Energies,
Journal Year:
2023,
Volume and Issue:
16(19), P. 6846 - 6846
Published: Sept. 27, 2023
Lithium-ion
batteries
are
widely
recognised
as
the
leading
technology
for
electrochemical
energy
storage.
Their
applications
in
automotive
industry
and
integration
with
renewable
grids
highlight
their
current
significance
anticipate
substantial
future
impact.
However,
battery
management
systems,
which
charge
of
monitoring
control
batteries,
need
to
consider
several
states,
like
state
health,
cannot
be
directly
measured.
To
estimate
these
indicators,
algorithms
utilising
mathematical
models
basic
measurements
voltage,
or
temperature
employed.
This
review
focuses
on
a
comprehensive
examination
various
models,
from
complex
but
close
physicochemical
phenomena
computationally
simpler
ignorant
physics;
estimation
problem
formal
basis
development
algorithms;
used
Li-ion
monitoring.
The
objective
is
provide
practical
guide
that
elucidates
different
helps
navigate
existing
techniques,
simplifying
process
new
applications.
Energies,
Journal Year:
2024,
Volume and Issue:
17(5), P. 1250 - 1250
Published: March 6, 2024
With
increasing
concerns
about
climate
change,
there
is
a
transition
from
high-carbon-emitting
fuels
to
green
energy
resources
in
various
applications
including
household,
commercial,
transportation,
and
electric
grid
applications.
Even
though
renewable
are
receiving
traction
for
being
carbon-neutral,
their
availability
intermittent.
To
address
this
issue
achieve
extensive
application,
the
integration
of
storage
systems
conjunction
with
these
becoming
recommended
practice.
Additionally,
transportation
sector,
increased
demand
EVs
requires
development
that
can
deliver
rigorous
driving
cycles,
lithium-ion-based
batteries
emerging
as
superior
choice
due
high
power
densities,
length
life
cycle,
low
self-discharge
rates,
reasonable
cost.
As
result,
battery
(BESSs)
primary
system.
The
high-performance
on
BESS
have
severe
negative
effects
internal
operations
such
heating
catching
fire
when
operating
overcharge
or
undercharge
states.
Reduced
efficiency
poor
charge
result
at
higher
temperatures.
mitigate
early
degradation,
management
(BMSs)
been
devised
enhance
ensure
normal
operation
under
safe
conditions.
Some
BMSs
capable
determining
precise
state
estimations
reduce
hazards.
Precise
estimation
health
computed
by
evaluating
several
metrics
central
factor
effective
systems.
In
scenario,
accurate
indicators
(HIs)
becomes
even
more
important
within
framework
BMS.
This
paper
provides
comprehensive
review
discussion
different
BESSs,
suitable
classification
based
key
characteristics.
Scientific Reports,
Journal Year:
2024,
Volume and Issue:
14(1)
Published: July 11, 2024
Abstract
Accurately
estimating
Battery
State
of
Charge
(SOC)
is
essential
for
safe
and
optimal
electric
vehicle
operation.
This
paper
presents
a
comparative
assessment
multiple
machine
learning
regression
algorithms
including
Support
Vector
Machine,
Neural
Network,
Ensemble
Method,
Gaussian
Process
Regression
modelling
the
complex
relationship
between
real-time
driving
data
battery
SOC.
The
models
are
trained
tested
on
extensive
field
collected
from
diverse
drivers
across
varying
conditions.
Statistical
performance
metrics
evaluate
SOC
prediction
accuracy
test
set.
process
demonstrates
superior
precision
surpassing
other
techniques
with
lowest
errors.
Case
studies
analyse
model
competence
in
mimicking
actual
charge/discharge
characteristics
responding
to
changing
drivers,
temperatures,
drive
cycles.
research
provides
reliable
data-driven
framework
leveraging
advanced
analytics
precise
monitoring
enhance
management.
Energies,
Journal Year:
2024,
Volume and Issue:
17(7), P. 1643 - 1643
Published: March 29, 2024
This
study
explores
the
challenges
and
advances
in
estimation
of
state
charge
(SOC)
lithium-ion
batteries
(LIBs),
which
are
crucial
to
optimizing
their
performance
lifespan.
review
focuses
on
four
main
techniques
SOC
estimation:
experimental
measurement,
modeling
approach,
data-driven
joint
highlighting
limitations
potential
inaccuracies
each
method.
suggests
a
combined
incorporating
correction
parameters
closed-loop
feedback,
improve
measurement
accuracy.
It
introduces
multi-physics
model
that
considers
temperature,
charging
rate,
aging
effects
proposes
integration
models
algorithms
for
optimal
SOC.
research
emphasizes
importance
considering
temperature
factors
approaches.
fusion
different
methods
could
lead
more
accurate
predictions,
an
important
area
future
research.
