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r
e,
ch
ar
g
e/d
is
ch
ar
g
e
r
ates,
d
ep
t
h
o
f
d
is
ch
ar
g
e,
an
d
m
ater
ial
ch
a
r
ac
ter
is
tics
[
5
]
.
T
r
ad
itio
n
al
p
h
y
s
ics
-
b
ased
o
r
eq
u
i
v
alen
t
-
cir
cu
it
m
o
d
els
r
eq
u
i
r
e
ex
te
n
s
iv
e
ex
p
er
t
k
n
o
wled
g
e
a
n
d
ex
p
er
im
e
n
tal
ca
lib
r
atio
n
,
wh
ich
lim
its
th
eir
ap
p
licab
ilit
y
ac
r
o
s
s
d
iv
er
s
e
o
p
e
r
atin
g
e
n
v
ir
o
n
m
en
ts
[
6
]
.
Mo
r
eo
v
er
,
th
ese
m
o
d
els
m
ay
f
ail
t
o
g
en
er
alize
ac
r
o
s
s
ch
em
is
tr
ies
o
r
u
s
ag
e
p
r
o
f
iles
,
r
esu
ltin
g
in
in
ac
cu
r
ate
SOH
p
r
ed
ictio
n
s
in
r
ea
l
-
wo
r
ld
s
ce
n
ar
io
s
.
T
h
is
lim
itatio
n
h
i
g
h
lig
h
ts
th
e
n
ee
d
f
o
r
p
r
ed
ic
tiv
e
f
r
am
ewo
r
k
s
ca
p
ab
le
o
f
tr
ac
k
in
g
tem
p
o
r
al
d
eg
r
ad
atio
n
b
eh
a
v
io
r
,
h
an
d
lin
g
ch
em
is
tr
y
v
a
r
iab
ilit
y
,
an
d
s
u
p
p
o
r
tin
g
s
tan
d
ar
d
ize
d
test
in
g
p
r
o
to
co
ls
.
R
ec
en
t
r
esear
ch
h
as
s
h
if
ted
t
o
war
d
s
d
ata
-
d
r
i
v
en
m
et
h
o
d
s
,
p
ar
ticu
lar
ly
m
ac
h
in
e
lear
n
i
n
g
(
ML
)
an
d
d
ee
p
lear
n
in
g
(
DL
)
,
f
o
r
b
atter
y
h
ea
lth
p
r
e
d
ictio
n
.
ML
m
o
d
e
ls
s
u
ch
as
r
an
d
o
m
f
o
r
est
(
R
F)
,
g
r
a
d
ien
t
b
o
o
s
tin
g
(
GB
)
,
an
d
e
x
tr
em
e
g
r
ad
ien
t
b
o
o
s
tin
g
(
XGBo
o
s
t
)
h
av
e
d
e
m
o
n
s
tr
ated
s
tr
o
n
g
p
er
f
o
r
m
an
ce
in
ca
p
tu
r
in
g
n
o
n
lin
ea
r
r
elatio
n
s
h
i
p
s
b
etwe
en
b
atter
y
f
ea
tu
r
es
an
d
ca
p
ac
ity
[
7
]
,
[
8
]
.
DL
m
o
d
els,
p
ar
ticu
lar
ly
m
u
lti
-
lay
er
p
er
ce
p
tr
o
n
s
(
ML
Ps
)
an
d
r
ec
u
r
r
en
t
n
e
u
r
al
n
etwo
r
k
s
(
R
NNs),
o
f
f
er
an
ad
d
itio
n
al
ad
v
an
tag
e
b
y
lea
r
n
in
g
co
m
p
le
x
f
ea
tu
r
e
r
ep
r
esen
tatio
n
s
d
ir
ec
tly
f
r
o
m
d
ata
with
o
u
t
m
an
u
al
f
ea
tu
r
e
e
n
g
in
e
er
in
g
[
9
]
.
Hy
b
r
i
d
ap
p
r
o
ac
h
es,
wh
ich
co
m
b
in
e
t
h
e
s
tr
en
g
th
s
o
f
ML
an
d
DL
,
h
av
e
em
er
g
e
d
as
p
r
o
m
is
in
g
s
o
lu
tio
n
s
,
as
th
ey
lev
er
ag
e
DL
’
s
f
ea
tu
r
e
ex
tr
ac
tio
n
ca
p
ab
ilit
ies
with
ML
’
s
r
o
b
u
s
t
p
r
e
d
ictio
n
m
ec
h
an
is
m
s
[
1
0
]
.
