This research explores the range of tangible benefits that the implementation of transportation electrification programs can achieve for disadvantaged communities.
This project will study the response in electric vehicle charging behavior by implementing a carbon-based pricing scheme and studying activity patterns of drayage trucks operations by simulating different opportunity charging scenarios to estimate state of charge and load profiles.
This project will focus on electric vehicle integration into the smart grid through covering the following topics: 1) A centralized optimization approach for integrated distributed energy
In this dissertation, an optimal framework is introduced to find out the best PEV charging/discharging strategy using microgrids that includes all the Distributed Energy Resources present in a typical modern building microgrid.
This project is aimed to develop a deep reinforcement learning based smart charging technology for multiple chargers at the University of California, Riverside.
California has a long history of reducing greenhouse gas (GHG) emissions, and has been working to accelerate the adoption of battery electric heavy-duty trucks (BEHDTs) that have a restricted, load-dependent driving range, which makes charging planning an important role in the use of BEHDTs as an alternative to DHDTs. This research study investigates a mixed fleet drayage routing problem (MFDRP) with non-linear charging times.
The report involves a discussion of smart charging for electric vehicles, a comprehensive literature review on the subject, development of models for analyzing EV charging and grid impact, and algorithms for solving the smart charging problem.
