Hybrid Renewable Energy and Smart App‐Based Management for Efficient and Sustainable EV Charging Infrastructure

This paper presents a hybrid renewable energy system integrated with a smart application‐based management solution to enhance the efficiency, sustainability, and scalability of electric vehicle (EV) charging stations. The system utilizes photovoltaic (PV) panels, wind turbines (WTs), and battery storage to reduce reliance on grid power and improve energy resilience. A robust energy management system (EMS) employs an adaptive neuro‐fuzzy inference system (ANFIS) to optimize PV production under changing conditions, while machine learning algorithms control the dynamic distribution of energy among the grid, storage, and EVs. A smart application leverages IoT, real‐time data analytics, and a scheduling algorithm to optimize charging operations across numerous stations. This app‐based management optimizes energy use, reduces grid overload, and offers dynamic charging suggestions. Simulation findings in MATLAB/Simulink demonstrate that the proposed system improves power balance, grid stability, and user convenience, while decreasing grid reliance by more than 30%. Furthermore, a blockchain‐based payment solution provides secure transactions, lowering station owners’ operating expenses by up to 25%, while saving users as much as 18%. This study provides a comprehensive foundation for future EV charging infrastructure supporting the transition to sustainable electric mobility.

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Publication Details

Journal
International Journal of Energy Research
Published
2025-01-01
DOI
https://doi.org/10.1155/er/5872792
Citations
4
Primary Topic
Electric Vehicles and Infrastructure
Type
article
Field-Weighted Citation Impact
2.92
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article

Hybrid Renewable Energy and Smart App‐Based Management for Efficient and Sustainable EV Charging Infrastructure

Ammar Ahmed Alkahtani, Mohamed A. El‐Hameed, Ibrahem E. Atawi, Ali Q. Al‐Shetwi et al.
4 citations
International Journal of Energy Research
Electric Vehicles and Infrastructure
2.92
article

Hybrid Renewable Energy and Smart App‐Based Management for Efficient and Sustainable EV Charging Infrastructure

Ammar Ahmed Alkahtani, Mohamed A. El‐Hameed, Ibrahem E. Atawi, Ali Q. Al‐Shetwi, Fahad A. Sahli, Mousa H. Adaqriri
article en
4 citations

Abstract

This paper presents a hybrid renewable energy system integrated with a smart application‐based management solution to enhance the efficiency, sustainability, and scalability of electric vehicle (EV) charging stations. The system utilizes photovoltaic (PV) panels, wind turbines (WTs), and battery storage to reduce reliance on grid power and improve energy resilience. A robust energy management system (EMS) employs an adaptive neuro‐fuzzy inference system (ANFIS) to optimize PV production under changing conditions, while machine learning algorithms control the dynamic distribution of energy among the grid, storage, and EVs. A smart application leverages IoT, real‐time data analytics, and a scheduling algorithm to optimize charging operations across numerous stations. This app‐based management optimizes energy use, reduces grid overload, and offers dynamic charging suggestions. Simulation findings in MATLAB/Simulink demonstrate that the proposed system improves power balance, grid stability, and user convenience, while decreasing grid reliance by more than 30%. Furthermore, a blockchain‐based payment solution provides secure transactions, lowering station owners’ operating expenses by up to 25%, while saving users as much as 18%. This study provides a comprehensive foundation for future EV charging infrastructure supporting the transition to sustainable electric mobility.

International Journal of Energy ResearchVol. 2025(1)
Shaqra University (SA), Fahd bin Sultan University (SA), University of Tabuk (SA), Jazan University (SA)
Openalex Percentile: Top 9%
Electric Vehicles and Infrastructure
2.92
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Hybrid Renewable Energy and Smart App‐Based Management for Efficient and Sustainable EV Charging Infrastructure — Ammar Ahmed Alkahtani, Mohamed A. El‐Hameed, et al. · International Journal of Energy Research (2025) | TGRS Research Map | TGRS