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Article · IEEE Access · January 1, 2025

High-Precision and Robust DNN Model for Predicting Quality Factor of WPT-Oriented Slotted Ground Resonators

Kassen Dautov, Gulnur Tolebi, Mohammad Hashmi, Anwar Jarndal, E'qab R. Almajali, Galymzhan Nauryzbayev

Mohammad HashmiGalymzhan Nauryzbayev
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Abstract

Machine learning (ML) has emerged as an effective approach for optimizing circuit design and bringing a paradigm shift in the development of wireless power transfer (WPT) systems. Being the main building blocks of near-field WPT, the slotted ground plane (SGP) resonators with a high quality factor (Q) enhance power transfer efficiency. However, it is pertinent to note that the resonator size, slot shape, and location result in distinct Q outcomes. Therefore, this work delves into the use of ML for predicting Q of SGP resonators. It can be predicted through a deep learning approach, owing to its capacity to learn from the implicit associations between input and output data. Hence, a deep neural network (DNN) model was designed using 20006 data files generated by electromagnetic (EM) simulations. DNN demonstrated its effectiveness, achieving an accuracy of 99.26%, thereby outperforming other benchmark ML models. Furthermore, the model proved its robustness in predicting Q of variously sized resonators and showed 98.3% accuracy. Subsequently, this enables anticipating the Q metric of scaled resonators without the need for exhaustive EM simulations. The predicted Q values were supported through experimental measurements. Finally, the SGP resonators were aptly employed to exhibit the near-field WPT system.

Wireless Power Transfer SystemsAdvanced DC-DC ConvertersInduction Heating and Inverter Technologywireless power transferdeep neural networknear-field power transfer

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