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2
,
2
3
]
b
y
ad
d
in
g
t
h
e
ef
f
ec
t o
f
te
m
p
er
atu
r
e
an
d
li
f
e
c
y
cle
to
t
h
e
o
p
en
cir
cu
it
v
o
lta
g
e.
I
n
r
ec
en
t
y
ea
r
s
,
r
esear
c
h
er
s
o
n
th
e
b
atter
y
m
o
d
el
h
a
v
e
b
ec
o
m
e
v
er
y
p
o
p
u
lar
.
D
u
e
to
t
h
e
n
ec
ess
it
y
o
f
b
atter
y
m
o
d
els
i
n
b
atter
y
m
a
n
ag
e
m
e
n
t
s
y
s
te
m
s
an
d
in
th
e
s
t
ate
o
f
c
h
ar
g
e
e
s
ti
m
atio
n
,
w
h
i
ch
r
ep
r
esen
t
a
b
ig
p
r
o
b
lem
in
B
MS.
T
o
s
o
lv
e
th
i
s
p
r
o
b
lem
,
m
an
y
r
esear
ch
er
s
h
av
e
p
r
o
p
o
s
ed
v
ar
io
u
s
m
et
h
o
d
s
to
esti
m
ate
So
C
:
T
h
e
f
ir
s
t
t
y
p
es
o
f
m
e
th
o
d
s
b
ased
o
n
d
ir
ec
t
m
ea
s
u
r
e
m
e
n
t
.
T
h
e
m
o
s
t
c
o
m
m
o
n
tech
n
iq
u
e
f
o
r
ca
lcu
lati
n
g
th
e
SO
C
i
s
t
h
e
C
o
u
lo
m
b
co
u
n
ti
n
g
m
eth
o
d
s
,
al
s
o
k
n
o
w
n
a
s
a
m
p
er
e
h
o
u
r
co
u
n
ti
n
g
a
n
d
cu
r
r
e
n
t
in
te
g
r
atio
n
[
2
6
,
2
7
]
.
T
h
is
m
e
th
o
d
u
t
ilizes
b
atter
y
cu
r
r
en
t
r
ea
d
in
g
s
m
at
h
e
m
a
ticall
y
i
n
te
g
r
ated
o
v
er
ti
m
e
to
ca
lcu
late
SOC
v
al
u
es.
T
h
e
i
n
itial
s
tate
o
f
ch
ar
g
e
r
ep
r
esen
t
t
h
e
p
r
o
b
le
m
s
o
f
th
is
m
eth
o
d
s
b
ec
au
s
e
is
u
n
k
n
o
w
n
.
T
h
er
ef
o
r
e
th
is
m
et
h
o
d
is
ef
f
icie
n
t
if
t
h
e
i
n
itia
l
s
tate
o
f
c
h
ar
g
e
i
s
k
n
o
w
n
.
Op
en
C
ir
c
u
it
Vo
lta
g
e
(
OC
V)
m
et
h
o
d
is
s
elec
ted
to
d
eter
m
i
n
e
So
C
v
ia
th
e
r
elatio
n
s
h
ip
b
et
w
ee
n
O
C
V
an
d
So
C
[
2
8
,
2
9
]
.
T
h
is
m
e
th
o
d
i
s
s
p
ec
i
all
y
u
s
ed
f
o
r
b
atter
y
lead
ac
id
.
An
o
t
h
e
r
m
et
h
o
d
is
d
is
c
u
s
s
ed
in
[
3
0
,
3
1
]
b
ased
o
n
t
h
e
ca
lc
u
lates
o
f
b
atter
y
p
ar
a
m
eter
s
v
ia
e
lectr
o
ch
e
m
ical
s
p
ec
tr
o
s
co
p
ies
i
m
p
ed
a
n
ce
,
o
n
ce
t
h
e
p
ar
a
m
e
ter
s
o
f
th
e
m
o
d
el
ar
e
k
n
o
w
n
,
th
e
So
C
ca
n
b
e
esti
m
a
ted
,
n
ev
er
t
h
eles
s
th
i
s
m
et
h
o
d
s
it
is
n
o
t
s
u
itab
le
f
o
r
o
n
lin
e
ap
p
licatio
n
s
.
T
h
e
s
ec
o
n
d
ty
p
e
is
b
ased
o
n
b
atter
y
s
tate
s
p
ac
e
m
at
h
e
m
atica
l
m
o
d
el
s
o
r
elec
tr
ical
cir
cu
it
b
atter
y
m
o
d
els
to
d
esig
n
an
o
b
s
er
v
er
f
o
r
r
ea
l
-
ti
m
e
So
C
esti
m
atio
n
.
I
n
[
3
2
]
a
Kalm
a
n
Fil
ter
(
KF)
w
as
p
r
o
p
o
s
ed
f
o
r
esti
m
ate
So
C
o
f
a
lead
-
ac
id
b
atter
y
u
s
i
n
g
t
h
e
lin
ea
r
m
o
d
el.
T
h
en
a
n
E
x
te
n
d
ed
Kal
m
an
Fil
ter
(
E
KF)
w
as
s
t
u
d
ied
in
[
3
3
]
,
b
asin
g
o
n
n
o
n
li
n
ea
r
b
atter
y
m
o
d
els.
I
n
d
ee
d
th
i
s
f
ilter
p
er
f
o
r
m
s
a
n
an
al
y
tical
lin
ea
r
izi
n
g
w
h
ic
h
ca
u
s
es
n
u
m
er
ical
p
r
o
b
lem
s
i
n
th
e
J
ac
o
b
ian
ca
lc
u
la
te
o
f
t
h
e
m
o
d
el
[
3
4
]
.
T
h
e
J
ac
o
b
ian
co
m
p
u
tatio
n
al
p
r
o
b
lem
is
s
o
l
v
ed
b
y
U
n
s
ce
n
ted
Kal
m
an
F
ilter
(
UKF)
b
ec
au
s
e
t
h
e
J
ac
o
b
ian
in
UKF
is
ca
lcu
late
u
s
i
n
g
a
s
tatic
lin
ea
r
izi
n
g
.
I
n
t
h
e
r
ef
er
en
ce
[
3
5
]
,
a
UKF
is
u
s
ed
to
esti
m
ate
th
e
So
C
o
f
a
L
it
h
i
u
m
-
I
o
n
b
atter
y
b
ased
o
n
a
n
o
n
-
li
n
ea
r
ele
ctr
o
ch
e
m
ical
b
atter
y
m
o
d
el.
L
ater
a
n
ad
ap
tiv
e
u
n
s
ce
n
ted
Kal
m
a
n
f
ilter
w
as
d
ev
elo
p
ed
f
o
r
o
n
lin
e
So
C
e
v
a
lu
atio
n
o
f
a
L
it
h
iu
m
-
I
o
n
b
atte
r
y
[
3
6
]
.
T
h
e
m
aj
o
r
d
r
a
w
b
ac
k
o
f
t
h
is
ap
p
r
o
ac
h
is
th
at
a
KF
n
ee
d
s
a
s
u
itab
le
m
o
d
el
f
o
r
b
atter
y
,
h
o
w
ev
e
r
,
u
s
i
n
g
t
h
e
f
ee
d
b
ac
k
s
in
th
is
m
o
d
el
w
ill r
eq
u
ir
e
a
p
r
o
p
er
s
tate
in
itia
lizi
n
g
f
o
r
th
e
co
n
v
er
g
e
n
ce
o
f
t
h
e
m
o
d
el.
