Innovative Energy Approach for Design and Sizing of Electric Vehicle Charging Infrastructure
Autor(en): |
Daniele Martini
Martino Aimar Fabio Borghetti Michela Longo Federica Foiadelli |
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Medium: | Fachartikel |
Sprache(n): | Englisch |
Veröffentlicht in: | Infrastructures, 20 Dezember 2023, n. 1, v. 9 |
Seite(n): | 15 |
DOI: | 10.3390/infrastructures9010015 |
Abstrakt: |
In Italy, the availability of service areas (SAs) equipped with charging stations (CSs) for electric vehicles (EVs) on highways is limited in comparison to the total number of service areas. The scope of this work is to create a prototype and show a different approach to assessing the number of inlets required on highways. The proposed method estimates the energy requirements for the future electric fleet on highways. It is based on an energy conversion that starts with the fuel sold in the highway network and ends with the number of charging inlets. A proposed benchmark method estimates energy requirements for the electric fleet using consolidated values and statistics about refueling attitudes, with factors for range correction and winter conditions. The results depend on assumptions about future car distribution, with varying numbers of required inlets. The analysis revealed that vehicle traffic is a critical factor in determining the number of required charging inlets, with significant variance between different SAs. This study highlights the necessity of incorporating factors like weather, car charging power, and the future EV range into these estimations. The findings are useful for planning EV charging infrastructure, especially along major traffic routes and in urban areas with high-range vehicles relying on High-Power DC (HPDC) charging. The model’s applicability to urban scenarios can be improved by considering the proportion of energy recharged at the destination. A key limitation is the lack of detailed origin–destination (OD) highway data, leading to some uncertainty in the calculated range ratio coefficient and underscoring the need for future research to refine this model. |
Copyright: | © 2023 the Authors. Licensee MDPI, Basel, Switzerland. |
Lizenz: | Dieses Werk wurde unter der Creative-Commons-Lizenz Namensnennung 4.0 International (CC-BY 4.0) veröffentlicht und darf unter den Lizenzbedinungen vervielfältigt, verbreitet, öffentlich zugänglich gemacht, sowie abgewandelt und bearbeitet werden. Dabei muss der Urheber bzw. Rechteinhaber genannt und die Lizenzbedingungen eingehalten werden. |
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