Big data analytics of ev charging stations

Big Data Analytics of EV Charging
Stations in India
Muskan Rath
Department of Computer Science and Engineering
International Institute of Information Technology,
Bhubaneswar
Email Id: b517022@iiit-bh.ac.in
Abstract—In recent times, the scientific community has
been collecting substantial data from various experiments and
analysis to extract relevant information from it. This information,
primarily obtained from diverse outlets, multidimensional and
unstructured in design, refers to Big Data. At present, it has
played a pivotal role in areas of research like IOT which includes
sub-areas like Smart Grids, Hybrid Vehicles, Electric Vehicles,
etc. However, the major challenge faced by Big Data today is
to handle the vast volume of data of a wide variety; possessing
high data transmission rate; produced by devices, automobiles,
establishments, power grids, and many other things connected to
the Internet. Of the areas related to IoT, smart grid and electric
vehicles also come across the challenge of handling large and
varied datasets, as they produce and consume Big Data, by pro-
ducing databases for the same entity, creating copies of the same
and passing it along to the other systems. Henceforth, there is an
incredible need to look at the created information to reveal data
like concealed models, obscure connections, advertise patterns,
and client inclinations that can empower associations (Govt. or on
the other hand Private) to settle on educated choices. The study
and systematic retrieval of this information is a Herculean task
as performed by conventional application software for processing
data. Thus, Big Data Analytics is a necessity for EVs. The
current analysis paper discusses the applications of big data and
its methods in harnessing the data produced from EVs,drivers,
charging stations and infrastructures.In electric vehicles, we can
get data from various sources like sensors and travel records.
We would then use big data techniques to analyse this data. The
analyzed data is used to plan ideas and policies for positioning
of charging stations, smarter charging mechanisms, addressing
energy management problems, and assessing the ability of power
delivery networks to accommodate additional loads. Meanwhile,
we would also look at the market cost of the services provided
by EVs (i.e. V2G opportunities). This paper will primarily focus
on big data analytics strategies for EV Charging Stations.
Index Terms—Big Data Analytics, Electric Vehicles
(EVs), EV Charging Stations, Smart grid
I. INTRODUCTION
Today, individuals and systems use the network
in this age, having an exponential and tremendous size of
information, measured in Exabyte (EB) and Petabyte (PB). By
2025, the forecast is that the Internet will outperform the brain
size of people living in the entire world. This firm development
of information directly results from advances in computed
sensors, calculations, correspondences, and capacity that have
made the enormous scale of information. Roger Magoulas,
an analyst, contrived the name Big Data to portray this
peculiarity. Gartner Company expressed that information will
be the 21st-century oil. Over the most recent 25 years, it has
developed enormously in different fields with unmistakable
sorts. As shown by the measurable report of International Data
Corporation (IDC), in the year 2011, the general volume of
the information made on the planet was 1.8ZB that improved
by almost multiple times in the following five years.
As per the groundbreaking analysis by International Data Cor-
poration ( IDC) on ”The Virtual Universe of Opportunities,”
in 2013 the commonly produced and duplicated quantity of
data was 4.4 zettabytes (ZB).The amount of data is doubling
at regular intervals so by 2020 the overall output would
outperform 44 ZB (44 trillion GB). Other than the volume, the
rate at which information is collected straightforwardly results
from the advances in correspondence developments and IoT.
These giant datasets with top speed, precision represent the
Big Data phenomenon [1, 2]. Despite the associated complex-
ities, the paramount aim of the analysis is to explore these
details without missing valuable knowledge. One approach to
performing such an analysis is to break it into tiny subsets and
apply statistical techniques to every sample. Data visualization
and analytics is yet another approach that allows grasping
these data comprehensively [3]. Subsequently, it is significant
that we derive this information from repositories, change it
into reasonable structures, prepare it for storage, and handle
it for investigation in order to effortlessly and accurately
interpret it through media like outlines, tables, maps, plots,
dashboards, etc settling on effective, educated choices. Data
Analytics pertains to core IoT users, such as Smart Grids and
Electric Cars, because they comprise the wide spectrum of
connected devices and enormous associated system of things,
such as automobiles, charging points, savvy electronic meters,
intelligent electronic devices (IEDs) and phasor measurement
units (PMUs). We likewise foresee them to be the driving
force behind green smart cities by empowering the proficient
combination of the sustainable power source and lower dis-
charges. The urban smart city envisions nearly all level sur-
faces, including sidewalks, lined with solar panels to optimize
the usage of solar resources [4]. Electric vehicles, including
Plug-in Hybrid Electric Vehicles (PHEVs) and battery electric
vehicles (BEVs), are promising choices to supplant Internal
Combustion Engine (ICE) vehicles to diminish energy depen-
dence, lower GHG discharges, and improve urban air quality.