T
h
e
th
ir
d
ca
teg
o
r
y
o
f
tech
n
iq
u
es
is
b
ased
o
n
t
h
e
b
lack
-
b
o
x
b
atter
y
m
o
d
el
s
[
3
7
-
4
1
]
.
I
t
p
r
o
v
id
es
th
e
b
est
So
C
esti
m
ate
s
d
u
e
to
t
h
e
ef
f
ec
ti
v
e
ab
ili
t
y
o
f
co
m
p
u
tatio
n
a
l
in
te
lli
g
en
ce
to
ap
p
r
o
x
i
m
ate
n
o
n
-
li
n
ea
r
f
u
n
ctio
n
.
Se
v
er
al
au
t
h
o
r
s
[
3
7
,
3
8
]
h
av
e
p
r
o
p
o
s
ed
n
e
w
m
e
th
o
d
s
d
ep
en
d
in
g
o
n
A
r
ti
f
icia
l
Neu
r
al
Net
w
o
r
k
(
A
NN)
ap
p
r
o
ac
h
.
As
r
ep
o
r
ted
in
[
4
2
]
,
a
n
e
w
Neu
r
al
Ne
t
w
o
r
k
(
NN)
m
o
d
el
w
a
s
d
ev
elo
p
ed
to
esti
m
ate
lead
-
ac
id
b
atter
y
So
C
,
b
ased
o
n
cu
r
r
en
t
m
ea
s
u
r
e
m
e
n
t
s
o
f
d
i
s
ch
ar
g
e
an
d
te
m
p
er
at
u
r
e.
An
o
th
er
m
o
d
el
w
a
s
p
r
esen
ted
ex
p
lo
iti
n
g
th
e
r
ad
ia
l
b
asis
o
f
th
e
n
e
u
r
al
n
et
w
o
r
k
f
u
n
cti
o
n
f
o
r
esti
m
ati
n
g
t
h
e
S
o
C
o
f
a
lead
ac
id
b
atter
y
an
d
d
etec
t
th
e
d
eg
r
ad
ed
p
ile
[
3
7
]
.
I
n
[
3
9
]
also
s
u
g
g
e
s
ted
a
f
ee
d
-
f
o
r
w
ar
d
n
eu
r
al
n
et
w
o
r
k
to
esti
m
at
e
So
C
o
f
Ni
-
MH
b
atter
ies.
Ot
h
er
A
r
ti
f
icial
i
n
tel
lig
e
n
t
b
ased
m
et
h
o
d
s
h
a
v
e
b
ee
n
i
n
v
esti
g
at
ed
to
co
m
p
u
te
th
e
So
C
o
f
b
atter
ies li
k
e
F
u
zz
y
lo
g
ic
[
4
0
]
,
Su
p
p
o
r
t V
ec
to
r
Ma
c
h
in
e
[
4
1
]
.
R
ec
en
t
l
y
,
h
y
b
r
id
m
et
h
o
d
s
w
er
e
d
ev
elo
p
ed
to
i
m
p
r
o
v
e
t
h
e
es
ti
m
atio
n
ac
c
u
r
ac
y
.
a
h
y
b
r
id
m
eth
o
d
f
o
r
So
C
esti
m
at
io
n
b
ased
o
n
AN
N
an
d
UKF
w
as
p
r
o
p
o
s
ed
b
y
W
ei,
He
in
R
ef
er
e
n
ce
[
4
3
]
.
T
h
e
s
tate
o
f
c
h
ar
g
e
So
C
i
s
d
eter
m
i
n
ed
ac
co
r
d
in
g
to
th
e
cu
r
r
e
n
t,
v
o
ltag
e,
a
n
d
te
m
p
er
atu
r
e
m
ea
s
u
r
ed
b
y
A
N
N.
T
h
e
u
n
s
ce
n
ted
Kal
m
a
n
f
ilter
is
u
s
ed
to
r
ed
u
ce
A
NN
er
r
o
r
s
.
T
h
en
,
R
ad
ial
b
asic
n
e
u
r
al
n
et
w
o
r
k
w
a
s
u
s
ed
w
it
h
E
KF
f
o
r
th
e
So
C
es
ti
m
atio
n
in
[
4
4
]
.
T
h
is
co
m
b
i
n
ed
m
o
d
el
d
eliv
er
s
t
h
e
b
est
p
er
f
o
r
m
a
n
ce
i
n
e
s
ti
m
ati
n
g
ac
c
u
r
ac
y
w
h
ic
h
er
r
o
r
b
ein
g
les
s
th
a
n
1
%
b
u
t
t
h
e
E
KF
p
er
f
o
r
m
s
an
an
a
l
y
t
ica
l
lin
ea
r
izi
n
g
w
h
ic
h
ca
u
s
es
n
u
m
er
ical
p
r
o
b
lem
s
in
th
e
J
ac
o
b
ian
ca
lcu
late
o
f
th
e
m
o
d
el.
T
h
er
ef
o
r
e,
in
t
h
is
p
ap
e
r
,
a
b
atter
y
m
o
d
el
o
f
t
h
e
b
atte
r
y
is
co
n
s
id
er
ed
as a
b
lack
b
o
x
u
s
i
n
g
a
N
A
R
X
m
o
d
el
(
No
n
lin
ea
r
Au
to
R
e
g
r
es
s
i
v
e
m
o
d
el
w
i
th
e
Xo
g
e
n
o
u
s
in
p
u
t)
.
T
h
en
,
w
e
u
s
ed
in
th
e
s
ec
o
n
d
f
ee
d
-
f
o
r
w
ar
d
n
e
u
r
al
n
et
w
o
r
k
to
es
ti
m
ate
So
C
.
T
h
e
p
r
o
p
o
s
ed
m
o
d
el
i
s
d
esi
g
n
ed
an
d
test
ed
o
n
a
L
it
h
i
u
m
-
I
o
n
b
atter
y
.
T
h
e
s
i
m
u
latio
n
r
e
s
u
lts
s
h
o
w
g
o
o
d
ac
cu
r
ac
y
a
n
d
q
u
ick
co
n
v
er
g
e
n
ce
f
o
r
esti
m
ati
n
g
t
h
e
S
o
C
o
f
L
i
-
I
o
n
b
atter
ies.
T
h
is
p
ap
er
is
o
r
g
an
ized
as
f
o
llo
w
s
.
Sec
tio
n
I
I
d
escr
ib
es
t
h
e
t
h
eo
r
y
o
f
n
e
u
r
al
n
et
w
o
r
k
.
Sectio
n
I
I
I
p
r
esen
t
th
e
e
x
p
er
i
m
en
t
d
ata
u
s
ed
i
n
th
is
p
ap
er
an
d
A
N
N
d
esi
g
n
.
Sectio
n
I
V
d
etail
s
a
p
r
o
p
o
s
ed
b
atter
y
m
o
d
el
an
d
So
C
es
ti
m
atio
n
m
et
h
o
d
s
.
