Principal InvestigatorSeshadri Srinivasa Raghavan, Ph.D.
University of California, Davis
The goal of this study intends to understand the relative share of vehicle miles traveled (VMT) between a plug-in hybrid vehicle and a battery electric vehicle.
This research aims to develop an Artificial Neural Network (ANN) to forecast EVs’ trip destinations and charging behavior–information that is essential for electricity load aggregators to effectively manage charging loads.
Principal InvestigatorLuis Fernando Enriquez-Contreras
University of California, Riverside
This project is focused on developing a free and open-source Python library for integrating various microgrid components and electric vehicle charging infrastructure.
This research is focused on the optimization of transportation system management by developing statistical models involving dynamic network modeling, spatiotemporal analysis, and theories of AI.
The goal of this study is to investigate the combined impact of fuel type, after treatment strategies, and driving mode on primary and secondary emissions form heavy duty vehicles.
This project examined the effects of road configurations and urban vegetation on the air quality impact of vehicle-related emissions and used this understanding to suggest methods to mitigate the impact of these emissions on urban air quality.
This research will evaluate revenue-neutral mechanisms to encourage zero emission vehicle (ZEV) sales in California, with no net cost to taxpayers: The analysis is being done for both private
