Paper Info
Title
Adaptive Resonant Beam Charging for Intelligent Wireless Power Transfer.
Abstract
As a long-range high-power wireless power transfer (WPT) technology, resonant beam charging (RBC) can transmit watt-level power over long distance for the devices in the Internet of Things (IoT). Due to its open-loop architecture, RBC faces the challenge of providing dynamic current and voltage to optimize battery charging performance. In RBC, battery overcharge may cause energy waste, thermal effects, and even safety issues. On the other hand, battery undercharge may lead to charging time extension and significant battery capacity reduction. In this paper, we present an adaptive RBC (ARBC) system for battery charging optimization. Based on RBC, ARBC uses a feedback system to control the supplied power dynamically according to the battery preferred charging values. Moreover, in order to transform the received current and voltage to match the battery preferred charging values, ARBC adopts a dc–dc conversion circuit. Relying on the analytical models for RBC power transmission, we obtain the end-to-end power transfer relationship in the approximate linear closed-form of ARBC. Thus, the battery preferred charging power at the receiver can be mapped to the supplied power at the transmitter for feedback control. Numerical evaluation demonstrates that ARBC can save 61% battery charging energy and 53%–60% supplied energy compared with RBC. Furthermore, ARBC has high energy-saving gain over RBC when the WPT is unefficient. ARBC in WPT is similar to link adaption in wireless communications. Both of them play the important roles in their respective areas.
Year
DOI
Venue
2019
10.1109/jiot.2018.2867457
IEEE Internet of Things Journal
Keywords
DocType
Volume
Batteries,Receivers,Transmitters,Internet of Things,Power supplies,Monitoring,DC-DC power converters
Journal
abs/1809.09364
Issue
ISSN
Citations 
1
IEEE Internet of Things Journal, 2018
2
PageRank 
References 
Authors
0.38
0
6
Name
Order
Citations
PageRank
Qingqing Zhang110214.76
Wen Fang220.72
Mingliang Xiong342.11
Qingwen Liu496562.14
Jun Wu533744.25
Pengfei Xia623711.65