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Novel stand-alone, completely autonomous and renewable energy based charging station for charging plug-in hybrid electric vehicles (PHEVs)

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  • Fathabadi, Hassan

Abstract

A novel stand-alone charging station (CS) powered by a combination of solar and wind energy in presence of a fuel cell (FC) system is designed and constructed for charging plug-in hybrid electric vehicles (PHEVs). The built CS is high-efficient due to putting to practical use a proposed novel variable step-size maximum power point tracking (MPPT) scheme applied to both photovoltaic (PV) and wind parts of the CS. The main defect of a stand-alone CS is its necessity to battery banks which not only are expensive but also provide short lifetime due to a considerable number of daily charge and discharge imposed to them in a CS. This problem has been solved in this study by utilizing a FC system as supporting power source playing two roles. First, whenever PV and wind power production is less than charge demand, the FC system produces extra electric energy required. Second, whenever PV and wind power production is more than charge demand, the electrolyzer of the FC system produces hydrogen by absorbing extra electric power available in the system. Thus, the FC system acts as a high-capacity storage device in continuously regulating charging power to instant charge demand. The CS has been built, and experimental measurements obtained from its realistic operation are presented that prove the novelty and contributions of the constructed CS compared to those available as:

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  • Fathabadi, Hassan, 2020. "Novel stand-alone, completely autonomous and renewable energy based charging station for charging plug-in hybrid electric vehicles (PHEVs)," Applied Energy, Elsevier, vol. 260(C).
    • Handle: RePEc:eee:appene:v:260:y:2020:i:c:s0306261919318811
    DOI: 10.1016/j.apenergy.2019.114194
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    Cited by:

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    5. Muhammad Irfan & Sara Deilami & Shujuan Huang & Binesh Puthen Veettil, 2023. "Rooftop Solar and Electric Vehicle Integration for Smart, Sustainable Homes: A Comprehensive Review," Energies, MDPI, vol. 16(21), pages 1-29, October.
    6. Shi, Jiaqi & Liu, Nian & Huang, Yujing & Ma, Liya, 2022. "An Edge Computing-oriented Net Power Forecasting for PV-assisted Charging Station: Model Complexity and Forecasting Accuracy Trade-off," Applied Energy, Elsevier, vol. 310(C).
    7. Phiraphat Antarasee & Suttichai Premrudeepreechacharn & Apirat Siritaratiwat & Sirote Khunkitti, 2022. "Optimal Design of Electric Vehicle Fast-Charging Station’s Structure Using Metaheuristic Algorithms," Sustainability, MDPI, vol. 15(1), pages 1-22, December.
    8. Zhu, Rui & Kondor, Dániel & Cheng, Cheng & Zhang, Xiaohu & Santi, Paolo & Wong, Man Sing & Ratti, Carlo, 2022. "Solar photovoltaic generation for charging shared electric scooters," Applied Energy, Elsevier, vol. 313(C).
    9. Eltoumi, Fouad M. & Becherif, Mohamed & Djerdir, Abdesslem & Ramadan, Haitham.S., 2021. "The key issues of electric vehicle charging via hybrid power sources: Techno-economic viability, analysis, and recommendations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    10. Ehtisham Lodhi & Fei-Yue Wang & Gang Xiong & Ghulam Ali Mallah & Muhammad Yaqoob Javed & Tariku Sinshaw Tamir & David Wenzhong Gao, 2021. "A Dragonfly Optimization Algorithm for Extracting Maximum Power of Grid-Interfaced PV Systems," Sustainability, MDPI, vol. 13(19), pages 1-27, September.
    11. Shang, Yitong & Yu, Hang & Niu, Songyan & Shao, Ziyun & Jian, Linni, 2021. "Cyber-physical co-modeling and optimal energy dispatching within internet of smart charging points for vehicle-to-grid operation," Applied Energy, Elsevier, vol. 303(C).
    12. Mirza, Adeel Feroz & Mansoor, Majad & Zhan, Keyu & Ling, Qiang, 2021. "High-efficiency swarm intelligent maximum power point tracking control techniques for varying temperature and irradiance," Energy, Elsevier, vol. 228(C).

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