Developing Innovative GIS Solutions for Optimal Placement of Electric Vehicle Charging Stations in the Urban Area

L’unité de recherche AVENUES (EA 7284) est une unité de recherche interdisciplinaire ayant comme objet de recherche la ville, une approche basée sur les dynamiques urbaines et une méthode qui repose sur l'interdisciplinarité vue comme une intégration systémique des approches disciplinaires se co-transformant par leur mutuelle confrontation à l'objet. Les enseignants-chercheurs de l'équipe sont fortement impliqués dans la formation d'ingénieurs UTC en Génie Urbain (GU) et assurent ainsi la cohérence et la complémentarité entre la formation et la recherche en modélisation urbaine.

Description of the research subject:

As cities shift towards a more sustainable future with electric vehicles (EVs), the demand for well-planned charging infrastructure is rapidly increasing. One of the most pressing challenges urban planners face is determining where to place these charging stations to maximize accessibility, convenience, and efficiency. In this context, the placement of charging stations and their integration into existing cityscapes and infrastructure becomes crucial to ensuring a seamless transition to electric mobility. This internship is an opportunity to contribute directly to solving these challenges by developing innovative strategies and tools for optimizing EV charging infrastructure.

During this internship, your primary focus will be on developing a cutting-edge method for the strategic deployment of charging stations. This will involve several key objectives:

- Identifying optimal locations: You'll analyze various factors, such as traffic patterns, user behaviors, vehicle density, and proximity to key urban amenities, to determine the best sites for installing EV charging stations. By integrating existing infrastructure data with user mobility trends, you'll ensure that stations are both accessible and useful for drivers. 

- Calculating the ideal number of stations: You'll create models to estimate the number of charging stations needed in different locations to meet current and future demand. This includes finding a balance between convenience for users and minimizing costs, ensuring that cities can expand their networks efficiently without unnecessary expenditures. 

- Assessing power grid impacts: As EV adoption grows, the demand on electrical grids will increase. You'll model the energy consumption of charging stations and project how they will affect local power grids. By forecasting future needs, you'll help cities anticipate and mitigate potential strains on energy resources.  

- Developing a GIS-based tool: A critical part of the internship will be creating a Geographic Information System (GIS) tool, such as a model-builder or plugin, that enables city planners to identify the best locations for EV charging stations. This tool will combine spatial data, user trends, and infrastructure insights to generate location recommendations that can be easily visualized and adapted to different urban environments.

As an intern, you'll be responsible for building models and conducting advanced spatial data analysis using Geographic Information Systems (GIS). You'll work closely with a multidisciplinary research team, combining your technical expertise in data analysis and modeling with insights from urban planning, electrical engineering, and sustainable mobility. This collaboration will provide you with a deep understanding of the complex factors influencing EV infrastructure planning.

Funding:
The internship will be funded through an SFRI international mobility scholarship of 7,500 euros, which will cover the intern's living expenses in Compiègne, France. The internship will only take place if the student is selected for the SFRI scholarship.

We are looking for a final-year student (engineering or master’s level) in one of the following fields:  
- Urban Planning, Geography, and/or Enegy, with strong knowledge of Geographic Information Systems (GIS). 
- Proficiency in GIS tools (QGIS, ArcGIS) as well as data processing tools (Python, MATLAB, etc.).  
- Interest in topics related to sustainable mobility and the electrification of urban infrastructure.  
- Ability to work independently with a strong analytical mindset.