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Self-Inductance-Based Metal Object Detection With Mistuned Resonant Circuits and Nullifying Induced Voltage for Wireless EV Chargers

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Abstract
In this paper, a metal object detection (MOD) system, a kind of foreign object detection (FOD), which is based on mis-tuned resonant circuits and utilizes the variation of self-inductance of a sensing pattern, is newly proposed for wireless electric vehicle (EV) chargers. The sensing pattern that consists of multiple loop coil sets is mounted on the transmitting (Tx) pad of an EV charger, where a loop coil set has two coils connected in series with the opposite polarity to cancel out the induced voltage generated by the Tx coil. Variation of self-inductance of the loop coil set is detected by a parallel-resonant circuit, driven by a current source and operating at near 1 MHz, in order to enhance the resolution of the proposed MOD system. To increase the detection sensitivity of the proposed MOD system, instead of an exact resonant frequency, a mistuned operating frequency near the -3 dB point is utilized for the parallel-resonant circuit. In this way, the proposed MOD system can detect very small metal objects regardless of their position and orientation on the Tx coil without any blind zone. Through simulations and experiments, it is found that the proposed MOD system detects not only horizontal but also standing upright metal objects. A prototype MOD system, operating at 85 kHz to satisfy the standard J2954, was fabricated to verify its feasibility. The results showed that output voltage change of the proposed MOD system becomes 22.7% for a piece of the aluminum foil of 3 x 3 cm(2) and 40.9% for 100 Korean Won coin.
Author(s)
Jeong, Seog Y.Thai, Van X.Park, Jun H.Rim, Chun T.
Issued Date
2019-01
Type
Article
DOI
10.1109/TPEL.2018.2813437
URI
https://scholar.gist.ac.kr/handle/local/12944
Publisher
Institute of Electrical and Electronics Engineers
Citation
IEEE Transactions on Power Electronics, v.34, no.1, pp.748 - 758
ISSN
0885-8993
Appears in Collections:
Department of Electrical Engineering and Computer Science > 1. Journal Articles
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