Inter
national
J
our
nal
of
A
pplied
P
o
wer
Engineering
(IJ
APE)
V
ol.
14,
No.
3,
September
2025,
pp.
569
∼
578
ISSN:
2252-8792,
DOI:
10.11591/ijape.v14.i3.pp569-578
❒
569
AI-dri
v
en
solutions
f
or
Li-ion
battery
perf
ormance
and
pr
ediction
Sthitprajna
Mishra
1
,
Chinmoy
K
umar
P
anigrahi
1
,
Subhra
Debdas
1
,
Atri
Bandy
opadh
yay
2
,
Srikanth
V
elpula
3
,
Amit
K
umar
Sahoo
4
,
P
abitra
K
umar
T
ripath
y
5
1
School
of
Electrical
Engineering,
KIIT
Deemed
to
be
Uni
v
ersity
,
Bhubanesw
ar
,
India
2
School
of
Computer
Engineering,
KIIT
Deemed
to
be
Uni
v
ersity
,
Bhubanesw
ar
,
India
3
Department
of
Electrical
and
Electronics
Engineering,
SR
Uni
v
ersity
,
W
arang
al,
India
4
Department
of
Electrical
and
Electronics
Engineering,
Centurion
Uni
v
ersity
T
echnology
and
Management,
Bhubanesw
ar
,
India
5
Department
of
Computer
Science
and
Engineering,
Kalam
Institute
of
T
echnology
,
Berhampur
,
India
Article
Inf
o
Article
history:
Recei
v
ed
Jul
10,
2024
Re
vised
Jan
11,
2025
Accepted
Jan
19,
2025
K
eyw
ords:
Battery
management
system
Lithium-ion
battery
Remaining
useful
life
State
of
char
ge
State
of
health
ABSTRA
CT
Batteries
serv
e
as
crucial
po
wer
sources
for
essential
port
able
de
vices
lik
e
elec-
tric
v
ehicles,
smartphones,
and
laptops.
The
widespread
adoption
of
Li-ion
bat-
teries,
while
bene
cial,
has
unfortunately
led
to
a
sur
ge
in
adv
erse
incidents.
The
sudden
f
ailure
of
batteries
in
both
industrial
and
lightweight
applications
poses
signicant
economic
risks
across
v
arious
industries.
Consequently
,
researchers
are
intensifying
their
focus
on
enhancing
battery
state
estimation,
and
manage-
ment
systems
and
predicting
remaining
useful
life
(R
UL).
This
paper
is
struc-
tured
into
three
main
sections.
Firstly
,
it
delv
es
into
the
acquisition
of
battery
data,
encompassing
both
commercially
a
v
ailable
and
freely
accessible
Li-ion
battery
datasets.
Secondly
,
the
e
xploration
e
xtends
to
techniques
for
estimating
battery
states
through
adv
anced
battery
management
systems.
The
paper
in
v
es-
tig
ates
battery
R
UL
estimation,
c
ate
gorizing
and
e
v
aluating
di
v
erse
prognostic
methods
appl
ied
to
Li-ion
batteries
based
on
crucial
performance
parameters.
The
re
vie
w
includes
scrutin
y
of
commercially
and
publicly
a
v
ailable
datasets
for
v
arious
battery
models
and
conditions,
considering
dif
ferent
battery
states
and
the
role
of
adv
anced
battery
management
system
(BMS).
In
the
nal
section,
the
paper
concludes
with
a
comparati
v
e
analysis
of
Li-ion
battery
R
UL
prediction,
incorporating
e
xploration
into
v
arious
R
UL
prediction
algorithms,
and
mathe-
matical
models,
and
introducing
an
AI-based
cloud
monitoring
system.
This
is
an
open
access
article
under
the
CC
BY
-SA
license
.
Corresponding
A
uthor:
Sthitprajna
Mishra
School
of
Electrical
Engineering,
KIIT
Deemed
to
be
Uni
v
ersity
Bhubanesw
ar
,
Odisha,
India
Email:
sthitprajnamishra26@gmail.com
1.
INTR
ODUCTION
It
has
turned
out
that
the
electric
v
ehicles
(EVs)
as
well
as
the
clean
ener
gy
technologies
continue
t
o
e
xpand
at
a
f
ast
pace,
which
has
become
one
of
the
dominant
w
ays
of
tackling
global
en
vironmental
and
ener
gy
issues.
The
con
v
entional
ener
gy
sources
such
as
those
that
produce
toxic
g
ases
are
gradually
being
substituted
by
en
vironmentally
friendly
electric
automobiles.
Central
to
EV
technology
is
the
battery
,
which
comprises
four
critical
components:
electric
design,
mechanical
design,
thermal
design
as
well
as
battery
management
system
(BMS)
[1].
Due
to
the
social
de
v
elopment
and
manuf
acturing
of
hi
ghly
ef
cient
and
instantaneous
po
wer
control
technologies
as
a
result
of
the
adv
ancing
modern
technologies,
batteries
particularly
those
used
within
J
ournal
homepage:
http://ijape
.iaescor
e
.com
Evaluation Warning : The document was created with Spire.PDF for Python.
570
❒
ISSN:
2252-8792
EVs
ha
v
e
become
e
xtremely
vital
in
an
attempt
at
of
fering
e
v
er
reliable
green
ener
gy
storage
mechanisms.
