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

Manuele Bertoluzzo, Paolo Di Barba, Michele Forzan, Maria Evelina Mognaschi and Elisabetta Sieni ()
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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

Energies, 2023, vol. 16, issue 23, 1-17

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.

Keywords: deep learning; dynamic wireless power transfer system; fast surrogate model; optimization; magnetic field; finite element analysis; field-circuit model (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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