***Electric Vehicle Promotion in Rural Communities for Environmental, Social, and Economic Sustainability

Electric Vehicle, Sustainability, Machine Learning, Data Analysis

Research Mentor: Dr. Guang Wang,
Department, College, Affiliation: Computer Science, Arts and Sciences
Contact Email: gw22g@fsu.edu
Research Assistant Supervisor (if different from mentor):
Research Assistant Supervisor Email:
Faculty Collaborators:
Faculty Collaborators Email:

Looking for Research Assistants: Yes
Number of Research Assistants: 2
Relevant Majors: Computer Science, Statistics, Civil Engineering, Industria Engineering, Geography, Social Science Majors
Project Location: On FSU Main Campus
Research Assistant Transportation Required:
Remote or In-person: Partially Remote
Approximate Weekly Hours: 10 hours, Flexible schedule (Combination of business and outside of business. TBD between student and research mentor.)
Roundtable Times and Zoom Link: Not participating in the Roundtable

Project Description

Despite billions of dollars of federal investments that promote transportation electrification, rural communities are still largely left behind with very low EV penetration and few charging resources. EV usage in rural communities may be more beneficial for environmental, social, and economic sustainability due to the longer driving distances and high driving frequencies of rural communities for different personal and professional uses (e.g., for purchasing daily necessities, recreation, work, and transporting agricultural implements, etc). Therefore, adopting EVs in rural communities not only helps improve environmental benefits but also has the potential to promote an equitable transition to clean energy and mitigate the wealth disparity between rural communities and urban communities. In this project, we propose to develop a data-driven socially informed framework to help promote EV adoption for rural innovation towards environmental, social, and economic sustainability. In doing so, we aim to address multiple complicated technical and real-world challenges by performing the following tasks.
(i) Investigating drivers and barriers to EV adoption in rural communities. We plan to conduct comprehensive analyses of barriers and drivers from different stakeholders’ perspectives (e.g., rural residents, charging infrastructure operators, utility agencies, and governments). We will use mixed methods, including surveys, panels, and big data analytics.
(ii) Predicting charging demand in rural areas. Based on our accessed large-scale fine-grained human mobility data and socioeconomic data from both urban and rural areas, combined with EV charging data from urban areas, we plan to extract social activity patterns (e.g., visitation frequency and dwelling time) and charging patterns (e.g., charging duration and frequencies). We then plan to predict charging demand in rural areas based on rural mobility patterns, socioeconomic status, and charging patterns in urban areas.
(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 to satisfy charging demand of EVs, (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 literature review about electric vehicle (EV) adoption and deployment. Different datasets including survey data, mobility 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: Literature review experience is recommended.
Data collection and analysis skills are required.
Experience in Python is recommended.