Ener
gy
storage
has
hence
emer
ged
as
one
of
the
most
promising
industries
due
to
the
increasing
need
for
ener
gy-intensi
v
e
products
such
as
consumer
electronics
and
adv
ancement
in
the
utilization
of
rene
w
able
ener
gy
[2],
[3].
Ne
w
ener
gy
sources
displacing
the
traditional
and
continuous
supply
systems
including
nuclear
,
coal,
and
oil
and
replacing
them
with
rene
w
able
ener
gy
systems
including
wind
and
solar
ener
gy
is
causing
disruption
of
the
systems
especially
in
de
v
eloping
countries.
This
shift
is
putting
in
place
conditions
that
require
ne
w
adv
anced
ener
gy
st
orage
systems
to
respond
to
comple
x
ener
gy
mark
et
structures
[4],
[5].
Dif
fe
rent
types
of
storage
such
as
electrochemical
which
has
a
high
ef
cienc
y
,
mechanical,
chemical,
and
thermal
storage
ha
v
e
dif
ferent
capacities,
and
days
of
storage.
From
these,
the
rechar
geable
electrochemical
systems
bearing
the
most
popularity
due
to
con
v
eniences
such
as
high
ener
gy
density
,
light
weight
and
e
xibility
particularly
the
lithium-
ion
(Li-ion)
batteries.
Li-ion
batteries
are
higher
performing
than
other
battery
technologies
including
lead-
acid,
redox
o
w
,
sodium
sulphur
batteries
among
others
that
mak
e
its
application
in
a
viation
industry
,
satellite
communication,
marine
applications
and
EVs.
The
y
also
dri
v
e
v
arious
home
appliances
lik
e
refrigerators,
laptops
and
w
andering
de
vices
lik
e
mobile
phones
among
others
[6].
This
report
implies
that
Li-ion
batteries
ha
v
e
some
of
the
unique
benets
such
as
long
c
ycle-life,
high
ener
gy
density
and
lo
w
maintenance
hence
the
technology
is
uni
v
ersally
applied.
But
the
y
also
ha
v
e
some
disadv
antages
for
e
xample,
the
y
are
e
xpensi
v
e,
are
easily
damaged
when
the
y
are
fully
char
ged
or
cause
re
risks.
In
the
case
of
the
electrical
v
ehicles,
tw
o
v
ariables
namely
the
stat
e
of
health,
and
the
state
of
char
ge
signicantly
af
fect
both
the
safety
,
and
output
of
the
v
ehicle.
Battery
ener
gy
management
(BEM)
strate
gies
therefore
seek
to
enhance
the
state
of
health
(SoH)
and
state
of
char
ge
(SoC)
of
Li-ion
batteries
for
increased
life
c
ycle
of
the
battery
bank
together
with
increasing
ef
cienc
y
of
the
induction
motor
[7],
[8].
While
there
are
certain
con
v
entional
w
ays
of
controlling
speed
such
as
using
dynamo-meter
and
other
similar
technologies,
the
recent
inno
v
ati
v
e
BEM
methods
in
v
olv
e
model-in-loop
t
echniques
that
mimic
battery
performance
and
there-
fore
reduces
the
battery’
s
life
c
ycle.
The
y
pre
v
ent
the
reduction
of
SoC
rate
and
slo
w
do
wn
the
SoH
decline
that
distorts
the
general
battery
dependability
[9].
As
t
he
use
of
Li-ion
batteries
e
xpanded
widely
there
has
been
a
signicant
focus
on
the
number
of
char
ge
dischar
ge
c
ycles,
remaining
useful
life
(R
UL),
and
de
gradation
anal-
ysis.
It
is
v
ery
important
for
accurate
estimation
of
R
UL
pre
v
enting
battery
f
ailures
and
maintaining
superior
system
performance
[10].
Although
there
are
so
man
y
benets
associated
with
Li-ion
batteries,
the
y
e
xperience
high
de
gradation
and
f
ailure
rates,
this
mak
es
battery
management
system
and
accurate
R
UL
models
to
be
more
crucial.
Better
estimation
of
R
UL
leads
to
the
necessity
of
ha
ving
better
datasets,
and
se
v
eral
or
g
anizations
ha
v
e
been
de
v
eloping
datasets
for
dif
ferent
battery
models
[11],
[12].
These
datasets
are
v
ery
useful
in
enhancing
battery
health
estimations,
and
also
in
minim
izing
the
time
tak
en
in
de
v
eloping
ne
w
datasets
needed
for
battery
research,
as
well
as
enhancing
the
dependability
of
systems
used
in
battery
management.
Using
these
datasets,
researchers
will
be
able
to
increase
the
accurac
y
of
R
UL
estimations
as
well
as
battery
health
assessment
[13],
[14].
Man
y
approaches
ha
v
e
been
made
to
ef
fecti
v
e
and
accurate
assessment
of
SoH
and
R
UL
in
the
Li-ion
bat-
teries.
Through
a
system
simulation
approach
in
v
olving
electrochemical
techniques
and
data
analysis
methods
of
statistics,
e
v
aluation
of
state
estimation
algorithms
is
pro
vided
[15].
Furthermore,
support
v
ector
machines
(SVM)
has
been
used
to
impro
v
e
the
accurac
y
of
R
UL
of
battery
through
impro
ving
the
accurac
y
of
the
men-
tioned
model.
