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A Deep Learning Approach to Improve the Control of Dynamic Wireless Power Transfer Systems

Author

Listed:
  • Manuele Bertoluzzo

    (Department of Industrial Engineering, University of Padua, 35131 Padua, Italy)

  • Paolo Di Barba

    (Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy)

  • Michele Forzan

    (Department of Industrial Engineering, University of Padua, 35131 Padua, Italy)

  • Maria Evelina Mognaschi

    (Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100 Pavia, Italy)

  • Elisabetta Sieni

    (Department of Theoretical Applied Sciences, University of Insubria, 21100 Varese, Italy)

Abstract

In this paper, an innovative approach for the fast estimation of the mutual inductance between transmitting and receiving coils for Dynamic Wireless Power Transfer Systems (DWPTSs) is implemented. To this end, a Convolutional Neural Network (CNN) is used; an image representing the geometry of two coils that are partially misaligned is the input of the CNN, while the output is the corresponding inductance value. Finite Element Analyses are used for the computation of the inductance values needed for CNN training. This way, thanks to a fast and accurate inductance estimated by the CNN, it is possible to properly manage the power converter devoted to charge the battery, avoiding the wind up of its controller when it attempts to transfer power in poor coupling conditions.

Suggested Citation

  • Manuele Bertoluzzo & Paolo Di Barba & Michele Forzan & Maria Evelina Mognaschi & Elisabetta Sieni, 2023. "A Deep Learning Approach to Improve the Control of Dynamic Wireless Power Transfer Systems," Energies, MDPI, vol. 16(23), pages 1-17, December.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:23:p:7865-:d:1292164
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