IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v313y2024ics0360544224037964.html

Optimization of electromagnetic vibration for integrated electric drive systems based on electric vehicle driving cycle considering energy consumption

Author

Listed:
  • Sun, Zhicheng
  • Hu, Jianjun
  • Yao, Zutang
  • Xue, Shouzhi

Abstract

To reduce the vibration of the integrated electric drive system (IEDS) in actual operation condition and enhance vehicle comfort, the method is proposed to characterize the working characteristics of the IEDS in the electric vehicle driving cycle (EVDC) that can reflect the daily travel conditions of electric vehicles by using representative points. Then, based on the representative points, the multi-objective optimization method was proposed to optimize the electromagnetic force harmonics and motor loss of the electric drive system under complex operating conditions. This method utilizes particle swarm optimization algorithm to obtain the Pareto solution set of the optimization objectives, and uses weighted sum method (WSM) to determine the best-balanced solution. The optimization results show that the multi-objective optimization method proposed in this paper reduces the electromagnetic harmonic as the optimization objective by more than 13 %. Additionally, it expands the high-efficiency region of the IEDS, reducing the energy consumption of the vehicle by 4.7 % in the EVDC, thereby decreasing vehicle energy consumption and improving vehicle comfort.

Suggested Citation

  • Sun, Zhicheng & Hu, Jianjun & Yao, Zutang & Xue, Shouzhi, 2024. "Optimization of electromagnetic vibration for integrated electric drive systems based on electric vehicle driving cycle considering energy consumption," Energy, Elsevier, vol. 313(C).
    • Handle: RePEc:eee:energy:v:313:y:2024:i:c:s0360544224037964
    DOI: 10.1016/j.energy.2024.134018
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544224037964
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2024.134018?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. 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).
    2. Yang, Yang & He, Qiang & Fu, Chunyun & Liao, Shuiping & Tan, Peng, 2020. "Efficiency improvement of permanent magnet synchronous motor for electric vehicles," Energy, Elsevier, vol. 213(C).
    3. Li Zhao & Kun Li & Wu Zhao & Han-Chen Ke & Zhen Wang, 2022. "A Sticky Sampling and Markov State Transition Matrix Based Driving Cycle Construction Method for EV," Energies, MDPI, vol. 15(3), pages 1-19, January.
    4. Liu, Qin & Zhang, Wencan & Zhang, Zhongbo & Qin, Qichao, 2022. "A drive system global control strategy for electric vehicle based on optimized acceleration curve," Energy, Elsevier, vol. 248(C).
    5. Zhang, Yahui & Wang, Zimeng & Tian, Yang & Wang, Zhong & Kang, Mingxin & Xie, Fangxi & Wen, Guilin, 2024. "Pre-optimization-assisted deep reinforcement learning-based energy management strategy for a series–parallel hybrid electric truck," Energy, Elsevier, vol. 302(C).
    6. Ye, Yiming & Wang, Hanchen & Xu, Bin & Zhang, Jiangfeng, 2023. "An imitation learning-based energy management strategy for electric vehicles considering battery aging," Energy, Elsevier, vol. 283(C).
    7. Hu, Jianjun & Guo, Qi & Sun, Zhicheng & Yang, Dianzhao, 2023. "Study on low-frequency torsional vibration suppression of integrated electric drive system considering nonlinear factors," Energy, Elsevier, vol. 284(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhu, Ming & Li, Yingkun & Chen, Xiong & Zhou, Changsheng & Mi, Junjie & Liu, Tiantian & Li, Weixuan, 2025. "Nonlinear adaptive robust control strategy of thrust regulation for pintle solid-propellant rocket motors," Energy, Elsevier, vol. 329(C).
    2. Liu, Feng & Wang, Xiuhe & Sun, Lingling & Wei, Hongye & Li, Changbin & Ren, Jie, 2025. "Improved 3D hybrid thermal model for global temperature distribution prediction of interior permanent magnet synchronous motor," Energy, Elsevier, vol. 315(C).
    3. Lee, Jung-Hwan & Lim, Sang-Kil & Kwon, Kihan, 2025. "Optimization of multi-motor and multi-speed powertrain system for electric vehicles based on efficiency characteristics between motor and inverter," Energy, Elsevier, vol. 337(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    2. Osman Emre Özçiflikçi & Mikail Koç & Serkan Bahçeci, 2025. "Evaluation of Maximum Torque per Ampere Control Method for Interior Permanent Magnet Machine Drives on dSpace with Emphasis on Potential Practical Issues for High Energy Efficiency," Energies, MDPI, vol. 18(15), pages 1-20, August.