The
current
de
v
elopments
in
the
Li-ion
battery
technology
call
for
more
e
xtensi
v
e
and
up-to-date
re
vie
ws
of
the
methods
aimed
at
the
estim
ation
of
R
UL
[16],
[17].
Gaps
in
current
research:
Although
there
are
adv
ancements
being
made
on
the
study
of
battery
health
and
performance
de
gradation,
there
are
still
loopholes
in
measuring
dif
ferent
methodologies
a
v
ailable.
Se
v
eral
studies
are
still
missing
in
the
current
research
that
addresses
the
interaction
between
the
battery
management
algorithms
and
the
estimation
of
R
UL
[18],
[19].
This
g
ap
is
important
for
maximizing
the
performance
of
batteries
as
well
as
eliminating
f
ai
lures.
Thus,
there
is
a
need
to
e
xplore
BMS
and
R
UL
estimation
models
to
obtain
the
best
results
in
battery
control
[20].
Research
contrib
utions
and
ne
w
directions:
This
research
aims
at
lling
these
g
aps
by
pro
viding
an
assessment
of
both
the
public
and
commercial
datasets
in
batteries
storage.
It
asses
ses
superior
state
estima-
tion
methods
utilizing
BMS
and
dissects
dif
ferent
types
of
R
UL
prediction
techniques.
The
study
focuses
on
three
k
e
y
areas:
another
sub-eld
is
the
battery
data
acquis
ition,
deep
estimation
of
battery
health
utilizing
progressi
v
e
BMS,
and
methods
of
R
UL
estimation
[21],
[22].
This
w
ork
also
identies
the
pros
and
cons
of
the
approaches
presented
when
analyzing
datasets
and
comparing
R
UL
prediction
models.
The
contrib
ution
of
this
study
is
therefore
in
comparing
a
number
R
UL
prediction
al
g
or
ithms
and
mathematical
model
[23].
The
paper
presents
a
cloud
monitoring
system
that
is
inte
n
de
d
to
increase
the
capabilities
of
stream
processing
and
upgrade
battery
health
predictions.
This
study
establishes
that
it
is
possible
to
impro
v
e
the
accurac
y
of
the
R
UL
Int
J
Appl
Po
wer
Eng,
V
ol.
14,
No.
3,
September
2025:
569–578
Evaluation Warning : The document was created with Spire.PDF for Python.
Int
J
Appl
Po
wer
Eng
ISSN:
2252-8792
❒
571
of
Li-ion
cells
by
incorporating
ndings
from
data
analysis
with
other
suitable
methods
[24],
[25].
Future
di-
rections:
In
the
future,
the
studies
will
continue
on
ho
w
to
add
machine
learning
and
big
data
implemented
into
the
battery
c
on
t
rol
systems.
The
highlighted
adv
anced
technologies
in
papers
described
ha
v
e
the
capabilities
to
enhance
battery
ef
ci
enc
y
,
its
durability
and
ener
gy
density
.
Furthermore,
other
methods
such
as
correlation
matrices,
pair
plot,
time
series
plot
and
box
plot
will
also
be
utilized
to
understand
certain
beha
viour
of
Li-ion
battery
particularly
during
dischar
ge
c
ycles.
These
visualization
tools
of
fer
v
aluable
insights
into
performance
changes
and
de
gradation
patterns,
further
adv
ancing
battery
health
monitoring
and
R
UL
prediction
beha
viour
during
dischar
ge
c
ycles.
F
or
the
prediction
of
R
UL
e
xplanation
sho
wn
in
Figure
1.
Figure
2
depicts
a
fundamental
battery
management
system
concept.
This
method
aids
in
kno
wledge
of
the
dif
ferences
among
numerous
battery
states
and
R
UL,
in
particular
within
the
conte
xt
of
real-global
b
usiness
and
commercial
conditions.
The
number
one
recognition
of
this
o
v
ervie
w
is
to
pro
vide
a
comparati
v
e
look
at
battery
R
UL
estimation
procedures,
incorporating
battery
management,
and
of
fering
complete
statistics
on
commercially
and
freely
a
v
ailable
online
battery
datasets.
Figure
1.
Predicting
R
UL
through
Li-ion
battery
data
acquisition
process
AI-driven
solutions
for
Li-ion
battery
performance
and
pr
ediction
(Sthitpr
ajna
Mishr
a)
Evaluation Warning : The document was created with Spire.PDF for Python.
572
❒
ISSN:
2252-8792
Figure
2.
V
isualizing
battery
management
procedures
2.
PR
OPOSED
METHOD
The
proposed
frame
w
ork
gi
v
es
an
impro
v
ed
methodology
and
high
le
v
el
of
sophistication
for
perfor
-
mance
e
v
aluation
and
accurate
estimation
of
the
predicti
v
e
capability
of
lithium-ion
batteries
supported
by
state
of
art
data
acquisition,
BMS,
and
R
UL
assessment
techniques.
It
unfolds
across
three
k
e
y
pillars:
2.1.
Battery
data
acquisition
This
step
is
about
the
ef
fecti
v
e
use
of
the
lithium-ion
battery
data
pool
which
includes
both
proprietary
and
open-sourced
data
sets.
