Internati
o
nal
Journal of P
o
wer Elect
roni
cs an
d
Drive
S
y
ste
m
(I
JPE
D
S)
Vol.
4, No. 4, Decem
ber
2014, pp. 557~
566
I
S
SN
: 208
8-8
6
9
4
5
57
Jo
urn
a
l
h
o
me
pa
ge
: h
ttp
://iaesjo
u
r
na
l.com/
o
n
lin
e/ind
e
x.ph
p
/
IJPEDS
Fuzzy Adaptive Cont
rol
for Di
rect T
o
rque
in Electric Vehicle
Med
j
d
o
ub kh
essam,
Abdel
d
ejb
a
r Haz
z
a
b,
Bousm
a
h
a
Bouchib
a
,
M Bendjim
a
F
acult
y
of th
e s
c
ienc
es
and
te
chn
o
log
y
, B
éch
ar U
n
ivers
i
t
y
,
ALGE
RIA
Article Info
A
B
STRAC
T
Article histo
r
y:
Received J
u
n
6, 2013
R
e
vi
sed Oct
3,
2
0
1
3
Accepted Oct 15, 2013
This
paper
pres
e
n
ts
a t
echniqu
e t
o
control
the
el
e
c
tri
c
veh
i
cl
e (E
V) s
p
eed an
d
torque at an
y
cur
v
e.
Our
propulsion
m
odel consists of two perman
ent magnet
s
y
nchronous (PMSM)
motors.
The fuzzy
ad
aptive PI controller is used to
adjus
t
th
e diff
e
r
ent s
t
a
t
i
c
error
cons
tants
,
as
per the
s
p
eed
error.
Th
e
suggested based
on the direct tor
que
fuzzy
contro
l (DTFC). A Mamdani ty
p
e
fuzzy
dir
ect
torq
ue controller is
firs
t develop
e
d
and then rules are modified
using stator
curr
ent membership
functi
ons. The computations ar
e
ensured
b
y
the
ele
c
troni
c d
i
fferent
ial
,
this d
r
iving pro
cess p
e
rm
it to
stee
r e
ach dr
iving
wheels
at an
y
cu
rve s
e
parat
e
l
y
.M
odeling
and simulation ar
e carr
i
ed out using
the Matlab/Sim
ulink too
l
to
in
ves
tigate th
e performan
ce of
th
e proposed
sy
s
t
e
m
.
Keyword:
DTFC
Electric ve
hicle
El
ect
roni
c
di
f
f
e
rent
i
a
l
Fuzzy a
d
apti
ve
PI
controller
MSA
P
Copyright ©
201
4 Institut
e
o
f
Ad
vanced
Engin
eer
ing and S
c
i
e
nce.
All rights re
se
rve
d
.
Co
rresp
ond
i
ng
Autho
r
:
Med
j
do
ub
kh
essam
,
Facu
lty of th
e
scien
ces an
d tech
no
log
y
,
Béchar Unive
r
sity,
B.P 417
BEC
H
A
R
(08
000
), ALG
E
RIA
.
Em
a
il: k
h
e
ssam
.
med
j
do
ub@h
o
t
m
a
il.fr
1.
INTRODUCTION
Perm
anent
m
a
gnet
sy
nc
hr
on
ous
m
o
t
o
rs
(P
M
S
M
)
are
wi
d
e
l
y
used
i
n
hi
g
h
-
p
er
f
o
rm
ance d
r
i
v
es
su
c
h
as i
n
d
u
st
ri
al
r
o
b
o
t
s
an
d m
a
chi
n
e t
o
ol
s t
h
ank
s
t
o
t
h
ei
r
kn
o
w
n a
d
vant
ages o
f
:
hi
gh
po
we
r de
nsi
t
y
, hi
g
h
-
to
rq
u
e
/in
ertia ratio
, an
d free
m
a
in
ten
a
n
ce
[1
].
In
rece
nt
y
ears t
h
e
DT
C
becom
e
s wi
del
y
use
d
i
n
t
e
rm
of
cont
rol
.
H
o
we
ver
,
t
h
e
r
e i
s
a
n
ot
he
r ap
p
r
oac
h
w
h
i
c
h
has ac
h
i
eved a
si
g
n
i
f
i
c
ant
succe
ss a
n
d c
oul
d be
re
fe
rre
d as
the intelligent base
d DTC dri
v
es. In this case, one
of
the
m
o
st
successful types of PMSM DTC schemes are
t
hose
based
o
n
f
u
zzy
l
ogi
c
sy
st
em
. In fact
, fuzzy
l
o
gi
c b
a
sed di
re
ct
t
o
r
que c
ont
rol
(
D
TFC
)
has
bec
o
m
e
a
com
p
etitive control technique
to traction m
o
tor in EV dri
v
e c
o
m
p
ared
t
o
vector c
ont
rol m
e
thod.Wh
ere the
cl
assi
cal
schem
e
s
m
i
ght
fa
i
l
t
o
operat
e
pr
o
p
erl
y
.
Fu
rt
herm
ore, wi
t
h
a wel
l
desi
gned
DTFC
sc
hem
e
,
si
gni
fi
ca
nt
i
m
p
r
o
v
em
ent
s
i
n
t
e
rm
s of fl
u
x
, t
o
r
q
ue ri
p
p
l
e
s c
oul
d be at
t
a
i
n
e
d
.
