Junjie Qin, Assistant Professor of Electrical and Computer Engineering

Junjie Qin, assistant professor in Purdue University’s Elmore Family School of Electrical and Computer Engineering, has received a National Science Foundation (NSF) CAREER Award. These prestigious awards support junior faculty who exemplify the role of teacher-scholars through research and education, and the integration of these endeavors in the context of their organizations' missions. The awards, presented once each year, include a federal grant for research and education activities for five consecutive years.

Qin’s grant will support a project entitled “Towards Grid-Responsive Electrified Transportation Systems: Modeling, Aggregation, and Market Integration.” This project focuses on the growing trend of using electricity for transportation, such as electric cars. This shift brings opportunities and challenges where electric power systems meet transportation systems. The goal is to make electric transportation systems responsive to the electricity grid, which can bring economic benefits. The project aims to create a solid foundation and tools for this grid-responsive transportation system.

Qin says he is grateful to the NSF for this support.

“With this project, our lab aims to unlock the flexibility in electric vehicle charging loads for grid services by establishing a rigorous theoretical foundation for coupled power and transportation systems and developing tools useful for stakeholders in electricity and transportation industries,” said Qin. “This has the potential to save billions in investments for infrastructure upgrades as the electric vehicle adoption grows.” 

By doing this research, Qin wants to support the use of electric vehicles, help integrate renewable energy sources, and speed up the process of reducing carbon emissions from both transportation and electricity. He plans to develop new concepts and models that connect power and transportation systems. Additionally, Qin wants to create a spatiotemporal toolkit to optimize when and where electric vehicles are charged to better align with electricity market dynamics, demand patterns, and other factors. The project involves expertise from various fields like control theory, optimization, game theory, economics, and statistical learning.