Through
highly
tar
geted
choice
of
the
best
battery
cells
and
the
subsequent
collec-
tion
of
highly
accurate
parameter
data
related
to
the
cell
state
of
char
ge,
v
oltage,
current,
and
temperature,
the
study
aims
to
arri
v
e
at
a
set
of
v
ery
solid
health
indicators
(HI)
on
which
the
accurate
calculation
of
R
ULs
will
be
based.
Some
of
the
main
trends
observ
ed
in
the
process
of
data
acquisition
include
the
follo
wing
stringent
requirements
imposed
on
the
functional
performance
of
the
data
acquisition
systems
to
achie
v
e
e
xtremely
high
dependability
and
v
ersatility
with
respect
to
the
operating
conditions.
2.2.
Estimation
of
battery
states
A
wireless
BMS
on
the
programmable
logic
controllers
(PLC),
supervisory
control
and
data
acquisi-
tion
(SCAD
A),
and
GSM
modules
is
a
modern
solution
that
allo
ws
the
monit
oring
of
the
battery’
s
prerequisites
such
as
v
oltage,
current,
and
temperature.
This
dynamic
monitoring
approach
is
highly
reliable
and
e
xible
which
mak
es
it
possible
to
perform
well
in
man
y
dif
ferent
en
vironments.
W
ireless
technology
inte
gration
of
fers
enhanced
data
transfer
and
remote
system
management
capabilities
that
enhance
battery
ef
fecti
v
eness
as
well
as
decision-making.
2.3.
R
UL
estimation
methods
R
UL
est
imation
methods:
As
in
the
pre
vious
phase,
a
detailed
comparati
v
e
e
v
aluation
of
dif
ferent
prognostic
models
is
made
with
an
emphasis
put
on
R
UL
prediction.
The
technology
comprises
the
AI-based
methods
of
cloudy
monitoring
and
the
latest
mathematical
calculations
f
ac
ilitating
the
further
impro
v
ement
of
battery
performance
predictions.
The
y
also
augment
the
estimated
R
UL
’
s
precision
while
also
pro
viding
insights
into
the
battery
beha
vior
under
v
arious
scenarios,
which
is
a
paradigm
shift
in
batt
ery
management.
The
proposed
study
is
something
unique
i
n
the
eld
as
i
t
embeds
modern
approaches
to
data
acquisition,
adv
anced
algorithms
of
prediction,
and
sophisticated
BMS
technologies
into
a
single
research
for
the
purpose
of
maximizing
ener
gy
storage
solutions
in
electric
v
ehicle
and
portable
applications.
3.
METHODOLOGY
W
ireless
implementation
of
battery
management
systems
(BMS)
is
carried
out
with
the
help
of
PLC
for
data
processing
in
real-t
ime
with
the
help
of
SCAD
A
systems
and
GSM
modules
for
industrial
temperature
monitoring.
This
inte
gration
ensures
seamless
data
handling
and
monitoring,
enabl
ing
operators
to
k
eep
track
of
system
parameters
ef
ciently
.
It
also
enhances
s
y
s
tem
e
xibility
and
reduces
wiring
comple
xity
,
leading
to
easier
maintenance
and
scalability
.
Int
J
Appl
Po
wer
Eng,
V
ol.
14,
No.
3,
September
2025:
569–578
Evaluation Warning : The document was created with Spire.PDF for Python.
Int
J
Appl
Po
wer
Eng
ISSN:
2252-8792
❒
573
3.1.
Battery
monitoring
system
(BMS)
A
2.4
GHz
wireless
communication
module
also
ensures
lo
w
po
wer
and
lo
w-cost
signal
for
transmi
t-
ting
other
important
issues
lik
e
v
oltage,
current,
and
temperature
to
f
acilitate
data
acquisition
from
the
battery
system.
The
use
of
such
modules
enhances
the
reliability
of
wireless
transmission
while
maintaining
ener
gy
ef
cienc
y
and
cost-ef
fecti
v
eness,
making
it
suitable
for
lar
ge-scale
industrial
applications.
Additionally
,
it
sup-
ports
seamless
inte
gration
with
IoT
platforms,
enabli
ng
remote
monitoring
and
adv
anced
data
analytics.
This
contrib
utes
to
predicti
v
e
maintenance
and
im
pro
v
ed
o
v
erall
system
performance.
Furthermore,
the
scalability
of
these
modules
allo
ws
easy
e
xpansion
of
the
monitoring
netw
ork
as
system
requirements
gro
w
.
3.2.
Safety
assessment
In
order
to
pre
v
ent
early
battery
f
ailures,
a
ne
w
safety
assessment
approach
is
established
to
detect
and
pre
v
ent
possible
battery
f
ailures.
It
monitors
constantly
all
internal
v
ariables,
including
chemicals
and
ph
ysical
changes
and
e
xternal
f
actors
for
instance,
thermal
and
electrical
loads.
By
analyzing
condi
tions
in
real-time,
the
system
is
able
to
pre
v
ent
o
v
erchar
ging,
o
v
er
-dischar
ging,
and
o
v
erheating
thus
increasing
battery
life
and
pre
v
enting
e
xplosions.
3.3.
Data
acquisition
and
analysis
In
this
w
ork,
a
number
of
high-accurac
y
sensing
elements
are
used
in
order
to
measure
some
char
acter
-
istics
of
the
battery
such
as
its
v
oltage,
its
resistance,
the
current
through
it,
the
temperature
and
the
capacitance.
These
measurements
are
tak
en
across
eight
modules;
each
of
the
modules
comprises
twelv
e
cells.
