Plug-in electric vehicles (PEVs) play a significant role in the development of green cities since they generate less pollution than conventional vehicles. To promote PEV adoption and mitigate range anxiety, charging infrastructure should be deployed at strategic locations that are readily accessible to the public. Nebraska is working on the expansion of charging infrastructure around the state; however, stakeholders face several difficulties in trying to minimize irregular charging behaviors. Most electric vehicle users plug in and leave their vehicles for an extended time at public parking lots designated for PEVs. Some users even leave their vehicles for longer than 24 hours. Prolonged idle time is a concern for other PEV users who need to charge their vehicles to complete their planned trip. This thesis proposes several well-known regression methods to predict the idle time to help policymakers minimize the impact of irregular charging behaviors. In addition, PEV user charging behavior has a significant influence on the distribution network and its reliability. In addition, to increase efficiency in management of the electric grid, this thesis also proposes several well-known regression methods to predict the energy consumption of a charging session. The performance of different regression methods for predicting the idle time as well as energy consumption are characterized using established statistical metrics.
Adviser: Mahmoud Alahma