Sectio
n
I
V
p
r
ese
n
ts
t
h
e
s
i
m
u
la
tio
n
r
es
u
lts
o
f
t
h
e
p
r
o
p
o
s
ed
m
o
d
el,
an
d
s
ec
tio
n
V
d
r
a
w
s
s
o
m
e
co
n
cl
u
s
io
n
s
a
n
d
g
iv
e
s
d
ir
ec
tio
n
s
f
o
r
th
e
f
u
t
u
r
e
w
o
r
k
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
Lit
h
iu
m
-
io
n
b
a
tter
ies mo
d
elin
g
a
n
d
s
ta
te
o
f c
h
a
r
g
e
esti
m
a
tio
n
u
s
in
g
a
r
tifi
cia
l n
eu
r
a
l
...
(
Yo
u
n
es B
o
u
j
o
u
d
a
r
)
3417
2.
T
H
E
O
RY
O
F
NE
URA
L
NE
T
WO
RK
T
h
e
A
r
t
i
f
icial
Ne
u
r
al
Net
w
o
r
k
is
a
s
y
s
te
m
m
o
ti
v
ated
b
y
th
e
f
u
n
ctio
n
i
n
g
o
f
b
io
lo
g
ical
n
e
u
r
o
n
s
.
I
t
is
a
p
r
o
ce
s
s
in
g
ar
ch
itect
u
r
e
b
ased
o
n
th
e
h
u
m
an
b
r
ain
f
o
cu
s
in
g
o
n
i
n
f
o
r
m
atio
n
r
ep
r
esen
tat
i
o
n
b
y
it
s
ab
ilit
y
to
lear
n
an
d
ad
ap
t.
T
h
e
y
ar
e
ap
p
lied
in
p
ar
tic
u
lar
to
s
o
l
v
e
p
r
o
b
le
m
s
o
f
cla
s
s
i
f
icatio
n
,
p
r
ed
ictio
n
,
ca
te
g
o
r
izatio
n
,
an
d
o
p
ti
m
izatio
n
.
A
N
Ns
ar
e
co
n
s
tit
u
ted
b
y
a
m
at
h
e
m
atica
l
m
o
d
el
o
f
b
io
lo
g
ical
n
e
u
r
o
n
ca
lled
p
er
ce
p
tr
o
n
ar
r
an
g
ed
in
n
o
d
es
an
d
co
n
n
ec
ted
b
y
w
ei
g
h
in
g
v
ec
to
r
s
o
r
s
im
p
l
y
ca
lled
w
ei
g
h
ts
.
A
NN
s
c
an
m
o
d
el
an
y
ac
t
u
al
d
ata
v
ar
iatio
n
s
b
y
co
n
s
ta
n
tl
y
c
h
an
g
i
n
g
t
h
e
w
ei
g
h
ts
b
et
w
ee
n
t
h
e
n
o
d
es
b
ased
o
n
i
n
f
o
r
m
atio
n
f
lo
w
t
h
r
o
u
g
h
t
h
e
n
et
w
o
r
k
d
u
r
i
n
g
t
h
e
lear
n
i
n
g
p
h
ase.
A
NN
is
w
el
l
s
u
i
ted
f
o
r
m
o
d
eli
n
g
co
m
p
lex
r
elatio
n
s
h
i
p
s
b
et
w
ee
n
i
n
p
u
t
s
an
d
o
u
tp
u
t
s
w
it
h
an
ab
ilit
y
to
lear
n
a
n
d
ad
ap
t,
th
er
ef
o
r
e
is
at
th
e
s
a
m
e
ti
m
e
a
v
er
y
p
o
w
e
r
f
u
l
to
o
l
to
m
o
d
el
n
o
n
li
n
ea
r
s
tati
s
tical
d
ata.
T
h
e
b
asic
m
at
h
e
m
atica
l
m
o
d
el
o
f
A
N
Ns is
s
h
o
w
n
i
n
Fi
g
u
r
e
1.
Fig
u
r
e
1
.
Neu
r
o
n
e
p
r
o
ce
s
s
T
h
e
m
at
h
e
m
atica
l e
q
u
a
tio
n
o
f
th
is
n
e
u
r
o
n
ca
n
b
e
ex
p
r
ess
ed
a
s
in
(
1
)
:
=
(
∑
(
∗
+
)
)
(
1
)
w
h
er
e
X
i
is
th
e
i
n
p
u
t
o
f
th
is
n
eu
r
o
n
,
W
i
is
t
h
e
w
ei
g
h
t
o
f
th
e
in
ter
co
n
n
ec
tio
n
b
et
w
ee
n
i
n
p
u
t
X
i
an
d
n
eu
r
o
n
,
an
d
B
i is th
e
b
ias o
f
t
h
is
n
e
u
r
o
n
.
All th
e
w
ei
g
h
ts
a
n
d
b
ias ar
e
d
eter
m
in
ed
a
f
ter
th
e
tr
ai
n
i
n
g
p
h
a
s
e.
3.
E
XP
E
R
I
M
E
NT
DA
T
A
AND
ANN
DE
S
I
G
N
Li
-
io
n
b
atter
ies
ar
e
r
u
n
t
h
r
o
u
g
h
t
w
o
d
if
f
er
en
t
o
p
er
atio
n
al
p
r
o
f
iles
(
ch
ar
g
e,
d
is
c
h
ar
g
e)
at
a
m
b
ien
t
te
m
p
er
atu
r
es
4
4
oC
.
T
h
e
ch
ar
g
in
g
w
as
ca
r
r
ied
o
u
t
i
n
a
C
o
n
s
tan
t
C
u
r
r
en
t
/
C
o
n
s
tan
t
Vo
lta
g
e
(
C
C
/
C
V)
m
o
d
e.
W
h
en
t
h
e
b
atter
y
i
s
e
m
p
t
y
t
h
e
ch
ar
g
i
n
g
s
tar
ted
b
y
co
n
s
ta
n
t
cu
r
r
en
t
at
1
.
5
A
u
n
til
t
h
e
b
atter
y
v
o
lta
g
e
ar
r
iv
e
s
at
4
,
2
V
,
an
d
th
e
ch
ar
g
in
g
co
n
t
in
u
ed
i
n
a
co
n
s
ta
n
t
v
o
ltag
e
(
C
V
)
m
o
d
e
u
n
ti
l
t
h
e
cu
r
r
en
t
d
r
o
p
p
ed
to
20
mA
.
T
h
e
lo
ad
cu
r
r
en
t
is
f
ix
ed
at
4
A
,
an
d
th
e
d
is
ch
ar
g
e
v
o
lta
g
e
r
u
n
s
w
er
e
s
to
p
p
ed
at
2
.
7
V.
A
ll
ex
p
er
i
m
e
n
t
d
atab
ase
u
s
ed
i
n
t
h
i
s
p
ap
er
is
d
o
w
n
lo
ad
ed
f
r
o
m
th
e
N
A
S
A
p
r
o
g
n
o
s
tic
ce
n
ter
o
f
e
x
ce
lle
n
ce
w
eb
s
i
te
[
4
5
]
.
T
h
e
eq
u
ip
m
e
n
t
r
eq
u
ir
ed
f
o
r
th
is
test
is
B
atter
y
Hea
lt
h
Mo
n
ito
r
in
g
(
B
HM
)
,
s
en
s
o
r
s
to
m
ea
s
u
r
e
b
atter
y
v
o
ltag
e,
c
u
r
r
en
t
an
d
te
m
p
er
at
u
r
e,
lo
ad
b
an
k
,
ch
ar
g
er
s
,
d
ata
ac
q
u
is
itio
n
s
y
s
te
m
a
n
d
a
co
m
p
u
ter
f
o
r
co
n
tr
o
l
an
d
an
al
y
s
i
s
.