T
h
e
h
y
b
r
id
ap
p
r
o
ac
h
in
teg
r
ates
d
ee
p
lea
r
n
in
g
-
b
ased
f
ea
tu
r
e
ex
tr
ac
tio
n
with
en
s
em
b
le
lear
n
i
n
g
m
o
d
els
f
o
r
im
p
r
o
v
ed
ac
cu
r
ac
y
a
n
d
ca
lib
r
atio
n
in
b
o
th
r
e
g
r
ess
io
n
an
d
class
if
icatio
n
task
s
.
T
h
is
m
eth
o
d
o
lo
g
y
i
s
ap
p
lied
to
a
r
ea
l
-
wo
r
ld
d
at
aset c
o
n
tain
in
g
elec
t
r
o
ch
em
ical
im
p
e
d
an
ce
p
ar
am
e
ter
s
,
tem
p
er
atu
r
e,
a
n
d
o
th
er
o
p
er
atio
n
al
d
ata.
R
ec
en
t
b
atter
y
p
r
o
g
n
o
s
tics
s
tu
d
ies
h
av
e
in
v
esti
g
ated
in
d
ir
ec
t
h
ea
lth
in
d
icato
r
s
,
Gau
s
s
ian
-
p
r
o
ce
s
s
SOH/R
UL
m
o
d
elin
g
,
h
y
b
r
i
d
d
ata
-
d
r
iv
en
esti
m
atio
n
,
th
er
m
al
-
f
au
lt
p
r
o
g
n
o
s
is
,
AI
-
ass
is
ted
B
MS
s
tr
ateg
ies,
an
d
m
ac
h
in
e
-
lear
n
i
n
g
-
b
ased
th
e
r
m
al
an
d
p
o
wer
-
elec
tr
o
n
ics
d
iag
n
o
s
tics
[
11
]
-
[
1
5
]
.
R
elate
d
AI
-
d
r
iv
en
s
tu
d
ies
in
elec
tr
ic
-
v
eh
icle
co
n
tr
o
l
a
n
d
p
o
wer
p
r
ed
ictio
n
f
u
r
t
h
er
s
h
o
w
th
e
v
alu
e
o
f
s
y
s
tem
atic
m
o
d
el
co
m
p
ar
is
o
n
an
d
d
ata
-
ce
n
tr
ic
lear
n
in
g
in
en
e
r
g
y
ap
p
licatio
n
s
[
1
6
]
,
[
1
7
]
,
wh
ile
co
m
p
ar
ativ
e
d
ee
p
-
lear
n
in
g
e
v
alu
atio
n
s
in
o
th
e
r
d
o
m
ain
s
also
u
n
d
e
r
lin
e
th
e
im
p
o
r
tan
ce
o
f
r
o
b
u
s
t
b
e
n
ch
m
ar
k
in
g
an
d
ca
lib
r
atio
n
a
n
aly
s
is
[
1
8
]
,
[
1
9
]
.
Mo
tiv
ate
d
b
y
th
ese
d
ev
elo
p
m
en
ts
,
th
e
p
r
esen
t
wo
r
k
in
co
r
p
o
r
ates
h
y
p
er
p
ar
am
eter
tu
n
in
g
,
k
-
f
o
ld
c
r
o
s
s
-
v
alid
atio
n
,
an
d
u
n
ce
r
tain
ty
q
u
an
tific
atio
n
to
i
m
p
r
o
v
e
th
e
r
eliab
ilit
y
a
n
d
r
e
p
r
o
d
u
cib
ilit
y
o
f
SOH
p
r
ed
ictio
n
s
.
Fu
r
th
er
m
o
r
e,
th
e
f
r
am
ewo
r
k
ac
k
n
o
wled
g
es
g
ap
s
in
tim
e
-
s
er
ies
m
o
d
elin
g
an
d
o
u
tlin
es
th
e
f
u
tu
r
e
in
teg
r
atio
n
o
f
lo
n
g
s
h
o
r
t
-
te
r
m
m
em
o
r
y
(
L
STM
)
a
n
d
g
ated
r
ec
u
r
r
en
t
u
n
it
(
GR
U
)
-
b
ased
t
em
p
o
r
al
a
r
ch
itectu
r
es
f
o
r
ca
p
tu
r
in
g
cy
cle
-
wis
e
d
eg
r
ad
atio
n
tr
en
d
s
.
C
h
em
is
tr
y
-
s
p
ec
if
ic
b
eh
av
i
o
r
(
e
.
g
.
,
lith
iu
m
n
ick
el
m
a
n
g
an
ese
co
b
alt
o
x
id
e
(
NM
C
)
,
lith
iu
m
ir
o
n
p
h
o
s
p
h
ate
(
L
FP
)
,
an
d
lit
h
iu
m
co
b
alt
o
x
id
e
(
L
C
O)
)
is
also
r
ec
o
g
n
ized
,
an
d
p
ath
way
s
f
o
r
tr
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s
f
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lear
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in
g
an
d
d
o
m
ain
ad
a
p
tatio
n
ar
e
d
is
cu
s
s
ed
to
b
r
o
a
d
en
ap
p
licab
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ac
r
o
s
s
d
atasets
an
d
b
atter
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c
h
em
is
tr
ies.
