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Pubblicazioni

2025

• Signorile, F., Mastromarino, F., Scarabaggio, P., Gialò, V., Carli, R. & Dotoli, M. (2025) Optimal Positioning of Electric Vehicle Chargers for Efficient Land Use in Smart Cities: Integration with Fuel Stations IN Volta, M. (Ed.), IFAC-PapersOnLine.Elsevier B.V., 315-320. doi:10.1016/j.ifacol.2025.08.156
  [BibTeX] [Abstract] [Download PDF]

  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. © 2025 Elsevier B.V., All rights reserved.

  @CONFERENCE{Signorile2025315,
  author = {Signorile, Federico and Mastromarino, Fabio and Scarabaggio, Paolo and Gialò, Valeria and Carli, Raffaele and Dotoli, Mariagrazia},
  title = {Optimal Positioning of Electric Vehicle Chargers for Efficient Land Use in Smart Cities: Integration with Fuel Stations},
  year = {2025},
  journal = {IFAC-PapersOnLine},
  volume = {59},
  number = {9},
  pages = {315 - 320},
  doi = {10.1016/j.ifacol.2025.08.156},
  url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105016601583&doi=10.1016%2Fj.ifacol.2025.08.156&partnerID=40&md5=1355076b28a3acbd873b765c23e1594f},
  affiliations = {Politecnico di Bari, Department of Electronic and Information Engineering, Bari, Italy},
  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. © 2025 Elsevier B.V., All rights reserved.},
  author_keywords = {Charging stations; Electric vehicles; Land use; Maximal covering location problem; Optimal positioning; Smart cities},
  keywords = {Charging (batteries); Charging stations; Electric vehicles; Optimization; Smart city; Sustainable development; Charging infrastructures; Charging station; Covering location problems; Effective solution; Electric vehicle charging; Electric vehicles chargers; Fuel station; Maximal covering location problem; Optimal placements; Optimal positioning; Land use},
  correspondence_address = {F. Signorile; Department of Electrical and Information Engineering, Polytechnic of Bari, Bari, 70125, Italy; email: ti.ab1765433850ilop.1765433850dhp@21765433850eliro1765433850ngis.1765433850f1765433850; F. Mastromarino; Department of Electrical and Information Engineering, Polytechnic of Bari, Bari, 70125, Italy; email: ti.ab1765433850ilop@1765433850onira1765433850morts1765433850am.oi1765433850baf1765433850; P. Scarabaggio; Department of Electrical and Information Engineering, Polytechnic of Bari, Bari, 70125, Italy; email: ti.ab1765433850ilop@1765433850oigga1765433850barac1765433850s.olo1765433850ap1765433850; V. Gialò; Department of Electrical and Information Engineering, Polytechnic of Bari, Bari, 70125, Italy; email: ti.ab1765433850ilop.1765433850itned1765433850uts@o1765433850laig.1765433850v1765433850; R. Carli; Department of Electrical and Information Engineering, Polytechnic of Bari, Bari, 70125, Italy; email: ti.ab1765433850ilop@1765433850ilrac1765433850.elea1765433850ffar1765433850; M. Dotoli; Department of Electrical and Information Engineering, Polytechnic of Bari, Bari, 70125, Italy; email: ti.ab1765433850ilop@1765433850iloto1765433850d.aiz1765433850argai1765433850ram1765433850},
  editor = {Volta, M.},
  publisher = {Elsevier B.V.},
  issn = {24058963; 24058971; 14746670},
  isbn = {9783902661869; 9788374810357; 8374810351; 9783902661463; 9783902661586; 9783902661906; 9783902661104; 9783902823007; 9783902823243; 9783902823106},
  language = {English},
  abbrev_source_title = {IFAC-PapersOnLine},
  type = {Conference paper},
  publication_stage = {Final},
  source = {Scopus},
  note = {Cited by: 0; All Open Access; Gold Open Access}
  }