T
h
e
NN
tr
ain
in
g
ca
n
b
e
m
ad
e
m
o
r
e
ef
f
ic
ien
t
a
n
d
r
o
b
u
s
t
th
r
o
u
g
h
p
r
o
p
er
n
o
r
m
aliza
tio
n
o
f
th
e
d
ata
[
4
3
]
.
T
h
er
ef
o
r
e,
b
ef
o
r
e
tr
ain
in
g
,
t
h
e
in
p
u
ts
w
er
e
n
o
r
m
alize
d
to
th
e
r
an
g
e
[
-
1
;
1
]
b
y
:
=
2
(
−
)
−
−
1
(
2
)
w
h
er
e
X
m
i
n
an
d
X
m
a
x
ar
e
th
e
m
in
i
m
u
m
a
n
d
m
a
x
i
m
u
m
in
th
e
in
p
u
t
v
ec
to
r
X
o
f
th
e
NN.
I
n
th
e
test
in
g
s
tep
,
th
e
test
in
g
d
ata
w
as scaled
u
s
i
n
g
t
h
e
s
a
m
e
X
m
i
n
an
d
X
m
ax
u
s
ed
in
t
h
e
tr
ai
n
in
g
d
ata.
Af
ter
b
u
ild
i
n
g
t
h
e
d
atab
ase
p
a
s
s
ed
to
th
e
s
ep
ar
atio
n
o
f
th
e
lear
n
in
g
an
d
v
alid
atio
n
b
asis
.
Gen
er
all
y
,
th
er
e
is
n
o
p
r
ec
is
e
r
u
le
co
n
ce
r
n
in
g
t
h
i
s
s
ep
ar
atio
n
b
u
t
in
a
g
e
n
er
al
w
a
y
t
h
e
v
alid
atio
n
d
atab
ase
r
ep
r
esen
t
s
f
r
o
m
1
0
%
to
2
5
%
o
f
t
h
e
g
en
e
r
al
d
atab
ase.
On
ce
th
e
t
w
o
d
at
ab
ases
ar
e
cr
ea
ted
,
it
w
ill
b
e
n
ec
ess
ar
y
to
d
ef
i
n
e
an
ar
ch
itect
u
r
e
o
f
th
e
n
e
u
r
a
l
n
et
w
o
r
k
.
W
e
u
s
e
an
FF
N
N
an
d
NARX
m
o
d
els
tr
ain
ed
w
ith
t
h
e
b
ac
k
-
p
r
o
p
ag
atio
n
lear
n
i
n
g
al
g
o
r
ith
m
,
to
i
ts
ca
p
ac
it
y
to
s
o
l
v
e
n
o
n
lin
ea
r
p
r
o
b
le
m
.
T
h
e
r
i
s
k
o
f
o
v
er
-
lear
n
in
g
h
a
s
al
w
a
y
s
e
x
i
s
ted
w
h
e
n
w
e
u
s
ed
an
ar
ti
f
icial
n
eu
r
al
n
et
w
o
r
k
.
T
h
er
ef
o
r
e
th
e
o
p
ti
m
izatio
n
is
a
p
r
im
ar
y
p
h
a
s
e
i
n
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
9
,
No
.
5
,
Octo
b
er
2
0
1
9
:
3
4
1
5
-
3
4
2
2
3418
th
e
d
esi
g
n
o
f
t
h
e
n
e
u
r
al
n
et
wo
r
k
.
T
h
e
o
b
j
ec
tiv
e
o
f
o
p
ti
m
iz
atio
n
s
tep
is
to
lo
ca
te
t
h
e
o
p
ti
m
al
d
esi
g
n
o
f
t
h
e
n
eu
r
al
n
et
w
o
r
k
,
w
e
p
er
f
o
r
m
e
d
s
ev
er
al
test
s
to
f
in
d
t
h
e
o
p
ti
m
al
n
u
m
b
er
s
o
f
h
id
d
en
la
y
er
an
d
n
u
m
b
er
s
o
f
n
eu
r
o
n
p
er
la
y
er
.
T
h
e
p
e
r
f
o
r
m
a
n
ce
s
o
f
th
e
A
N
N
w
ill
b
e
m
ea
s
u
r
ed
b
y
Me
a
n
Sq
u
ar
ed
E
r
r
o
r
(
MSE
)
.
T
h
e
r
esu
lts
o
f
n
eu
r
al
n
et
w
o
r
k
p
er
f
o
r
m
a
n
ce
i
n
t
h
e
o
p
ti
m
izatio
n
p
r
o
ce
s
s
ar
e
p
r
esen
ted
i
n
T
ab
le
1
.
T
h
e
o
p
ti
m
al
ar
c
h
itect
u
r
e
i
s
w
it
h
1
0
n
e
u
r
o
n
s
i
n
th
e
f
ir
s
t a
n
d
th
e
s
ec
o
n
d
h
id
d
en
la
y
er
.
Ta
b
le
1
.
Neu
r
al
n
et
w
o
r
k
o
p
tim
is
a
tio
n
r
es
u
lts
N
e
u
r
a
l
n
e
t
w
o
r
k
a
r
c
h
i
t
e
c
t
u
r
e
P
ER
F
O
R
M
A
N
C
E(
M
S
E)
M
a
x
e
r
r
o
r
a
t
v
a
l
i
d
a
t
i
o
n
[
5
,
5
]
1
.
5
6
4
e
-
01
0
.
5
6
[
8
,
8
]
6
.
9
5
4
e
-
03
0
.
2
3
[
1
0
,
1
0
]
2
.
2
2
4
9
e
-
04
0
.
1
0
[
1
2
,
1
2
]
2
.
2
1
2
e
-
04
0
.
1
2
[
1
2
,
1
4
]
2
.
1
8
7
4
9
e
-
04
0
.
1
3
2
4.
P
RO
P
O
SE
D
B
AT
T
E
R
Y
M
O
DE
L
Li
-
io
n
b
atter
y
i
s
a
co
m
p
licat
ed
s
y
s
te
m
to
m
o
d
elin
g
d
u
e
to
th
e
n
o
n
li
n
ea
r
it
y
o
f
v
o
ltag
e
r
esp
o
n
s
e
.
A
N
Ns,
ar
e
f
o
u
n
d
to
b
e
g
o
o
d
u
n
i
v
er
s
al
ap
p
r
o
x
i
m
a
tes
w
h
ic
h
ap
p
r
o
x
i
m
ate
a
n
y
f
u
n
c
tio
n
to
d
esire
d
ac
cu
r
ac
y
.
T
h
e
p
r
o
p
o
s
ed
m
o
d
el
is
d
esig
n
ed
w
it
h
t
w
o
n
e
u
r
al
n
et
w
o
r
k
s
,
th
e
f
ir
s
t
b
ased
o
n
N
A
R
X
m
o
d
el
to
f
i
n
d
th
e
b
atter
y
v
o
lta
g
e
at
th
e
s
a
m
p
l
in
g
ti
m
e
k
a
s
a
f
u
n
ctio
n
o
f
th
e
v
o
ltag
e,
So
C
at
th
e
s
a
m
p
l
in
g
ti
m
e
k
−1
,
an
d
t
h
e
cu
r
r
en
t,
te
m
p
er
atu
r
e
at
t
h
e
s
a
m
p
lin
g
ti
m
e
k
.
a
s
it
is
p
r
esen
t
ed
in
th
e
Fi
g
u
r
e
2.
