This study aims to conduct a time-series to explore how electric vehicle (EV) adoption has increased over time and to predict how future EV adoption will continue to expand in the future.
This study explores how has EV adoption increased over the past years and predicts how will the EV market continue to expand and penetrate in the future years using a case study with regional granular travel survey data.
Using a choice model, the project will identify behavioral differences between those who purchase versus lease plug-in electric vehicles (PEVs), discuss how the characteristics of a vehicle technology influence the decision, and will investigate the impact of incentives on the decision to lease a PEV.
Using stated preference choice experiments, this study tries to fill the two gaps in the literature mentioned here: identify the drivers of choice of charging location during non-routine charging events and quantitative estimates of consumer preference for pricing strategies and other charging infrastructure attributes in case of routine nonhome charging events.
This project uses results from a cohort survey of electric vehicle owners in California conducted in 2019. The respondents of the survey are sampled from the pool of California Clean Vehicle Rebate Project (CVRP) recipients, and the CVRP is administered by the Center for Sustainable Energy & California Air Resources Board.
The market for plug-in electric vehicles (PEVs) that primarily include battery electric vehicles (BEVs) and plug-in hybrid vehicles (PHEVs) has been rapidly growing in California for the past few years. Given the targets for PEV penetration in the state, it is important to have a better understanding of the pattern of technology diffusion and the factors that are driving the process.
This data supports the project of the same name. The 2019 California Vehicle Survey data was used to analyze the driving behavior associated with more recent EV models (with potentially longer ranges).
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.
The study will utilize mathematical modeling, including artificial intelligence, to design a policy for the
optimal use of plug-in hybrid electric vehicles and identify charging locations for future battery electric vehicle
drivers
This report studies the responses given by PEV lessees and purchasers to the question of what they would do in the absence of the federal tax credit. To analyze the indications made by the responses, researchers sampled 7,000 California PEV drivers and used two logistic regression models and specified them.