As a major aspect of the endeavours to increment urban
sustainability, many nations have defined objectives for electric
vehicle adoption [5]. EVs assume a significant job to expand
energy security and to diminish emanations of ozone-depleting
substances and different contaminations. A crucial component
of technology for the future is wireless connectivity, where
individual transportation mutates to locomotion as a facility.
We acknowledge the fact that alternative energy for sustainable
growth is clean energy as a global advantage amid a severe
energy crisis. The production of such a resource is eco friendly
and is thus the primary choice for several nations around the
globe, such as the US, Japan, and Europe, and thus EVs are a
means of protecting the environment and addressing the essen-
tial question of the national grid. Therefore, the fundamental
strategy in many nations at present is to accomplish workable
transportation to address future vitality. By 2025, Navigant
Research predicts that over 1.2 billion automobiles worldwide
would be related to their surroundings and/or to one another by
embedded or distributed communications systems. The electric
vehicle requires a charging station, so we need to choose
its location carefully. EVs are plugged into charging stations
so that the batteries obtain electrical energy. These technolo-
gies would provide drivers with security warnings and real-
time traffic reports at a baseline; the more sophisticated and
completely integrated systems will enable semi-autonomous
driving systems. At present this form of interconnection is
important for EVs, having navigation systems that make a
driver aware of the available charging stations and monitor the
status of battery charging. EVs include hundreds of sensors,
including user drive behaviour, battery protection through the
Battery Management System (BMS), and charging station for
grid management. Drivers bring smart devices and wearables
contributing to road info. EVs manufacturing, the number of
e-vehicles in India is rising at 37.5 [6]. By 2030, India is
hoping to become a 100 percent EV country. However, the
availability and affordability of charging facilities in India
are a major obstacle in the growth of EVs envisioned by
SMEV. Despite this context, the goal is to access the economic
feasibility of EVs and the facilities needed for them. At least
one electric motor, using refillable electrical energy batteries
can drive the EVs. Moment torque is given by engines to
EVs, creating proficient and consistent increasing speed. From
an efficiency and environmental point of view, EVs are more
advanced technology than internal combustion engine vehicles
[7]. Hence, we propose to study the role of charging stations
in smart cities along with its policy.
II. LITERATURE REVIEW
This segment discusses the research on the signif-
icance of Electric Vehicle charging stations and role of big
data. Based on the literature, we define particular activities
taken around the world to encourage public recognition and
knowledge of charging stations for EVs. Lack of readily ac-
cessible charging facilities is, however, one pressure affecting
India’s EV industry. India’s government has recently begun
taking several steps to build safe and conveniently accessible
charging stations. Selecting the location for building charging
stations for EVs is necessary to ensure the acceptance of
EV and also to resolve some associated dangers such as
battery expenses and exhaustion, monetary perils, absence of
charging facilities, hazardous upkeep of EVs, problems with
its incorporation into a smart grid, range anxiety, auxiliary
loads and motorist attitude. Contingent upon the separation
went by EVs (in km) we have grouped EV charging stations
into three classes: Level 1 charging station, Level 2 charg-
ing station, and DC quick charging station [8]. Unsuitable
position of charging stations can affect the smooth working
of the power grid, resulting in voltage fluctuations, reduced
power loss, harmonics, and lower reliability indices [9]. The
system suggested by “ELECTRIC VEHICLE CHARGING
STATIONS. Technical Installation Guide.”, is a guideline for
assessing Agartala City’s implementation of the EV charging
network. To do this, we study three fields:
1) Various styles of charging points,
2) styles of EVs,
3) types of batteries.
