Design of DC-DC Converter for a Bifacial PV-Powered Stand-Alone Electric Vehicle Charging Station Using Secretary Bird Optimization Algorithm

Summary

This study develops a battery-storage-supported ANN-based MPPT system for a standalone electric vehicle charging station powered by a bifacial photovoltaic system. Additionally, the Secretary Bird Optimization Algorithm (SBOA) optimizes DC–DC converter, filter, and PI controller parameters to minimize total harmonic distortion (THD) and DC-bus voltage deviation.

Abstract

In response to the growing emphasis on sustainable mobility, this study presents a renewable-energy-based electric vehicle (EV) charging system incorporating a station battery energy storage (BES). The system employs a bifacial photovoltaic (PV) array with an artificial neural network (ANN)-assisted maximum power point tracking (MPPT) scheme. Additionally, the Secretary Bird Optimization Algorithm (SBOA) is used to optimize the DC–DC converter, filter, and PI controller parameters to minimize THD while maintaining a stable DC-bus voltage. Five EV models were evaluated, including one lead-acid battery-based model (BMW i3) and four lithium-ion battery-based models (Fiat 500e, Mercedes EQA 250, Volkswagen e-Golf, and Hyundai Kona Electric). The proposed MPPT configuration extracts maximum energy from the bifacial PV system while maintaining a stable DC-bus voltage under variable environmental and loading conditions. Validation through MATLAB/Simulink simulations under three operating scenarios (irradiance = 800–1000 W/m²; temperature = 20–25 °C) demonstrates high conversion efficiency, THDs of 2.85%, 2.26%, and 2.23%, and robust power management suitable for off-grid EV charging stations.

Introduction

Nowadays, integrating renewable sources, primarily solar, and EV charging stations into the distribution system is key. However, this integration leads to PQ issues and a need for advanced MPPT to extract power from renewable sources. However, due to the rapid growth in EV adoption, it became essential to have a fast, reliable charging infrastructure. The use of traditional grid-connected charging stations results in carbon emissions, stress, and power quality (PQ) issues. Additionally, integrating renewable energy sources, such as solar, with EVs can help reduce carbon emissions. However, battery storage is considered alongside solar energy, as solar energy is inhere....

Protocol

In this research, a stand-alone bifacial PV- and battery energy storage system (BESS)-fed EV charging system is developed, as shown in Figure 1, using an ANN-controlled MPPT method. A single-diode equivalent circuit is used to model the bifacial PV array, accounting for temperature, series resistance, shunt resistance, solar irradiance, and rear-side irradiance. The PV output is supplied to the EV charging station through a DC–DC boost converter, while the BESS supports DC-bus voltage regulation. The Secretary Bird Optimization Algorithm (SBOA) is used to optimize the PI controller gains and filter parameters. The modeling of the system c....

Results

The proposed stand-alone Bifacial PV system with ANNC-based MPPT for an EV charging station was constructed in Simulink/Matlab 2022b and executed on an Intel Core i7 with 16GB RAMS. The selected system and EV load ratings are displayed in Table 2 and Supplementary Table 3. In this work, three cases listed in Table 5 with various permutations of solar irradiation (G), temperature (T), AC loads, and five EV cars from different models and ratings are used to demonstrate the system's perform.......

Discussion

Overall, the outcomes indicate that this research work contributes to the scientific field by introducing a novel, optimized control strategy that enhances solar output, improves PQ, and supports a sustainable EV charging infrastructure appropriate for highways. The developed ANN-based MPPT with SBOA-optimized filter, converters, and controller achieves lower THD while maintaining a stable DC bus voltage for PV- and BES-supported standalone EV charging station¹⁷.

Litera.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
DC–AC converter	MathWorks	Simscape Electrical Three-Level Converter documentation: https://www.mathworks.com/help/sps/ref/threelevelconverterthreephase.html	Three-phase, three-level neutral-point-clamped DC–AC converter model.
DC–DC converter	MathWorks	Simscape Electrical Boost Converter documentation: https://www.mathworks.com/help/sps/ref/boostconverter.html	Boost DC–DC converter model used to regulate the PV output voltage.
EV1 battery	MathWorks	Battery model documentation: https://www.mathworks.com/help/autoblks/ref/datasheetbattery.html	Simulated lead-acid battery representing EV1.
EV2–EV5 batteries	MathWorks	Battery model documentation: https://www.mathworks.com/help/autoblks/ref/datasheetbattery.html	Simulated lithium-ion battery models representing EV2, EV3, EV4, and EV5.
MATLAB/Simulink	MathWorks	Software; https://www.mathworks.com/products/simulink.html	Software environment used to model and simulate the PV-powered electric-vehicle charging station.
Simscape Electrical	MathWorks	Software; https://www.mathworks.com/products/simscape-electrical.html	Component library used to model the power converters, electrical network, and associated control system.
Solar panel	Waaree Energies Ltd.; modeled in MATLAB/Simulink	Model WSM-315. Waaree product information: https://www.waaree.com/solar-pv-modules/; System Advisor Model: https://sam.nrel.gov/	WSM-315 solar panel parameters were entered as a user-defined PV module using data from the Waaree datasheet and the NREL System Advisor Model.
Storage battery	MathWorks	Lead-acid battery model documentation: https://www.mathworks.com/help/simscape/ug/lead-acid-battery.html	Simulated lead-acid battery used as the stationary energy-storage system.