| Title: |
A Deep Learning Approach to Improve the Control of Dynamic Wireless Power Transfer Systems |
| Authors: |
Bertoluzzo M.; Di Barba P.; Forzan M.; Mognaschi M. E.; Sieni E. |
| Contributors: |
Bertoluzzo, M.; Di Barba, P.; Forzan, M.; Mognaschi, M. E.; Sieni, E. |
| Publication Year: |
2023 |
| Collection: |
IRIS UNIPV (Università degli studi di Pavia) |
| Subject Terms: |
deep learning; dynamic wireless power transfer system; fast surrogate model; field-circuit model; finite element analysi; magnetic field; optimization |
| Description: |
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. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| Relation: |
info:eu-repo/semantics/altIdentifier/wos/WOS:001116960000001; volume:16; issue:23; journal:ENERGIES; https://hdl.handle.net/11571/1487581 |
| DOI: |
10.3390/en16237865 |
| Availability: |
https://hdl.handle.net/11571/1487581; https://doi.org/10.3390/en16237865 |
| Rights: |
info:eu-repo/semantics/openAccess |
| Accession Number: |
edsbas.36F00332 |
| Database: |
BASE |