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Prediction of Behavior of Low‐Power Noncontact Charger
Author(s) -
YONETSU DAIGO,
YAMAMOTO YASUSHI
Publication year - 2016
Publication title -
electrical engineering in japan
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.136
H-Index - 28
eISSN - 1520-6416
pISSN - 0424-7760
DOI - 10.1002/eej.22747
Subject(s) - rectifier (neural networks) , maximum power transfer theorem , electrical engineering , power factor , power (physics) , inductance , voltage , electromagnetic coil , engineering , capacitor , physics , computer science , quantum mechanics , stochastic neural network , machine learning , recurrent neural network , artificial neural network
SUMMARY This paper proposes a method to predict the charging current, the output power, and the power transfer efficiency of a low‐power, noncontact charger with reasonable accuracy. The low‐power, noncontact charger model considered in this paper consists of a sinusoidal voltage source, a sending and receiving coil, a full wave rectifier circuit, and an AA nickel metal‐hydride battery. The capacitor that is connected in series in the sending coils of the low‐power noncontact charger model to improve the power factor was also examined. The self‐inductance, the mutual inductance, and the resistances of the coils were calculated using axisymmetric finite element analysis, and were substituted into the circuit equations. The circuit equations were solved by using the Runge‐Kutta method. The calculated charging current, output power, and power transfer efficiency were in good agreement with the experimental results.