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Applied Sciences
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AI-Empowered Wireless Power Transfer Technology and Electromagnetic Metamaterials
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AI-Empowered Wireless Power Transfer Technology and Electromagnetic Metamaterials

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 3230

Special Issue Editors

Prof. Dr. Chunfang Wang

E-Mail Website
Guest Editor
Department of Electrical Engineering, Qingdao University, Qingdao 266000, China
Interests: wireless power transfer technologies; electromagnetic and thermal field simulation of inductor devices; small- and medium-sized photovoltaic converters
Dr. Cancan Rong

E-Mail Website
Guest Editor
School of Electrical and Power Engineering, China University of Mining and Technology, Xuzhou 221008, China
Interests: wireless power transfer; low-frequency metamaterial
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hongbo Ma

E-Mail Website
Guest Editor
School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China
Interests: high-efficiency/high-power density power converters; wireless power transfer system; MHz power supply module with wide bandgap devices; power supply for aeronautics; astronautics applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

AI-empowered wireless power transfer (WPT) technology is emerging as a pivotal area of research, driven by its potential to revolutionize the wireless industry. As the demand for convenient, reliable, and safe wireless power transfer continues to grow, WPT systems have garnered significant attention. However, despite the numerous advantages of WPT technology, challenges such as decreasing transfer efficiency with increasing distance and the inevitable generation of large electromagnetic fields (EMFs) in charging systems remain unresolved. Metamaterials, engineered composite materials or structures, have emerged as innovative solutions to address these limitations. Recent advancements in WPT systems have demonstrated their effectiveness in enhancing transfer efficiency and mitigating EMF-induced electromagnetic compatibility issues.

This Special Issue aims to explore innovative applications of AI in WPT systems, including electromagnetic compatibility in metamaterial-based WPT systems, the use of AI in optimizing the performance of metamaterial-based wireless power transfer systems, theoretical studies on metamaterial-based WPT systems, magnetic-inductive and resonant wave phenomena in WPT systems via metamaterials, programmable metamaterials and metasurfaces for WPT systems, and smart wireless power transfer (SWIPT) technologies based on information metamaterials and metasurfaces. By fostering interdisciplinary collaboration and knowledge sharing, this initiative seeks to drive the development and implementation of groundbreaking AI-driven WPT technologies.

Prof. Dr. Chunfang Wang
Dr. Cancan Rong
Prof. Dr. Hongbo Ma
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • wireless power transfer
  • energy efficiency
  • metasurfaces and metamaterials
  • artificial intelligence
  • Internet of Things
  • EM wave manipulation
  • electromagnetic compatibility
  • flexible wireless energy harvesting

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Published Papers (3 papers)