The
nominal
v
oltage
is
distrib
uted
with
each
of
the
indi
vidual
cells
featuring
3.7
v
olts
of
po
wer
potential,
that
tak
es
the
total
v
oltage
of
a
fully
char
ged
module
to
44.4
V
.
This
w
ay
of
connecting
the
battery
allo
ws
the
accurate
and
intricate
observ
ation
of
its
w
orking.
Data
acquisition
process
forms
the
type
of
c
yclic
beha
vior
of
the
battery
in
the
process
of
dischar
ging
processes.
Thus,
through
collecting
such
data,
the
researchers
can
plot
time
series
plots
illustrating
the
dynamic
of
these
paramet
ers.
Such
plots
assist
in
tracking
battery
de
gradation
and
the
performance
of
the
battery
for
each
dischar
ging
c
ycle
being
performed
on
the
battery
.
In
addition,
it
is
possible
to
perform
a
correlation
analysis
aimed
at
making
conclusions
on
interdependence
of
v
arious
parameters
with
each
other
,
for
instance,
ho
w
temperature
is
dependent
on
v
oltage
or
current.
High
accurac
y
sensors,
collection
of
data
at
dif
ferent
stages
during
the
dischar
ging
c
ycles
across
long
term
gi
v
es
a
clear
indication
of
the
battery
performance
across
the
c
ycles.
It
is
also
the
information
which
can
be
v
aluable
to
mak
e
the
proper
decisions
on
the
use
of
the
batteries
in
the
future
and
increase
the
o
v
erall
reliability
of
the
system,
as
sho
wn
in
Figure
3.
Figure
3.
V
isualizing
battery
management
procedures
AI-driven
solutions
for
Li-ion
battery
performance
and
pr
ediction
(Sthitpr
ajna
Mishr
a)
Evaluation Warning : The document was created with Spire.PDF for Python.
574
❒
ISSN:
2252-8792
3.4.
Statistical
techniques
and
data-set
selection
Exploratory
data
analysis
techniques
including
correlation
analysis
and
box
plots
are
emplo
yed
to
de-
termine
the
lik
elihood
of
a
relation
between
battery
parameters
as
well
as
their
trends
within
char
ge-dischar
ge
c
ycles.
The
data
collected
is
subject
to
rigorous
inclusion
and
e
xclusion
criteria,
which
means
that
only
high
quality
data
from
commercial
and
public
s
ource
is
used.
This
mak
e
the
outcome
of
the
analysis
reliable,
repeat-
able,
and
sound
ha
ving
a
solid
groundw
ork
for
future
in
v
estig
ations
on
lithium-ion
battery
performance
and
control.
After
the
acquisition
of
e
xperimental
battery
data,
it
becomes
imperati
v
e
to
conduct
assessments
for
impedance
aging
parameter
estimation
and
capacity
de
gradation
parameter
estimation.
These
e
v
aluations
are
crucial
in
determining
the
remaining
useful
life
(R
UL)
threshold
or
ascertaining
whether
it
meets
the
specied
criteria.
This
analytical
process
in
v
olv
es
measuring
impedance
aging
and
capacity
de
gradation
to
g
auge
the
op-
erational
longe
vity
and
performance
deterioration
of
the
battery
,
contrib
uting
to
a
comprehensi
v
e
understanding
of
its
life
c
ycle
dynamics.
4.
RESUL
TS
AND
DISCUSSION
Although
there
are
distinct
uses
of
R
UL
prediction
methodologies
for
lithium-ion
batteries
(LIBs),
most
methodologies
ha
v
e
not
yet
been
utilized
to
predict
the
R
UL
of
LIBs,
thus
pointing
t
o
areas
that
can
be
e
xplored
in
the
future.
These
une
xplored
areas
of
fer
potential
for
adv
ancing
battery
life
forecasti
n
g
tech-
niques.
Future
research
can
le
v
erage
emer
ging
tools
lik
e
machine
learning
and
h
ybrid
modeling
to
enhance
prediction
accurac
y
.
By
inte
grating
data
dri
v
en
approaches
with
ph
ysics-based
models,
researchers
can
better
capture
comple
x
de
gradation
mechanisms
under
v
aried
operating
conditions.
Additionally
,
incorporating
adap-
ti
v
e
algorithms
that
learn
from
real-time
battery
performance
data
can
signicantly
impro
v
e
long-term
R
UL
prediction
and
f
acilitate
smarter
battery
management
systems.
4.1.
Pr
ediction
of
the
beha
viour
of
the
battery
The
follo
wing
re
gions
present
huge
research
scopes
:
In
the
outlined
suggested
model,
there
are
three
forms
of
assessment
namely:
i)
The
complementary
nature
of
analytical
methodologies
with
the
similar
method
mak
es
it
promising
to
enhance
the
prognostication
accurac
y
of
the
R
UL
of
lithium-ion
cells.
Thus,
combining
the
identied
basic
approaches
to
data
analysis
with
the
other
methods,
Burns
and
Summer
will
belie
v
e
that
the
dependability
of
the
R
UL
results
can
be
impro
v
ed
considerably
in
practice.
This
combination
of
methods
is
necessary
especially
as
the
battery
characteristics
demonstrate
more
features
in
the
de
v
elopment
of
its
beha
vior
that
af
fects
the
predictions
accurac
y;
ii)
This
has
made
it
necessary
to
look
for
other
better
sources
of
data
with
which
the
accurac
y
of
estimating
the
remaining
useful
life
(R
UL)
can
be
enhanced.