T
h
e
s
ec
o
n
d
to
esti
m
ate
So
C
i
s
b
ased
o
n
FF
NN
(
Feed
-
Fo
r
w
a
r
d
Neu
r
al
Net
w
o
r
k
)
.
T
h
e
s
tr
u
ctu
r
e
o
f
FF
NN
is
s
h
o
w
n
i
n
F
ig
u
r
e
3
,
w
h
er
e
th
e
in
p
u
t
s
ar
e
th
e
b
atter
y
v
o
lta
g
e
at
s
a
m
p
li
n
g
ti
m
e
k
−
1
an
d
th
e
m
ea
s
u
r
e
m
e
n
t
o
f
cu
r
r
e
n
t
an
d
te
m
p
er
atu
r
e
at
s
a
m
p
li
n
g
ti
m
e
k
.
Fig
u
r
e
2
.
NARX M
o
d
el
Fig
u
r
e
3
.
FF
NN
f
o
r
So
C
e
s
ti
m
atio
n
Fro
m
t
h
e
b
o
th
m
o
d
els
o
f
Fi
g
.
2
an
d
3
w
e
ca
n
o
b
tain
ed
t
h
e
r
elatio
n
s
h
ip
o
f
th
e
o
u
tp
u
t
Y
(
k
)
as
a
f
u
n
ctio
n
o
f
I
n
p
u
t
U
(
k
)
an
d
p
r
ev
io
u
s
o
u
tp
u
t
Y
(
k
−
1)
,
Y
(
k
−
2
)
f
o
r
th
e
NARX
m
o
d
el:
(
)
=
(
(
)
,
(
−
1
)
.
.
.
,
(
−
)
,
(
−
1
)
,
.
.
.
(
−
)
)
(
3
)
T
h
e
f
u
n
c
tio
n
F
is
t
h
e
h
y
p
er
b
o
l
ic
tan
g
e
n
t,
o
f
te
n
u
s
ed
in
t
h
e
h
i
d
d
en
la
y
er
as
an
ac
ti
v
atio
n
f
u
n
ctio
n
a
n
d
lin
ea
r
tr
an
s
f
er
f
u
n
ctio
n
in
t
h
e
o
u
t
p
u
t la
y
er
.
(
)
=
2
1
+
(
−
2
∗
)
−
1
(
4
)
A
ll
p
ar
a
m
eter
s
o
f
t
h
e
t
w
o
n
e
u
r
al
n
e
t
w
o
r
k
s
ar
e
o
b
tai
n
ed
af
ter
th
e
s
tep
o
f
n
eu
r
al
n
et
w
o
r
k
tr
ai
n
in
g
u
s
i
n
g
b
ac
k
p
r
o
p
ag
atio
n
al
g
o
r
ith
m
s
.
T
h
e
b
atter
y
is
m
o
d
eled
u
s
i
n
g
a
N
A
R
X
m
o
d
el,
w
h
ich
is
tr
ain
ed
u
s
i
n
g
th
e
d
ata
o
b
tain
ed
f
r
o
m
th
e
b
atter
y
,
an
d
th
e
s
tate
o
f
ch
ar
g
e
is
e
s
ti
m
ated
u
s
in
g
F
FNN.
T
h
e
g
lo
b
al
p
r
o
p
o
s
ed
s
y
s
te
m
is
s
h
o
w
i
n
Fi
g
u
r
e
4.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
t J
E
lec
&
C
o
m
p
E
n
g
I
SS
N:
2
0
8
8
-
8708
Lit
h
iu
m
-
io
n
b
a
tter
ies mo
d
elin
g
a
n
d
s
ta
te
o
f c
h
a
r
g
e
esti
m
a
tio
n
u
s
in
g
a
r
tifi
cia
l n
eu
r
a
l
...
(
Yo
u
n
es B
o
u
j
o
u
d
a
r
)
3419
Fig
u
r
e
4
.
Stru
ct
u
r
e
o
f
p
r
o
p
o
s
e
d
b
atter
y
m
o
d
el
Sin
ce
t
h
e
So
C
o
f
th
e
b
atter
y
i
s
o
n
e
o
f
t
h
e
in
p
u
ts
to
t
h
e
NN,
it
is
n
ec
e
s
s
ar
y
to
m
ea
s
u
r
e
th
e
So
C
u
s
in
g
o
n
e
o
f
th
e
av
ailab
le
m
e
th
o
d
s
.
Fo
r
th
is
r
ea
s
o
n
,
th
e
a
m
p
er
e
-
h
o
u
r
co
u
n
ti
n
g
tech
n
iq
u
e,
g
i
v
en
i
n
[
2
6
,
2
7
]
,
is
e
m
p
lo
y
ed
f
o
r
co
llectin
g
t
h
e
tr
ain
i
n
g
d
ata.
5.
RE
SU
L
T
S AN
D
D
I
SCU
SS
I
O
N
T
h
is
p
ap
er
p
r
o
p
o
s
es
a
n
e
w
b
a
t
ter
y
m
o
d
el
a
n
d
So
C
est
i
m
a
tio
n
.
T
h
e
o
r
ig
i
n
alit
y
o
f
o
u
r
w
o
r
k
lies
i
n
th
e
f
ac
t t
h
at
b
atter
y
m
o
d
el
i
s
d
y
n
a
m
ic,
tak
e
i
n
to
ac
co
u
n
t t
h
e
ef
f
e
ct
o
f
te
m
p
er
atu
r
e
a
n
d
So
C
o
n
t
h
e
b
atter
y
m
o
d
els
,
an
d
w
e
ar
e
u
s
ed
t
h
i
s
m
o
d
el
to
esti
m
ate
So
C
.
T
h
e
b
atter
y
w
a
s
ch
ar
g
ed
f
r
o
m
0
%
to
1
0
0
%
an
d
d
is
c
h
ar
g
ed
f
r
o
m
100
%
to
0
%
,
s
o
th
e
in
teg
r
atio
n
er
r
o
r
w
as
n
e
g
li
g
ib
le
b
ec
au
s
e
th
e
cu
r
r
en
t
s
e
n
s
o
r
w
a
s
w
ell
c
alib
r
ated
,
th
er
ef
o
r
e
th
e
s
o
lid
c
u
r
v
e
i
s
r
eg
ar
d
ed
as
th
e
e
x
p
er
i
m
e
n
tal
So
C
[
4
3
]
.
W
e
ca
n
s
ee
in
t
h
e
n
e
x
t
f
ig
u
r
es
f
o
u
r
r
esu
lt
s
o
f
So
C
an
d
v
o
ltag
e
s
h
o
w
s
th
e
co
m
p
l
ete
s
i
m
ilar
it
y
b
et
w
ee
n
t
h
e
r
ef
er
en
ce
an
d
th
e
esti
m
ated
v
o
lt
ag
e
an
d
So
C
d
u
r
in
g
th
e
c
h
ar
g
e
an
d
d
i
s
ch
ar
g
e
p
r
o
ce
s
s
f
o
r
a
lear
n
i
n
g
an
d
v
alid
atio
n
d
ata.
