The
introduction
of
electric
cars
(EVs)
and
the
incorporation
cutting-edge
technologies
are
causing
a
major
revolution
in
automotive
sector.
Controller
Area
Network
(CAN)
bus
facilitates
component-to-component
communication
coordination,
which
is
an
essential
part
operation
vehicles.
Electric
vehicles'
CAN
integration
with
Internet
Things
(IoT)
solutions
can
meet
need
for
efficient
control
monitoring.
As
technology
advances,
this
not
only
improves
safety
during
development
testing
phases
but
also
opens
possibilities
remote
control,
monitoring,
other
use
cases
commercial
EV
systems.
Future Transportation,
Journal Year:
2025,
Volume and Issue:
5(1), P. 8 - 8
Published: Jan. 14, 2025
Transportation
5.0
is
an
advanced
and
sophisticated
system
combining
technologies
with
a
focus
on
human-centered
design
inclusivity.
Its
various
components
integrate
intelligent
infrastructure,
autonomous
vehicles,
shared
mobility
services,
green
energy
solutions,
data-driven
systems
to
create
efficient
sustainable
transportation
network
tackle
modern
urban
challenges.
However,
this
evolution
of
also
intended
improve
accessibility
by
creating
environmentally
benign
substitutes
for
traditional
fuel-based
even
when
addressing
traffic
management
control
issues.
Consequently,
promote
synergy
sustainability,
the
diversified
nature
ought
be
efficiently
effectively
managed.
Thus,
study
aims
reveal
involvement
core
component
prediction
in
through
systematic
literature
review.
This
contemplates
causal
model
under
dynamics
modeling
order
address
solutions
movement
toward
sustainability
context
5.0.
From
review,
addition
developed
model,
it
identified
that
every
method
reduces
environmental
impact
while
increasing
passenger
convenience
overall
efficiency
transport
network,
greater
improvements
developing
nations.
As
variety
options,
including
electric
successfully
integrated,
will
eventually
enable
mobility,
multimodal
transit
options.
Infrastructures,
Journal Year:
2024,
Volume and Issue:
9(1), P. 15 - 15
Published: Jan. 16, 2024
In
Italy,
the
availability
of
service
areas
(SAs)
equipped
with
charging
stations
(CSs)
for
electric
vehicles
(EVs)
on
highways
is
limited
in
comparison
to
total
number
areas.
The
scope
this
work
create
a
prototype
and
show
different
approach
assessing
inlets
required
highways.
proposed
method
estimates
energy
requirements
future
fleet
It
based
an
conversion
that
starts
fuel
sold
highway
network
ends
inlets.
A
benchmark
using
consolidated
values
statistics
about
refueling
attitudes,
factors
range
correction
winter
conditions.
results
depend
assumptions
car
distribution,
varying
numbers
analysis
revealed
vehicle
traffic
critical
factor
determining
inlets,
significant
variance
between
SAs.
This
study
highlights
necessity
incorporating
like
weather,
power,
EV
into
these
estimations.
findings
are
useful
planning
infrastructure,
especially
along
major
routes
urban
high-range
relying
High-Power
DC
(HPDC)
charging.
model’s
applicability
scenarios
can
be
improved
by
considering
proportion
recharged
at
destination.
key
limitation
lack
detailed
origin–destination
(OD)
data,
leading
some
uncertainty
calculated
ratio
coefficient
underscoring
need
research
refine
model.
Engineering Technology & Applied Science Research,
Journal Year:
2025,
Volume and Issue:
15(2), P. 20674 - 20680
Published: April 3, 2025
This
paper
presents
the
use
of
optimal
placement
and
number
Internet
Things
(IoT)
gateway
method
to
support
home
Electric
Vehicle
(EV)
charging
scheduling
within
an
IoT
system.
A
research
was
conducted
for
two
scenarios.
In
scenario
1,
a
single
placed,
while
in
2,
gateways
placed.
The
evaluation
both
scenarios
utilized
random
placement,
Equally
Distributed
Placement
(EDP),
Genetic
Algorithm
(GA)
placement.
optimization
result
ensures
that
Path
Loss
(PL)
value
communication
system
does
not
exceed
specified
PL
threshold.
aims
minimize
ensuring
quality
data
transmission,
specifically
maintaining
rate
above
31.72
kbps
throughput
24
kbps.
results
indicate
EDP
require
more
than
three
gateways.
Meanwhile,
GA
requires
only
gateways,
making
it
cost-effective
solution.
Abstract
To
meet
the
rising
demand
for
electric
vehicles
(EVs),
effective
and
dependable
fast-charging
reservation
systems
are
required.
Conventional
charging
frequently
lack
coordination
between
user
preferences,
real-time
station
status,
environmental
factors,
leading
to
poor
experiences
ineffectiveness.
