Machine Learning Based Metal Object Detection for Wireless Power Transfer Using Differential Coils

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  • Gong Yunyi
    Graduate School of information Science and Technology, Hokkaido University
  • Otomo Yoshitsugu
    Graduate School of information Science and Technology, Hokkaido University
  • Igarashi Hajime
    Graduate School of information Science and Technology, Hokkaido University

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<p>This paper presents the machine learning-based detection of foreign metal object for the wireless power transfer device including differential coils. To test the proposed method, the differential voltages are computed using finite element method for about 1500 cases with and without an aluminum cylinder at driving frequency of 85 kHz considering misalignment between the primal and secondary coils. It has been shown that gradient boosting decision tree and random forests classifier have the accuracy over 90% when input voltages and differential voltages are inputted together.</p>

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