IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v241y2025ics0960148124024029.html
   My bibliography  Save this article

Energy-efficient optimization of magnetized pavement structures for interoperability of inductive power transfer magnetic couplers in electric vehicles

Author

Listed:
  • Li, Yanjie
  • Li, Feng
  • Zhou, Siqi

Abstract

Integrating inductive power transfer (IPT) systems into pavements is an effective way to address the inconvenience of charging electric vehicles (EVs), thereby promoting renewable energy use. Magnetized pavements can enhance the efficiency of wireless charging systems. However, the factor of magnetized pavements has not been considered in coil interoperability studies. This paper investigated the interoperability of four types of coupling coils commonly used in IPT systems: circular (C), rectangular (R), double D (DD), bipolar (BP). A total of sixteen kinds of groups of couplers were composed within a magnetized pavement medium. The study compared the changes in coil coupling under air and magnetized pavement mediums, explored the effects of transmission distance and offset distance on coil coupling and IPT system performance, and clarified the evaluation principle of considering interoperability from the perspective of energy loss. The results showed that magnetized pavement enhanced the coupling performance of C-C, C-R, C-DD, C-BP, R-C, R-R, and R-BP couplers, while it weakened the coupling performance of others. The sixteen kinds of couplers can be divided into three categories, three categories: Type Ⅰ (C-C, C-R, R-C, R-R) with highest coupling coefficients, Type Ⅱ couplers, represented by DD-DD, reaching coupling zero at 80 mm offset in Y-direction, and Type Ⅲ couplers, represented by DD-C, reaching zero when aligned, requiring transverse offset for energy transfer. To reduce energy loss, transmission efficiency should be the primary evaluation index when comparing interoperability. The IPT system with C-C coupler achieved the highest output power of 63.8 W, while the R-R coupler achieved the highest transmission efficiency of 69.6 %. When the R coil was used as the primary coil, the efficiency of the corresponding four couplers was generally higher than others, and interoperability was the best. In the process of wireless charging technology promotion, it is recommended to adopt R-R type couplers to promote the development of electric vehicle industry and improve the utilization of renewable energy.

