UROP Project

Data Science, AI, Electric Vehicle, Smart Cities

Research Mentor: Guang Wang,
Department, College, Affiliation: Computer Science, Arts and Sciences
Contact Email: guang.wang@fsu.edu
Research Assistant Supervisor (if different from mentor): Lin Jiang
Research Assistant Supervisor Email:
Faculty Collaborators:
Faculty Collaborators Email:

Looking for Research Assistants: Yes
Number of Research Assistants: 2
Relevant Majors: Computer Science, Data Science
Project Location: On FSU Main Campus
Research Assistant Transportation Required:
Remote or In-person: Fully Remote
Approximate Weekly Hours: 10,
Roundtable Times and Zoom Link: Not participating in the Roundtable

Project Description

Despite billions of dollars of federal investments that promote transportation electrification, the state of Florida is still largely left behind with very low EV penetration and few charging resources. EV usage may be more beneficial for environmental, social, and economic sustainability. A key challenge for large-scale EV adoption is the inaccessibility of public charging infrastructure. Hence, in this project, we propose to develop a data-driven optimization framework for EV charging infrastructure deployment. In doing so, we aim to address multiple complicated technical and real-world challenges by performing the following tasks.
(i) Understanding the current charging station distribution in Florida.
(ii) Predicting charging demand in a fine-grained region, e.g., census block or zip code level.
(iii) Combining the predicted charging demand with other real-world conditions like population and POI distributions, we plan to design a decision-support tool to help deploy charging infrastructure by balancing different practical factors, such as (a) improving charging infrastructure accessibility and fairness to satisfy charging demand of EVs in different regions, (b) increasing charging resource utilization to improve the profitability of operators, and (c) reducing potential impacts on power grids, etc.


Research Tasks: Students will conduct a comprehensive literature review about electric vehicle (EV) adoption and charging infrastructure deployment. Different datasets including mobility data, charging station data, and US census data will be analyzed. Machine learning models will be built for charging station siting and charging demand prediction.

Skills that research assistant(s) may need: Data collection and analysis skills are required.
Experience in Python is required.