An Artificial Intelligence Based Control of a Superconducting Inductor Assisted DC–DC Converter for Ultra-Fast Electric Vehicle Charging

Authors

  • Sumalatha Kalakotla

Keywords:

Artificial Intelligence Control, Superconducting Inductor, DC–DC Converter, Double-Boost Converter, Electric Vehicle Charging, Ultra-Fast Charging, Power Conversion Efficiency

Abstract

This work presents the design and analysis of an artificial intelligence (AI)–based controlled superconducting inductor–assisted DC–DC double-boost converter for ultra-fast electric vehicle (EV) charging applications. The increasing demand for rapid and efficient charging solutions has highlighted the limitations of conventional AC–DC charging systems, particularly in terms of energy losses, thermal constraints, and multi-stage conversion inefficiencies. To address these challenges, the proposed system incorporates superconducting inductors using Bi₂₂₂₃ and MgB₂ materials, which offer near-zero resistance, high current-carrying capability, and improved power density. An AI-based controller is implemented to enhance switching performance, ensure stable voltage regulation, and improve dynamic response under varying load conditions. The system is modeled and simulated using MATLAB/Simulink to evaluate its electrical performance, efficiency, and transient characteristics. Simulation results demonstrate that the MgB₂-based converter achieves efficiency exceeding 95% at 15 kW output, outperforming conventional designs. Additionally, the AI controller significantly reduces voltage ripple, minimizes overshoot, and improves system stability. Overall, the proposed intelligent superconducting DC–DC converter provides a high-efficiency, reliable, and scalable solution for next-generation ultra-fast EV charging infrastructure.

 

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References

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Published

30.12.2024

How to Cite

Sumalatha Kalakotla. (2024). An Artificial Intelligence Based Control of a Superconducting Inductor Assisted DC–DC Converter for Ultra-Fast Electric Vehicle Charging. International Journal of Intelligent Systems and Applications in Engineering, 12(23s), 4299 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/8271

Issue

Vol. 12 No. 23s (2024)

Section

Research Article

License

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