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Integrating a spatio-temporal diffusion model with a multi-criteria decision-making approach for optimal planning of electric vehicle charging infrastructure

Author

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  • Mejia, Mario A.
  • Macedo, Leonardo H.
  • Pinto, Tiago
  • Franco, John F.

Abstract

Electric vehicles (EVs) allow a significant reduction in harmful gas emissions, thus improving urban air quality. However, the widespread adoption of this technology is limited by several factors, resulting in heterogeneous deployment in urban areas. This raises challenges regarding the planning of public electric vehicle charging infrastructure (EVCI), requiring adaptive strategies to ensure comprehensive and efficient coverage. This study introduces an innovative method that leverages geographic information systems to pinpoint appropriate sizes and suitable locations for public EVCI within urban environments. Initially, a Bass diffusion model is employed to estimate EV adoption rates by regions, enabling the determination of the appropriate sizes of EVCI necessary for each of them. Subsequently, a multi-criteria decision-making approach is applied to identify the suitable locations for EV charger installation within each region. In this way, EVCI locations are selected using spatial criteria, which ensure they are near common areas of interest and easily accessible through the road network. To validate the effectiveness and applicability of the proposed method, tests using geospatial data from a city in Brazil were carried out. The findings suggest that EVCI planning without proper spatial analysis may result in inefficient locations and inadequate sizes, which may discourage potential EV adopters and hinder widespread adoption of this technology.

Suggested Citation

  • Mejia, Mario A. & Macedo, Leonardo H. & Pinto, Tiago & Franco, John F., 2025. "Integrating a spatio-temporal diffusion model with a multi-criteria decision-making approach for optimal planning of electric vehicle charging infrastructure," Applied Energy, Elsevier, vol. 395(C).
    • Handle: RePEc:eee:appene:v:395:y:2025:i:c:s0306261925008906
    DOI: 10.1016/j.apenergy.2025.126160
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