Work

Planning urban EV charging stations with GIS based multi-criteria decision making

Year: 2025

Type: article

Abstract: With the accelerating adoption of electric vehicles (EV), strategic planning of charging infrastructure has become a critical component of sustainable urban mobility. The current EV charging points can be inadequately equipped to meet the rapidly increasing battery charging demand of EVs. Therefore, in this paper, a Geographic Information System (GIS)-based Multiple-Criteria Decision Analysis (MCDA) approach is adopted to help providing a solution of charging points location problem. Furthermore, the Analytical Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are used to select the optimal charging station site. A five-step approach is developed: determination of 14 criteria across four perspectives, spatial analysis using QGIS, criteria prioritization with AHP, site ranking in use of TOPSIS, and site capacity estimation. By integrating these methodologies, optimal charging point locations can be identified while ensuring effective deployment and utilization of EV charging infrastructure. Through a complete and considerate analysis, the study identifies the three most appropriate alternative locations for EVs with/without including photovoltaic (PV) system. The results derived from the proposed methodology were cross-referenced with the spatial distribution of existing charging stations. Although most of the current installations coincided with the priority zones identified by the model, the analysis also revealed additional high-potential locations that are not currently equipped. A distinctive advantage of the methodology lies in its integrative framework, which not only enables optimal site selection but also incorporates an evaluation of feasible energy sources, with a particular focus on the potential for solar energy utilization.

Source: Power Electronic Devices and Components

DOI: 10.1016/j.pedc.2025.100132

Authors: Yiwen Gu, Berk Celik, Maxime Hachette, Manuela Sechilariu, Fabrice Locment

Institutions: Université de Technologie de Compiègne, Modélisation multi-échelle des systèmes urbains

Language: English

Topic: Electric Vehicles and Infrastructure

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities, Affordable and clean energy, Climate action

Open Access status: gold

Estimated APC paid: $1,500