Deformation and failure properties of cylindrical battery packs under quasi-static and dynamic indentations
International Journal of Impact Engineering,
Год журнала:
2025,
Номер
unknown, С. 105239 - 105239
Опубликована: Янв. 1, 2025
Язык: Английский
Remaining Useful Life Prediction of Lead-Acid Battery Using Multi-phase Wiener Process-based Degradation Model
Process Safety and Environmental Protection,
Год журнала:
2025,
Номер
unknown, С. 106974 - 106974
Опубликована: Март 1, 2025
Язык: Английский
Comparative Analysis of Neural Network Models for Predicting Battery Pack Safety in Frontal Collisions
World Electric Vehicle Journal,
Год журнала:
2025,
Номер
16(2), С. 78 - 78
Опубликована: Фев. 5, 2025
Amid
concerns
about
environmental
degradation
and
the
consumption
of
non-renewable
energy,
development
electric
vehicles
(EVs)
has
accelerated,
with
increasing
focus
on
safety.
On
road,
battery
packs
are
exposed
to
potential
risks
from
unforeseen
objects
that
may
collide
or
scratch
system,
which
lead
damage
even
explosions,
thus
endangering
safety
transportation
participants.
In
this
study,
several
predictive
models
aimed
at
assessing
performances
proposed
provide
a
basis
for
data-driven
structural
optimization
by
numerically
simulating
deformation
base
plate.
Initially,
finite
element
model
pack
was
developed,
accuracy
verified
performing
modal
analysis
various
commercial
software
tools.
Then,
representative
samples
were
collected
using
optimal
Latin
hypercube
sampling,
followed
collision
simulations
gather
data
under
different
conditions.
Next,
prediction
three
models—PSO-BP
neural
network,
RIME-BP
RBF
network—was
compared
predicting
bottom
shell
deformation.
Finally,
based
error
functions.
The
results
indicate
these
network
can
accurately
predict
frontal
conditions
within
specified
limits,
yielding
best
performance
beyond
those
limits.
developed
is
able
assess
mechanical
response
collision,
providing
support
optimization.
It
also
provides
an
important
reference
improving
durability
design.
Язык: Английский
Multi‐objective optimization design of the automotive battery packs with fiber metal laminates under ground impact
Polymer Composites,
Год журнала:
2025,
Номер
unknown
Опубликована: Апрель 1, 2025
Abstract
Metal
is
commonly
used
due
to
its
high
absolute
energy
absorption
(
EA
)
value
and
low
mass
specific
SEA
m
),
while
carbon
fiber
reinforced
polymer
(CFRP)
boasts
a
but
costly.
To
address
this,
this
paper
suggests
the
application
of
metal
laminate
(FML)
material
in
automotive
battery
packs,
as
it
lightweight,
with
moderate
cost.
Regarding
ground
impact
car
that
poses
threat
safety,
resistance
FML
planes
investigated.
A
comparison
made
among
collision
responses
pack
enclosures
three
materials,
same
thickness
mass,
showing
suitable
shell
material.
Furthermore,
based
on
kriging
model
non‐dominated
sorting
genetic
algorithm
II
(NSGA‐II),
multi‐objective
optimization
design
developed
minimize
displacement
by
optimizing
layers.
The
Pareto
frontier
obtained,
leading
modification
decreases
compared
initial
design,
well
improvement
performance
absorption.
Highlights
FML,
7075
aluminum,
CFRP
were
validate
FML's
advantages.
for
improves
performance.
Optimization
values
adjusted
feasibility
processing
accuracy
limits.
Язык: Английский
Machine Learning in 3D and 4D Printing of Polymer Composites: A Review
Polymers,
Год журнала:
2024,
Номер
16(22), С. 3125 - 3125
Опубликована: Ноя. 8, 2024
The
emergence
of
3D
and
4D
printing
has
transformed
the
field
polymer
composites,
facilitating
fabrication
complex
structures.
As
these
manufacturing
techniques
continue
to
progress,
integration
machine
learning
(ML)
is
widely
utilized
enhance
aspects
processes.
This
includes
optimizing
material
properties,
refining
process
parameters,
predicting
performance
outcomes,
enabling
real-time
monitoring.
paper
aims
provide
an
overview
recent
applications
ML
in
composites.
By
highlighting
intersection
technologies,
this
seeks
identify
existing
trends
challenges,
outline
future
directions.
Язык: Английский