نهمین کنفرانس بین المللی فناوری و مدیریت انرژی

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کد مقاله icemt9-1199

عنوان Mutual Inductance Prediction of Coaxial Rectangular Planar Coils Using Artificial Neural Network Regression

نویسندگان Mahdi Asadi - Amir Musa Abazari

چکیده Wireless Power Transfer (WPT) systems, particularly those employing coils with inductive coupling, play a crucial role in diverse industries, requiring precise mutual inductance predictions to ensure effective energy transmission. This article explores the significance of mutual inductance in WPT, emphasizing its role in designing robust systems for applications such as electric vehicles and medical implants. Despite advancements, research gaps persist, prompting the use of data-driven algorithms, specifically Artificial Neural Networks (ANNs), to predict mutual inductance accurately. The methodology involves generating datasets using a numerical solution model, employing ANN regression models for training, and optimizing hyperparameters through a trial-and-error approach. The results demonstrate the effectiveness of the proposed approach, with the best-fitted model achieving an R2 score of 0.987.

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