| dc.description.abstract | In the emergence of greener transportation, Electric Vehicles (EVs) play an important
role, expected to outnumber conventional vehicles in the near future. However, the
installation of Fixed Charging Stations (FCSs) is not keeping up with the increased
demand, especially outside urban centers. Such a challenge is prohibiting many users
from owning EVs because of range anxiety. This thesis proposes a novel cooperative
mechanism where EVs can access charging services such as Vehicle-to-Vehicle (V2V)
charging schemes, private smart Home Charging Station (HCS), or Mobile Charging
Station (MCS) to complement existing FCS services in certain regions. To this end, the
proposed mechanism divides each region into geographically distributed zones managed
by cloud-fog nodes for charging service coordination. In each zone, we employ the Hungarian matching algorithm to optimally match EVs with the available charging services.
Unlike recent approaches that establish a one-to-one matching between supplier EVs and
demanding EVs, our mechanism matches multiple demanding EVs to charging services
with a larger capacity to maximize the service offering. Comparing results with existing studies shows that our model outperforms prior approaches across critical factors.
Furthermore, our proposed matching algorithm prioritizes EVs requiring charge based
on their maximum travel range given their current State of Charge (SoC). To address
the challenge of accurately estimating EV driving range, we introduce an ensemblebased Machine Learning (ML) model offering a compelling solution for enhancing the
estimation of EV driving range for practical applications. | en_US |