Al
so, i
t
s
h
o
u
l
d
be m
e
nt
i
oned t
h
at
for the
DTFC
schem
e
s whic
h are
base
d
on the classical
DTC structure, the inherite
d adva
ntages
of s
u
c
h
schem
e
s (qui
c
k
dy
nam
i
c respo
n
se
) c
oul
d
be m
a
i
n
t
a
i
n
ed.
T
he m
a
i
n
goa
l
of
usi
n
g a
DTFC
al
go
ri
t
h
m
for
PM
SM
dri
v
es
i
s
t
o
ove
rcom
e som
e
of t
h
e dra
w
backs
of
t
h
e ori
g
i
n
al
D
T
C
.
but
, t
h
i
s
r
e
duce
s
t
o
r
q
ue ri
p
p
l
e
g
r
eatly and the f
a
st
r
e
sp
on
se and
r
obu
stn
e
ss m
e
r
its o
f
the
classical DTC
In
order to present the
electronic
d
i
fferen
tial syste
m
fo
r an
electric v
e
h
i
cle driv
en
b
y
t
w
o
p
e
rm
anent
m
a
gnet
sy
nc
hr
on
o
u
s m
o
t
o
rs at
t
ached t
o
the rear wheel
using
fuzzy a
d
aptive c
ont
rol
l
er PI wi
t
h
di
r
ect
t
o
rq
ue fuzz
y
cont
r
o
l
,
di
f
f
e
r
ent
t
e
st
s have
been
carri
ed
o
u
t
:
dri
v
i
n
g
vehi
cl
e
o
n
st
rai
ght
r
o
ad
,
d
r
i
v
i
n
g
ve
hi
cl
e i
n
c
u
r
v
e
d
r
o
a
d
ri
g
h
t
an
d l
e
ft
[
10]
.
The
pa
per is
orga
nized as
follows. In secti
o
n II
is presented
,
m
a
th
e
m
ati
cal m
o
d
e
l o
f
an
PM
SM,
Electric Tractio
n System
Ele
m
en
ts Mo
deling
is
ou
tlin
ed in
section
III an
d th
e electric
d
i
fferen
tial syste
m
in
th
e VI section. Fin
a
lly th
e propo
se
d
fuzzy
adaptive PI c
ont
roller for
DTFC and PMSM drive in electric
v
e
h
i
cle
is p
r
esen
ted
with
simu
latio
n
resu
lts.
Evaluation Warning : The document was created with Spire.PDF for Python.
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No
.
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,
D
ecem
b
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2
014
:
55
7 – 566
55
8
2.
PMSM MODEL
AND DTC
FUNDAMENTALS
Fo
r th
e
d
e
sign
p
r
esen
ted
i
n
this p
a
p
e
r it is con
s
id
er
ed t
h
at the two rea
r
wheel
s of t
h
e electric vehicl
e
are drive
n
PM
SM.
2.
1.
Mac
h
ine Eq
u
a
ti
ons
The m
a
them
atical
m
odel
of t
h
e PM
SM
in
the
r
o
tor
re
fere
nc
e fram
e
(d
–
q
) i
s
gi
ven
by
[3
-
4]
:
f
r
q
d
q
s
d
r
q
r
d
s
q
d
w
i
i
L
R
L
w
L
w
L
R
v
v
p
p
*
0
*
*
(1)
R
s
: Stator
resista
n
ce.
L
L
q
d
,
: d,
q a
x
es
inductances.
f
: Perm
an
en
t mag
n
e
t
flux
linkag
e
.
i
d
,
i
q
: S
t
a
t
or
cu
rr
en
t .
v
d
,
v
q
: Stator
v
o
ltag
e
.
w
r
: Ro
tor an
gu
lar
v
e
lo
city.
Tran
sf
orm
i
ng (
1
)
f
r
om
d-
q t
o
α
-
β
co
o
r
di
nat
e
,
t
h
e fol
l
o
wi
n
g
vol
t
a
ge
eq
uat
i
o
n (2
)
a
r
e gi
ve
n by
:
e
i
L
L
w
pi
L
i
R
v
e
i
L
L
w
pi
L
i
R
v
q
d
r
d
s
q
d
r
d
s
(2)
Whe
r
e
e
and
e
are
phase BEMF
and,
)
(cos
)
)(
(
)
sin
(
)
)(
(
r
f
r
q
d
r
q
d
r
f
r
q
d
r
q
d
w
i
i
w
L
L
e
w
i
i
w
L
L
e
p
p
p
p
(3)
Whe
r
e
v
,
v
are
α
axis a
n
d
β
a
x
i
s
v
o
l
t
a
ge c
o
m
pone
nt
s,
i
α
and
i
,
i
are
α
ax
is
a
n
d
β
a
x
is
cu
rr
en
t
com
pone
nt
s,
r
is ro
tor an
gu
lar, p
is th
e
d
i
fferen
tial o
p
e
rator(=d
/d
t).