T
h
e
F
ig
u
r
e
5
s
h
o
w
s
t
h
e
co
m
p
ar
i
s
o
n
b
et
w
ee
n
t
h
e
e
x
p
er
i
m
e
n
t
So
C
an
d
t
h
e
o
u
tp
u
t
o
f
FF
NN
f
o
r
t
r
ain
in
g
d
ata,
an
d
t
h
e
Fi
g
u
r
e
6
f
o
r
t
h
e
v
alid
atio
n
d
ata.
T
h
e
Fig
u
r
e
7
p
r
esen
t
t
h
e
o
u
tp
u
t
o
f
N
A
R
X
m
o
d
el
a
n
d
ex
p
er
i
m
e
n
t
b
atter
y
v
o
ltag
e
f
o
r
tr
ain
i
n
g
d
ata,
an
d
th
e
Fi
g
u
r
e
8
f
o
r
th
e
v
alid
atio
n
d
ata.
Fig
u
r
e
5
.
E
x
p
er
i
m
en
tal
an
d
es
ti
m
ated
b
atter
y
So
C
(
tr
ain
i
n
g
d
ata)
Fig
u
r
e
6
.
E
x
p
er
i
m
en
tal
an
d
e
s
ti
m
ated
b
atter
y
S
OC
(
v
a
lid
ati
o
n
d
ata)
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
0
8
8
-
8708
I
n
t J
E
lec
&
C
o
m
p
E
n
g
,
Vo
l.
9
,
No
.
5
,
Octo
b
er
2
0
1
9
:
3
4
1
5
-
3
4
2
2
3420
Fig
u
r
e
7
.
E
x
p
er
i
m
en
tal
an
d
es
ti
m
ated
b
atter
y
v
o
ltag
e
(
tr
ai
n
i
n
g
d
ata)
Fig
u
r
e
8
.
E
x
p
er
i
m
en
tal
an
d
es
ti
m
ated
b
atter
y
v
o
ltag
e
(
v
alid
a
tio
n
d
ata)
T
h
e
m
a
x
i
m
u
m
er
r
o
r
o
f
NA
R
X
m
o
d
el
is
4
%
b
u
t
w
e
ca
n
s
e
e
s
o
m
e
er
r
o
r
p
ea
k
af
ter
ea
ch
d
is
ch
ar
g
e
o
f
b
atter
y
d
u
e
to
th
e
h
i
g
h
d
eg
r
ee
o
f
b
atter
y
d
is
c
h
ar
g
e.
T
h
is
p
ea
k
s
h
o
w
s
th
e
r
o
b
u
s
tn
e
s
s
o
f
o
u
r
m
o
d
el
b
ec
au
s
e
th
e
cu
r
v
e
o
f
o
u
r
m
o
d
el
co
n
v
er
g
es
q
u
ick
l
y
to
th
e
e
x
p
er
i
m
e
n
tal
c
u
r
v
e.
Fo
r
th
e
s
ec
o
n
d
m
o
d
el
F
FNN
th
e
m
a
x
i
m
u
m
er
r
o
r
is
1
0
%
,
d
u
e
to
t
h
e
p
e
ak
er
r
o
r
o
f
b
atter
y
v
o
ltag
e
a
f
ter
ea
c
h
d
is
c
h
ar
g
e
o
p
er
atio
n
,
an
d
d
u
e
to
er
r
o
r
p
r
o
p
ag
atio
n
in
b
o
th
n
e
u
r
al
n
et
w
o
r
k
.
6.
CO
NCLU
SI
O
N
A
So
C
es
ti
m
ato
r
s
y
s
te
m
f
o
r
L
i
-
I
o
n
b
atter
ies
u
s
in
g
A
NN
w
a
s
p
r
o
p
o
s
ed
in
th
is
p
ap
er
.
T
h
e
A
N
N
is
o
f
NARX
a
n
d
F
FNN
t
y
p
es.
T
h
e
NARX
w
a
s
tr
ai
n
ed
o
f
f
-
li
n
e
to
f
i
n
d
th
e
ap
p
r
o
p
r
iate
m
o
d
el
n
e
ed
ed
in
th
e
FF
N
N,
w
h
ic
h
es
t
i
m
a
tes
t
h
e
So
C
o
f
t
h
e
b
atter
y
.
A
l
l
ex
p
er
i
m
en
t
d
at
ab
ase
u
s
ed
i
n
t
h
is
p
ap
er
is
d
o
w
n
lo
ad
ed
f
r
o
m
t
h
e
NAS
A
p
r
o
g
n
o
s
tic
ce
n
ter
o
f
ex
ce
lle
n
ce
w
eb
s
ite.
T
h
e
s
i
m
u
latio
n
r
esu
l
ts
o
f
th
e
p
r
o
p
o
s
ed
esti
m
ato
r
s
h
o
w
ed
g
o
o
d
ac
cu
r
ac
y
an
d
f
as
t
co
n
v
er
g
en
ce
to
t
h
e
e
x
p
er
i
m
e
n
tal
v
ar
iab
le,
in
d
ep
en
d
e
n
t
o
f
t
h
e
ch
ar
g
i
n
g
co
n
d
itio
n
s
.
T
h
e
p
r
o
p
o
s
ed
m
o
d
el
co
u
ld
b
e
u
s
ed
f
o
r
s
e
v
er
al
r
ec
h
ar
g
ea
b
l
e
b
atter
ies.
T
h
er
ef
o
r
e
s
o
m
e
c
h
alle
n
g
e
s
ab
o
u
t
th
e
p
r
o
p
o
s
ed
m
o
d
el
n
ee
d
to
d
is
cu
s
s
h
er
e.
Fo
r
ap
p
ly
i
n
g
t
h
is
m
o
d
el
to
a
b
atter
y
p
ac
k
,
it
is
n
ec
ess
ar
y
to
ca
lc
u
late
So
C
f
o
r
ea
ch
ce
ll.
T
h
e
b
atter
ies
ar
e
u
s
ed
in
th
e
d
if
f
er
en
t
en
v
ir
o
n
m
e
n
tal
co
n
d
itio
n
.
T
h
er
ef
o
r
e
th
e
d
atab
ase
u
s
ed
in
t
h
e
d
esi
g
n
o
f
th
is
m
o
d
el
n
ee
d
s
to
co
n
tai
n
all
th
e
p
o
s
s
ib
le
o
p
er
atio
n
s
ce
n
ar
io
.
Fo
r
th
e
n
ex
t
w
o
r
k
.
RE
F
E
R
E
NC
E
S
[1
]
H.
R
.
E
ich
i,
e
t
a
l
.
,
“
Ba
tt
e
ry
m
a
n
a
g
e
m
e
n
t
s
y
ste
m
:
A
n
o
v
e
rv
ie
w
o
f
it
s
a
p
p
l
ica
ti
o
n
in
t
h
e
sm
a
rt
g
rid
a
n
d
e
lec
tri
c
v
e
h
icle
s,
”
IEE
E
In
d
u
stria
l
El
e
c
tro
n
ics
M
a
g
a
zin
e
,
v
o
l/
issu
e
:
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u
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t
a
l
.
,
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sig
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m
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w
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tt
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le
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tri
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V
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A
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ti
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,”
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ter
n
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ti
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a
l
J
o
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rn
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.
K.
B
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t
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l
.
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,”
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ter
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.
M
.
A
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ro
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.
,
“
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[5
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C.
