After introducing the methodology in the first part of the article, in this Part 2 the algorithm is applied to calculate and identify the optimal positions of the charging stations for electric vehicles in the Italian highway network. The main objective is to map the infrastructural needs of the country using the information acquired from the most recent data base. The results can also be used to facilitate the application of the Directive on the Deployment of Alternative Fuels Infrastructure. In the paper the market for electric vehicles circulating in Italy is initially analyzed and, after applying an appropriate filtering, the algorithm input variables relating to the vehicle system (vehicle autonomy and energy of the battery pack) are identified. For this purpose, both the registration data and the technical characteristics provided by the manufacturers have been used. Subsequently the road system (the flow of vehicles with related indicators), the infrastructures (the number of sockets and the charging station power), and driver behavior (range anxiety) are considered. The results show a map of candidate points to allocate the charging stations divided by region. After having sized and placed the charging infrastructures with different planning scenario, some future projections are also introduced.

Optimal allocation of electric vehicle charging stations in a highway network: Part 2. The Italian case study

Polimeni A.;Micari S.;
2019-01-01

Abstract

After introducing the methodology in the first part of the article, in this Part 2 the algorithm is applied to calculate and identify the optimal positions of the charging stations for electric vehicles in the Italian highway network. The main objective is to map the infrastructural needs of the country using the information acquired from the most recent data base. The results can also be used to facilitate the application of the Directive on the Deployment of Alternative Fuels Infrastructure. In the paper the market for electric vehicles circulating in Italy is initially analyzed and, after applying an appropriate filtering, the algorithm input variables relating to the vehicle system (vehicle autonomy and energy of the battery pack) are identified. For this purpose, both the registration data and the technical characteristics provided by the manufacturers have been used. Subsequently the road system (the flow of vehicles with related indicators), the infrastructures (the number of sockets and the charging station power), and driver behavior (range anxiety) are considered. The results show a map of candidate points to allocate the charging stations divided by region. After having sized and placed the charging infrastructures with different planning scenario, some future projections are also introduced.
2019
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11570/3219455
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