B
a
sed
on
(
2
)
,
t
h
e m
a
t
h
em
at
i
c
al
m
odel
s
of
P
M
SM
u
nde
r t
h
e st
at
i
onary
(
α
,
β
)
refe
rence
f
r
a
m
e
s are:
v
v
L
E
E
L
L
i
i
L
R
L
L
L
w
L
L
L
w
L
R
i
i
d
d
d
d
s
d
q
d
r
d
q
d
r
d
s
dt
d
dt
d
1
1
0
0
1
(4)
The ge
nerat
e
d
el
ect
ro
m
a
gneti
c t
o
rque
(
T
e
) of
PM
SM
can
be ex
pressed
i
n
t
e
rm
s of st
ator
fl
u
x
linkage and current as:
i
i
T
p
e
2
3
(5)
For a uni
fo
rm
ai
r gap surface
-m
ount
ed PM
SM
m
o
t
o
r,
L
L
L
s
q
d
, t
h
e st
ate fl
ux l
i
nkage i
n
th
e
α
-
β
fram
e
can al
so be
gi
ve
n by
:
i
R
v
d
i
R
v
d
s
s
dt
dt
(6)
The am
pl
i
t
ude
of
t
h
e st
at
o
r
fl
u
x
l
i
n
kage
(
)is:
Evaluation Warning : The document was created with Spire.PDF for Python.
I
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8-8
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9
4
Fuzzy
Adaptive Control for
Direct Tor
q
ue
in
Electric Vehicle (Medjdoub k
h
ess
a
m)
55
9
2
2
s
(7)
The m
echani
c
a
l
dy
nam
i
c equa
t
i
on i
s
gi
ve
n
b
y
:
w
T
T
dw
r
L
e
r
f
p
d
t
J
(8)
Whe
r
e
T
e
is electro
m
a
g
n
e
tic to
rq
u
e
, p is po
le pairs ,J is th
e inertia o
f
PMSM
, f is
friction
facto
r
an
d
T
L
is
lo
ad
t
o
rqu
e
.
Usi
ng (
2
)
-
(
8
),
a dy
nam
i
c
m
o
d
e
l
of t
h
e PM
sy
nchr
on
ous m
o
t
o
rs can
be desc
ri
bed as:
w
T
T
dw
i
i
T
v
v
L
E
E
L
L
i
i
L
R
L
R
i
i
r
L
e
r
r
r
f
e
d
d
d
d
s
d
s
J
f
J
p
dt
p
dt
d
dt
d
sin
cos
2
3
1
1
0
0
1
0
0
(9)
3.
ELECTRIC T
R
ACTION
SYS
TEM ELEMENTS MODELING
Fi
gu
re 1 re
pre
s
ent
s
gene
ral
di
ag
ram
of
a
n
el
ectric traction system
using an perm
anent m
a
gnet
syn
c
hr
ono
us mach
in
es (
P
M
S
M)
supp
lied
b
y
vo
ltag
e
inv
e
r
t
er
[6
].
Figure
1. Electrical traction c
h
ain
3.
1.
Energy Sourc
e
The so
urce o
f
energy
i
s
general
l
y
a Li
t
h
ium
-Ion bat
t
e
ry sy
st
em
. Li
t
h
ium
-Ion bat
t
e
ry t
echnol
ogy
offers
adva
nt
ag
es of speci
fi
c e
n
ergy
, s
p
eci
fi
c powe
r
, an
d l
i
f
e over
ot
her t
y
p
e
s of rec
h
argea
b
l
e
bat
t
e
ri
es [7-8]
.
3.
2.
Inver
t
er Model
In
th
is electric
tractio
n
syst
e
m
, we
use an inverter to obt
ain three
balanced phases of alternating
current
wi
t
h
va
ri
abl
e
freque
nc
y
from
the current battery
[5]
.
S
S
S
U
v
v
v
c
b
a
dc
c
b
a
2
1
1
1
2
1
1
1
2
3
(10)
3.
3.
Vehicle Dynamics Analysis
B
a
sed o
n
pri
n
ci
pl
es of
ve
hi
cl
e
m
echani
c
s and a
e
r
ody
na
m
i
cs [2]
,
t
h
e
roa
d
l
o
a
d
F
res
can be
descri
bed
with accuracy via (1).
The powe
r
P
v
,
re
qui
red to
dri
v
e
a ve
hicle at a
v s
p
ee
d
has t
o
com
p
ensate the roed loa
d
Fw.
Evaluation Warning : The document was created with Spire.PDF for Python.
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56
0
F
F
F
F
P
aero
slope
roll
res
v
v
v
(11)
F
roll
:is th
e ro
llin
g
resistan
ce.
F
slope
:is the slope
re
sistance.
F
aero
:is the aerody
na
m
i
c drag.
Mg
F
roll
(12)
)
sin(
Mg
F
slope
(13)
2
0
2
1
v
A
C
F
v
f
x
aero
(14)
The forces ac
ting on the
vehicle are shown
in Figure
2.
Fi
gu
re
2.
F
o
rce
s
act
i
n
g
o
n
ve
h
i
cl
e
4.
THE
ELECTRIC
DIFFE
R
E
NTIAL
AND
ITS
I
M
P
L
EM
E
N
TA
T
I
ON
Figure
3
illustrates the im
plem
ented system
(electric and m
echan
ical com
pone
nts) i
n
the
Matlab
Sim
u
l
i
nk en
vi
r
onm
ent
.
