Gradual electrification is widely considered as a feasible strategy for reducing the oil dependency and CO2 emissions of road transportation. In chase of these aims increasing importance has been attributed to Electric Vehicles (EVs). Although the European Commission has strongly supported sustainable mobility initiatives in recent years, with the purpose of decarbonizing road transport and mitigating urban air pollution, results are below expectations. Among the initiatives that can be implemented the most important is certainly the use of electric vehicles on a large scale but it will be necessary, in parallel, to plan an appropriate system of infrastructures that will be able to support the expansion. In this paper a methodology to provide optimal locations of electric vehicle infrastructures in a highway network is proposed. The procedure can also be used to support the implementation of the DAFI (Directive on the Deployment of Alternative Fuels Infrastructure). The goal is to estimate the basic number of charging stations and determine their correct allocation on the road network by analyzing the supply and demand and considering the psychological component of the driver. In Part 1, the subject of this research article, a model is presented to detect how many charging infrastructures are required within the service areas and to identify their location. After the description of the solution algorithm, a test application is performed in order to assess model and technique. With the aim of analysing a high-level system, the model will be used, in Part 2 of the work, to calculate and distribute the charging point on the Italian case study.

Optimal allocation of electric vehicle charging stations in a highway network: Part 1. Methodology and test application

Napoli G.
Primo
Conceptualization
;
Micari S.;Andaloro L.;
2020

Abstract

Gradual electrification is widely considered as a feasible strategy for reducing the oil dependency and CO2 emissions of road transportation. In chase of these aims increasing importance has been attributed to Electric Vehicles (EVs). Although the European Commission has strongly supported sustainable mobility initiatives in recent years, with the purpose of decarbonizing road transport and mitigating urban air pollution, results are below expectations. Among the initiatives that can be implemented the most important is certainly the use of electric vehicles on a large scale but it will be necessary, in parallel, to plan an appropriate system of infrastructures that will be able to support the expansion. In this paper a methodology to provide optimal locations of electric vehicle infrastructures in a highway network is proposed. The procedure can also be used to support the implementation of the DAFI (Directive on the Deployment of Alternative Fuels Infrastructure). The goal is to estimate the basic number of charging stations and determine their correct allocation on the road network by analyzing the supply and demand and considering the psychological component of the driver. In Part 1, the subject of this research article, a model is presented to detect how many charging infrastructures are required within the service areas and to identify their location. After the description of the solution algorithm, a test application is performed in order to assess model and technique. With the aim of analysing a high-level system, the model will be used, in Part 2 of the work, to calculate and distribute the charging point on the Italian case study.
2020
Istituto di Tecnologie Avanzate per l'Energia - ITAE
Charging station
Electric vehicles
Highway network
Location
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/492223
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ente

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 62
  • ???jsp.display-item.citation.isi??? ND
social impact