We consider the scenario that a multi-coil transmitter transfers energy to one or more single-coil receiver(s) based on magnetic resonance. The power transfer efficiency for fixed positions is determined by the activation pattern of the controllable transmit coil array, and the optimal activation pattern can be obtained offline. In order to efficiently charge the power receivers, online prediction of the receiver positions is necessary, and for this purpose, we consider two machine learning algorithms, including random forest (RF) and deep neural network (DNN). The prediction accuracy and training duration of the two algorithms are measured and compared. Simulation results indicate that both RF and DNN perform well for the single receiver case, and for the two-receiver case DNN still works well but RF does not.
Paper
Full text
Machine Learning-Assisted Wireless Power Transfer Based on Magnetic Resonance
Semantic Scholar · Engineering · 2019
Abstract
We consider the scenario that a multi-coil transmitter transfers energy to one or more single-coil receiver(s) based on magnetic resonance. The power transfer efficiency for fixed positions is determined by the activation pattern of the controllable transmit coil array, and the optimal activation pattern can be obtained offline. In order to efficiently charge the power receivers, online prediction of the receiver positions is necessary, and for this purpose, we consider two machine learning algorithms, including random forest (RF) and deep neural network (DNN). The prediction accuracy and training duration of the two algorithms are measured and compared. Simulation results indicate that both RF and DNN perform well for the single receiver case, and for the two-receiver case DNN still works well but RF does not.