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Research

17 pages, 7230 KB  
Article
Position Identification for UAV Wireless Charging Coupler Using Neural Network and Voltage Fingerprint
by Dechun Yuan, Linxuan Li, Zhihao Han, Jiali Liu and Chaoyue Zhao
Appl. Sci. 2026, 16(7), 3318; https://doi.org/10.3390/app16073318 - 30 Mar 2026
Viewed by 308
Abstract
In response to the significantly reduced efficiency of magnetic coupling wireless charging for unmanned aerial vehicles (UAVs) caused by their high sensitivity to transmitter and receiver coil alignment, as well as landing point errors, a position identification method based on the detection coil-induced [...] Read more.
In response to the significantly reduced efficiency of magnetic coupling wireless charging for unmanned aerial vehicles (UAVs) caused by their high sensitivity to transmitter and receiver coil alignment, as well as landing point errors, a position identification method based on the detection coil-induced voltage fingerprint and embedded neural network regression is proposed. This enables position alignment through a 2D mechanical structure. Firstly, by means of an S–S compensation topology with a bipolar (BP) symmetrical four-detection-coil array deployed at the transmitter, the system effectively suppresses primary direct coupling, ensuring that the position of the receiver coil predominantly determines the detection signals. Secondly, by establishing a voltage fingerprint database during the offline stage and utilizing a multi-layer perceptron–radial basis function (MLP-RBF) regression model, the system achieves high-precision end-to-end positioning and alignment control during the online stage through induced voltage acquisition and data processing. Finally, experiments demonstrate that the proposed method achieves centimeter-level positioning accuracy, with an average error of approximately 1.2 cm and a maximum error of less than 1.8 cm, presenting excellent deployability and engineering applicability. Full article
(This article belongs to the Special Issue AI-Empowered Wireless Power Transfer Technology and Electromagnetic Metamaterials)
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13 pages, 2187 KB  
Article
Inverse Design of Chessboard Metasurface for Broadband Monostatic RCS Reduction Based on CNN-KAN with Attention Mechanism
by Shuang Zeng, Shi Pu, Haoda Xia, Quanshi Qin and Ning Xu
Appl. Sci. 2026, 16(3), 1320; https://doi.org/10.3390/app16031320 - 28 Jan 2026
Viewed by 412
Abstract
An efficient deep-learning-based framework for optimization-based inverse design of electromagnetic metasurface design is proposed in this paper. A novel unit-cell parameterization strategy generates 16-element structures via symmetry operations governed by ten geometric parameters, overcoming the inefficiencies of pixel-based representations. A dataset of 16,000 [...] Read more.
An efficient deep-learning-based framework for optimization-based inverse design of electromagnetic metasurface design is proposed in this paper. A novel unit-cell parameterization strategy generates 16-element structures via symmetry operations governed by ten geometric parameters, overcoming the inefficiencies of pixel-based representations. A dataset of 16,000 parameter–reflection phase pairs is constructed, and a hybrid model combining Convolutional Neural Network (CNN), attention mechanisms, and the Kolmogorov–Arnold Network (KAN) is developed for broadband response prediction. The coefficient of determination (R2) of the proposed model is 0.8837 in the 2–18 GHz band, which is 44.87% higher than the R2 without KAN. The proposed chessboard metasurface achieves a 10 dB monostatic radar cross-section (RCS) reduction under normal incidence over a wide frequency band from 7.4 to 15.2 GHz, corresponding to a relative bandwidth of 69%. This approach provides a generalizable, data-efficient solution for intelligent metasurface design. Full article
(This article belongs to the Special Issue AI-Empowered Wireless Power Transfer Technology and Electromagnetic Metamaterials)
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14 pages, 6264 KB  
Article
A Wireless Power Transfer System for Unmanned Aerial Vehicles with CC/CV Charging Based on Topology Switching
by Jin Chang, Weizhe Cai, Haoyang Wang, Yingzhou Guo, Junhao Wu, Cancan Rong and Chenyang Xia
Appl. Sci. 2025, 15(22), 11932; https://doi.org/10.3390/app152211932 - 10 Nov 2025
Cited by 3 | Viewed by 1761
Abstract
To enhance the battery endurance of unmanned aerial vehicles (UAVs), this article addresses key issues in traditional wireless power transfer (WPT) systems. These issues occur during constant current/constant voltage (CC/CV) switching, such as poor stability, high payload, power loss, and charging instability. Accordingly, [...] Read more.
To enhance the battery endurance of unmanned aerial vehicles (UAVs), this article addresses key issues in traditional wireless power transfer (WPT) systems. These issues occur during constant current/constant voltage (CC/CV) switching, such as poor stability, high payload, power loss, and charging instability. Accordingly, a WPT system based on topology switching is proposed. First, a lightweight compensation topology based on LCC-Series compensated topology (LCC-S) is designed. A tuning capacitor is incorporated, and two switches regulate the switching of the compensation capacitor to realize CC/CV mode transition. Meanwhile, the impedance matrix model is built to find optimal compensation component values, maximizing energy transfer. To reduce sensitivity to misalignment, a “+” shaped compensation coil is added to the basic 2 × 2 square coil array. It improves magnetic field uniformity and suppresses flux leakage. Experimental results show that the system achieves stable load-independent output. Within horizontal offset [−150, 150] mm and diagonal offset [−150√2, 150√2] mm, it keeps output power over 150 W and efficiency over 70%, with strong anti-misalignment ability. This system effectively solves key challenges such as endurance bottlenecks, complex CC/CV switching, and weak anti-misalignment. It offers a reliable technical solution for efficient charging of autonomous UAVs. Full article
(This article belongs to the Special Issue AI-Empowered Wireless Power Transfer Technology and Electromagnetic Metamaterials)
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