A review of Bayesian-filtering-based techniques in RUL prediction for Lithium-Ion batteries
Journal of Energy Storage,
Год журнала:
2025,
Номер
111, С. 115371 - 115371
Опубликована: Янв. 18, 2025
Язык: Английский
Perspective and comparative analysis of physics-based models for sodium-ion batteries
Electrochimica Acta,
Год журнала:
2025,
Номер
unknown, С. 145573 - 145573
Опубликована: Янв. 1, 2025
Язык: Английский
Machine learning algorithms based approach on prediction of mechanical behaviour of PLA/brass composites manufactured by additive manufacturing
Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 19, 2025
Machine
learning
(ML)
has
become
a
potential
alternate
in
material
science
to
process
complex
datasets
containing
multiple
inputs/outputs
and
predict
the
properties
of
materials
using
algorithms
statistical
models.
The
mechanical
PLA/brass
infill
composites
fabricated
by
fused
deposition
modelling
based
additive
manufacturing
were
analysed
this
work.
Tensile
strength,
flexural
strength
impact
are
three
output
parameters
considered
for
study
input
density,
nozzle
temperature,
layer
thickness
printing
speed.
Strength
increased
with
rise
density
temperature
while
decreased
speed,
thickness.
Six
ML
namely
Artificial
Neural
Networks
(ANNs),
Decision
Tree
(DT),
Random
Forest
(RF),
Ada
Boost
(AB),
Gradient
Boost,
Support
Vector
(SVM)
employed
evaluate
strength.
Effectiveness
models
was
studied
tools
coefficient
determination,
mean
square
error
absolute
error.
ANN
model
exhibited
high
accuracy
AB
displayed
least
predicting
Scanning
electron
morphology
deployed
fracture
profile
composites.
squared
(MSE)
is
higher
(1.496)
MSE
value
obtained
(0.131)
lowest
(MAE)
(0.209)
highest
MAE
(0.812)
tensile
boost
(5.603)
(2.376)
DT
(0.773)
gradient
(2.200)
Язык: Английский
Advanced computational techniques: Bridging metaheuristic optimization and deep learning for material design through image enhancement
Elsevier eBooks,
Год журнала:
2025,
Номер
unknown, С. 197 - 228
Опубликована: Янв. 1, 2025
Язык: Английский
State of health as a function of voltage hysteresis in Li-ion battery half-cells
Journal of Solid State Electrochemistry,
Год журнала:
2024,
Номер
unknown
Опубликована: Июнь 1, 2024
Язык: Английский
Machine learning in advancing anode materials for Lithium-Ion batteries – A review
Inorganic Chemistry Communications,
Год журнала:
2024,
Номер
unknown, С. 113577 - 113577
Опубликована: Ноя. 1, 2024
Язык: Английский
An Advanced Methodology for Crystal System Detection in Li-ion Batteries
Electronics,
Год журнала:
2024,
Номер
13(12), С. 2278 - 2278
Опубликована: Июнь 10, 2024
Detecting
the
crystal
system
of
lithium-ion
batteries
is
crucial
for
optimizing
their
performance
and
safety.
Understanding
arrangement
atoms
or
ions
within
battery’s
electrodes
electrolyte
allows
improvements
in
energy
density,
cycling
stability,
safety
features.
This
knowledge
also
guides
material
design
fabrication
techniques,
driving
advancements
battery
technology
various
applications.
In
this
paper,
a
publicly
available
dataset
was
utilized
to
develop
mathematical
equations
(MEs)
using
genetic
programming
symbolic
classifier
(GPSC)
determine
type
structure
Li-ion
with
high
classification
performance.
The
consists
three
different
classes
transformed
into
binary
datasets
one-versus-rest
approach.
Since
target
variable
each
variation
imbalanced,
several
oversampling
techniques
were
employed
achieve
balanced
variations.
GPSC
trained
on
these
variations
five-fold
cross-validation
(5FCV)
process,
optimal
hyperparameter
values
searched
random
value
search
(RHVS)
method.
goal
find
combination
highest
After
obtaining
MEs
performance,
they
combined
tested
initial
Based
conducted
investigation,
ensemble
could
detect
accuracy
(1.0).
Язык: Английский
Property Prediction for Complex Compounds Using Structure-Free Mendeleev Encoding and Machine Learning
Journal of Chemical Information and Modeling,
Год журнала:
2024,
Номер
unknown
Опубликована: Дек. 12, 2024
Predicting
the
properties
for
unseen
materials
exclusively
on
basis
of
chemical
formula
before
synthesis
and
characterization
has
advantages
research
resource
planning.
This
can
be
achieved
using
suitable
structure-free
encoding
machine
learning
methods,
but
additional
processing
decisions
are
required.
In
this
study,
we
compare
a
variety
encodings
algorithms
to
predict
structure/property
relationships
battery
materials.
It
was
found
that
physical
units
used
measure
property
labels
have
an
important
impact
predictive
ability
models,
regardless
computational
approach.
Property
with
respect
weight
give
excellent
performance,
volume
cannot
predicted
confidence
only
information,
even
when
underlying
characteristics
same.
These
results
contrast
previous
studies
unsupervised
classification,
where
excelled,
highlight
how
structural
features
or
represented
plays
role
in
models.
Язык: Английский
Quantum tomographic reconstruction: a Bayesian approach using the extended Kalman filter
Опубликована: Апрель 19, 2024
Язык: Английский
A machine learning approach for estimating supercapacitor performance of graphene oxide nano-ring based electrode materials
Energy Advances,
Год журнала:
2024,
Номер
unknown
Опубликована: Янв. 1, 2024
A
novel
machine
learning
approach
for
supercapacitor
performance
prediction
of
graphene
oxide
nano-ring-based
electrode
materials.
Язык: Английский