The growing demand for energy storage systems and electric vehicles (EVs) has led to the development of Vehicle-to-Everything (V2X) architectures, whose technology enables a bidirectional exchange of energy and information between EVs and other system elements, such as the grid, buildings, homes and other loads. The adoption of V2X systems has achieved significant progress, but still faces major challenges such as the complexity of technology integration, the need for interoperability between different devices and infrastructures and the high cost of implementation. This paper presents a comprehensive review of V2X systems, analyzing hardware and communication technologies, with a focus on vehicle components. After discussing V2X communication standards, specific applications such as Vehicle-to-Grid (V2G), Vehicle-to-Building (V2B), Vehicle-to-Home (V2H) and Vehicle-to-Vehicle (V2V), will be described, highlighting their advantages and disadvantages that will enable their large-scale implementation. Furthermore, the contribution that artificial intelligence (AI) and machine learning (ML) can make to optimize energy flows, improve cybersecurity systems and facilitate the practical implementation of these technologies will be discussed, followed by an analysis of potential future development paths.

Vehicle-to-grid and vehicle-to-building systems integrated in smart infrastructures. A comprehensive review of technologies, challenges and opportunities / Mascioli, Lorenzo Frattale; Golino, Andrea; Loggia, Riccardo; Bonfiglio, Andrea; Minetti, Manuela; Palma, Alessandro Lorenzo; Moscatiello, Cristina; Falvo, Maria Carmen; Martirano, Luigi. - (2025), pp. 631-645. ( 2025 International Conference on Clean Electrical Power, ICCEP 2025 Villasimius; Italy ) [10.1109/iccep65222.2025.11143653].

Vehicle-to-grid and vehicle-to-building systems integrated in smart infrastructures. A comprehensive review of technologies, challenges and opportunities

Mascioli, Lorenzo Frattale;Golino, Andrea;Loggia, Riccardo;Moscatiello, Cristina;Falvo, Maria Carmen;Martirano, Luigi
2025

Abstract

The growing demand for energy storage systems and electric vehicles (EVs) has led to the development of Vehicle-to-Everything (V2X) architectures, whose technology enables a bidirectional exchange of energy and information between EVs and other system elements, such as the grid, buildings, homes and other loads. The adoption of V2X systems has achieved significant progress, but still faces major challenges such as the complexity of technology integration, the need for interoperability between different devices and infrastructures and the high cost of implementation. This paper presents a comprehensive review of V2X systems, analyzing hardware and communication technologies, with a focus on vehicle components. After discussing V2X communication standards, specific applications such as Vehicle-to-Grid (V2G), Vehicle-to-Building (V2B), Vehicle-to-Home (V2H) and Vehicle-to-Vehicle (V2V), will be described, highlighting their advantages and disadvantages that will enable their large-scale implementation. Furthermore, the contribution that artificial intelligence (AI) and machine learning (ML) can make to optimize energy flows, improve cybersecurity systems and facilitate the practical implementation of these technologies will be discussed, followed by an analysis of potential future development paths.
2025
2025 International Conference on Clean Electrical Power, ICCEP 2025
electric vehicle; machine learning; V2G; V2X; vehicle-to-everything; vehicle-to-grid
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Vehicle-to-grid and vehicle-to-building systems integrated in smart infrastructures. A comprehensive review of technologies, challenges and opportunities / Mascioli, Lorenzo Frattale; Golino, Andrea; Loggia, Riccardo; Bonfiglio, Andrea; Minetti, Manuela; Palma, Alessandro Lorenzo; Moscatiello, Cristina; Falvo, Maria Carmen; Martirano, Luigi. - (2025), pp. 631-645. ( 2025 International Conference on Clean Electrical Power, ICCEP 2025 Villasimius; Italy ) [10.1109/iccep65222.2025.11143653].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1754497
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