Fig
u
r
e
1
illu
s
tr
ates
th
e
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v
er
all
p
r
o
ce
s
s
o
f
lith
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m
-
io
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b
atter
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d
eg
r
a
d
atio
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m
o
d
el
in
g
.
T
h
e
co
m
p
u
tatio
n
al
s
etu
p
u
s
ed
in
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h
is
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d
y
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im
u
latio
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ased
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d
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eg
i
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with
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aw
o
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im
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e
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ce
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ata
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f
r
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r
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wo
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ld
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atter
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o
llo
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d
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y
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r
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,
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el
tr
ain
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g
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p
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o
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m
a
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Ma
ch
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e
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o
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ar
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ile
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y
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ip
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ates
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ased
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x
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en
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em
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le
ML
p
r
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r
f
o
r
im
p
r
o
v
ed
SO
H
esti
m
atio
n
.
T
h
e
wo
r
k
f
lo
w
s
h
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wn
in
Fig
u
r
e
1
is
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p
r
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e
m
eth
o
d
o
l
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ical
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cib
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Fig
u
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.
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m
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MSE
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ep
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ML
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ch
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F.
Mo
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e
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tical
d
ep
lo
y
m
en
t
co
n
s
id
e
r
atio
n
s
,
d
is
cu
s
s
in
g
h
o
w
s
u
ch
p
r
ed
ictiv
e
f
r
am
ew
o
r
k
s
ca
n
tr
an
s
late
to
r
ea
l
-
tim
e
em
b
ed
d
e
d
B
MS
p
latf
o
r
m
s
an
d
h
o
w
th
ey
r
elate
to
s
tan
d
ar
d
s
in
clu
d
in
g
I
E
C
6
2
6
6
0
,
I
SO
1
2
4
0
5
,
an
d
I
E
E
E
1
1
8
8
.
T
h
e
p
r
o
p
o
s
ed
f
r
a
m
ewo
r
k
ca
n
g
u
id
e
th
e
s
elec
tio
n
o
f
o
p
tim
al
p
r
ed
ictiv
e
s
tr
ateg
ies
in
B
MS
ap
p
licatio
n
s
,
th
er
eb
y
im
p
r
o
v
in
g
s
af
ety
,
ex
te
n
d
in
g
b
atter
y
life
s
p
an
,
an
d
r
ed
u
cin
g
o
p
er
atio
n
al
c
o
s
ts
.
2.
M
E
T
H
O
D
L
ith
iu
m
-
io
n
b
atter
y
d
eg
r
ad
ati
o
n
is
d
r
iv
en
b
y
co
u
p
led
ele
ctr
o
ch
em
ical,
th
e
r
m
al,
a
n
d
m
ec
h
an
ical
p
r
o
ce
s
s
es.
Fro
m
a
t
h
eo
r
etica
l
p
er
s
p
ec
tiv
e,
t
h
e
b
atter
y
’
s
s
tate
o
f
h
ea
lth
(
SOH)
is
a
f
u
n
ct
io
n
o
f
its
in
ter
n
al
r
esis
tan
ce
,
ca
p
ac
ity
,
an
d
v
o
ltag
e
ch
ar
ac
ter
is
tics
.
De
g
r
ad
atio
n
m
ec
h
a
n
is
m
s
s
u
ch
as
s
o
lid
el
ec
tr
o
ly
te
in
ter
p
h
ase
(
SEI
)
g
r
o
wth
,
lith
iu
m
p
latin
g
,
an
d
s
tr
u
ctu
r
al
b
r
ea
k
d
o
w
n
o
f
el
ec
tr
o
d
e
m
ater
ials
ca
u
s
e
ir
r
ev
er
s
ib
le
ca
p
ac
ity
lo
s
s
o
v
er
tim
e.
Data
-
d
r
iv
e
n
m
o
d
eli
n
g
lev
er
ag
es
h
is
to
r
ical
o
p
er
ati
o
n
al
an
d
im
p
e
d
an
ce
d
ata
to
ca
p
tu
r
e
th
e
co
m
p
lex
n
o
n
lin
ea
r
r
elatio
n
s
h
i
p
s
b
etwe
en
in
p
u
t
v
ar
iab
les
(
e.
g
.
,
am
b
ien
t
tem
p
er
atu
r
e,
cy
cle
co
u
n
t,
r
es
is
tan
ce
)
an
d
tar
g
et
o
u
tp
u
ts
(
ca
p
ac
ity
o
r
h
ea
lth
s
t
ate)
.
