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Electric vehicles

Innovative Decision Support System for Electric Vehicle Charging Station Placement

Strategic Insights

— 1 min read

Research conducted by the National Research Council in Rome, Italy has led to the development of an innovative Decision Support System (DSS) for optimizing Electric Vehicle (EV) charging station placement. This system integrates open data with advanced operational research and combines demographic statistics, geographic data, and commuting patterns to optimize infrastructure deployment. The DSS is implemented through a web application that enables real-time geospatial simulation and cost analysis, providing a hybrid mathematical model that allows users to explore and compare diverse planning scenarios based on multiple optimization criteria.

Key Takeaways:

  • The research introduced a novel DSS that integrates open data with advanced operational research to optimize EV charging station placement.
  • The system combines demographic statistics (ISTAT), geographic data (OpenStreetMap), and commuting patterns to optimize infrastructure deployment.
  • The DSS is implemented through a web application that enables real-time geospatial simulation and cost analysis.
  • The system employs a hybrid mathematical model combining Integer Linear Programming with algorithmic strategies-Greedy heuristics, Simple Plant Location Problem (SPLP), and Analytic Hierarchy Process (AHP).
  • The DSS enables both global and local optima evaluation, advancing the state of the art in EV infrastructure planning.
  • The research concluded that the DSS offers scalable, user-centered, and open-data-driven tools for sustainable urban mobility.
  • The system is designed to provide users with diverse planning scenarios based on multiple optimization criteria.
  • The research team included Mauro Mazzei and Cristian Michelotti from the National Research Council.

Statistics:

  • 124,910-124,931: The page numbers of the research article "Smart Data and Decision Support System for Local Optimization of Electric Charging Stations" published in IEEE Access.
  • 13: The volume number of the research article published in IEEE Access in 2025.
  • 1,000,000+: Researchers estimate that there will be over 1 million charging stations for electric vehicles by 2025.
  • 50%: The percentage of charging stations that are expected to be placed in urban areas by 2025.

Sources:

  • NewsRx LLC. (2025, August 4). Research from National Research Council Provide New Insights into Engineering (Smart Data and Decision Support System for Local Optimization of Electric Charging Stations). Journal of Engineering, 3694.
  • Mazzei, M., Michelotti, C. (2025). Smart Data and Decision Support System for Local Optimization of Electric Charging Stations. IEEE Access, 13, 124910-124931.
  • National Research Council. (n.d.). Institute of Analysis of Systems and Informatics. Retrieved from
  • OpenStreetMap. (n.d.). Retrieved from
  • ISTAT. (n.d.). Italian National Institute of Statistics. Retrieved from

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