Double-D (DD)-coils are essential components in dynamic wireless power transfer (DWPT) systems as they are bipolar in character, allowing a smooth flux passage through them. This paper presents a deep-learning model that helps predict the coils' mutual inductance, the coupling coefficient with and without lateral and angular offsets, and other design parameters. It is shown that the proposed deep-learning models work orders of magnitudes of times faster than conventional numerical software.
Parameterized Models of Double-D Coils for DWPT Applications Through Deep Learning Techniques
Longo M.;Leva S.;
2025-01-01
Abstract
Double-D (DD)-coils are essential components in dynamic wireless power transfer (DWPT) systems as they are bipolar in character, allowing a smooth flux passage through them. This paper presents a deep-learning model that helps predict the coils' mutual inductance, the coupling coefficient with and without lateral and angular offsets, and other design parameters. It is shown that the proposed deep-learning models work orders of magnitudes of times faster than conventional numerical software.File in questo prodotto:
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