As the global community transitions towards sustainable transportation, electric vehicles (EVs) have emerged as a promising alternative to traditional internal combustion engine vehicles. However, while EVs offer numerous benefits, such as reduced greenhouse gas emissions and lower operating costs, they pose unique challenges. This dissertation examines EV charging behavior in-depth to facilitate widespread EV adoption. The dissertation consists of three chapters that comprehensively analyze EV drivers' charging behavior, aiming to mitigate challenges related to EV adoption.
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. Identifying charging locations can assist policymakers in making informed decisions regarding infrastructure development. By understanding when and where future EV owners tend to charge their vehicles, charging infrastructure stakeholders can strategically deploy charging stations in the most convenient and necessary locations for EV owners. These findings can help reduce range anxiety, making EVs more appealing to potential buyers.
Moreover, the study aims to bridge the gap between the transportation and power system sectors by predicting charging behavior and locations. Furthermore, comprehending charging behavior can provide valuable insights into drivers' habits, avoiding unnecessary charging sessions. This study can instill confidence in drivers during their trips, increase trust in the technology, and potentially accelerate the adoption of EVs.