A sun flower optimization based modified high step up SEPIC converter for electric vehicle applications

Area of Science:

• Electrical Engineering
• Renewable Energy Systems
• Power Electronics

Background:

• Solar photovoltaic (PV) systems require efficient power conversion, especially under dynamic operating conditions.
• Maximizing power extraction from solar panels is crucial for energy efficiency.
• Hybrid electric vehicles (HEVs) can benefit from advanced power management solutions.

Purpose of the Study:

• To propose a high step-up modified SEPIC converter for solar PV maximum power point tracking (MPPT).
• To develop a Sun Flower Optimization (SFO) based MPPT controller for dynamic conditions.
• To validate the converter's performance under steady-state, variable irradiance, and variable load.

Main Methods:

• Design and simulation of a high step-up modified SEPIC converter for a 100W, 24V solar panel.
• Implementation of a Sun Flower Optimization (SFO) algorithm for maximum power point tracking.
• Performance analysis using MATLAB/Simulink and validation with a real-time experimental prototype.

Main Results:

• Achieved a maximum voltage conversion ratio of 7.92, yielding an output voltage of 190V.
• Attained a maximum conversion efficiency of 95.63%.
• Demonstrated effective operation under steady state, variable solar irradiance, and variable load conditions.

Conclusions:

• The proposed high step-up modified SEPIC converter with SFO-based MPPT is effective for solar PV power extraction.
• The converter is suitable for multi-source fed hybrid electric vehicle applications.
• Simulation and experimental results confirm the theoretical analysis and design.