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Cited by 512

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A High Efficiency 5 kW Inductive Charger for EVs Using Dual Side Control

Wu, Hunter H., Gilchrist, Aaron, Sealy, Kylee D., Bronson, Daniel·2012·IEEE Transactions on Industrial Informatics

Abstract

This paper presents the design of a 5 kW inductive charging system for electric vehicles (EVs). Over 90% efficiency is maintained from grid to battery across a wide range of coupling conditions at full load. Experimental measurements show that the magnetic field strength meets the stringent International Commission on Non-Ionizing Radiation Protection (ICNIRP) guidelines for human safety. In addition, a new dual side control scheme is proposed to optimize system level efficiency. Experimental validation showed that a 7% efficiency increase and 25% loss reduction under light load conditions is achievable. The authors believe this paper is the first to show such high measured efficiencies for a level 2 inductive charging system. Performance of this order would indicate that inductive charging systems are reasonably energy efficient when compared to the efficiency of plug-in charging systems.

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Crossref · 10.1109/tii.2012.2192283

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Metadata

Authors
Wu, H. H., Gilchrist, A., Sealy, K. D., & Bronson, D.
Venue
IEEE Transactions on Industrial Informatics
Published
2012-08
Volume
8
Issue
3
Pages
585-595
ISSN
  • 1551-3203 (ISSN-L)
  • 1941-0050
OpenAlex
W2014517525
Cited by
512
References
0
FWCI
18.11
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: A High Efficiency 5 kW Inductive Charger for EVs Using Dual Side Control. https://app.cochat.ai/papers/10.1109/tii.2012.2192283
The paper
Wu, H. H., Gilchrist, A., Sealy, K. D., & Bronson, D. (2012). A High Efficiency 5 kW Inductive Charger for EVs Using Dual Side Control. IEEE Transactions on Industrial Informatics. https://doi.org/10.1109/tii.2012.2192283

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.