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

Gong, Yunyi; Otomo, Yoshitsugu; Igarashi, Hajime

Journal of Advanced Simulation in Science and Engineering, 2022, 9(1), 20-29

Permalink : https://hdl.handle.net/2115/84953

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アクセス権	open access
URI	https://hdl.handle.net/2115/84953
タイトル	Machine Learning Based Metal Object Detection for Wireless Power Transfer Using Differential Coils
著者	Gong, Yunyi ; Otomo, Yoshitsugu ; Igarashi, Hajime
著者名 Gong, Yunyi
著者名 Otomo, Yoshitsugu
e-Rad_Researcher 90212737 著者名 Igarashi, Hajime
言語	英語
キーワード	wireless power transfer
	metal object detection
	differential coils
	machine learning
発行日	2022
出版者	Japan Society for Simulation Technology
出版者	日本シミュレーション学会
収録物名	Journal of Advanced Simulation in Science and Engineering
巻(号)	9(1)
ページ	20-29
抄録	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.
資源タイプ	学術雑誌論文
出版タイプ	Accepted Manuscript
権利情報	© 2022 Japan Society for Simulation Technology
PISSN	2188-5303
関連情報	NAID 130008149338
関連情報 (isVersionOf)	出版社版DOI https://doi.org/10.15748/jasse.9.20