Capacitance Prediction Using Multi-cascade Convolutional Neural Network for Efficient Wireless Power Transfer

Meng Wang; Mingshen Li; Qi Luo; Yanyan Shi

doi:10.1109/LAWP.2024.3390201

Capacitance Prediction Using Multi-cascade Convolutional Neural Network for Efficient Wireless Power Transfer

Meng Wang, Mingshen Li, Qi Luo, Yanyan Shi

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Abstract

The efficiency of the wireless power transfer is significantly impacted by misalignment between the transmitting and receiving coils due to impedance mismatching. To tackle this issue, an efficient power transfer solution is proposed, employing a capacitance prediction method based on a multi-cascade convolutional neural network. In the study, the impedance matching characteristic of a magnetic coupling resonant wireless power transfer system with an impedance matching network is analyzed. After that, a neural network-driven approach is introduced to establish a mapping between reflection impedance and the optimal capacitance, and the impedance matching performance of the system is assessed in the presence of coil misalignments.

Original language	English
Pages (from-to)	1-6
Number of pages	6
Journal	IEEE Antennas and Wireless Propagation Letters
DOIs	https://doi.org/10.1109/LAWP.2024.3390201
Publication status	E-pub ahead of print - 17 Apr 2024

Keywords

Coils
Capacitance
Impedance
Impedance matching
Capacitors
Wireless power transfer
Feature extraction
Multi-cascade convolutional neural network
impedance matching
wireless power transfer (WPT)

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10.1109/LAWP.2024.3390201Licence: Unspecified

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© 2024, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This is the accepted manuscript version of a conference paper which has been published in final form at https://doi.org/10.1109/LAWP.2024.3390201
Accepted author manuscript, 808 KBLicence: Unspecified

Cite this

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title = "Capacitance Prediction Using Multi-cascade Convolutional Neural Network for Efficient Wireless Power Transfer",

abstract = "The efficiency of the wireless power transfer is significantly impacted by misalignment between the transmitting and receiving coils due to impedance mismatching. To tackle this issue, an efficient power transfer solution is proposed, employing a capacitance prediction method based on a multi-cascade convolutional neural network. In the study, the impedance matching characteristic of a magnetic coupling resonant wireless power transfer system with an impedance matching network is analyzed. After that, a neural network-driven approach is introduced to establish a mapping between reflection impedance and the optimal capacitance, and the impedance matching performance of the system is assessed in the presence of coil misalignments.",

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N1 - © 2024, IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. This is the accepted manuscript version of a conference paper which has been published in final form at https://doi.org/10.1109/LAWP.2024.3390201

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AB - The efficiency of the wireless power transfer is significantly impacted by misalignment between the transmitting and receiving coils due to impedance mismatching. To tackle this issue, an efficient power transfer solution is proposed, employing a capacitance prediction method based on a multi-cascade convolutional neural network. In the study, the impedance matching characteristic of a magnetic coupling resonant wireless power transfer system with an impedance matching network is analyzed. After that, a neural network-driven approach is introduced to establish a mapping between reflection impedance and the optimal capacitance, and the impedance matching performance of the system is assessed in the presence of coil misalignments.

KW - Coils

KW - Capacitance

KW - Impedance

KW - Impedance matching

KW - Capacitors

KW - Wireless power transfer

KW - Feature extraction

KW - Multi-cascade convolutional neural network

KW - impedance matching

KW - wireless power transfer (WPT)

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JO - IEEE Antennas and Wireless Propagation Letters

JF - IEEE Antennas and Wireless Propagation Letters

ER -

Capacitance Prediction Using Multi-cascade Convolutional Neural Network for Efficient Wireless Power Transfer

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