EV Charging Stations could gather and transfer operational
data to a central location, from where one can aggregate,
process and analyze the data and view the findings through ac-
tionable dashboards, and other features such as Scan, Anomaly
Detection, and REST-based APIs. These results will allow
station operators to gain valuable information to help them
enhance in unfamiliar areas of operations; besides delivering
timely and meaningful data of great value to end-users. Here
we would apply Adaptive Particle Swarm Optimization to
address the complex issue. Beijing used the aforementioned
technique via a network of roads. Bendiabdellah et al. raised
the issue of allotting charging stations for the city of Cologne
in Germany [10]. The situations for the siting of charging
stations are determined in the principal stage by using a proba-
bilistic technique fixated on the Bayesian system [11]. Present
research models the issue of charging station positioning in
various cities in India. India has a host of smart cities to come.
In the upcoming future, a significant number of EVs will be
stationed on the roads of such cities. There would likewise
be a requirement for a dependable charging framework. High
traffic density alongside low network firmness makes it hard
to track down loader areas. Our paper presents approaches,
apparatuses, and execution markers to discover ideal loader
areas, considering both the traffic and the electrical matrix. It
proposes a two-phase model for the portion of the charging
station. In the main stage, the candidate areas for charging
focuses are recognized by another technique for the Bayesian
system. In the subsequent level, it completes wanting to pick
the correct destinations, the type of charging stations, and the
measure of charging focuses at the charging stations. The size
of the road network nodes from the closest bus of the delivery
network, the traffic volume, and the reliability of the grid
are considered crucial considerations for the position of the
charging stations. It uses the ability of the Bayesian network to

cope with confusion and contact between events in the current
research. The second period of the proposed arranging model
incorporates deciding the appropriate situations for charging
stations(p) from the candidate areas (pc), the all out number
and the measure of quick/slow charging stations. The area
issue is planned as a multi-target cost advancement issue, VRP
file, ease of use file, and hanging tight period for charging
stations as minimisation or augmentation capacities. The third
objective function is the accessibility of charging stations. In
order to measure accessibility, the distance matrix and the
shortened distance matrix is calculated first. It is troublesome
for EV car drivers to wait in the charging stations for a
long time. The aim of optimization is therefore to reduce the
waiting period. The waiting period in the loading points is
based on the M / M / c queuing principle. A multi-target
cross breed CSO-TLBO calculation acquainted was utilized
to tackle the streamlining issue. Charging stations must not
only be sufficiently widespread for the electrical grid to easily
reach a charging station in its driving range but should be
widely dispersed so that the electrical transmission systems
can cover the entire city after recharging. So, we drafted the
Electric Vehicle Charging Station Placement Problem based
on these fresh perspectives [12]. We then address EVCSPP by
the suggested four methods (Iterative Hybrid-Integer Linear
Program, Greedy Method, Effective Hybrid-Integer Linear
Program, Chemical Reaction, The Optimization of Chemical
Reactions). Taylor Haw et al. introduced a charging station
having a key-initiated controller for the charging cycle [13].
A magnetic Swipe Card, a data storage device (for example,
ROM), or an electronic tag (for example, an RFID tag) may
form part of the electronic key. A transmitter to relay a signal
to a transponder or a Radio Frequency Identification (RFID)
tag can be given to the charging station. To receive a signal
transmitted from a transponder or RFID tag, it may provide
the charging station with a receiver. Thus, the charging station
may challenge a transponder or RFID tag to automatically
enable the controller to get user information, such as account
details. To augment the EV traffic stream that can be charged
under the up-and-comer plan of the EV charging stations,
a battery limit confined EV stream catch area model was
proposed [14]. A case study comprising a distributive system
with 33 nodes and a traffic network system with 25 nodes
was performed to show the efficacy of the proposed process.
