AI Based Quasi-Resonant Converter for Electric Vehicle Charging

Authors

  • Harini Sampath,Dr N Chellammal , Dr. Sridhar Patthi

Keywords:

Electric Vehicle Charging, Quasi-Resonant Converter, Artificial Intelligence, Machine Learning, Power Electronics, Efficiency Optimization, Soft Switching, Power Factor Correction, Electromagnetic Interference Reduction

Abstract

The fast increase in electric vehicles (EVs) has created an urgent requirement for improvedcharging infrastructure that can support the needs of future transportation. This study introduces anew method for EV charging systems that combines artificial intelligence (AI) methods with quasiresonant converters. Our research examines how machine learning algorithms can enhance the

References

A. Ahmad, M. S. Alam, and R. Chabaan, "A Comprehensive Review of Wireless Charging Technologies for Electric Vehicles," IEEE Transactions on Transportation Electrification, vol. 4, no. 1, pp. 38-63, 2018.

M. Yilmaz and P. T. Krein, "Review of Battery Charger Topologies, Charging Power Levels, and Infrastructure for Plug-In Electric and Hybrid Vehicles," IEEE Transactions on Power Electronics, vol. 28, no. 5, pp. 2151-2169, 2013.

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Published

2024-05-20

How to Cite

Harini Sampath,Dr N Chellammal , Dr. Sridhar Patthi. (2024). AI Based Quasi-Resonant Converter for Electric Vehicle Charging. Journal of Computational Analysis and Applications (JoCAAA), 33(05), 2262–2278. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/3307

Issue

Vol. 33 No. 05 (2024): JOCAAA

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.