As
mentioned
earlier
,
to
achie
v
e
high
le
v
els
of
R
UL
prediction
it
is
imperati
v
e
to
w
ork
with
high
quality
data
therefore,
future
research
should
focus
primary
on
seeking
better
data
sources.
This
will
be
especially
helpful
because
it
will
co
v
er
data
obtained
under
v
arying
operational
conditions
as
well
as
bat
tery
states
that
will
impro
v
e
R
UL
estimation;
iii)
Multi-state
joint
estimation
can
be
used
to
se
v
eral
states
such
as
state
of
char
ge
(SoC),
state
of
ener
gy
(SoE),
state
of
po
wer
(SoP),
state
of
health
(SoH),
state
of
tempera
ture
(SoT),
as
well
as
state
of
stress
(SoS)
to
e
xpose
the
enhanced
battery
management
methodologies.
In
other
w
ords,
by
jointly
estimating
these
dif
ferent
states,
the
BMS
pro
vides
a
more
accurate
representation
of
battery
conditions
hence
impro
ving
its
capacity
to
estimate
R
UL
whil
e
at
the
same
time
impro
ving
its
o
v
erall
performance;
and
i
v)
Hence
other
research
potentia
lities
in
the
future
of
battery
management
may
also
include,
AI
and
machine
learning,
and
big
data
analysis.
Thus,
the
combination
of
dif
ferent
approaches
in
the
estimation
enhances
the
le
v
el
of
precision
and
can
form
a
basis
for
enhancement
on
the
estimation
of
R
UL
with
re
g
ard
to
BMS.
4.2.
Dischar
ging
beha
viour
of
Li-ion
battery
The
conducted
visualizations
present
a
comprehensi
v
e
analysis
of
the
beha
vior
of
lithium-ion
(Li-ion)
batteries
during
the
dischar
ge
process.
Through
a
series
of
analytical
techniques,
including
the
generation
of
a
correlation
matrix
and
the
visualization
of
pair
plots,
the
interrelationships
between
v
arious
parameters
such
as
cell
v
oltage,
current,
and
temperature
were
e
xamined.
T
ime
series
plots
were
emplo
yed
to
elucidate
the
temporal
dynamics
of
these
v
ariables
throughout
the
dischar
ge
c
ycles,
pro
viding
v
aluable
insights
into
their
beha
vior
o
v
er
tim
e.
Additionally
,
boxplots
were
utilized
to
illustrate
the
distri
b
ution
of
ce
ll
v
oltage,
current,
and
tempera
ture
across
dif
ferent
c
ycle
numbers,
enabling
the
identication
of
trends
and
patterns
associated
with
c
ycle
progression.
By
systematically
conducting
these
visualizations,
a
holistic
understanding
of
the
beha
vior
of
Li-ion
batteries
during
dischar
ge
w
as
attained,
f
acilitating
informed
decision-making
and
Int
J
Appl
Po
wer
Eng,
V
ol.
14,
No.
3,
September
2025:
569–578
Evaluation Warning : The document was created with Spire.PDF for Python.
Int
J
Appl
Po
wer
Eng
ISSN:
2252-8792
❒
575
optimization
strate
gies
for
battery
performance
enhancement.
This
approach
contrib
utes
to
the
adv
ancement
of
battery
research
and
the
de
v
elopment
of
ef
cient
ener
gy
storage
solutions.
W
e
mentioned
the
dif
ferent
techniques
in
Figure
4
and
Figures
5(a),
and
5(b)
as
correlation
and
box
plot
respecti
v
ely
.
Figure
4.
Correlation
matrix
of
Li-ion
battery
while
dischar
ging
in
dif
ferent
c
ycle
(a)
(b)
Figure
5.
Dischar
ging
battery
box
plot
according
to
the
c
ycles:
(a)
v
oltage
of
the
battery
and
(b)
temperature
of
the
battery
5.
CONCLUSION
In
conclusion,
this
paper
highlights
the
critical
importance
of
adv
ancing
battery
state
estimation,
and
management
systems,
and
forecasting
the
remaining
useful
life
(R
UL)
of
lithium-ion
(Li-ion)
batteries.
The
widespread
adoption
of
Li-ion
batteries
in
essential
portable
de
vices
such
as
smartphones,
laptops,
and
EV
AI-driven
solutions
for
Li-ion
battery
performance
and
pr
ediction
(Sthitpr
ajna
Mishr
a)
Evaluation Warning : The document was created with Spire.PDF for Python.
576
❒
ISSN:
2252-8792
has
led
to
increased
attention
on
mitig
ating
adv
erse
incidents
and
economic
risks
associated
with
battery
f
ail-
ures.
Through
a
structured
re
vie
w
,
the
paper
emphasizes
three
main
sections:
the
acquisition
of
battery
data,
techniques
for
estimating
battery
stat
es
using
adv
anced
battery
management
systems,
and
battery
R
UL
esti-
mation
methods.
By
scrutinizing
commercially
and
publicly
a
v
ailable
datasets
for
v
arious
battery
models
and
conditions,
as
well
as
e
v
aluating
di
v
erse
prognostic
methods,
the
paper
pro
vides
insights
into
the
current
land-
scape
of
Li-ion
battery
research.