[15] provides a multifunctional design model that involves
a complete configuration of the charging station for EVs
that takes into account factors like sustainable production
of EVs, characteristics of charging station, market dynamics,
retail demand, power grid, and urban planning factors. The
question of positioning at charging stations worried researchers
around the globe. Deb et al. in [16] analyzed different facets
of network charging strategy, such as the global scenario,
modelling methods, goal roles, and constraints. He developed
the question of the location of charging stations taking into
consideration just the transport network [10]. In “A Survey
on Energy Internet: Architecture, Approach, and Emerging
Technologies”, Liu et al. regarded building costs and operating
costs as the primary roles in their formulation, together with
the charging requirement as a limitation. In [17] Wei et al.
created the multi-level input queue, an automated charging
pattern. Our model uses grid demand data, data from the
station charging, data from the EV generator, data from the
customer, and data from a central delivery network. We would
suggest processing data in parallel, using MapReduce over the
Hadoop frame to accommodate the vast volume of data from
varied sources. To overcome the dynamic problem, Adaptive
Particle Swarm Optimization (APSO) can be implemented.
In [18], Tu et al. proposed paying facilities for the road
network of the city of Shenzhen in China. Maximizing the
driving time of the EVs and reducing waiting time at the
charging stations are primary features. The range of EVs is
the limitation of the design framework. Implementation of the
GA has overcome the allocation issue. In [19], the question
on the locations of charging stations has been devised by
examining only distribution networks. A statistical model is
provided in [20] for forecasting demand for EVs charging
focused on big data technology, taking into consideration real-
world traffic flow details as well as environmental factors
in projecting the market for charging EVs. Additionally, the
chronicled traffic information and climate information of South
Korea were utilized to figure the forecasting model, which
incorporates a group examination to arrange traffic designs,
a social investigation to distinguish influential factors, and a
decision tree to build up classification criteria. The factors
included in this analysis were the beginning period for charg-
ing, defined by the real-world traffic conditions and the initial
battery charging condition. Model contextual investigations for
electric vehicle charging were introduced during weekdays
and ends of the week in summer and winter were introduced
to show the distinctive charging load profiles of EVs in
the private and business locales. The introduced forecasting
model may permit power framework specialists to envision
electric vehicle charging requests dependent on recorded traffic
information and climate information. Along these lines, the
proposed electric vehicle charging request model can be the
establishment for the exploration of the effect of charging EVs
on the force framework in India too.
III. RESULT AND DISCUSSIONS
In outline, the EVs need fast, successful Data An-
alytics philosophies to associate continuously with the savvy
lattice and the shrewd city. Versatile edge processing innova-
tions will support those techniques. Security and protection
concerns heighten with the brought together exchange of EV
information through different EVs. Among all the techniques,
we found that the one recommended by the paper [20] is the
most suitable strategy. Paper [20] proposes a program to decide
an EV charging demand whose specific structure configuration
relies upon MATLAB worked in limits. The particular plan
of this application contains four components, including data
sources, data storage, management, data handling.A local plate

on the PC takes care of the data sources at the fundamental
level.. These snippets of data are collections of plain substance
records, made up of chronic traffic data, atmosphere data
from various avenues in South Korea assembled conflictingly.
The MATLAB data store work redoes the wide assortment
of data put away. This can be implemented in India. Such
capacity to enter the information varieties from the funda-
mental layer which can gracefully subtleties for energetically
productive presentation to basic data irregularly keenly. This
contains the individual road’s traffic insights and temperature
measurements. The third layer manages the past layer of
information put in a safe spot. MATLAB’s Map Reduce work
can perform calculations on immense data groupings, hence,
is the reason for this technique. This limit has three phases:
map stage, widely appealing stage, and diminishing stage. A
piece of data enters the guide stage, which masterminds this
data for taking care of. By then, the midway data is in the
decrease stage, which joins the widely appealing and makes
the last outcome. It merges the moderate results to deliver an
indisputable outcome. This examination used the guide work
that structures set aside traffic flow data and atmospheric de-
tails in the optimal partnership with data processing.Then, the
transitional data encounters the decrease stage, which unites
the widely appealing outcomes to make a convincing result.
