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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
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    References listed on IDEAS

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    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.
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