A multiplexing LCL module using individual transmitters for dynamic wireless charging of electric vehicles

Shaocong Zhou, Chunbo Zhu, Chunlai Yu, C. C. Chan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)

Abstract

This paper proposed a novel multiplexing module based on LCL topology for dynamic wireless charging of electric vehicles. The LCL compensation network is widely used due to its insensitivity of output characteristics to load variation. But the design of an LCL topology requires a compensation inductor with the same value of a transmitter. The proposed multiplexing LCL module (MLM) can make the primary transmitter work as compensation inductor, result in no additional inductor achieving cost benefits.A mutual coupling model and a finite element simulated model are utilized to analyze the working conditions of the MLM. A distribution method is proposed to realize the function of MLMs reliably. Finally a 173W prototype is built to verify the feasibility of an MLM.

Original languageEnglish
Title of host publication2017 IEEE Energy Conversion Congress and Exposition, ECCE 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2728-2733
Number of pages6
ISBN (Electronic)9781509029983
DOIs
Publication statusPublished - 3 Nov 2017
Externally publishedYes
Event9th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2017 - Cincinnati, United States
Duration: 1 Oct 20175 Oct 2017

Publication series

Name2017 IEEE Energy Conversion Congress and Exposition, ECCE 2017
Volume2017-January

Conference

Conference9th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2017
Country/TerritoryUnited States
CityCincinnati
Period1/10/175/10/17

Keywords

  • Distribution
  • Dynamic wireless charging
  • Electric vehicle
  • LCL topology

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Renewable Energy, Sustainability and the Environment
  • Control and Optimization

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