The
pr
op
ose
d
c
ont
r
o
l
sy
st
em
pri
n
ci
p
l
e coul
d
be s
u
m
m
a
ri
zed as fol
l
o
w
s
:
(2
) A
cur
r
en
t
loop, ba
sed
on fuzzy m
ode cont
rol, is use
d
to cont
rol eac
h m
o
tor torque, The s
p
eed
of each rea
r
wheel is
cont
rol
l
e
d
usi
n
g s
p
ee
ds
di
ffe
r
e
nce
feed
bac
k
.
Fi
gu
re
3.
EV
p
r
o
p
u
l
s
i
o
n
an
d
cont
rol
sy
st
em
s schem
a
t
i
c
di
agram
Since the two rear wheels are direc
tly d
r
iven
b
y
two
separate
m
o
to
rs, t
h
e sp
eed
of the o
u
t
er
wh
eel
will requ
ire
b
e
i
n
g h
i
g
h
e
r t
h
an
th
e sp
eed of the in
n
e
r
wh
eel
du
ri
n
g
steering
man
e
u
v
e
rs (and
v
i
ce-v
e
rsa) [1
4
]
.
In
t
h
is case
h
o
wev
e
r can
be easily
m
e
t if a po
sitio
n
en
co
d
e
r is u
s
ed
t
o
sense th
e angu
lar
p
o
s
ition
o
f
the steering
wheel. T
h
e re
fe
rence s
p
ee
d
Wre
f is the
n
s
e
t by the acce
lerator
pe
dal command. T
h
e
actual
refe
rence
s
p
ee
d fo
r t
h
e l
e
ft
dri
v
e
Wref – l
e
f
t
and t
h
e ri
ght
dri
v
e
W
r
e
f
– r
i
ght
are t
h
en
o
b
t
a
i
n
ed
by
adj
u
st
i
n
g
th
e referen
ce sp
eed
Wref
u
s
i
n
g th
e
o
u
t
p
u
t
sig
n
a
l fro
m
th
e p
o
s
ition
en
coder.
If t
h
e v
e
h
i
cle is tu
rn
i
n
g ri
g
h
t
, th
e
left wheel spe
e
d
is i
n
crease
d
a
n
d the
right
wheel
spee
d rem
a
ins e
qual t
o
the
refe
re
nce s
p
ee
d
Wre
f.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
PED
S
I
S
SN
:
208
8-8
6
9
4
Fuzzy
Adaptive Control for
Direct Tor
q
ue
in
Electric Vehicle (Medjdoub k
h
ess
a
m)
56
1
If th
e v
e
h
i
cle is tu
rn
ing
left th
e rig
h
t
wh
eel sp
eed is increased a
nd the
left wheel speed rem
a
ins
equal t
o
th
e re
f
e
rence
spee
d
Wre
f
[9
-1
0]
.
M
ode
rn
cars
can’t
use
p
u
re
Ac
kerm
ann-
J
eant
a
u
d
st
eeri
n
g
,
part
l
y
bec
a
use i
t
i
g
no
re
s im
port
a
nt
d
y
n
a
m
i
c an
d
co
m
p
lian
t
effects, bu
t th
e
princip
l
e is soun
d
for low sp
eed
man
e
u
v
e
rs
[1
1]. It is illu
strat
e
d
in
Fi
gu
re 4.
Fi
gu
re
4.
D
r
i
v
i
n
g
t
r
a
j
ect
o
r
y
m
odel
The
differe
n
ce
betwee
n the
a
n
gula
r
s
p
eeds
of the
wh
eel
drives is expressed b
y
th
e relation:
w
L
d
w
w
v
w
w
mes
mes
w
tan
2
1
(15)
And t
h
e steeri
n
g a
ngle i
ndicat
es
the tra
j
ect
ory direction.
ahead
straight
right
turn
left
turn
...
0
...
0
...
0
(16)
In accordance
with the ab
ove desc
ribe
d equation, Fi
gure 5 sh
ow the
electric differe
ntial syste
m
bl
oc
k di
ag
ram
as
use
d
f
o
r si
m
u
l
a
t
i
ons
.
Fi
gu
re
5.
B
l
oc
k
di
ag
ram
sho
w
use
of
t
h
e el
ect
ro
ni
c di
ffe
re
nt
i
a
l
.
5.
FUZ
Z
Y
ADA
PTIVE PI
C
O
NTROL
AL
G
O
RITH
M
The
fuzzy
a
d
a
p
t
i
v
e P
I
c
ont
r
o
l
l
e
rs ha
ve bee
n
wi
del
y
ap
pl
i
e
d t
o
i
n
d
u
st
ri
al
pr
ocess
.
T
h
e a
ppl
i
cat
i
o
n o
f
th
is tech
n
i
qu
e
in
th
e sp
eed
con
t
ro
l o
f
EV is
sh
own
in
Fi
g
u
r
e
3. The a
ppl
i
c
at
i
on co
nt
ai
ns
t
w
o st
eps t
h
e
fi
rst
i
s
a sim
p
le PI controller and the second is fuzzy logic
co
n
t
ro
ller. th
e fu
zz
y adaptive control sy
ste
m
select the
p
a
ram
e
ter o
f
th
e PI co
n
t
ro
l
by th
e m
a
m
d
an
i
ru
les. Th
is
will b
e
produ
ce au
to
m
a
tic co
n
t
ro
l strateg
i
es for our
syste
m
.Th
e
fu
zzy ad
ap
tive PI
co
n
t
ro
ller is illu
strated in
Figu
re 6(a).