The
com
parati
v
e
analysis
of
R
UL
prediction
algorithms
and
mathematical
models,
alongside
the
introduction
of
an
AI-based
cloud
monitoring
system,
underscores
the
need
for
inno
v
a-
ti
v
e
approaches
to
enhance
battery
performance
and
safety
in
both
industri
al
and
lightweight
applications.
As
researchers
continue
to
e
xplore
and
de
v
elop
adv
anced
battery
management
techniques,
this
paper
serv
es
as
a
v
aluable
resource
for
guiding
future
research
directions
and
addressing
the
challenges
associated
with
Li-ion
battery
technology
.
FUNDING
INFORMA
TION
The
authors
declare
that
this
research
did
not
r
ecei
v
e
an
y
specic
grant
from
public,
commercial,
or
not-for
-prot
funding
agencies.
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UTHOR
CONTRIB
UTIONS
ST
A
TEMENT
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utor
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axonomy
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vidual
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tions,
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disputes,
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acilitate
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C
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sualization
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esources
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The
data
presented
in
this
study
are
h
ypothetical
and
were
generated
solely
for
the
purpose
ofconcep-
tual
analysis
and
methodological
illustration.
As
such,
no
real-w
orld
datasets
were
used
or
made
a
v
ailable.
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un,
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en
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health
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e
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motion
model,
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Y
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J.
W
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H.
He,
S.
Peng,
and
M.
Pecht,
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battery
health
prognosis
based
on
a
real
battery
manage-
ment
system
used
in
electric
v
ehicles,
”
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T
echnology
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B.
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Sax
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R.
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Meng
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A
re
vie
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on
prognostics
and
health
management
(PHM)
methods
of
lithium-ion
batteries,
”
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w
able
and
Sustainable
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Reliability
and
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met
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of
SOC
and
SOH
estimation
for
lithium-
ion
batteries,
”
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of
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A
re
vie
w
of
the
state
of
health
for
lithium-ion
batteries:
Research
status
and
suggestions,
”
Jour
-
nal
of
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Zhou,
Z.
P
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X.
Han,
L.
Lu,
and
M.
Ouyang,
“
An
easy-to-implement
multi-point
impedance
technique
for
monitori
ng
aging
of
lithium-ion
batteries,
”
Journal
of
Po
wer
Sources
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v
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doi:
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wsour
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K.
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and
C.
D.
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xtended
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determining
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in
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and
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Mierlo,
and
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V
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den
Bossche,
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re
vie
w
of
state
of
health
estimation
methods
of
Li-ion
batteries
for
real
applications,
”
Rene
w
able
and
Sustainable
Ener
gy
Re
vie
ws
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pp.
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2016,
doi:
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R.
Zhou,
Y
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Ren,
M.
Jiao,
H.
Liu,
and
C.
Lian,
“
Adv
anced
data-dri
v
en
techniques
in
AI
for
predicting
lithium-ion
battery
remaining
useful
life:
a
comprehensi
v
e
re
vie
w
,
”
Green
Chemical
Engineering
,
2024,
doi:
10.1016/j.gce.2024.09.001.
BIOGRAPHIES
OF
A
UTHORS
Sthitprajna
Mishra
holds
a
Bachelor
of
T
echnology
in
Electrical
and
Electr
onics
Engi-
neering
from
GIT
A,
BBSR,
and
a
Master’
s
de
gree
in
Po
wer
Electronics
and
Dri
v
es
from
IGIT
Sarang.
He
is
currently
pursuing
his
Ph.D.
at
KIIT
in
the
area
of
IoT
-based
optimized
smart-grid
battery
man-
agement
system
(BMS),
and
also
serv
es
as
an
IEEE
chair
member
of
the
student
branch
at
KIIT
.
Mr
.
Mishra’
s
academic
focus
lies
in
the
intersection
of
IoT
technology
and
smart
gr
id
optimization,
aim-
ing
to
contrib
ute
to
adv
ancements
in
ener
gy
mana
gement
and
grid
ef
cienc
y
.
He
can
be
contacted
at
email:
sthitprajnamishra26@gmail.com.
AI-driven
solutions
for
Li-ion
battery
performance
and
pr
ediction
(Sthitpr
ajna
Mishr
a)
Evaluation Warning : The document was created with Spire.PDF for Python.
578
❒
ISSN:
2252-8792
Chinmoy
K
umar
P
anigrahi
is
a
Professor
and
Director
at
KIIT
DU’
s
School
of
Electrical
Engineering.
His
e
xpertise
includes
soft
computing,
po
wer
systems,
rene
w
able
ener
gy
,
and
battery
management
systems.
He
has
supervised
29
Ph.D.
and
72
M.T
ech.
scholars,
and
guided
four
jointly
.
He
has
authored
182
research
articles
and
presented
148
papers
at
conferences.
He
recei
v
ed
se
v
eral
accolades,
including
being
rank
ed
among
the
T
op
3
Ph.D.
supervisors
at
KIIT
(2022),
and
a
w
ards
such
as
Outstanding
Scientist
(2020)
and
Best
T
eacher
(2015).
He
is
the
Chair
of
the
IEEE
K
olkata
Section
Industrial
Electronics
Society
Chapter
–
Bhubanesw
ar
and
holds
senior
IEEE
memberships.
