Energies,
Journal Year:
2024,
Volume and Issue:
17(11), P. 2487 - 2487
Published: May 22, 2024
Battery
state
of
health
(SOH),
which
is
a
crucial
parameter
the
battery
management
system,
reflects
rate
performance
degradation
and
aging
level
lithium-ion
batteries
(LIBs)
during
operation.
However,
traditional
machine
learning
models
face
challenges
in
accurately
diagnosing
SOH
complex
application
scenarios.
Hence,
we
developed
deep
framework
for
estimation
without
prior
knowledge
capacity.
Our
incorporates
series
neural
networks
(DNNs)
that
utilize
direct
current
internal
resistance
(DCIR)
feature
to
estimate
SOH.
The
correlation
DCIR
with
fade
capacity
quantified
as
strong
under
various
conditions
using
Pearson
coefficients.
We
K-fold
cross-validation
method
select
hyperparameters
DNN
optimal
hyperparameter
compared
significant
advantages
reliable
prediction
accuracies.
proposed
algorithm
subjected
robustness
validation,
experimental
results
demonstrate
model
achieves
precision,
mean
absolute
error
(MAE)
less
than
0.768%
root
square
(RMSE)
1.185%,
even
when
LIBs
are
varying
study
highlights
superiority
reliability
combining
DNNs
features
estimation.
Energies,
Journal Year:
2025,
Volume and Issue:
18(3), P. 746 - 746
Published: Feb. 6, 2025
The
sustainable
reuse
of
batteries
after
their
first
life
in
electric
vehicles
requires
accurate
state-of-health
(SoH)
estimation
to
ensure
safe
and
efficient
repurposing.
This
study
applies
the
systematic
ProKnow-C
methodology
analyze
state
art
SoH
using
machine
learning
(ML).
A
bibliographic
portfolio
534
papers
(from
2018
onward)
was
constructed,
revealing
key
research
trends.
Public
datasets
are
increasingly
favored,
appearing
60%
studies
reaching
76%
2023.
Among
12
identified
sources
covering
20
from
different
lithium
battery
technologies,
NASA’s
Prognostics
Center
Excellence
contributes
51%
them.
Deep
(DL)
dominates
field,
comprising
57.5%
implementations,
with
LSTM
networks
used
22%
cases.
also
explores
hybrid
models
emerging
role
transfer
(TL)
improving
prediction
accuracy.
highlights
potential
applications
predictions
energy
informatics
smart
systems,
such
as
grids
Internet-of-Things
(IoT)
devices.
By
integrating
estimates
into
real-time
monitoring
systems
wireless
sensor
networks,
it
is
possible
enhance
efficiency,
optimize
management,
promote
practices.
These
reinforce
relevance
machine-learning-based
resilience
sustainability
systems.
Finally,
an
assessment
implemented
algorithms
performances
provides
a
structured
overview
identifying
opportunities
for
future
advancements.
World Electric Vehicle Journal,
Journal Year:
2023,
Volume and Issue:
14(7), P. 188 - 188
Published: July 14, 2023
The
state
of
health
(SOH)
a
lithium
ion
battery
is
critical
to
the
safe
operation
such
batteries
in
electric
vehicles
(EVs).
However,
regeneration
phenomenon
capacity
has
significant
impact
on
accuracy
SOH
estimation.
To
overcome
this
difficulty,
paper
we
propose
method
for
estimating
based
incremental
energy
analysis
(IEA)
and
bidirectional
long
short-term
memory
(BiLSTM).
First,
IE
curve
that
effectively
describes
complex
chemical
characteristics
obtained
according
data
calculated
from
constant
current
(CC)
charging
phase.
Then,
relationship
between
degradation
analyzed
peak
height
extracted
as
aging
characteristic
battery.
Further,
Pearson
correlation
utilized
determine
linear
proposed
SOH.
Finally,
BiLSTM
employed
capture
underlying
mapping
SOH,
estimation
model
developed.
results
demonstrate
able
estimate
under
two
different
conditions
with
root
mean
square
error
less
than
0.5%
coefficient
determination
above
98%.
Additionally,
combined
select
an
high
correlation,
reducing
required
input
computational
burden.
