors for optimum energy efficiency: A computationally efficient design," Energy, Elsevier, vol. 203(C).
    • Handle: RePEc:eee:energy:v:203:y:2020:i:c:s0360544220308860
    DOI: 10.1016/j.energy.2020.117779
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    References listed on IDEAS

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    1. Li, Shuangqi & He, Hongwen & Li, Jianwei, 2019. "Big data driven lithium-ion battery modeling method based on SDAE-ELM algorithm and data pre-processing technology," Applied Energy, Elsevier, vol. 242(C), pages 1259-1273.
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    5. 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:

    1. Nuria Novas & Alfredo Alcayde & Isabel Robalo & Francisco Manzano-Agugliaro & Francisco G. Montoya, 2020. "Energies and Its Worldwide Research," Energies, MDPI, vol. 13(24), pages 1-41, December.
    2. Kahourzade, Solmaz & Mahmoudi, Amin & Roshandel, Emad & Cao, Zhi, 2021. "Optimal design of Axial-Flux Induction Motors based on an improved analytical model," Energy, Elsevier, vol. 237(C).
    3. Selvin Raj, Jaya Antony Perinba & Asirvatham, Lazarus Godson & Angeline, Appadurai Anitha & Manova, Stephen & Rakshith, Bairi Levi & Bose, Jefferson Raja & Mahian, Omid & Wongwises, Somchai, 2024. "Thermal management strategies and power ratings of electric vehicle motors," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    4. 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).
    5. Kang Ma & Ye Liu & Ziqiang Wei & Jianfei Yang & Baocheng Guo, 2022. "A Novel High-Speed Permanent Magnet Synchronous Motor for Hydrogen Recirculation Side Channel Pumps in Fuel Cell Systems," Energies, MDPI, vol. 15(23), pages 1-13, November.
    6. Xiong, Yongqing & Cheng, Qian, 2023. "Effects of new energy vehicle adoption on provincial energy efficiency in China: From the perspective of regional imbalances," Energy, Elsevier, vol. 281(C).
    7. Dimitrios Rimpas & Stavrοs D. Kaminaris & Dimitrios D. Piromalis & George Vokas & Konstantinos G. Arvanitis & Christos-Spyridon Karavas, 2023. "Comparative Review of Motor Technologies for Electric Vehicles Powered by a Hybrid Energy Storage System Based on Multi-Criteria Analysis," Energies, MDPI, vol. 16(6), pages 1-24, March.

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