A study of wireless power transfer using series-series magnetic resonant coupling topology
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Uday Kumar Mudhigollam
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Nagesh Kumar Kamma Veera Venkata
Abstract
Wireless Power Transfer (WPT) technology has got wide range of applications in which Electric Vehicle (EV) wireless charging is gaining more popularity as there is a provision of dynamic charging in addition to static charging. The main objective of this paper is to design and analyze 5 kW rated Magnetic Resonant Coupling (MRC) wireless power transfer charging circuit for EV. The Series-Series MRC WPT circuit’s design formulae are derived in this paper. The circuit parameters like inductances and capacitances could be determined for any power rating and mutual coupling coefficient between transmitter and receiver coils by using the design formulae derived in this paper. The performance of SS topology based EV charging circuit has been studied analytically at different coupling coefficients by estimating the power transfer efficiency. The analytical design of Series-Series MRC WPT circuit has been verified by comparing the output power and load current with the results obtained from MATLAB and TINA (Toolkit for Interactive Network Analysis) software simulation. An experimental set-up is established for SS topology-based charging circuit rated for output power of 5 W with circuit elements designed by considering coupling coefficient of 0.2 between the coils. The measurements are conducted to verify the constant current characteristics under various load conditions. Finally, the effect on output power and load current of SS topology based EV charging circuit at different coupling coefficients between transmitter and receiver coils is discussed in the paper.
Acknowledgments
The authors are thankful to BHEL for encouraging to carry out this research work and submit this manuscript for publication.
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Research ethics: Not applicable.
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Informed consent: Consent was obtained from all individuals included in this study.
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Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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