Wanshi Hong recently joined Lawrence Berkeley National Laboratory as a postdoc in the Sustainable Energy & Environmental Systems Department. She received her Ph.D. degree at the University of Virginia, in 2020. Her graduate research work mainly focused on developing adaptive learning algorithms and control approaches for transportation-related applications including vehicle fuel optimization and intelligent traffic control schemes. Wanshi's research interests include adaptive control, optimal control, on-line parameter estimation, and machine learning.
2023 R&D 100 Award: HEVI-LOAD: A Tool for Projecting Infrastructure Needs for Medium- and Heavy-Duty Electric Vehicles - August 23rd 2023
As the decarbonization of the transportation sector continues, government agencies, transportation planners, private clean energy practitioners, and medium and heavy-duty (MHD) electric vehicle drivers are wondering what kind of infrastructure will be needed and where it will be needed. Berkeley Lab scientists have developed a tool to help answer these and related questions: Medium- and Heavy-Duty Electric Vehicle Infrastructure – Load Operation and Deployment (HEVI-LOAD). HEVI-LOAD is a software tool that helps predict the needs of MHD electric vehicles. Its projections include requirements for the electric grid and charging infrastructure such as the type, number, and location of charging stations for the state of California and beyond. The data is visualized and presented in a dashboard application that allows the user to view large and small results down to the county level. The tool has been supported by the California Energy Commission and the US Department of Energy, Vehicle Technology Office’s Analysis Program.