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN
:
2
088
-86
94
I
J
PED
S
Vo
l.
4
,
No
.
4
,
D
ecem
b
er
2
014
:
55
7 – 566
56
2
Fi
gu
re 6(a
)
.
St
r
u
ct
u
r
e of
fuzzy ada
p
tive PI c
o
ntroller
The e
x
pressi
on
o
f
t
h
e
PI
i
s
gi
v
e
n i
n
t
h
e
Eq
uat
i
on
(
1
7
)
.
t
i
p
dt
t
e
t
e
t
x
K
K
0
)
(
*
)
(
*
)
(
(17)
The m
e
m
b
ersh
i
p
fu
nct
i
o
n us
ed by
f
u
zzy
cont
rol
l
e
r are defines as
Nega
tive large (NL
)
, Ne
gative
Sm
a
ll (NS),
n
e
g
a
tiv
e m
e
d
i
u
m
(NM
)
,
Zero
(Z), Po
sitiv
e
Sm
a
ll (PS), an
d Positiv
e Big
(PB).
The c
o
ntrol rul
e
s are
fram
ed
t
o
ac
hi
eve
t
h
e
b
e
st
pe
rf
orm
a
nce o
f
the fuzzy
cont
roller. T
h
e
s
e rules a
r
e
gi
ve
n i
n
t
h
e
Ta
bl
e 1
an
d
2.
Tabl
e 1. K
p
F
u
zzy
cont
rol
rul
e
Tabl
e 2. Ki
F
u
zzy
cont
rol
rul
e
e(
w)
de(
w
)
NL
NM
NS
Z
PS
PM
PB
N
L
M
S
M
S
M
L
Z L
M
L
Z
L
M
L
P L
M
L
Z
L
M
L
e(w)
de(
w
)
NL
NM
NS
Z
PS PM
PB
N Z
S
M
L
M
S
Z
Z Z
S
M
L
M
S
Z
P Z
M
L
L
L
M
Z
the
s
u
rface view of Kp, Ki
a
r
e
shown
i
n
Figure
6(b), and
6(c), res
p
ectivel
y.
Figure 6(b). Surface view
of Kp
Figure 6(c
)
.
Surface view
of Ki
6.
F
U
ZZY
DIRECT
TORQ
UE
CO
NTR
O
L
In
th
is presen
t p
a
p
e
r,
We p
r
esen
t DTFC of PMSM d
r
iv
e con
t
ro
lled
b
y
fu
zzy Ad
ap
tiv
e PI is th
e let
t
er
i
s
ge
neral
l
y
ba
sed
o
n
cl
assi
ca
l
DTC
sc
hem
e
.
A bl
oc
k di
a
g
ra
m
of t
h
e pr
op
o
s
ed d
r
i
v
e sche
m
e
i
s
i
l
l
u
st
rat
e
d i
n
Fi
g
u
re 3
.
I
t
coul
d be see
n
t
h
at
i
t
has
structure
similar to t
h
e classi
cal DTC
sche
mes, while
the hystersis
controllers
a
r
e re
placed
by
a s
i
ngl
e
fuzzy
c
o
ntrolle
r.
The
fuzzy
c
ont
roller
is a
M
a
m
d
an
i typ
e
with two
inpu
ts and
on
e
ou
tpu
t
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
PED
S
I
S
SN
:
208
8-8
6
9
4
Fuzzy
Adaptive Control for
Direct Tor
q
ue
in
Electric Vehicle (Medjdoub k
h
ess
a
m)
56
3
It recei
ves
two i
n
puts
of t
o
rque e
r
ror
(
e
T
)
f
l
ux
er
ro
r
(
e
)
an
d fu
zzity the
m
with
ad
equ
a
te
num
ber
o
f
f
u
zzy
su
bset
s.
The
n
,
base
d
o
n
t
h
e
p
r
o
v
i
d
e
d
fuzzy re
asoning
rules,
for eac
h
state
of flux
and t
o
rque
e
r
ror
val
u
es
the m
o
st appropriate
cont
ro
l sig
n
a
l
is
chosen
,
u
s
ed
alon
g
w
ith
stator
f
l
ux
p
o
s
ition
section
t
o
ind
e
x
grid
of
op
ti
m
u
m vo
ltag
e
v
ectors.
A
fu
zzy l
o
gic co
n
t
ro
ller is
ap
p
l
ed
to th
e
direct
t
o
r
que c
ont
rol
s
[1
1]
-[
12]
so a
s
t
o
m
i
nim
i
zethe t
o
r
q
ue ri
p
p
l
e
and t
o
m
a
xi
m
i
ze t
h
e dri
v
e effi
ci
ency
. T
h
e
m
a
jor
pr
o
b
l
e
m
wi
t
h
swi
t
c
hi
n
g
t
a
bl
e base
d
DTC
dri
v
e i
s
hi
g
h
t
o
r
q
ue a
n
d
cu
rr
ent
ri
ppl
es.
T
o
i
n
crease
p
r
eci
si
on
o
f
trad
itio
n
a
l DTC o
f
in
du
ction
m
o
to
r con
t
ro
l
an
d
d
e
creas
e
large torque ri
pple, a fuzzy DTC control syste
m
along with a
fuzzy adaptive
P
I
co
nt
r
o
l
l
e
r i
s
pr
o
pose
d
.