He
has
conducted
collaborati
v
e
research
at
the
Uni
v
ersity
of
Shef
eld
and
the
Uni
v
ersity
of
Zurich
(UZH),
German
y
.
He
can
be
contacted
at
email:
panigrahichinmo
y@gmail.com.
Subhra
Debdas
recei
v
ed
his
B.E.
in
Electrical
Engineering
and
M.E.
in
Po
wer
Syst
em
En-
gineering
f
rom
Indian
Institute
of
Engineering
Science
and
T
echnology
,
Shibpur
,
and
his
Ph.D.
from
Sainath
Uni
v
ersity
,
Ranchi
.
Extensi
v
e
design
po
wer
engineer
e
xperience
at
DCPL
and
L
and
T
Sar
-
gent
and
Lundy
,
managing
impactful
national
and
inte
rnational
projects.
Ov
er
21
years
of
teaching
e
xperience,
including
8
years
at
Uni
v
ersity
of
T
echnology
and
Applied
Sciences
in
Nizw
a,
Sultanate
of
Oman.
No
w
full-time
f
aculty
at
KIIT
Deemed
Uni
v
ersity’
s
School
of
Electrical
Engineering.
His
academic
interests
is
in
rene
w
able
ener
gy
,
smart
grid
technologies,
Industry
4.0,
IoT
,
cloud
comput-
ing,
and
focusing
on
pract
ical
applications.
He
can
be
contacted
at
email:
subhra.debdas@gmail.com.
Atri
Bandy
opadh
yay
is
a
dynamic
computer
scientist,
systems
engineer
from
Purulia,
W
est
Beng
al.
Inno
v
ator
in
AI,
deep
learning,
cryptograph
y
through
impactful
internships
at
High-
Radius
and
DRDO.
Kaling
a
Institute
of
Industrial
T
echnology
graduate,
e
xcelling
in
projects
lik
e
EmoSense,
dyna
mic
train
price
prediction.
St
ellar
academic
record,
numerous
certications,
publi-
cations
in
IEEE
and
Springer
.
T
railblazer
in
machine
learning
and
IoT
.
UiP
ath
Stude
nt
De
v
eloper
Champion,
accolades
at
DRDO
ITR.
Leading
research,
inno
v
ation,
and
shaping
future
of
technology
.
He
can
be
contacted
at
email:
atricc03@gmail.com.
Srikanth
V
elpula
recei
v
ed
the
B.T
ech.
and
M.T
ech.
de
grees
from
Ja
w
aharlal
Nehru
T
echnological
Uni
v
ersity
,
Hyderabad,
India,
in
2009
and
2012,
respecti
v
ely
.
He
completed
his
Ph.D.
de
gree
in
the
year
2020
at
V
ellore
Institute
of
T
echnology
,
V
ellore,
T
amilnadu,
India.
He
w
ork
ed
as
an
Assistant
Professor
in
the
Department
of
Electrical
and
Electronics
Engineering
at
v
arious
engineering
colle
ges
in
India
during
2011-2022.
Currently
,
he
is
w
orking
as
Assistant
Professor
at
SR
Uni
v
ersity
,
W
arang
al,
T
elang
ana,
India.
His
research
interests
include
con
v
erter
controls,
DFIG
based
systems,
electrical
v
ehicle
dri
v
es
and
battery
management
system,
and
the
inte
gration
of
rene
w
able
ener
gies
into
the
po
wer
systems.
He
can
be
contacted
at
email:
srikv
elpula@gmail.com.
Amit
K
u
mar
Sahoo
recei
v
ed
his
Ph.D.
from
Birla
Institute
of
T
echnology
,
Mesra,
India
in
Control
System
and
Master’
s
de
gree
from
National
Institute
of
T
echnology
,
Rourk
ela,
India.
He
is
presently
w
orking
as
an
Associate
Professor
in
the
Department
of
Electrical
and
Electronics
Engi-
neering,
Centurion
Uni
v
ersity
of
T
echnology
and
Management,
Odisha,
India.
He
has
more
than
13
years
of
teaching
e
xperience.
He
is
a
life
member
of
IEI,
India.
His
spec
ializations
include
system
identication,
linear
and
non-linear
control
system,
control
and
automation,
inte
gra
l
and
fractional
order
controller
design,
soft
and
e
v
olutionary
computing,
and
machine
learning.
He
can
be
contacted
at
email:
amitkumar2687@gmail.com.
P
abitra
K
umar
T
ripath
y
is
a
Professor
at
Kalam
Institute
of
T
echnology
,
Berhampur
,
af
liated
with
Biju
P
atnaik
Uni
v
ersity
of
T
echnology
,
Odisha,
specializes
in
Machine
Learning
and
E-Commerce.
He
holds
a
M
aster’
s
de
gree
in
Mathematics
from
Berhampur
Uni
v
ersity
,
an
M.T
ech.
in
Computer
Science,
and
a
Ph.D.
from
Kaling
a
Uni
v
ersity
,
Ra
ipur
.
His
e
xpertise
includes
theory
of
computations,
compiler
design,
cryptograph
y
,
and
computational
number
theory
.
He’
s
authored
tw
o
books
published
by
CRC
and
W
ile
y
.
He
can
be
contacted
at
email:
pabitratripath
y81@gmail.com.
Int
J
Appl
Po
wer
Eng,
V
ol.
14,
No.
3,
September
2025:
569–578
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