ML
alg
o
r
ith
m
s
s
u
ch
as
R
F
an
d
GB
p
ar
titi
o
n
th
e
f
ea
tu
r
e
s
p
ac
e
an
d
c
r
ea
te
en
s
em
b
le
d
ec
is
io
n
r
u
les
to
ap
p
r
o
x
im
ate
th
ese
r
elatio
n
s
h
ip
s
,
wh
ile
DL
m
o
d
els
s
u
ch
as
m
u
lti
-
lay
er
p
er
ce
p
tr
o
n
(
ML
Ps
)
lear
n
h
ig
h
-
lev
el
r
ep
r
e
s
en
tatio
n
s
th
r
o
u
g
h
n
eu
r
al
n
et
wo
r
k
lay
er
s
.
I
n
alig
n
m
en
t
with
r
ev
iewe
r
f
ee
d
b
ac
k
,
th
is
s
tu
d
y
in
co
r
p
o
r
ates
ad
d
itio
n
al
m
eth
o
d
o
l
o
g
ical
r
ig
o
r
th
r
o
u
g
h
h
y
p
er
p
ar
am
eter
tu
n
in
g
,
k
-
f
o
ld
cr
o
s
s
-
v
alid
atio
n
,
an
d
u
n
ce
r
tain
t
y
q
u
an
tific
atio
n
to
i
m
p
r
o
v
e
r
o
b
u
s
tn
ess
,
r
ep
r
o
d
u
cib
ilit
y
,
an
d
ca
lib
r
atio
n
r
eliab
ilit
y
.
T
h
e
tu
n
in
g
p
r
o
c
ess
in
v
o
lv
es
g
r
id
/r
an
d
o
m
s
ea
r
ch
o
v
er
p
ar
am
eter
s
s
u
c
h
a
s
t
r
ee
d
e
p
t
h
,
n
u
m
b
e
r
o
f
e
s
t
i
m
a
t
o
r
s
,
le
a
r
n
i
n
g
r
a
t
e
,
M
L
P
l
a
y
e
r
w
i
d
t
h
,
d
r
o
p
o
u
t
r
a
t
e
,
a
n
d
a
c
t
i
v
a
ti
o
n
f
u
n
c
t
i
o
n
s
.
C
r
o
s
s
-
v
alid
atio
n
m
itig
ates
d
ata
s
p
litt
in
g
b
ias,
wh
ile
u
n
c
er
tain
ty
esti
m
atio
n
v
ia
en
s
e
m
b
le
v
ar
ia
n
ce
an
d
ca
lib
r
atio
n
cu
r
v
es a
s
s
ess
e
s
p
r
ed
ictio
n
co
n
f
id
en
ce
,
p
ar
ticu
lar
l
y
im
p
o
r
ta
n
t f
o
r
B
MS
d
ec
is
io
n
-
m
ak
in
g
[
2
0
]
-
[
2
2
]
.
T
h
e
f
lo
w
in
Fig
u
r
e
2
s
tar
ts
with
d
ata
p
r
ep
r
o
ce
s
s
in
g
,
wh
e
r
e
id
en
tifie
r
s
a
r
e
r
e
m
o
v
e
d
,
c
o
n
tin
u
o
u
s
f
ea
tu
r
es a
r
e
s
tan
d
ar
d
ized
,
an
d
th
e
tar
g
et
is
d
ef
in
ed
f
o
r
r
e
g
r
ess
io
n
(
ca
p
ac
ity
)
o
r
class
if
icatio
n
(
>
0
.
8
th
r
esh
o
ld
)
.
T
h
e
co
m
p
u
tat
io
n
al
p
ip
elin
e
c
o
n
s
is
ts
o
f
f
o
u
r
s
tag
es:
d
ataset
p
r
ep
ar
atio
n
,
m
o
d
el
d
ev
el
o
p
m
en
t,
v
alid
atio
n
,
an
d
d
iag
n
o
s
tic
ev
alu
atio
n
.
M
o
d
el
d
ev
elo
p
m
e
n
t
co
v
er
s
ML
(
R
F
an
d
GB
)
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DL
(
ML
P
with
r
ec
tifie
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lin
ea
r
u
n
it
(
R
eL
U
)
ac
tiv
atio
n
,
d
r
o
p
o
u
t,
a
n
d
ea
r
ly
s
to
p
p
in
g
)
,
an
d
a
h
y
b
r
id
m
o
d
el
th
at
co
m
b
i
n
es
DL
f
ea
tu
r
e
ex
tr
ac
tio
n
with
en
s
em
b
le
lea
r
n
in
g
.