Fi
g
u
r
e 3 s
h
ows t
h
e
cont
rol
sc
hem
e
wi
t
h
t
h
e
fuzzy
l
ogi
c
co
n
t
ro
ller
wh
ich
m
o
d
i
fies th
e DTC b
y
in
co
rpo
r
ating
fu
zzy lo
g
i
c in
t
o
it. A
Fu
zzy log
i
c meth
od
is u
s
ed
in
th
is
st
udy
t
o
i
m
pro
v
e t
h
e st
ea
dy
st
at
e perf
orm
a
nce of a c
o
n
v
e
n
t
i
onal
DTC
sy
st
em
.Fi
g
6.
d s
c
hem
a
t
i
c
al
l
y
sho
w
s a
direct torque fuzzy cont
ro
l in which the fuzzy cont
rolle
rs re
place th
e flux linka
ge and torque
hysteresis
cont
rollers
.
The s
w
itching table is the sum
e
as the one used
i
n
a co
nve
nt
i
o
nal
DT
C
sy
st
em
[13]
. B
a
si
cal
l
y
, a
Fuzzy
controller
is c
o
m
pose
d
of
a
fuzzification
part
,
a
fuzzy
infere
nc
e part
and a defuzzi
fi
cat
i
o
n part
.
T
h
e
i
n
p
u
t
m
e
m
b
e
r
s
h
i
p
f
u
n
c
t
i
o
n
s
f
o
r
t
h
i
s
f
u
z
z
y
c
o
n
t
r
o
l
l
e
r
a
r
e
s
h
o
w
n
i
n
F
i
g
u
r
e
4
.
I
t
c
o
u
l
d
b
e
c
l
e
a
r
l
y
seen
th
at to
rqu
e
erro
r is fuzzified
in
to
fi
ve fu
zzy su
b
s
ets o
f
EZ (zero), SP (sm
a
l
l
p
o
s
itiv
e), SN (smal
l
n
e
g
a
tiv
e), LP
(larg
e
p
o
sitiv
e), LN (larg
e
neg
a
tiv
e),
MP (mean
p
o
s
itiv
e), MN (m
ean
n
e
g
a
tiv
e), in
o
r
d
e
r to
pr
o
v
i
d
e
a pr
ope
r
t
o
rq
ue
cont
rol
by
us
i
n
g
ze
ro
v
o
l
t
a
ge vect
o
r
s.
Ad
di
t
i
onal
l
y
, t
h
i
s
m
e
t
hode enabl
e
s
m
o
re app
r
op
ri
at
e co
nt
rol
act
i
ons
t
o
be
t
a
ken
f
o
r t
h
e
s
m
al
l
and l
a
r
g
e t
o
r
q
ue e
r
r
o
r
val
u
es.
In
t
e
r
m
s o
f
flux
erro
r, three m
e
m
b
ersh
ip
fun
c
tion
s
o
f
EZ
(
zero
)
, SP (sm
a
ll p
o
s
itiv
e),
SN (sm
a
ll
n
e
g
a
tiv
e), LP
(larg
e
p
o
s
itiv
e), LN
(larg
e
n
e
g
a
tiv
e), MP (m
ean
p
o
s
itiv
e), MN
(m
ean
n
e
g
a
tiv
e), are em
p
l
o
y
ed
.
Actu
ally, th
e
fu
rt
he
r s
u
bset
s
f
o
r
fl
u
x
e
r
r
o
r i
n
p
u
t
w
oul
d
be
exce
ssi
ve
and
w
o
ul
d a
d
d
t
o
t
h
e
sy
st
em
com
p
l
e
xi
t
y
.
Fi
gu
re 6(
d
)
. In
put
m
e
m
b
ershi
p
f
u
nct
i
ons
f
o
r (a
) T
o
rq
ue e
r
ro
r,
(
b
)
Flu
x
e
r
r
o
r
Fi
gu
re 6(e
)
.
O
u
t
p
ut
m
e
m
b
ershi
p
f
unct
i
o
ns f
o
r
re
fere
nce vo
l
t
a
ge
As m
e
nt
i
oned
earl
i
e
r, fo
r a com
p
l
e
t
e
fuzzy
cont
r
o
l
schem
e
besi
des fuzzy
i
n
put
s
and
out
put
s
,
a list of
fuz
z
y
rules
is als
o
require
d
which
basica
lly relates each s
e
t of
possible
inputs
state to t
h
e
p
r
o
p
e
r
o
u
t
p
u
t
.
I
n
t
h
i
s
c
o
n
t
r
o
l
s
c
h
e
m
e
,
a
f
u
z
z
y
i
n
f
e
r
e
n
c
e
s
y
s
t
e
m
w
i
t
h
f
o
r
t
y
-
n
i
n
e
f
u
z
z
y
r
u
l
e
s
i
s
e
m
ployed.
In
fact, t
h
e
number of
re
qui
re
d
fuz
z
y ru
les
could
be
eas
ily calculated
from
the pres
ented
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN
:
2
088
-86
94
I
J
PED
S
Vo
l.
4
,
No
.
4
,
D
ecem
b
er
2
014
:
55
7 – 566
56
4
fuzzy s
ubsets
for each
one
of the i
n
puts
. A desira
bl
e fuzzy control
would
be
s
e
ized just with the
conve
r
ge
nce
of all
possibl
e
in
pu
t states.
