Optimal Positioning of Electric Vehicle Chargers for Efficient Land Use in Smart Cities: Integration with Fuel Stations

Expanding the charging infrastructure is essential for the widespread adoption of electric vehicles (EVs). A promising and effective solution is integrating EV charging points into existing fuel stations, thus optimizing land use while enhancing accessibility. Hence, the optimal placement of EV chargers within fuel station networks is a critical challenge. Traditional approaches rely on the so-called Maximal Covering Location Problem (MCLP), which assumes binary coverage and overlooks capacity constraints. This paper extends the MCLP framework by introducing a novel distance-based scalable coverage function and incorporating capacity limitations to prevent stations overload. The proposed model enhances the accuracy of demand distribution and offers a realistic, scalable approach to planning EV charging infrastructure. To validate its effectiveness, the proposed model is tested on a real-world case study involving the city of Bari, Italy.

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Publication Details

Journal
IFAC-PapersOnLine
Published
2025-01-01
DOI
https://doi.org/10.1016/j.ifacol.2025.08.156
Primary Topic
Smart Parking Systems Research
Type
article
Field-Weighted Citation Impact
0.00
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article

Optimal Positioning of Electric Vehicle Chargers for Efficient Land Use in Smart Cities: Integration with Fuel Stations

Mariagrazia Dotoli, Paolo Scarabaggio, Raffaele Carli, Federico Signorile et al.
IFAC-PapersOnLine
Smart Parking Systems Research
article

Optimal Positioning of Electric Vehicle Chargers for Efficient Land Use in Smart Cities: Integration with Fuel Stations

Mariagrazia Dotoli, Paolo Scarabaggio, Raffaele Carli, Federico Signorile, Fabio Mastromarino, Valeria Gialò
article en

Abstract

Expanding the charging infrastructure is essential for the widespread adoption of electric vehicles (EVs). A promising and effective solution is integrating EV charging points into existing fuel stations, thus optimizing land use while enhancing accessibility. Hence, the optimal placement of EV chargers within fuel station networks is a critical challenge. Traditional approaches rely on the so-called Maximal Covering Location Problem (MCLP), which assumes binary coverage and overlooks capacity constraints. This paper extends the MCLP framework by introducing a novel distance-based scalable coverage function and incorporating capacity limitations to prevent stations overload. The proposed model enhances the accuracy of demand distribution and offers a realistic, scalable approach to planning EV charging infrastructure. To validate its effectiveness, the proposed model is tested on a real-world case study involving the city of Bari, Italy.

IFAC-PapersOnLineVol. 59(9)
Polytechnic University of Bari (IT)
Openalex Percentile: Top 73%
Smart Parking Systems Research
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