T
h
e
d
ataset
is
s
p
lit
in
a
7
5
/2
5
r
atio
f
o
r
tr
ain
i
n
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d
test
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an
d
p
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m
an
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s
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eg
r
ess
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m
e
tr
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R
MSE
,
MA
E
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an
d
R
²)
a
n
d
class
if
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m
etr
ics
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a
cc
u
r
ac
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F1
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s
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e,
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d
R
OC
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AU
C
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s
u
p
p
o
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ch
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esid
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an
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ca
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r
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Fig
u
r
e
2
.
Flo
w
d
ia
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r
am
f
o
r
lit
h
iu
m
-
io
n
b
atter
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h
ea
lth
p
r
ed
ic
tio
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
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2
0
8
8
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8
6
9
4
I
n
t J Po
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&
Dr
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s
t
,
Vo
l.
1
7
,
No
.
3
,
Sep
tem
b
er
20
2
6
:
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-
1
5
9
0
1584
Ad
d
itio
n
al
d
iag
n
o
s
tic
to
o
ls
wer
e
in
co
r
p
o
r
ated
,
in
cl
u
d
in
g
cy
cle
-
wis
e
d
eg
r
ad
atio
n
p
lo
t
s
,
m
o
d
el
-
r
esid
u
al
tr
en
d
v
is
u
aliza
tio
n
s
,
an
d
s
ca
tter
-
alig
n
m
e
n
t
ev
al
u
atio
n
f
o
r
h
y
b
r
id
r
e
g
r
ess
io
n
.
T
h
e
s
e
an
aly
s
es
s
u
p
p
o
r
t
th
e
in
ter
p
r
etatio
n
o
f
th
e
R
MS
E
d
is
cr
ep
an
cy
.
Alth
o
u
g
h
th
e
h
y
b
r
id
m
o
d
el
ex
h
ib
its
a
s
lig
h
tly
h
ig
h
er
R
MSE
th
an
th
e
s
tan
d
alo
n
e
R
F m
o
d
el,
it
d
e
m
o
n
s
tr
ates im
p
r
o
v
e
d
ca
lib
r
ati
o
n
an
d
r
eliab
ilit
y
.
Th
e
RF
m
o
d
el
is
an
en
s
em
b
le
lear
n
in
g
tech
n
iq
u
e
th
at
co
n
s
tr
u
cts
m
u
ltip
le
d
ec
is
io
n
tr
ee
s
f
r
o
m
r
an
d
o
m
s
u
b
s
ets
o
f
th
e
d
ataset
an
d
f
ea
tu
r
es,
a
v
er
ag
in
g
th
eir
o
u
tp
u
ts
f
o
r
r
eg
r
ess
io
n
o
r
u
s
in
g
m
ajo
r
ity
v
o
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g
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o
r
class
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f
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n
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th
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lly
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f
o
r
r
eg
r
ess
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e
p
r
ed
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n
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g
iv
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b
y
(
1
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.
^
=
(
1
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∑
ℎ
=
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(
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(
1
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W
h
er
e
T
is
th
e
n
u
m
b
er
o
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tr
ee
s
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h
t
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x
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f
r
o
m
th
e
t
th
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ee
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T
h
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er
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in
g
p
r
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ce
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s
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ce
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esu
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lo
we
r
R
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d
MA
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d
o
f
ten
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ld
s
h
ig
h
R
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co
r
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in
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tr
o
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at
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s
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r
r
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t th
e
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als f
r
o
m
th
e
p
r
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s
iter
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ts
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p
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ate
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s
ar
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2
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n
d
(
3
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(
)
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(
2
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1
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−
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3
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er
e
F
m
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1
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h
e
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r
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s
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s
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r
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ictio
n
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h
m
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n
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ee
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ain
e
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d
γ
m
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ate.
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h
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r
co
r
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to
m
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ec
tiv
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d
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r
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lar
izatio
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h
iev
e
lo
w
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ile
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ain
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ain
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g
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ig
h
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r
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h
e
m
u
lti
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lay
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ce
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tr
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n
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ML
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ty
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e
o
f
f
ee
d
f
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r
war
d
n
eu
r
al
n
etwo
r
k
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m
p
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f
f
u
lly
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n
n
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ted
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y
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s
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d
n
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lin
ea
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f
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n
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s
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E
ac
h
n
e
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r
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n
c
o
m
p
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tes
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4
)
.
(
)
=
(
(
)
(
l
-
1
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+
(
)
)
(
4
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W
h
er
e
W
(l)
an
d
b
(l)
ar
e
weig
h
t
s
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d
b
iases
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d
f
is
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ac
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tio
n
f
u
n
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n
s
u
ch
as R
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o
r
s
ig
m
o
id
.