Th
e list
o
f
p
r
ov
id
ed
fu
zzy
ru
l
e
s is sho
w
n
i
n
Tab
l
e
3.
Tabl
e
3.
Sh
o
w
s t
h
e t
a
bl
e p
r
op
ose
d
f
o
r t
h
e
sel
ect
i
on
of
t
h
e
a
ngl
e
P
Z
N
T
P Z
N
P Z
N
P
Z
N
3
0
3
2
2
2
3
2
3
2
Table
4. List
of fuzzy in re
ference
rules
e
e
T
NG
NM
NP
EZ
PP
PM
PG
NG
PG
PM
PS PS PS
PM
PG
NM
PG
PM
PS PS PS
PM
PG
NP
PG
PM
PS
EZ
PS PM
PG
EZ
PG
PM
PS
EZ
PS PM
PG
PP
PG
PM
PS
EZ
PS PM
PG
PM
PG
PM
PS PS PS
PM
PG
PG
PG
PM
PS PS PS
PM
PG
7.
SIMULATION
RESULTS
In
or
der t
o
c
h
a
r
act
eri
ze t
h
e d
r
i
v
i
ng
wh
eel
sy
st
em
behavi
o
r
,
sim
u
l
a
t
i
ons w
e
re carri
e
d
usi
ng t
h
e m
odel
o
f
Figu
r
e
3. Th
ey show
m
o
to
r
cur
r
e
n
t
an
d th
e
v
a
r
i
atio
n of spee
d fo
r
ea
ch
m
o
tor. The
follo
win
g
res
u
lts
was
si
m
u
lated
in
M
A
TLAB
7.
1.
Str
a
igh
t
Road
In
th
is step
th
e sp
eed
o
f
th
e EV is equ
a
l 6
0
Km
/h
. Th
e Fig
u
re 5
sh
ows th
at th
e sp
eed
of EV
h
a
s
two
ph
ases t
h
e
first is
b
e
tween [0
4
]
s th
e secon
d
i
s
bet
w
ee
n [
4
5]
s wi
t
h
s
p
ee
d e
qual
8
0
Km
/
h
.
As we rem
a
rk the spee
d of the tow bac
k
wheel
s are equal this improve that the electronic
d
i
fferen
tial d
o
esn
’
t
work
i
n
th
is cas
e.Whe
n
we apply resistive torques a
t
3
s
th
e figu
re sh
ows th
at the o
n
l
y
chan
ge
d i
s
i
n
t
h
e di
rect
t
o
r
q
ues, t
h
e d
e
vel
ope
d m
o
t
o
r t
o
rq
ue i
s
not
i
c
e
d
. T
h
e sl
ope
e
ffect
re
sul
t
s
i
n
hi
g
h
i
m
p
r
ov
em
en
t i
n
th
e electro
mag
n
e
tic m
o
to
r
to
rq
u
e
, bo
th
on
t
h
e l
e
ft
a
nd t
h
e ri
ght
of eac
h m
o
t
o
r. T
h
e s
y
st
em
b
e
h
a
v
i
or
is
illustrated
b
y
Figure 7
(
a), 7
(
b), 7
(
d
)
,
an
d
7(e). R
e
sistiv
e to
rqu
e
s are
sho
w
n in
Fig
u
re
7
(
f) .
Fi
gu
re 7.
St
rai
ght
r
o
ad
0
1
2
3
4
5
0
20
40
60
80
te
m
p
s
[
s
]
V
R
[K
m/h
]
F
i
g
.
7a
– R
i
ght
W
h
eel
s
pee
d.
0
1
2
3
4
5
0
20
40
60
80
te
m
p
s
[
s
]
V
L
[K
m/h
]
F
i
g
.
7b –
L
e
f
t
W
hee
l
s
p
eed
0
1
2
3
4
5
0
50
100
150
te
m
p
s
[
s
]
Te
R
[N
.m]
F
i
g.
7c
–
R
i
g
h
t
m
o
t
o
r
E
l
ec
t
r
om
agne
t
i
c
T
o
r
q
ue.
0
1
2
3
4
5
0
50
10
0
15
0
te
m
p
s
[
s
]
Te
L
[N
.m]
F
i
g.
7
d
–
Le
f
t
m
o
t
o
r
E
l
ec
t
r
om
a
gnet
i
c
To
r
q
u
e
.
0
1
2
3
4
5
0
20
40
60
80
te
m
p
s [s]
V
h
[K
m
/
h
]
F
i
g.
7e
–
V
e
hi
c
l
e s
p
e
e
d
0
1
2
3
4
5
0
50
10
0
15
0
te
m
p
s
[s
]
v
ehi
c
l
e r
e
s
i
s
t
ant
t
o
r
q
u
e
[
N
.
m
]
F
i
g
.
7f
–
R
es
i
s
t
i
v
e
T
orqu
es
.
0
1
2
3
4
5
0
50
10
0
15
0
te
m
p
s [s]
Te
R
L
[N
.m
]
F
i
g
.
7
g
– E
l
ec
t
r
o
m
ag
ne
t
i
c
T
orqu
e.
-0.
1
-0
.
0
5
0
0.
0
5
0.