A
lao
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”
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En
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,
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13
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pp.
3
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[6
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.
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.
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.
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“
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”
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o
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El
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[8
]
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.
W
a
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d
V
.
S
rin
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,
“
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a
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s (
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]
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.
R.
S
u
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ian
,
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.
,
“
M
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,
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t
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l
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“
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tu
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”
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o
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E
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p
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1
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.
Jo
h
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“
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3
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[1
2
]
M
.
Du
rr,
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a
l
.
,
“
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m
,
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o
u
rn
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[1
3
]
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Ch
a
n
,
“
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n
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ste
m
s,”
in
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,
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p
.
4
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.
[1
4
]
Z.
M
.
S
a
lam
e
h
,
e
t
a
l
.
,
“
A
m
a
th
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m
a
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,
”
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E
T
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n
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y
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n
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n
,
v
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l/
issu
e
:
7
(
1
)
,
pp.
93
-
98
,
19
92
.
[1
5
]
M
.
Nik
d
e
l,
e
t
a
l
.,
“
V
a
rio
u
s
b
a
t
ter
y
m
o
d
e
ls
f
o
r
v
a
rio
u
s
sim
u
lat
io
n
stu
d
ies
a
n
d
a
p
p
li
c
a
ti
o
n
s,
”
Ren
e
wa
b
le
a
n
d
S
u
sta
in
a
b
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n
e
rg
y
Rev
iews
,
v
o
l.
32
,
p
p
.
4
7
7
-
4
8
5
,
2
0
1
4
.
[1
6
]
H.
He
,
e
t
a
l
.
,
“
Co
m
p
a
riso
n
stu
d
y
o
n
th
e
b
a
tt
e
ry
m
o
d
e
ls
u
se
d
f
o
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th
e
e
n
e
rg
y
m
a
n
a
g
e
m
e
n
t
o
f
b
a
tt
e
ries
in
e
lec
tri
c
v
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h
icle
s,
”
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e
rg
y
Co
n
v
e
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a
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d
M
a
n
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me
n
t
,
v
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l.
64
,
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p
.
1
1
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1
2
.
[1
7
]
J.
A
p
p
e
lb
a
u
m
a
n
d
R.
W
e
iss,
“
A
n
e
le
c
tri
c
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l
m
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a
d
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in
T
e
lec
o
mm
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n
ica
ti
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n
s
En
e
rg
y
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2
.
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T
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9
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2
.
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n
ter
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l,
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E
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p
.
3
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-
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0
7
,
1
9
8
2
.
[1
8
]
F
.
M
.
G
.
L
o
n
g
a
tt
,
“
Circu
it
b
a
se
d
b
a
tt
e
ry
m
o
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e
ls:
A
re
v
ie
w
,
”
in
Co
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g
re
so
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ro
a
me
ric
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n
o
d
e
e
stu
d
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a
n
tes
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De
In
g
e
n
ier
i
a
El
e
c
trica
.
C
ib
e
lec
,
2
0
0
6
.
[1
9
]
M
.
Ch
e
n
a
n
d
G
.
A
.
R
.
M
o
ra
,
“
A
c
c
u
ra
te
e
lec
tri
c
a
l
b
a
tt
e
r
y
m
o
d
e
l
c
a
p
a
b
le
o
f
p
re
d
ict
in
g
ru
n
ti
m
e
a
n
d
i
v
p
e
rf
o
r
m
a
n
c
e
,
”
IEE
E
tra
n
sa
c
ti
o
n
s
o
n
e
n
e
rg
y
c
o
n
v
e
rs
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n
,
v
o
l/
issu
e
:
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(
2
)
,
p
p
.
5
0
4
-
511
,
2
0
0
6
.
[2
0
]
O.
T
re
m
b
la
y
,
e
t
a
l
.
,
“
A
g
e
n
e
ric
b
a
tt
e
ry
m
o
d
e
l
f
o
r
th
e
d
y
n
a
m
ic
si
m
u
latio
n
o
f
h
y
b
rid
e
lec
tri
c
v
e
h
icle
s,
”
in
Veh
icl
e
Po
we
r a
n
d
Pr
o
p
u
lsio
n
Co
n
fer
e
n
c
e
,
2
0
0
7
.
VP
PC
2
0
0
7
.
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E
,
p
p
.
2
8
4
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2
8
9
,
2
0
0
7
.
[2
1
]
O.
T
re
m
b
la
y
a
n
d
L.
A
.
De
ss
a
in
t,
“
Ex
p
e
ri
m
e
n
tal
v
a
li
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a
ti
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a
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c
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ti
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n
s,
”
W
o
rl
d
El
e
c
tric V
e
h
icle
J
o
u
rn
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l
,
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l/
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e
:
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)
,
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p
.
1
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10
,
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0
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.
[2
2
]
H.
He
m
i,
e
t
a
l
.
,
“
D
y
n
a
m
ic
m
o
d
e
li
n
g
a
n
d
sim
u
latio
n
o
f
te
m
p
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ra
tu
re
a
n
d
c
u
rre
n
t
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f
fe
c
ts
o
n
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n
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lec
tri
c
v
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h
icle
s
li
th
i
u
m
io
n
b
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tt
e
ry
,
”
in
El
e
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trica
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a
n
d
C
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ter
En
g
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g
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),
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0
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n
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p
.
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5
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0
1
5
.
[2
3
]
Y.
Bo
u
jo
u
d
a
r,
e
t
a
l
.
,
“
Li
-
io
n
b
a
tt
e
ry
p
a
ra
m
e
ters
e
sti
m
a
ti
o
n
u
sin
g
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e
u
ra
l
n
e
t
w
o
rk
s,
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i
n
W
ir
e
les
s
T
e
c
h
n
o
lo
g
ies
,
Emb
e
d
d
e
d
a
n
d
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n
telli
g
e
n
t
S
y
ste
ms
(
W
IT
S
),
2
0
1
7
I
n
ter
n
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ti
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l
Co
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fer
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o
n
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E
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p
p
.
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,
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0
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7
.
[2
4
]
K.
A
.
S
m
it
h
,
e
t
a
l
.
,
“
M
o
d
e
l
-
b
a
se
d
e
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tro
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h
e
m
ica
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ra
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li
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p
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5
]
A
.
Kh
a
li
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t
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l
.
,
“
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h
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im
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f
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e
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m
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tri
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e
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icle
s,
”
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u
sta
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le Ci
t
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n
d
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o
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iety
,
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o
l.
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,
p
p
.
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5
-
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7
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2
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.
[2
6
]
K.
S
.
Ng
,
e
t
a
l
.
,
“
En
h
a
n
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o
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.
[2
7
]
S
.
Je
o
n
,
e
t
a
l
.
,
“
Co
m
p
a
ra
ti
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d
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o
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,
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In
d
i
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n
J
o
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rn
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l
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f
S
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e
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h
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l
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ss
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e
:
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(2
6
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5
.
[2
8
]
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X
in
g
,
e
t
a
l
.
,
“
S
tate
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h
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stim
a
ti
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s a
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e
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s,
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p
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p
p
.
1
0
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1
5
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0
1
4
.
[2
9
]
K
.
S
.
Ng
,
e
t
a
l
.
,
“
S
tate
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of
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h
a
rg
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stim
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ies
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Po
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rg
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0
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n
2
0
0
8
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n
d
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te
rn
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l,
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EE
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p
p
.
