Renewable Energy-Fed EV Charging Station Utilizing Z-Source Quadratic Improved Boost Zeta Converter with Optimized RBFNN MPPT Control

Articles in Press

Document Type : Research Article

Authors

1 Research scholar, Department of Electrical and Electronics Engineering, School of Engineering and Technology, Dhanalakshmi Srinivasan University, Samayapuram, 621112, India.

2 Dean, School of Engineering and Technology, Dhanalakshmi Srinivasan University, Samayapuram, 621112, India.

Abstract

PV-based EV charging stations offer economic advantages, including decreased operational costs, reduced grid dependency during peak hours, and enhanced consistency through the addition of local energy storage systems. This work presents a novel approach for integrating Renewable Energy Sources (RESs), specifically Photovoltaic (PV) systems, into Electric Vehicle (EV) charging stations. Proposed research utilizes a Z-source Quadratic Improved Boost Zeta (Z-SQ-IBZ) converter coupled with a Crayfish Optimization Algorithm based Radial Basis Function Neural Network (COA-RBFNN) Maximum Power Point Tracking (MPPT) algorithm for enhancing power extraction efficiency from PV arrays. Energy management and power balancing between PV energy generation and storage system are enabled through bidirectional DC-DC converter interfacing battery storage and DC link. Proposed PV-based EV charging system is implemented in MATLAB Simulink, and novel Z-SQ-IBZ converter attains enhanced efficiency of 96.63% with improved voltage and minimized voltage stress. The novel COA optimised RBFNN MPPT effectively tracks optimal power with improved tracking efficiency of 99.81%. MATLAB simulation results demonstrate that proposed topology effectively manages energy flow, optimises PV power utilisation, and constantly supports EV charging demands, making it appropriate for efficient EV charging station applications.

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AUT Journal of Electrical Engineering

Articles in Press, Accepted Manuscript
Available Online from 26 December 2025

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