1 research outputs found
A Machine to Machine framework for the charging of Electric Autonomous Vehicles
Electric Autonomous Vehicles (EAVs) have gained increasing attention of
industry, governments and scientific communities concerned about issues related
to classic transportation including accidents and casualties, gas emissions and
air pollution, intensive traffic and city viability. One of the aspects,
however, that prevent a broader adoption of this technology is the need for
human interference to charge EAVs, which is still mostly manual and
time-consuming. This study approaches such a problem by introducing the
Inno-EAV, an open-source charging framework for EAVs that employs
machine-to-machine (M2M) distributed communication. The idea behind M2M is to
have networked devices that can interact, exchange information and perform
actions without any manual assistance of humans. The advantages of the Inno-EAV
include the automation of charging processes and the collection of relevant
data that can support better decision making in the spheres of energy
distribution. In this paper, we present the software design of the framework,
the development process, the emphasis on the distributed architecture and the
networked communication, and we discuss the back-end database that is used to
store information about car owners, cars, and charging stations