9
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0
8
.
[3
0
]
A
.
G
u
h
a
,
e
t
a
l
.
,
“
Re
m
a
in
in
g
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se
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l
li
f
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stim
a
ti
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o
f
li
th
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m
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a
tt
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b
a
se
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o
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th
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tern
a
l
re
sista
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e
g
ro
w
th
m
o
d
e
l,
”
in
Co
n
tro
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o
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fer
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e
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0
1
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d
ia
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EE
E
,
p
p
.
3
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,
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0
1
7
.
[3
1
]
J.
P
.
R
.
Ba
rre
ra
,
e
t
a
l
.
,
“
S
o
c
e
s
ti
m
a
ti
o
n
f
o
r
li
th
iu
m
-
io
n
b
a
tt
e
ries
:
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v
ie
w
a
n
d
f
u
tu
re
c
h
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ll
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n
g
e
s
,
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e
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tro
n
ics
,
v
o
l/
issu
e
:
6
(
4
)
,
p
p
.
1
0
2
,
2
0
1
7
.
[3
2
]
B.
S
.
Bh
a
n
g
u
,
e
t
a
l
.
,
“
No
n
li
n
e
a
r
o
b
se
rv
e
rs
f
o
r
p
re
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ictin
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sta
te
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h
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rg
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n
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te
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lt
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o
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a
tt
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rie
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y
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rid
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e
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tri
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v
e
h
icle
s,
”
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E
tra
n
s
a
c
ti
o
n
s o
n
v
e
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icu
l
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r
tec
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p
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4
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0
0
5
.
[3
3
]
G
.
L
.
P
lett,
“
Ex
ten
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e
d
k
a
lm
a
n
f
i
lt
e
rin
g
f
o
r
b
a
tt
e
r
y
m
a
n
a
g
e
m
e
n
t
s
y
ste
m
s
o
f
li
p
b
-
b
a
se
d
h
e
v
b
a
tt
e
r
y
p
a
c
k
s:
P
a
rt
3
.
sta
te an
d
p
a
ra
m
e
ter es
ti
m
a
ti
o
n
,
”
J
o
u
rn
a
l
o
f
P
o
we
r so
u
rc
e
s
,
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l/
issu
e
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4
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p
p
.
2
7
7
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2
9
2
,
2
0
0
4
.
[3
4
]
A
.
M
u
ra
n
g
ira,
“
No
u
v
e
ll
e
s
a
p
p
ro
c
h
e
s
e
n
f
il
trag
e
p
a
rti
c
u
laire
.
a
p
p
li
c
a
ti
o
n
a
u
re
c
a
lag
e
d
e
la
n
a
v
ig
a
ti
o
n
in
e
rti
e
ll
e
,
”
P
h
.
D.
th
e
sis,
Un
iv
e
rsite d
e
T
e
c
h
n
o
lo
g
ie d
e
T
ro
y
e
s
-
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,
2
0
1
4
.
[3
5
]
S
.
S
a
n
t
h
a
n
a
g
o
p
a
lan
a
n
d
R.
E.
W
h
it
e
,
“
S
tate
o
f
c
h
a
rg
e
e
sti
m
a
ti
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n
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sin
g
a
n
u
n
sc
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n
ted
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il
ter
f
o
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ig
h
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o
w
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r
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th
iu
m
io
n
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ll
s,
”
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n
ter
n
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ti
o
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l
J
o
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E
n
e
rg
y
Res
e
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rc
h
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v
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l/
iss
u
e
:
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4
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)
,
p
p
.
1
5
2
-
1
6
3
,
2
0
1
0
.
[3
6
]
F
.
S
u
n
,
e
t
a
l
.
,
“
A
d
a
p
ti
v
e
u
n
sc
e
n
ted
k
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l
m
a
n
f
il
terin
g
f
o
r
sta
te
o
f
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h
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rg
e
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sti
m
a
ti
o
n
o
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li
th
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m
-
io
n
b
a
tt
e
ry
f
o
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tri
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v
e
h
icle
s,
”
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e
rg
y
,
v
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l/
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e
:
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p
.
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7
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W
.
S
h
e
n
,
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t
a
l
.
,
“
A
n
e
w
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a
tt
e
r
y
a
v
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il
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a
p
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to
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g
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w
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rk
,
”
En
e
rg
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Co
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v
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io
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a
n
d
M
a
n
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g
e
me
n
t
,
v
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l
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ss
u
e
:
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(6
)
,
p
p
.
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b
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0
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4
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3422
[3
8
]
Y.
M
o
rit
a
,
e
t
a
l
.
,
“
On
-
li
n
e
d
e
tec
ti
o
n
o
f
sta
te
-
of
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c
h
a
rg
e
in
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id
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a
tt
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ra
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ial
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a
sis
f
u
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ti
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tw
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rk
,
”
Asia
n
J
o
u
rn
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l
o
f
Co
n
tr
o
l
,
v
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l/
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e
:
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)
,
p
p
.
2
6
8
-
2
7
3
,
2
0
0
6
.
[3
9
]
B.
Ch
e
n
g
,
e
t
a
l
.
,
“
Ni
–
m
h
b
a
tt
e
ries
sta
te
-
of
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c
h
a
rg
e
p
re
d
ictio
n
b
a
se
d
o
n
imm
u
n
e
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lu
ti
o
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n
e
t
w
o
rk
,
”
En
e
rg
y
Co
n
v
e
rs
io
n
a
n
d
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a
n
a
g
e
me
n
t
,
v
o
l
/i
ss
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e
:
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2
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p
.
3
0
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8
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0
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6
,
2
0
0
9
.
[4
0
]
A
.
J.
S
a
lk
in
d
,
e
t
a
l
.
,
“
De
ter
m
in
a
ti
o
n
o
f
sta
te
-
of
-
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h
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rg
e
a
n
d
sta
te
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of
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e
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lt
h
o
f
b
a
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ries
b
y
f
u
z
z
y
lo
g
ic
m
e
th
o
d
o
l
o
g
y
,”
J
o
u
rn
a
l
o
f
P
o
we
r so
u
rc
e
s
,
v
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l/
iss
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e
:
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,
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p
.
2
9
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-
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0
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9
9
9
.
[4
1
]
J.
A
.
A
n
t´
o
n
,
e
t
a
l
.
,
“
Ba
tt
e
r
y
sta
te
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of
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h
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e
´
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sti
m
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to
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u
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g
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e
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m
te
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e
,
”
A
p
p
li
e
d
M
a
t
h
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ma
ti
c
a
l
M
o
d
e
ll
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n
g
,
v
o
l/
issu
e
:
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7
(9
)
,
p
p
.
6
2
4
4
-
6
2
5
3
,
2
0
1
3
.
[4
2
]
O.
V
.
S
.
R.
Ku
m
a
r
a
n
d
K.
S
rik
a
n
th
,
“
En
e
rg
y
lo
ss
e
s
a
n
d
g
lo
b
a
l
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issio
n
s
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d
u
c
ti
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h
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ted
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ra
ti
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n
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se
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m
p
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re
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it
i
o
n
s w
it
h
i
n
a
m
icro
g
rid
.
”
[4
3
]
W
.
He
,
e
t
a
l
.
,
“
S
tate
o
f
c
h
a
rg
e
e
s
ti
m
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n
f
o
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-
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ries
u
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ra
l
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o
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