IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i7p1615-d1365670.html
   My bibliography  Save this article

Mutual Inductance Estimation Using an ANN for Inductive Power Transfer in EV Charging Applications

Author

Listed:
  • Gonçalo C. Abrantes

    (Department of Electrical and Computer Engineering, University of Coimbra, Polo 2, 3030-290 Coimbra, Portugal)

  • Valter S. Costa

    (Department of Electrical and Computer Engineering, University of Coimbra, Polo 2, 3030-290 Coimbra, Portugal
    Instituto de Telecomunicações, University of Coimbra, Polo 2, 3030-290 Coimbra, Portugal)

  • Marina S. Perdigão

    (Instituto de Telecomunicações, University of Coimbra, Polo 2, 3030-290 Coimbra, Portugal
    Polytechnic Institute of Coimbra, Coimbra Institute of Engineering, Rua Pedro Nunes-Quinta da Nora, 3030-199 Coimbra, Portugal)

  • Sérgio Cruz

    (Department of Electrical and Computer Engineering, University of Coimbra, Polo 2, 3030-290 Coimbra, Portugal
    Instituto de Telecomunicações, University of Coimbra, Polo 2, 3030-290 Coimbra, Portugal)

Abstract

In the context of inductive power transfer (IPT) for electric vehicle (EV) charging, the precise determination of the mutual inductance between the magnetic pads is of critical importance. The value of this inductance varies depending on the EV positioning, affecting the power transfer capability. Therefore, the precise determination of its value yields various advantages, particularly by contributing to the optimization of the charging process of the EV batteries, since it offers the possibility of adjusting the position of the vehicle depending on the level of misalignment. Within this framework, algorithms grounded in artificial intelligence (AI) techniques emerge as promising solutions. This research work revolves around the estimation of the mutual inductance in a wireless inductive power transfer system using a resonant converter topology, implemented in MATLAB/Simulink ® R2021b. The system output was developed to emulate the behavior of a battery charger. To estimate this parameter, an artificial neural network (ANN) was developed. Given the characteristics of the system, the features were chosen in a way that they could provide a clear indication to the ANN if the vehicle position changed, independently of the charging power. In the pursuit of creating a robust AI model, the training dataset contained approximately 1% of the available data. Upon the analysis of the results, it was verified that the largest estimation error observed was around 3%, occurring at the lowest charging power considered. Hence, it can be inferred that the proposed ANN exhibits the capability to accurately estimate the value of mutual inductance in this type of system.

Suggested Citation

  • Gonçalo C. Abrantes & Valter S. Costa & Marina S. Perdigão & Sérgio Cruz, 2024. "Mutual Inductance Estimation Using an ANN for Inductive Power Transfer in EV Charging Applications," Energies, MDPI, vol. 17(7), pages 1-19, March.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:7:p:1615-:d:1365670
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/7/1615/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/7/1615/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ainur Rakhymbay & Anvar Khamitov & Mehdi Bagheri & Batyrbek Alimkhanuly & Maxim Lu & Toan Phung, 2018. "Precise Analysis on Mutual Inductance Variation in Dynamic Wireless Charging of Electric Vehicle," Energies, MDPI, vol. 11(3), pages 1-21, March.
    Full references (including those not matched with items on IDEAS)

    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. Jaber Abu Qahouq & Yuan Cao, 2018. "Control Scheme and Power Electronics Architecture for a Wirelessly Distributed and Enabled Battery Energy Storage System," Energies, MDPI, vol. 11(7), pages 1-20, July.
    2. Shijun Chen & Huwei Chen & Shanhe Jiang, 2019. "Optimal Decision-Making to Charge Electric Vehicles in Heterogeneous Networks: Stackelberg Game Approach," Energies, MDPI, vol. 12(2), pages 1-20, January.
    3. Yong Tian & Jindong Tian & Dong Li & Shijie Zhou, 2018. "A Multiple Legs Inverter with Real Time–Reflected Load Detection Used in the Dynamic Wireless Charging System of Electric Vehicles," Energies, MDPI, vol. 11(5), pages 1-20, May.
    4. Vladimir Kindl & Martin Zavrel & Pavel Drabek & Tomas Kavalir, 2018. "High Efficiency and Power Tracking Method for Wireless Charging System Based on Phase-Shift Control," Energies, MDPI, vol. 11(8), pages 1-19, August.
    5. Stoyka, Kateryna & Di Mambro, Gennaro & Femia, Nicola & Maffucci, Antonio & Ventre, Salvatore & Villone, Fabio, 2021. "Behavioral modeling of Wireless Power Transfer System coils," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 183(C), pages 208-220.

    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:gam:jeners:v:17:y:2024:i:7:p:1615-:d:1365670. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.