Adaptive Resonant Beam Charging for Intelligent Wireless Power Transfer

Published on 2019in IEEE Internet of Things Journal9.52
· DOI :10.1109/jiot.2018.2867457
Qingqing Zhang2
Estimated H-index: 2
(Tongji University),
Wen Fang2
Estimated H-index: 2
(Tongji University)
+ 3 AuthorsPengfei Xia13
Estimated H-index: 13
(Tongji University)
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.
  • References (25)
  • Citations (3)
#1Qingqing Zhang (Tongji University)H-Index: 2
#2Wen Fang (Tongji University)H-Index: 2
Last.Liuqing Yang (CSU: Colorado State University)H-Index: 36
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#1Qingqing Zhang (Tongji University)H-Index: 2
#2Xiaojun ShiH-Index: 1
Last.Yong LiaoH-Index: 1
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#1Bohao Feng (Beijing Jiaotong University)H-Index: 4
#2Hongke Zhang (Beijing Jiaotong University)H-Index: 13
Last.Shui Yu (Deakin University)H-Index: 30
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#1Muhammad Shahzad Aziz (National University of Sciences and Technology)H-Index: 1
#2Sohaib Ahmad (IUB: Islamia University)H-Index: 1
Last.Umair Saleem (National University of Sciences and Technology)H-Index: 1
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#1Sancheng Peng (Zhaoqing University)H-Index: 10
#2Shui Yu (Deakin University)H-Index: 30
Last.Aimin Yang (Guangdong University of Foreign Studies)H-Index: 2
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Jul 1, 2012 in ICIEA (Conference on Industrial Electronics and Applications)
#1Weixiang Shen (Swinburne University of Technology)H-Index: 21
#2Thanh Tu Vo (Swinburne University of Technology)H-Index: 4
Last.A. Kapoor (Swinburne University of Technology)H-Index: 26
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#1Andre B. Kurs (MIT: Massachusetts Institute of Technology)H-Index: 5
#2Aristeidis Karalis (MIT: Massachusetts Institute of Technology)H-Index: 10
Last.Marin Soljacic (MIT: Massachusetts Institute of Technology)H-Index: 59
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