Abstract:
In today’s world, electric vehicles (EVs) are trending due to many positive
factors associated with them. They are taking over the conventional internal
combustion (IC) engine-based vehicles in many countries around the world
due to the limited resources of fossil fuels. Many countries are going to ban
conventional vehicles by 2030. EVs due to their high performance and low
maintenance cost have gained its popularity among the users. In spite of
all that, still there are some challenges for the manufacturers of EVs which
are considered very important from the user point of view. EVs charging
has been an important area for the researchers from the last two decades.
Our conventional power line infrastructure, carbon emissions and long waiting time for charging an EV are affecting the adaptation rate of EVs. The
problem that we want to pursue in this thesis work is associated with the
energy management in a smart charging network for EVs. We have seen
some complexities when the number of EVs become greater than the number of charging ports which causes EVs congestion and as a result the user
faces longer waiting time. Also, the charging cost is high during on-peak
hours and grid stability is also not ensured during peak load demand. These
problems limit the performance of a smart charging network and lower the
EVs adaptation rate.
For this purpose, we have developed a fuzzy inference system along with
improved optimal scheduling algorithm to optimally manage the energy generated to charge the EVs by considering all the important parameters associated with EVs charging. Moreover, average daily trip distance of each EV
is also considered as an input to the smart charging network to overcome
the peak load demand and charge the EVs according to the associated trip
distance of each EV in order to reduce charging cost for the owner as well as
peak to average ratio. Every EV will be charged based on the combination
of some priorities that will help the EV owners in reducing their waiting time
as well as charging cost. In this way, EVs adaptation rate will increase. The
proposed methodology will be verified by simulations.
