IET Power Electronics,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Nov. 19, 2024
Abstract
An
improved
dual‐source
SC‐MLI
topology
is
developed
in
this
article
for
medium‐voltage
and
high‐power
applications.
This
can
perform
symmetrically
asymmetrically
to
generate
9
levels
13
levels,
respectively.
It
consists
of
10
unidirectional
switches,
a
dual
DC
source,
two
capacitors
provide
high‐gain
output
voltage
with
lower
TSV.
Since
the
capacitor's
voltages
are
self‐balanced,
therefore
no
need
an
auxiliary
circuit
or
sensors,
which
brings
down
complexity
circuit.
To
check
viability
proposed
topology,
simple
fundamental
control
strategy
based
on
nearest‐level
pulse
width
modulation
opted
for.
From
comparative
analysis,
it
was
observed
that
outperformed
similar
topologies
terms
switch
counts,
cost
factor,
power
quality,
total
standing
voltage.
The
topology's
feasibility
evaluated
using
MATLAB/Simulink
under
both
static
dynamic
loads.
Furthermore,
thermal
analysis
conducted
PLECS
software
calculate
losses
across
components
consecutively
efficiency
has
been
found
while
having
over
96%
symmetric
asymmetric
configurations,
Finally,
simulation
results
verified
by
experimental
prototype
validate
performance
different
loading
conditions.
Journal of Electrochemical Energy Conversion and Storage,
Journal Year:
2024,
Volume and Issue:
22(4)
Published: Sept. 30, 2024
Abstract
Heat
removal
and
thermal
management
are
critical
for
the
safe
efficient
operation
of
lithium-ion
batteries
packs.
Effective
dynamically
generated
heat
from
cells
presents
a
substantial
challenge
optimization.
This
study
introduces
novel
liquid
cooling
method
aimed
at
improving
temperature
uniformity
in
battery
pack.
A
complex
nonlinear
hybrid
model
is
established
through
traditional
full-factor
design
back
propagation
neural
network
(BPNN)
approximation.
links
input
parameters
such
as
number
baffles,
baffle
angle,
inlet
speed
to
output
including
maximum
temperature,
difference,
pressure
drop.
Global
multiobjective
optimization
carried
out
using
Nondominated
Sorting
Genetic
Algorithm
II
sidestep
locally
optimal
solutions.
Pareto
solutions
sorted
multiple
criteria
decision-making
techniques.
Through
optimization,
rise
relative
initial
controlled
within
7.68
K,
difference
4.22
K
(below
commonly
required
5
K),
drop
only
83.92
Pa.
Results
presented
this
work
may
help
enhance
performance
efficiency
battery-based
energy
conversion
storage.
The
technique
used
helps
maximize
benefit
an
innovative
technique.
Scientific Reports,
Journal Year:
2025,
Volume and Issue:
15(1)
Published: Feb. 20, 2025
The
critical
necessity
for
sophisticated
predictive
maintenance
solutions
to
optimize
performance
and
extend
lifespan
is
underscored
by
the
widespread
adoption
of
lithium-ion
batteries
across
industries,
including
electric
vehicles
energy
storage
systems.
This
study
introduces
a
comprehensive
framework
that
incorporates
real-time
health
diagnostics
with
state-of-charge
(SOC)
estimation,
utilizing
an
Improved
Random
Forest
(IRF)
algorithm
address
current
limitations
in
battery
management
integrates
physics-informed
methodologies
data-driven
machine
learning
models
facilitate
dynamic
assessment
production
precise
predictions.
achieved
analysing
features
such
as
SOC,
efficiency,
capacity
decline.
IRF
outperforms
state-of-the-art
methods
Gradient
Boosting
standard
Forest,
obtaining
lowest
Root
Mean
Square
Error
1.575
R2
score
0.9995.
demonstrates
exceptional
accuracy.
Furthermore,
model
guarantees
adaptability
robust
anomaly
detection,
classification
accuracy
99.99%
no
false
negatives.
These
developments
proactive
interventions,
reduce
operational
risks,
life
substantial
margin.
innovative
provides
conditions
establishing
connection
between
empirical
data
analysis
theoretical
modelling.
positioned
transformative
solution
sustainable
systems,
addition
addressing
challenges
scalability
computational
research
demonstrates.
results
emphasize
its
potential
tool
assuring
reliability,
safety,
longevity
contemporary
applications.
International Transactions on Electrical Energy Systems,
Journal Year:
2025,
Volume and Issue:
2025(1)
Published: Jan. 1, 2025
There
has
been
expeditious
development
and
significant
advancements
accomplished
in
the
electrified
transportation
system
recently.
The
primary
core
component
meant
for
power
backup
is
a
lithium‐ion
battery.
One
of
keys
to
assuring
vehicle’s
safety
dependability
an
accurate
remaining
useful
life
(RUL)
forecast.
Hence,
exact
prediction
RUL
plays
vital
part
management
battery
conditions.
However,
because
its
complex
working
characteristics
intricate
deterioration
mechanism
inside
battery,
predicting
by
evaluating
exterior
factors
exceedingly
difficult.
As
result,
developing
improved
health
technology
successfully
massive
effort.
Because
complexity
ageing
mechanisms,
single
model
unable
describe
mechanisms.
this
paper
review
organised
into
three
sections.
First
study
about
degradation
mechanism,
second
data
collections
using
mercantile
openly
accessible
Li‐ion
sets
third
estimation
RUL.
important
performance
parameters
distinct
forecast
are
categorised,
analysed
reviewed.
In
end,
brief
explanation
given
various
error
indices.
This
article
classifies
summarises
data‐dependent
models
machine
learning
(ML),
deep
(DL)
ensemble
(EL)
algorithms
suggested
last
few
years.
goal
work
context
present
overview
all
recent
utilising
data‐driven
models.
also
followed
categorisation
several
types
ML,
DL
EL
prediction.
Finally,
review‐based
includes
pros
cons