Suggested Citation

  • Li, Yanjie & Li, Feng & Zhou, Siqi, 2025. "Energy-efficient optimization of magnetized pavement structures for interoperability of inductive power transfer magnetic couplers in electric vehicles," Renewable Energy, Elsevier, vol. 241(C).
    • Handle: RePEc:eee:renene:v:241:y:2025:i:c:s0960148124024029
    DOI: 10.1016/j.renene.2024.122334
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148124024029
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2024.122334?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. Yao Pei & Yann Le Bihan & Mohamed Bensetti & Lionel Pichon, 2021. "Comparison of Coupling Coils for Static Inductive Power-Transfer Systems Taking into Account Sources of Uncertainty," Sustainability, MDPI, vol. 13(11), pages 1-13, June.
    2. Qiao, Baihao & Liu, Jing, 2020. "Multi-objective dynamic economic emission dispatch based on electric vehicles and wind power integrated system using differential evolution algorithm," Renewable Energy, Elsevier, vol. 154(C), pages 316-336.
    3. Han, Xiaojuan & Wei, Zixuan & Hong, Zhenpeng & Zhao, Song, 2020. "Ordered charge control considering the uncertainty of charging load of electric vehicles based on Markov chain," Renewable Energy, Elsevier, vol. 161(C), pages 419-434.
    4. Zhou, Junfeng & Zhang, Yanhui & Zhang, Yubo & Shang, Wen-Long & Yang, Zhile & Feng, Wei, 2022. "Parameters identification of photovoltaic models using a differential evolution algorithm based on elite and obsolete dynamic learning," Applied Energy, Elsevier, vol. 314(C).
    5. Kai Song & Yu Lan & Xian Zhang & Jinhai Jiang & Chuanyu Sun & Guang Yang & Fengshuo Yang & Hao Lan, 2023. "A Review on Interoperability of Wireless Charging Systems for Electric Vehicles," Energies, MDPI, vol. 16(4), pages 1-22, February.
    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. Erel, Mehmet Zahid & Özdemir, Mehmet Akif & Aydemir, Mehmet Timur, 2025. "Solar energy-powered wireless charging system for three-wheeled e-scooter applications," Renewable Energy, Elsevier, vol. 246(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. Chermite, Charaf & Douiri, Moulay Rachid, 2025. "A novel hybrid approach combining Differentiated Creative Search with adaptive refinement for photovoltaic parameter extraction," Renewable Energy, Elsevier, vol. 245(C).
    2. Sheng, Wanxing & Li, Rui & Yan, Tao & Tseng, Ming-Lang & Lou, Jiale & Li, Lingling, 2023. "A hybrid dynamic economics emissions dispatch model: Distributed renewable power systems based on improved COOT optimization algorithm," Renewable Energy, Elsevier, vol. 204(C), pages 493-506.
    3. Nawal Rai & Amel Abbadi & Fethia Hamidia & Nadia Douifi & Bdereddin Abdul Samad & Khalid Yahya, 2023. "Biogeography-Based Teaching Learning-Based Optimization Algorithm for Identifying One-Diode, Two-Diode and Three-Diode Models of Photovoltaic Cell and Module," Mathematics, MDPI, vol. 11(8), pages 1-30, April.
    4. Phillip K. Agbesi & Rico Ruffino & Marko Hakovirta, 2024. "Creating an optimal electric vehicle ecosystem: an investigation of electric vehicle stakeholders and ecosystem trends in the US," SN Business & Economics, Springer, vol. 4(3), pages 1-36, March.
    5. Qiao, Dongdong & Wei, Xuezhe & Fan, Wenjun & Jiang, Bo & Lai, Xin & Zheng, Yuejiu & Tang, Xiaolin & Dai, Haifeng, 2022. "Toward safe carbon–neutral transportation: Battery internal short circuit diagnosis based on cloud data for electric vehicles," Applied Energy, Elsevier, vol. 317(C).
    6. Li, Zening & Su, Su & Jin, Xiaolong & Chen, Houhe, 2021. "Distributed energy management for active distribution network considering aggregated office buildings," Renewable Energy, Elsevier, vol. 180(C), pages 1073-1087.
    7. Pradeep Vishnuram & Suresh Panchanathan & Narayanamoorthi Rajamanickam & Vijayakumar Krishnasamy & Mohit Bajaj & Marian Piecha & Vojtech Blazek & Lukas Prokop, 2023. "Review of Wireless Charging System: Magnetic Materials, Coil Configurations, Challenges, and Future Perspectives," Energies, MDPI, vol. 16(10), pages 1-31, May.
    8. Edwidge Raissa Mache Kengne & Alain Soup Tewa Kammogne & Thomas Tatietse Tamo & Ahmad Taher Azar & Ahmed Redha Mahlous & Saim Ahmed, 2023. "Photovoltaic Systems Based on Average Current Mode Control: Dynamical Analysis and Chaos Suppression by Using a Non-Adaptive Feedback Outer Loop Controller," Sustainability, MDPI, vol. 15(10), pages 1-24, May.
    9. Roy, Nibir Baran & Das, Debapriya, 2024. "Stochastic power allocation of distributed tri-generation plants and energy storage units in a zero bus microgrid with electric vehicles and demand response," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    10. Rajeshkumar Ramraj & Ehsan Pashajavid & Sanath Alahakoon & Shantha Jayasinghe, 2023. "Quality of Service and Associated Communication Infrastructure for Electric Vehicles," Energies, MDPI, vol. 16(20), pages 1-28, October.
    11. Ni, Fangyuan & Xiang, Yue & Wang, Shiqian & Hu, Zechun & Liu, Fang & Xu, Xiao & Jiang, Yi & Wang, Yang, 2025. "Charging management of electric vehicles with consumption of renewable energy," Energy, Elsevier, vol. 321(C).
    12. Qun Niu & Ming You & Zhile Yang & Yang Zhang, 2021. "Economic Emission Dispatch Considering Renewable Energy Resources—A Multi-Objective Cross Entropy Optimization Approach," Sustainability, MDPI, vol. 13(10), pages 1-33, May.
    13. Caleb Dunlap & Charles W. Van Neste, 2025. "Magnetic Gear Wireless Power Transfer System: Prototype and Electric Vehicle Charging," Energies, MDPI, vol. 18(3), pages 1-18, January.
    14. Shufu Yuan & Yuzhang Ji & Yongxu Chen & Xin Liu & Weijun Zhang, 2023. "An Improved Differential Evolution for Parameter Identification of Photovoltaic Models," Sustainability, MDPI, vol. 15(18), pages 1-28, September.
    15. Yin, Wanjun & Ji, Jianbo, 2024. "Research on EV charging load forecasting and orderly charging scheduling based on model fusion," Energy, Elsevier, vol. 290(C).
    16. Valentin Rigot & Tanguy Phulpin & Jihen Sakly & Daniel Sadarnac, 2022. "A New 7 kW Air-Core Transformer at 1.5 MHz for Embedded Isolated DC/DC Application," Energies, MDPI, vol. 15(14), pages 1-16, July.
    17. Adrian Chmielewski & Piotr Piórkowski & Jakub Możaryn & Stepan Ozana, 2023. "Sustainable Development of Operational Infrastructure for Electric Vehicles: A Case Study for Poland," Energies, MDPI, vol. 16(11), pages 1-43, June.
    18. Dey, Bishwajit & Misra, Srikant & Garcia Marquez, Fausto Pedro, 2023. "Microgrid system energy management with demand response program for clean and economical operation," Applied Energy, Elsevier, vol. 334(C).
    19. Gaith Baccouche & Mohamed Haikel Chehab & Chokri Ben Salah & Mehdi Tlija & Abdelhamid Rabhi, 2024. "Hybrid PVP/Battery/Fuel Cell Wireless Charging Stations Using High-Frequency Optimized Inverter Technology for Electric Vehicles," Energies, MDPI, vol. 17(14), pages 1-24, July.
    20. Zhang, Xiaofeng & Kong, Xiaoying & Yan, Renshi & Liu, Yuting & Xia, Peng & Sun, Xiaoqin & Zeng, Rong & Li, Hongqiang, 2023. "Data-driven cooling, heating and electrical load prediction for building integrated with electric vehicles considering occupant travel behavior," Energy, Elsevier, vol. 264(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:renene:v:241:y:2025:i:c:s0960148124024029. 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/renewable-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.