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h
e
ML
P
is
tr
ain
ed
b
y
m
in
im
izin
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lo
s
s
f
u
n
ctio
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m
m
o
n
ly
th
e
m
ea
n
s
q
u
ar
ed
er
r
o
r
,
wh
ich
d
ir
ec
tl
y
in
f
lu
en
ce
s
R
MSE
an
d
im
p
r
o
v
es R
²
b
y
ca
p
tu
r
in
g
co
m
p
lex
n
o
n
lin
ea
r
r
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n
s
h
i
p
s
.
=
1
∑
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=
1
−
^
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2
(
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h
e
h
y
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r
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d
DL
+M
L
ap
p
r
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ac
h
co
m
b
in
es
th
e
r
e
p
r
esen
tatio
n
al
lear
n
in
g
a
b
ilit
y
o
f
ML
P
with
th
e
p
r
ed
ictiv
e
s
tab
ilit
y
o
f
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ML
m
o
d
el
s
u
ch
as
g
r
ad
ie
n
t
b
o
o
s
ti
n
g
o
r
lo
g
is
tic
r
eg
r
ess
io
n
.
First,
th
e
ML
P
p
r
o
ce
s
s
es
th
e
in
p
u
t f
ea
t
u
r
es to
p
r
o
d
u
ce
l
aten
t r
ep
r
esen
tatio
n
s
.
z
= MLP(
x)
(
6
)
ŷ
= ML_
mo
d
el
(
[
x,
z
]
)
(
7
)
T
h
ese
f
ea
tu
r
es,
al
o
n
g
with
th
e
o
r
ig
in
al
in
p
u
ts
,
ar
e
th
e
n
f
e
d
i
n
to
an
ML
m
o
d
el
t
o
g
e
n
er
ate
th
e
f
in
al
p
r
ed
ictio
n
.
T
h
is
h
y
b
r
id
izatio
n
o
f
te
n
y
iel
d
s
s
u
p
er
io
r
p
er
f
o
r
m
an
ce
i
n
b
o
th
r
eg
r
ess
io
n
an
d
class
if
icatio
n
task
s
,
lo
wer
in
g
R
MSE
an
d
MA
E
f
o
r
r
eg
r
ess
io
n
wh
ile
im
p
r
o
v
i
n
g
R
OC
-
AUC
an
d
F1
-
s
co
r
e
f
o
r
c
lass
if
icatio
n
d
u
e
to
m
o
r
e
in
f
o
r
m
ativ
e
d
ec
is
io
n
b
o
u
n
d
a
r
i
es
[
2
3
]
-
[
2
5
]
.
Fig
u
r
e
3
p
r
o
v
id
es
th
e
s
tr
u
ct
u
r
al
in
s
ig
h
t
a
n
d
g
o
v
e
r
n
in
g
e
q
u
atio
n
s
f
o
r
all
f
o
u
r
m
o
d
el
f
am
ilies
.
I
t
v
is
u
ally
s
u
m
m
ar
izes
h
o
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ch
m
o
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d
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ig
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if
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i
n
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in
g
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eq
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en
tial
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r
r
o
r
co
r
r
ec
tio
n
,
a
n
d
p
r
ed
ictio
n
f
u
s
io
n
.
Stru
ct
u
r
al
ar
ch
itectu
r
es
a
n
d
eq
u
atio
n
s
o
f
Fig
u
r
e
3
(
a
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R
F,
wh
er
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m
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ltip
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is
io
n
tr
ee
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ag
g
r
eg
ated
to
o
b
tain
t
h
e
f
in
al
p
r
ed
ictio
n
.
Fig
u
r
e
3
(
b
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GB
,
wh
er
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s
eq
u
e
n
tial
wea
k
lear
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e
tr
ain
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r
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Fig
u
r
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(
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s
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m
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lly
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ted
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n
lay
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Fig
u
r
e
3
(
d
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s
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th
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h
y
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r
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m
o
d
el,
wh
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f
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r
/class
if
ier
to
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ate
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e
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al
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t
p
u
t.
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S
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n
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Dev
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1585
(
a)
(
b
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(
c)
(
d
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Fig
u
r
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3
.
Stru
ctu
r
al
ar
ch
itectu
r
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n
d
eq
u
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n
s
o
f
(
a)
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c)
m
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an
d
(
d
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h
y
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r
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m
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3.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
e
ac
cu
r
ate
p
r
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o
f
li
th
iu
m
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b
atter
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ca
p
ac
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d
eg
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is
p
i
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tal
f
o
r
en
h
an
cin
g
th
e
r
eliab
ilit
y
an
d
life
s
p
an
o
f
en
er
g
y
s
to
r
ag
e
s
y
s
tem
s
,
p
ar
tic
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lar
ly
in
ap
p
licatio
n
s
s
u
ch
a
s
elec
tr
ic
v
eh
icles,
r
en
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b
le
en
er
g
y
in
teg
r
atio
n
,
an
d
p
o
r
tab
le
elec
tr
o
n
ics.