Existing
methods
EV
fail
account
dynamic
real-world
conditions
such
as
changing
traffic
patterns,
uptime,
IoT
sensor
inputs,
resulting
in
suboptimal
allocation
failed
reservations.
This
study
fills
a
gap
by
proposing
an
IoT-Coordinated
Fast
Charging
Reservation
Approach
(IoT-CFCRA),
which
uses
data
predict
success
suggest
best
stations
under
different
conditions.
The
IoT-CFCRA
IoT-Enhanced
Dataset,
contains
attributes,
vehicle
data,
IoT-enhanced
like
type,
battery
level,
distance
station,
traffic.
Data
preprocessing
entails
normalization,
encoding,
feature
selection
find
important
features.
A
Support
Vector
Machine
(SVM)
model
is
trained
through
hyperparameter
tuning
80
−
20
splitting.
algorithm
also
includes
scoring
method
that
considers
distance,
conditions,
membership
status
provide
ranked
suggestions.
Users
receive
notifications
help
them
adapt
results.
experimental
results
show
proposed
approach
outperforms
other
methods.
It
achieved
accuracy
of
87%,
outperforming
baseline
(Gradient
Boosting)
4%
improving
on
Logistic
Regression
10%.
AUC
score
0.92
indicates
excellent
discriminative
capability,
5-point
improvement
over
Random
Forest.
F1-Score
0.84
demonstrates
strong
balance
precision
recall,
SVM
8%.
Furthermore,
RMSE
was
reduced
0.25,
indicating
19.4%
decrease
prediction
error
when
compared
KNN
(RMSE
=
0.36).
cross-validation
88%
confirms
model's
robustness,
next-best
performing
4%.
These
metrics
highlight
resilience
creating
precise
reservations
suggesting
optimum
stations.
seamlessly
combines
capacities
with
machine
learning
tackle
factors
influence
encourages
user-centered
decision-making
resource
allocation,
laying
groundwork
future
advances
IoT-driven
infrastructure.
International Journal of Consumer Studies,
Journal Year:
2025,
Volume and Issue:
49(3)
Published: April 24, 2025
ABSTRACT
The
large‐scale
expansion
of
vehicle‐to‐grid
(V2G)
technology
requires
the
full
support
electric
vehicle
(EV)
users.
However,
existing
studies
lack
a
comprehensive
review
V2G
acceptance,
especially
preferences
and
attitudes
potential
consumers.
To
this
end,
our
study
conducts
systematic
literature
to
understand
acceptance
behaviour
explore
its
future
research
directions.
By
reviewing
87
related
literatures,
we
obtain
key
information
about
adoption
in
terms
publication
trends,
keywords,
theories,
contexts,
methods,
antecedents,
decisions,
outcomes.
Results
show
that
antecedents
mainly
influencing
are
those
product
(e.g.,
battery
life
EV
flexibility),
individual
range
anxiety
risk
awareness),
economy
costs).
three
most
important
decisions
for
intention
join
an
aggregator,
decision
sign
contract,
willingness
EVs.
outcome
with
votes
is
impact
on
energy,
particular
by
enhancing
grid
flexibility,
efficiency,
stability.
In
addition,
context
primarily
focused
United
States,
China,
Netherlands,
notable
from
other
countries.
Based
these
results,
also
further
discuss
directions
acceptance.
Technologies,
Journal Year:
2024,
Volume and Issue:
12(8), P. 137 - 137
Published: Aug. 20, 2024
Electric
vehicles
(EVs)
are
becoming
of
significant
interest
owing
to
their
environmental
benefits;
however,
energy
efficiency
concerns
remain
unsolved
and
require
more
investigation.
A
major
issue
is
a
lack
EV
charging
infrastructure,
which
can
lead
operational
difficulties.
Effective
infrastructure
development,
including
well-placed
stations
(CS),
critical
enhancing
connectivity.
To
overcome
this,
consumers
want
real-time
data
on
station
availability,
neighboring
locations,
access
times.
This
work
leverages
the
Distance
Vector
Multicast
Routing
Protocol
(DVMRP)
enhance
information
collection
process
for
through
Internet
Things
(IoT).
The
evolving
IoT
paradigm
enables
use
sensors
transfer
give
information.
Strategic
sensor
placement
helps
forecast
server
stations,
optimize
vehicle
scheduling,
estimate
wait
recommender
system
designed
identify
with
rapidly
rates,
along
uniform
pricing.
In
addition,
routing
protocol
has
privacy
protection
strategy
prevent
unauthorized
safeguard
during
exchanges
between
user
locations.
simulated
MATLAB
2020a,
controlled
secured
in
cloud.
predicted
algorithm’s
performance
evaluated
using
several
kinds
standards,
power
costs,
counts,
consumption,
optimization
values.
