Authors :
Saumya Kakaria; Sanjana Mohan; Saumyaa Shah; Sharan Shankarnarayan
Volume/Issue :
Volume 6 - 2021, Issue 10 - October
Google Scholar :
http://bitly.ws/gu88
Scribd :
https://bit.ly/3nyDIGl
Abstract :
The ever-increasing population, rising global
warming and depleting non-renewable resources
indicate the pressing need of shifting to more sustainable
and environment friendly ways of transportation.
Electric Vehicles have zero tailpipe emissions and very
low dependency on fossil fuels. Widespread EV adoption
is hence the key to advancing sustainable mobility.
However, there are multiple challenges hindering EV
adoption, especially in India. In our research, we have
investigated what range anxiety is and how it inhibits
people from purchasing Electric Vehicles. This research
is in alignment with the United Nations Sustainable
Development Goal 9 of industry, innovation and
infrastructure and Sustainable Development Goal 12 of
responsible consumption and production. The Indian
Government aims to make India a 100% electric-vehicle
nation by 2030. In order to achieve the aforementioned
aim, an extensive charging infrastructure will be
essential so that range anxiety of EV’s can be tackled.
We have proposed a way for providing optimal locations
of charging stations by using genetic algorithm. We have
applied the algorithm only to the city of New Delhi for
the purpose of this research, however its application can
be extended to other cities as well. We’ve focused on the
optimal placement of electric vehicle charging stations
after taking into consideration many aspects and
constraints in the model without excessive technical
details. There are a few limitations in our research such
as the geographical area into consideration and we’ve
also assumed that consumers will not be biased towards
any charging station based on time and cost factors,
which may not be the case in reality. Further research
bridging these gaps will allow us to arrive at a more reallife like problem and thereby more accurate solutions
Keywords :
Electric Vehicles; Range Anxiety; Genetic Algorithm; Charging Infrastructure; Charging Stations; Operations Research; Climate Change
The ever-increasing population, rising global
warming and depleting non-renewable resources
indicate the pressing need of shifting to more sustainable
and environment friendly ways of transportation.
Electric Vehicles have zero tailpipe emissions and very
low dependency on fossil fuels. Widespread EV adoption
is hence the key to advancing sustainable mobility.
However, there are multiple challenges hindering EV
adoption, especially in India. In our research, we have
investigated what range anxiety is and how it inhibits
people from purchasing Electric Vehicles. This research
is in alignment with the United Nations Sustainable
Development Goal 9 of industry, innovation and
infrastructure and Sustainable Development Goal 12 of
responsible consumption and production. The Indian
Government aims to make India a 100% electric-vehicle
nation by 2030. In order to achieve the aforementioned
aim, an extensive charging infrastructure will be
essential so that range anxiety of EV’s can be tackled.
We have proposed a way for providing optimal locations
of charging stations by using genetic algorithm. We have
applied the algorithm only to the city of New Delhi for
the purpose of this research, however its application can
be extended to other cities as well. We’ve focused on the
optimal placement of electric vehicle charging stations
after taking into consideration many aspects and
constraints in the model without excessive technical
details. There are a few limitations in our research such
as the geographical area into consideration and we’ve
also assumed that consumers will not be biased towards
any charging station based on time and cost factors,
which may not be the case in reality. Further research
bridging these gaps will allow us to arrive at a more reallife like problem and thereby more accurate solutions
Keywords :
Electric Vehicles; Range Anxiety; Genetic Algorithm; Charging Infrastructure; Charging Stations; Operations Research; Climate Change