In Paper [19], the researchers rejected moderate stage and the
diminish stage from this assessment considering how a specific
road was if perused for predicting Electric Vehicle charging
demand to abstain from joining similar data. The distinction
for a solitary road is definitely not an immediate result of
the count of the effectiveness of the new Electric Vehicle
charging demand structure, however, of the compelled space
for this article. Regardless, it is important to recollect these two
stages to assess the interest for EV charging in exceedingly
muddled road cases, which are beyond this article. The line of
subtleties will contain the day of the year, and the subtleties
area would include the hour of the day. Data incorporates
traffic stream, climate, tenacity, wind speed and style of day.
The last layer shapes this information to depict the Electric
Vehicle charging demand measuring structure and coordinates
the accompanying advances: a gathering assessment, a social
examination and a choice tree. The starting phase to deal with
data produced is to apply an AI method to recognize customary
traffic instances of recorded traffic details. The accompanying
stage is identifying components to develop a decision tree for
determining the model approach for electric vehicle charging.
They chose the examination gage days for fleeting dissects to
foresee the Electric Vehicle charging request during working
days and ends of the week in winter and summer and the
Electric car charging requirement in summer and winter across
these four particular days. They expected the accompanying
arrangements:
(1) Charging electric vehicles once every day either in the
working climate during the day or at home during night time
with a humble charge rate.By and by we can charge an electric
vehicle all the time either at their transport stations or in their
diverse, high-charging parking structures;
(2) the Gussian traffic, which relies on the predetermined
traffic models decides the starting period for charging electric
vehicles
(3) In examination, the hidden SOC for half and half ve-
hicles expect a Gaussian movement before charging, while
the basic SOC for an ordinary vehicle before charging is
0.20. They detached the solicitation for charging into two
parts, including business and private goals. They treated the
confidence in electric vehicle charging at home as Electric
Vehicle charging charges in private goals. Electric vehicles
charged in the working environment and in their particular
leaving regions, transport stations reflected charging their costs
in the organization premises. They charged high EV charging
requests during night time at home because of the substantial
congestion of electric vehicles all over town at the end of the
week during the daytime. They accounted for most extraordi-
nary calls for charging during the non-functional hours. The
daytime charge on a working day in winter is more prominent
than that during evening. Also, the daytime charging is a
necessity as there are different segments of EVs charged at the
work environment and electrical transportation charged at the
daytime transportation stations. The suggested EV charging
request envisioned as a model could help in the assessment
on the impact of EV charging demand on the smart grid.
Furthermore, this application checking model may empower
efficient chairmen to later build up the activity and age profiles
on power frameworks by foreseeing the EV charging demand
in both private and public goals. Similarly, it will add to the
choice of forecast and activity plans for adaptable EV charging
structures subject to EV charging demands in different Electric
Vehicle Charging Stations in India.
IV. CONCLUSION AND FUTURE SCOPE
We recognize that alternative energy for sustainable
growth is clean energy as a global advantage amid a severe
energy crisis. The creation of such an asset is without con-
tamination and is in this manner the principal choice of a few
nations around the globe, for example, US, Europe and Japan
and in this way, the production of EVs is a method of securing
nature and of tending to the basic inquiry of national network
arranging. Some of the problems confronting the EV charging
industry can be resolved by allowing the Internet of things-
enabled stations – that is, by instrumenting and integrating
them into the Internet. Battery technology will be a crucial
factor for e-vehicle development in India. There are several
opportunities for accelerated growth in the IoT market in the
Smart Grid field and in the EV Charging business in particular.
Rightly, IoT data analysis produced by EV Charging Stations
will dramatically change the economics of the service of such
stations–and thereby eliminate main obstacles that impede
their acceptance in the large marketplace. The government
must play a significant role here because it needs to find
out policies and resources for the indigenous development of
Li-ion batteries. This would enormously diminish the battery

cost, as the vast majority of the batteries as of now being
used are imported. Indian Oil has marked a Memorandum of
Understanding (MoU) with Israel on innovative work for other
metal-air batteries that can decrease foundation needs by half.
This will help both the buyer and the seller ease up on the
initial investment. In order to attract investors to manufacture
e-vehicles and set up charging stations, the Indian government
plays a vital part here. A longitudinal study is required as
the electric vehicle population increases/battery technology to
better analyze the infrastructure requirement.
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