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PIER PIER B PIER C PIER M PIER Letters
2025-06-01
Machine Learning Assisted Long-Range Wireless Power Transfer
By
Likai Wang,

    Dr. Likai Wang

    School of Physics Science and Engineering

    Tongji University

    China

    Homepage

Yuqian Wang,

    Dr. Yuqian Wang

    School of Physics Science and Engineering

    Tongji University

    China

    Homepage

Shengyu Hu,

    Dr. Shengyu Hu

    School of Physics Science and Engineering

    Tongji University

    China

    Homepage

Yunhui Li,

    Prof. Yunhui Li

    Department of Physics

    Tongji University

    China

    Homepage

Hong Chen,

    Dr. Hong Chen

    Tongji University

    China

    Homepage

Ce Wang,

    Dr. Ce Wang

    School of Physics Science and Engineering

    Tongji University

    China

    Homepage

Zhiwei Guo

    Dr. Zhiwei Guo

    School of Physics Science and Engineering

    Tongji University

    China

    Homepage

Progress In Electromagnetics Research, Vol. 183, 59-66, 2025
doi:10.2528/PIER25020801
Abstract
Long-range near-field magnetic resonance wireless power transfer (WPT) technology holds broad application prospects in fields such as medical implants and industrial manufacturing robots. However, it faces challenges of low efficiency and poor robustness in long-distance transmission. This study proposes an innovative collaborative optimization approach that integrates the machine learning gradient descent optimization algorithm (GDOA) with non-Hermitian topological physics to precisely regulate the coupling strength distribution, thereby realizing a highly flexible, efficient, and robust WPT system capable of anchoring transmission frequencies and accommodating an arbitrary number of resonators. Experimental results demonstrate that the GDOA-optimized Su-Schrieffer-Heeger (SSH)-like topological chain achieves a transmission efficiency of 65% at the target frequency and maintains 57.9% efficiency under 30% structural perturbations, significantly outperforming the SSH chain (45.6%) and uniform chain (24.1%) in control groups. This research provides theoretical and experimental support for the design of machine learning-based topological long-range WPT systems, offering substantial practical value, particularly in medical electronic power supply and wireless industrial equipment applications.
Supplementary Information
Supporting Information-25020801.docx.docx
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Citation
Likai Wang, Yuqian Wang, Shengyu Hu, Yunhui Li, Hong Chen, Ce Wang, and Zhiwei Guo, "Machine Learning Assisted Long-Range Wireless Power Transfer," Progress In Electromagnetics Research, Vol. 183, 59-66, 2025.
doi:10.2528/PIER25020801
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