T
h
e
ev
alu
atio
n
s
p
an
s
r
eg
r
ess
io
n
task
s
(
p
r
e
d
ictin
g
co
n
tin
u
o
u
s
ca
p
ac
ity
v
alu
es)
a
n
d
class
if
icatio
n
task
s
(
d
is
tin
g
u
is
h
in
g
h
ea
lth
y
v
s
.
d
eg
r
a
d
e
d
b
atter
ies,
u
s
in
g
a
th
r
esh
o
ld
o
f
0
.
8
n
o
r
m
alize
d
ca
p
ac
ities
)
.
Per
f
o
r
m
an
ce
m
etr
ics
in
clu
d
e
v
is
u
al
s
ca
tter
p
lo
ts
f
o
r
p
r
ed
ictio
n
ac
cu
r
ac
y
,
r
o
o
t
m
ea
n
s
q
u
ar
e
d
er
r
o
r
(
R
MSE
)
f
o
r
r
eg
r
ess
io
n
er
r
o
r
,
r
ec
eiv
er
o
p
e
r
atin
g
c
h
ar
ac
ter
is
tic
(
R
OC
)
cu
r
v
es
an
d
ar
ea
u
n
d
er
th
e
cu
r
v
e
(
AUC)
f
o
r
class
if
icatio
n
d
is
cr
im
in
atio
n
,
an
d
ca
lib
r
atio
n
cu
r
v
es
f
o
r
p
r
o
b
a
b
ilit
y
r
eliab
ilit
y
.
T
h
e
r
esu
lts
ar
e
p
r
esen
ted
ac
r
o
s
s
two
m
ain
f
ig
u
r
es:
o
n
e
f
o
cu
s
in
g
o
n
r
e
g
r
ess
io
n
p
r
ed
ictio
n
s
v
ia
s
ca
tter
p
lo
ts
,
a
n
d
an
o
t
h
er
o
n
co
m
p
ar
ativ
e
m
e
tr
ics f
o
r
b
o
t
h
r
eg
r
ess
io
n
an
d
cl
ass
if
icatio
n
.
3
.
1
.
Sca
t
t
er
p
lo
t
s
T
h
e
s
ca
tter
p
lo
t
in
Fig
u
r
e
4
(
a)
illu
s
tr
ates
th
e
r
elatio
n
s
h
ip
b
etwe
en
ac
tu
al
n
o
r
m
alize
d
b
atter
y
ca
p
ac
ity
(x
-
ax
is
)
a
n
d
R
F
p
r
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n
s
(
y
-
ax
is
)
.
I
d
ea
lly
,
all
p
o
in
ts
w
o
u
ld
lie
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n
th
e
d
iag
o
n
al
(
y
=
x
)
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t
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o
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ate
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s
ter
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g
with
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o
ticea
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le
m
id
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r
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g
e
s
ca
tter
(
0
.
4
–
0
.
8
)
is
o
b
s
er
v
ed
.
T
h
is
in
d
icate
s
th
at
wh
ile
th
e
m
o
d
el
ca
p
tu
r
es
th
e
g
en
er
al
tr
en
d
we
ll,
p
r
ed
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n
v
ar
ia
n
ce
in
c
r
ea
s
es
f
o
r
p
ar
tially
d
e
g
r
ad
e
d
s
tates.
I
n
b
atter
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lth
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o
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ian
ce
ca
n
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ed
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ce
ac
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r
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esti
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atin
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r
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ain
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g
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s
ef
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l lif
e
f
o
r
ce
lls
n
ea
r
in
g
en
d
-
of
-
life
.
Fig
u
r
e
4
(
b
)
c
o
m
p
a
r
es
ac
tu
al
a
n
d
p
r
ed
icted
ca
p
ac
ities
f
o
r
th
e
GB
m
o
d
el,
s
h
o
win
g
p
o
in
ts
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o
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ely
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n
ed
with
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e
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d
in
d
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im
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r
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v
e
d
ac
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r
ac
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v
er
RF
.
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h
e
r
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u
ce
d
s
ca
tter
ac
r
o
s
s
th
e
ca
p
ac
ity
r
an
g
e
r
ef
lects
GB
’
s
ab
ilit
y
to
co
r
r
ec
t
er
r
o
r
s
iter
ativ
el
y
an
d
m
o
d
el
co
m
p
le
x
b
atter
y
b
e
h
av
io
r
s
.
