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Cited by 312

The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.

Tripolar Pad for Inductive Power Transfer Systems for EV Charging

Open access
Kim, Seho, Covic, Grant A., Boys, John T.·2017·IEEE Transactions on Power Electronics

Abstract

A recently proposed magnetic structure called Tripolar Pad (TPP) is investigated as a primary pad in an inductive power transfer battery charging system for electric vehicles. In this paper, a TPP primary is evaluated for 3.3 kW power transfer to both a circular pad and a bipolar pad as secondary pads. A mathematical model is presented to describe the power transfer from the TPP primary to the secondary pads then a control scheme is proposed which exploits the mutual decoupling between the TPP coils to find optimal primary currents. The optimization of the primary currents in the TPP improved the effective coupling factor over traditional topologies, particularly when the secondary was misaligned, and maintained leakage magnetic flux below International Commission on Non-Ionizing Radiation Protection guidelines. The result for the proposed TPP is validated with practical measurements.

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Metadata

Authors
Kim, S., Covic, G. A., & Boys, J. T.
Venue
IEEE Transactions on Power Electronics
Published
2017-07
Volume
32
Issue
7
Pages
5045-5057
ISSN
  • 0885-8993 (ISSN-L)
  • 1941-0107
OpenAlex
W2508845814
Cited by
312
References
25
FWCI
8.84
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Tripolar Pad for Inductive Power Transfer Systems for EV Charging. https://app.cochat.ai/papers/10.1109/tpel.2016.2606893
The paper
Kim, S., Covic, G. A., & Boys, J. T. (2017). Tripolar Pad for Inductive Power Transfer Systems for EV Charging. IEEE Transactions on Power Electronics. https://doi.org/10.1109/tpel.2016.2606893

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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.