    3. Sun, Peng & Song, Qiang & Wang, Wei, 2024. "Minimum loss modulation method for various power factor angles," Energy, Elsevier, vol. 310(C).
    4. Hu, Jianjun & Guo, Qi & Sun, Zhicheng & Yang, Dianzhao, 2023. "Study on low-frequency torsional vibration suppression of integrated electric drive system considering nonlinear factors," Energy, Elsevier, vol. 284(C).
    5. 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).
    6. He, Qiang & Yang, Yang & Luo, Chang & Zhai, Jun & Luo, Ronghua & Fu, Chunyun, 2022. "Energy recovery strategy optimization of dual-motor drive electric vehicle based on braking safety and efficient recovery," Energy, Elsevier, vol. 248(C).
    7. Kwon, Kihan & Lee, Jung-Hwan & Lim, Sang-Kil, 2023. "Optimization of multi-speed transmission for electric vehicles based on electrical and mechanical efficiency analysis," Applied Energy, Elsevier, vol. 342(C).
    8. Vasileios I. Vlachou & Georgios K. Sakkas & Fotios P. Xintaropoulos & Maria Sofia C. Pechlivanidou & Themistoklis D. Kefalas & Marina A. Tsili & Antonios G. Kladas, 2024. "Overview on Permanent Magnet Motor Trends and Developments," Energies, MDPI, vol. 17(2), pages 1-48, January.
    9. Liu, Weirong & Yao, Pengfei & Wu, Yue & Duan, Lijun & Li, Heng & Peng, Jun, 2025. "Imitation reinforcement learning energy management for electric vehicles with hybrid energy storage system," Applied Energy, Elsevier, vol. 378(PA).
    10. Mateusz Oszczypała & Jarosław Ziółkowski & Jerzy Małachowski, 2022. "Analysis of Light Utility Vehicle Readiness in Military Transportation Systems Using Markov and Semi-Markov Processes," Energies, MDPI, vol. 15(14), pages 1-24, July.
    11. Wang, Shaoshuai & Zhang, Jianzhong & Deng, Fujin, 2024. "Design and optimization of high torque density flux modulated multi-winding permanent magnet machines for electric vehicles," Energy, Elsevier, vol. 304(C).
    12. Xin Liu & Wujing Li & Xiangyang Guo & Biao Su & Shuyu Guo & Yiran Jing & Xi Zhang, 2025. "Advancements in Energy-Storage Technologies: A Review of Current Developments and Applications," Sustainability, MDPI, vol. 17(18), pages 1-39, September.
    13. Hayatullah Nory & Ahmet Yildiz & Serhat Aksun & Cansu Aksoy, 2025. "Design and Analysis of an IE6 Hyper-Efficiency Permanent Magnet Synchronous Motor for Electric Vehicle Applications," Energies, MDPI, vol. 18(17), pages 1-14, September.
    14. Sun, Xiaodong & Dong, Ziyin & Cai, Yingfeng & Jin, Zhijia & Lei, Gang & Tian, Xiang, 2025. "A comprehensive review of design optimization methods for hybrid electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 217(C).
    15. Lu, Qizhe & Fang, Jiayi & Yang, Chao & Tang, Wenbin, 2025. "Maximum entropy deep inverse reinforcement learning-based energy management strategy for hybrid electric logistics trucks," Energy, Elsevier, vol. 338(C).
    16. Liu, Feng & Wang, Xiuhe & Sun, Lingling & Wei, Hongye & Li, Changbin & Ren, Jie, 2025. "Improved 3D hybrid thermal model for global temperature distribution prediction of interior permanent magnet synchronous motor," Energy, Elsevier, vol. 315(C).
    17. Yang, Wanli & Guo, Xin & Dai, Wei, 2026. "Improved soft actor-critic based health energy management strategy design for a dual-motor battery electric vehicle," Energy, Elsevier, vol. 344(C).
    18. Mateusz Oszczypała & Jarosław Ziółkowski & Jerzy Małachowski, 2023. "Modelling the Operation Process of Light Utility Vehicles in Transport Systems Using Monte Carlo Simulation and Semi-Markov Approach," Energies, MDPI, vol. 16(5), pages 1-31, February.
    19. Wenna Xu & Hao Huang & Chun Wang & Shuai Xia & Xinmei Gao, 2025. "A Comparative Study of Energy Management Strategies for Battery-Ultracapacitor Electric Vehicles Based on Different Deep Reinforcement Learning Methods," Energies, MDPI, vol. 18(5), pages 1-18, March.
    20. Gu, Jianqiang & Wu, Zhan & Song, Yubing & Nicolescu, Ana-Cristina, 2024. "A win-win relationship? New evidence on artificial intelligence and new energy vehicles," Energy Economics, Elsevier, vol. 134(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:313:y:2024:i:c:s0360544224037964. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.