T
h
is
tig
h
ter
f
it
s
u
g
g
ests
lo
wer
r
eg
r
ess
io
n
e
r
r
o
r
s
,
m
ak
in
g
it
well
s
u
ited
f
o
r
p
r
ec
is
e
b
atter
y
-
ca
p
a
city
f
o
r
e
ca
s
tin
g
in
p
r
ac
tical
ap
p
licatio
n
s
.
T
h
e
M
u
lti
-
L
ay
er
Per
ce
p
tr
o
n
’
s
ac
tu
al
-
v
er
s
u
s
-
p
r
ed
icted
ca
p
ac
ity
p
lo
t
in
Fig
u
r
e
4
(
c)
s
h
o
ws
g
r
ea
ter
s
ca
tter
th
an
GB
,
esp
ec
ial
ly
at
lo
w
ca
p
ac
ities
(
<0
.
4
)
,
wh
er
e
i
t
ten
d
s
to
u
n
d
er
p
r
e
d
ict
s
ev
er
e
d
eg
r
ad
atio
n
.
W
h
ile
th
e
m
o
d
el
ca
n
lear
n
co
m
p
lex
b
atter
y
p
atter
n
s
th
r
o
u
g
h
its
lay
er
ed
s
tr
u
ctu
r
e,
its
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er
f
o
r
m
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ce
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ay
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e
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er
e
d
b
y
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r
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g
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n
d
d
ata
im
b
al
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ce
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in
d
icatin
g
th
e
n
ee
d
f
o
r
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tr
o
n
g
er
r
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g
u
lar
izatio
n
i
n
s
u
c
h
ca
s
es.
Th
e
h
y
b
r
id
m
o
d
el
in
Fig
u
r
e
4
(
d
)
,
wh
ich
u
s
es
d
ee
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n
in
g
f
o
r
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tr
ac
tin
g
h
ig
h
-
lev
el
f
ea
tu
r
es
an
d
GB
f
o
r
f
in
al
p
r
ed
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n
,
s
h
o
ws
a
co
m
p
ac
t
p
o
in
t
d
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tr
ib
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tio
n
n
ea
r
th
e
d
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o
n
al
an
d
b
e
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p
r
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b
ab
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r
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th
an
th
e
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tan
d
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n
e
DL
m
o
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el.
Alth
o
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g
h
R
F
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ield
s
th
e
lo
west
R
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T
ab
le
1
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th
e
h
y
b
r
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n
f
ig
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r
atio
n
r
em
ai
n
s
attr
ac
tiv
e
b
ec
au
s
e
it
co
m
b
in
es
co
m
p
etitiv
e
r
e
g
r
ess
io
n
p
er
f
o
r
m
an
ce
with
i
m
p
r
o
v
e
d
ca
lib
r
atio
n
b
eh
a
v
io
r
f
o
r
class
if
icatio
n
-
o
r
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ted
b
atter
y
-
h
ea
lth
d
ec
is
io
n
s
.
3
.
2
.
Ca
lib
ra
t
i
o
n a
nd
RO
C
curv
es
f
o
r
hea
lt
hy
v
s
deg
ra
de
d ba
t
t
er
ies
T
h
e
ca
lib
r
atio
n
cu
r
v
es
s
h
o
wn
in
Fig
u
r
e
5
(
a)
i
n
d
icate
h
o
w
well
p
r
ed
icted
p
r
o
b
ab
ilit
ies
m
atch
ac
tu
al
d
eg
r
ad
atio
n
r
ates.
T
h
e
h
y
b
r
id
_
DL
+L
R
m
o
d
el
alig
n
s
m
o
s
t
clo
s
ely
with
th
e
id
ea
l
d
iag
o
n
al,
in
d
icatin
g
h
ig
h
l
y
r
eliab
le
p
r
o
b
ab
ili
ty
esti
m
ates.
GB
is
al
s
o
well
ca
lib
r
ated
with
m
in
o
r
d
ev
iatio
n
s
,
R
F
ten
d
s
to
u
n
d
e
r
p
r
e
d
ict
at
m
id
-
p
r
o
b
ab
ilit
ies,
an
d
ML
P
s
h
o
ws
o
v
er
co
n
f
id
e
n
ce
at
lo
w
p
r
o
b
ab
ilit
ies.
Acc
u
r
ate
ca
l
ib
r
atio
n
is
v
ital
f
o
r
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ain
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ical
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u
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es 6
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an
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.
(
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(
b
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c)
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d
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Fig
u
r
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4
.
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m
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p
ac
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e
g
r
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n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8
6
9
4
I
n
t J Po
w
E
lec
&
Dr
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s
t
,
Vo
l.
1
7
,
No
.
3
,
Sep
tem
b
er
20
2
6
:
1581
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1
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4.
CO
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