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Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

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Published on: February 14, 2025

Machine learning-based MPPT integration with quadratic double-extended DC-DC converter for grid-connected PV-powered

D Karthikeyan¹, Vinod Kumar Shukla², K Rajesh³

¹Department of Information Technology, Post-Doctoral fellow School of Engineering, Architecture and Interior Design, Amity University Dubai, UAE and Associate professor, Department of Electrical and Electronics Engineering, SRM Institute of science and Technology, Kattankulathur, Chennai, 603203, India. karthikeyand992@gmail.com.

Scientific Reports

|March 1, 2026

Summary

This study introduces a novel converter and Machine Learning controller for electric vehicles (EVs) powered by solar energy. The system enhances efficiency and reduces harmonic distortion for cleaner transportation.

Keywords:

BLDC motor Electric Vehicles Grid and battery Quadratic Double Extended Converter RES STFO-RBFNN

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Area of Science:

Electrical Engineering
Renewable Energy Systems
Artificial Intelligence

Background:

Growing environmental concerns necessitate emission-free transportation solutions like Electric Vehicles (EVs).
Brushless Direct Current (BLDC) motors are crucial for EV efficiency, requiring stable power from Renewable Energy Sources (RES), such as solar Photovoltaic (PV) systems.
PV systems often face challenges with insufficient DC output voltage to meet load demands.

Purpose of the Study:

To propose a novel DC-DC converter and an advanced Machine Learning (ML) based Maximum Power Point Tracking (MPPT) controller for solar-powered EVs.
To improve the voltage output from PV systems and ensure maximum power extraction.
To enhance the overall performance and energy management of EV powertrains.

Main Methods:

A Quadratic Double Extended (QDE) DC-DC converter was designed to boost PV voltage with reduced stress.
A Sea Turtle Foraging optimized Radial Bias Function Neural Network (STFO-RBFNN) was developed for MPPT.
A system integrating PV, grid, battery storage, a bidirectional converter, and a 3-phase Voltage Source Inverter (VSI) was simulated in MATLAB/Simulink.

Main Results:

The proposed QDE converter improved voltage gain and reduced voltage stress.
The STFO-RBFNN controller effectively tracked maximum power from the PV system.
The integrated system demonstrated significantly enhanced performance, achieving 95.43% converter efficiency and 1.14% Total Harmonic Distortion (THD).

Conclusions:

The novel converter and ML-based MPPT controller offer a robust solution for solar-powered EVs.
The system provides efficient energy conversion and flexible energy management through supplementary sources.
The proposed methodology represents a significant advancement in optimizing EV performance and reliability for sustainable transportation.