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Optimal design of wireless power transfer coils for biomedical implants using machine learning and meta-heuristic algorithms

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Abstract

The classical methods for optimizing wireless power transfer (WPT) systems using mathematical equations or finite element methods can be time-consuming and may only sometimes yield optimal designs. In order to overcome this challenge, this paper introduces a novel approach integrating machine learning techniques with meta-heuristic methods to design and optimize a miniaturized, high-efficiency WPT receiving coil for biomedical applications. The objective is to achieve dimensions below 20 mm, a depth of 30 mm within the tissue, and a frequency of 13.56 MHz. Our approach leverages a neural network (NN) model to predict efficiency based on geometric coil parameters, eliminating the need for complex equations. The NN was trained on a dataset generated via finite element method simulations. We employ two meta-heuristic algorithms, the genetic algorithm and the coyote optimization method, to find optimal parameters that maximize efficiency. Our NN model demonstrates exceptional accuracy, exceeding 97%. Furthermore, the proposed WPT coil design approach enhances transfer efficiency by up to 76%, significantly reducing computation time compared to classical methods. Finally, we validate our results using finite element simulation with Ansys Maxwell 3D.

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Authors and Affiliations

  1. Laboratoire Ingénierie des Systèmes et Télécommunications (LIST), Department of Electrical Systems Engineering, Faculty of Technology, University M’Hamed Bougara, Avenue de l’Indépendance, 35000, Boumerdes, Algeria

    Fatima Bennia, Aimad Boudouda & Fares Nafa

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  1. Fatima Bennia
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  2. Aimad Boudouda
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  3. Fares Nafa
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FB and AB wrote the main manuscript text, and FN corrected the English language of the manuscript. All authors reviewed the manuscript.

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Correspondence to Fatima Bennia.

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Bennia, F., Boudouda, A. & Nafa, F. Optimal design of wireless power transfer coils for biomedical implants using machine learning and meta-heuristic algorithms. Electr Eng (2024). https://doi.org/10.1007/s00202-024-02345-4

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