E3S Web of Conferences,
Год журнала:
2025,
Номер
601, С. 00057 - 00057
Опубликована: Янв. 1, 2025
Precise
voltage
and
current
regulation
is
essential
to
ensure
the
required
power
output
maximum
efficiency
in
charging
stations,
particularly
those
utilizing
Wireless
Power
Transfer
(WPT)
systems.
Effective
techniques
are
necessary
manage
Battery
Electric
Vehicles
(BEVs)
operating
under
various
modes.
This
study
outlines
controller
design
for
different
methods
lithium-ion
batteries
a
WPT
charger.
Initially,
fundamental
concepts
of
systems
their
equivalent
circuit
introduced.
Subsequently,
control
strategy
detailed
Constant
Current
(CC)
mode,
Multi-stage
Method
(MCM),
Pulse
Charging
(PCM).
Finally,
resilience
validity
this
innovative
approach
controlling
demonstrated
through
simulations.
Energy Strategy Reviews,
Год журнала:
2024,
Номер
54, С. 101471 - 101471
Опубликована: Июнь 29, 2024
Electrification
of
transport
industry
in
China
presents
several
new
prospects
to
fulfil
requirements,
which
are
necessary
encounter
increasing
issues
energy
security,
air
quality,
and
lower
the
dependence
on
fossil
fuels.
The
Chinese
government
is
paying
significant
attention
EV
market
penetration
consumer
adoption
through
numerous
demonstration
programs/plans
with
attractive
transportation
policies.
In
this
study,
key
factors
included
barriers
policies
comprehensively
reviewed,
can
enhance
intention
adopt
EVs.
This
research
study
extensively
demonstrates
positive
impact
two
distinguished
types
including
financial
preferential
consumer's
EVs
by
implementing
an
extended
improved
version
Theory
Planned
behavior
(TPB).
A
case
Shanghai
performed
survey
314
respondents,
further
evaluated
structural
equation
modelling
(SEM)
assess
aspects
adoption.
particular,
construct
items
TPB
attitude,
subjective
norm
(SN),
perceived
behavioral
control
(PBC)
investigated
detail
present
their
joint
purchasing
consumers.
confirmatory
factor
analysis
(CFA)
AMOS
for
assessment
findings.
findings
from
reveal
that
have
a
considerable
towards
shaping
attitude
consumers
significantly
related
However,
Shanghainese,
more
positively
associated
purchase
comparison
Consequently,
playing
crucial
role
controlling
China.
principal
policy
suggestions
various
provide
multifaceted
perceptions
stakeholders
envision
electrified
transportation.
Batteries,
Год журнала:
2025,
Номер
11(3), С. 107 - 107
Опубликована: Март 13, 2025
Artificial
Neural
Networks
(ANNs)
improve
battery
management
in
electric
vehicles
(EVs)
by
enhancing
the
safety,
durability,
and
reliability
of
electrochemical
batteries,
particularly
through
improvements
State
Charge
(SOC)
estimation.
EV
batteries
operate
under
demanding
conditions,
which
can
affect
performance
and,
extreme
cases,
lead
to
critical
failures
such
as
thermal
runaway—an
exothermic
chain
reaction
that
may
result
overheating,
fires,
even
explosions.
Addressing
these
risks
requires
advanced
diagnostic
strategies,
machine
learning
presents
a
powerful
solution
due
its
ability
adapt
across
multiple
facets
management.
The
versatility
ML
enables
application
material
discovery,
model
development,
quality
control,
real-time
monitoring,
charge
optimization,
fault
detection,
positioning
it
an
essential
technology
for
modern
systems.
Specifically,
ANN
models
excel
at
detecting
subtle,
complex
patterns
reflect
health
performance,
crucial
accurate
SOC
effectiveness
applications
this
domain,
however,
is
highly
dependent
on
selection
datasets,
relevant
features,
suitable
algorithms.
Advanced
techniques
active
are
being
explored
enhance
improving
models’
responsiveness
diverse
nuanced
behavior.
This
compact
survey
consolidates
recent
advances
estimation,
analyzing
current
state
field
highlighting
challenges
opportunities
remain.
By
structuring
insights
from
extensive
literature,
paper
aims
establish
ANNs
foundational
tool
next-generation
systems,
ultimately
supporting
safer
more
efficient
EVs
robust
safety
protocols.
Future
research
directions
include
refining
dataset
quality,
optimizing
algorithm
selection,
precision,
thereby
broadening
ANNs’
role
ensuring
reliable
vehicles.
World Electric Vehicle Journal,
Год журнала:
2024,
Номер
15(7), С. 324 - 324
Опубликована: Июль 21, 2024
The
rapid
proliferation
of
electric
vehicles
(EVs)
presents
both
opportunities
and
challenges
for
the
electrical
grid.
While
EVs
offer
a
promising
avenue
reducing
greenhouse
gas
emissions
dependence
on
fossil
fuels,
their
uncoordinated
charging
behavior
can
strain
grid
infrastructure,
thus
creating
new
operators
EV
owners
equally.
nature
vehicle
may
lead
to
emergence
peak
loads.
Grid
typically
plan
demand
periods
deploy
resources
accordingly
ensure
stability.
Uncoordinated
introduce
unpredictability
variability
into
load
patterns,
making
it
more
challenging
manage
loads
effectively.
This
paper
examines
implications
address
this
challenge
proposes
novel
dynamic
optimization
algorithm
tailored
schedules
efficiently,
mitigating
power
peaks
while
ensuring
user
satisfaction
requirements.
proposed
“Proof
Need”
(PoN)
aims
schedule
based
collected
data
such
as
state
charge
(SoC)
EV’s
battery,
charger
power,
number
connected
per
household,
end-user’s
preferences,
local
distribution
substation’s
capacity.
PoN
calculates
priority
index
each
coordinates
all
at
times
in
way
that
does
not
exceed
maximum
allocated
was
tested
under
different
scenarios,
results
comparison
between
an
baseline
scenario
coordinated
model,
proving
efficiency
our
algorithm,
by
40.8%
with
no
impact
overall
total
time.