1
-0.
1
-0.
05
0
0.
05
0.
1
F
l
u
x
A
[W
b
]
F
l
ux
B
[
W
b]
F
i
g.
7h
-
F
l
ux
Evaluation Warning : The document was created with Spire.PDF for Python.
I
J
PED
S
I
S
SN
:
208
8-8
6
9
4
Fuzzy
Adaptive Control for
Direct Tor
q
ue
in
Electric Vehicle (Medjdoub k
h
ess
a
m)
56
5
7.
2.
Cur
v
ed Road on the Ri
ght
at
Spe
ed of
60km/h
Th
e v
e
h
i
cle is d
r
iv
i
n
g
o
n
a
cu
rv
ed
ro
ad
on
th
e righ
t side with
6
0
k
m
/h
. In
th
is case
th
e d
r
i
v
ing
wheel
s
f
o
l
l
o
w
di
ffe
re
nt
pat
h
s,
an
d t
h
ey
t
u
r
n
i
n
t
h
e
sam
e
di
re
ct
i
on
but
wi
t
h
di
ffe
re
nt
spee
d
s
.
At tim
e equal 4s
(st
r
aight road)
we c
h
a
n
ge t
h
e
s
p
ee
d
to
80Km
/h. In t
h
is step the electronic
di
ffe
re
nt
i
a
l
change t
h
e spee
d
of t
h
e t
w
o m
o
t
o
r
by
decrea
sing t
h
e spee
d of the drivin
g
wheel on the ri
ght side,
and
i
n
c
r
ease t
h
e spee
d
of
t
h
e l
e
ft
w
h
eel
. T
h
e
beha
vi
o
r
of
t
h
e
s
e spee
ds
i
s
gi
ven
by
Fi
g
u
re
8(a
)
,
8
(
b
)
a
n
d
8(c
)
.
On
ce th
is
sp
eed
stab
ilizes, th
e t
o
rq
u
e
ret
u
rn
s to its in
itial v
a
lu
e wh
ich
co
rresp
ond
s to
t
h
e to
tal
resi
st
i
v
e t
o
rq
ue
ap
pl
i
e
d
on
t
h
e
m
o
t
o
r w
h
eel
s;
t
h
e
beha
vi
o
r
i
s
sh
ow
n i
n
Fi
g
u
r
e
8(
f).
Figu
re 8.
Cu
rv
ed roa
d
o
n
the
rig
h
t
8.
CO
NCL
USI
O
N
O
u
r
stud
y is d
e
p
e
nd
o
n
t
h
e speed
con
t
ro
l of
th
e EV
th
rou
gh lef
t
o
r
r
i
gh
t ro
ad. Th
is p
a
p
e
r
pr
opo
sed
a
Ada
p
t
i
v
e
Fuz
z
y
Logi
c
bas
e
d S
p
ee
d C
o
nt
r
o
l
O
f
PM
S
M
. The
p
r
o
p
o
se
d co
nt
r
o
l
m
e
t
hod c
o
nsi
d
ers
t
h
e
di
st
ur
ba
nce i
n
put
s
re
prese
n
t
i
n
g
t
h
e
sy
st
em
n
onl
i
n
ea
ri
t
y
or
t
h
e
u
n
m
odel
l
e
d u
n
cert
a
i
n
t
y
t
o
gua
ra
nt
ee t
h
e
ro
b
u
st
ness
u
n
d
e
r m
o
t
o
r para
m
e
t
e
r and l
o
a
d
t
o
r
q
ue va
ri
at
i
ons
. Si
m
u
l
a
t
i
on a
n
d ex
peri
m
e
nt
al
resul
t
s
cl
earl
y
dem
onst
r
at
ed t
h
at
t
h
e pr
o
pos
ed co
nt
r
o
l
sy
st
em
can not
on
ly at
ten
u
a
te th
e ch
attering
to
th
e ex
ten
t
of o
t
h
e
r
cont
rol
m
e
t
hod
s (e.
g
.
,
P
I
c
o
nt
r
o
l
,
fuzzy
c
o
nt
r
o
l
,
et
c.
)
but
ca
n al
s
o
gi
ve a
b
e
t
t
e
r t
r
ansi
e
n
t
per
f
o
r
m
a
nce.
REFERE
NC
ES
[1]
AT lemcan
i, O
Bouchhida, K B
e
nmans
our, D B
oudana, MS Boucherit. Direct
Torque
Con
t
rol Strat eg
y
(DT
C
)
Based on Fuzzy Logic Controller for
a Perm
an
ent Magn
et S
y
n
c
hronous Machine Drive.
Journ
a
l of
Elect rical
Engineering &
T
echno
logy
. 200
9; 4(1): 66~78.
[2]
Ali Arif, Achou
r Betka, Abderezak Guett af
. I
m
provement
t he DTC S
y
st
em for Elect r
i
c V
e
hicles Induct ion
Motors.
serbian Journal
Of El
ect
Ri
cal
Engin
eer
i
ng.
2010; 7(2): 1
49- 165.
[3]
P Pragasen, R
Krishnan. Modeling, Simu
lat io
n,
andAnaly
s
i
s of
Permanent
M
a
gnet s Motor
Drives, Part I:
The
Permanent Magnets S
y
nchronou
s Mot or
Drive.
IEEE Transact ion s on
Industry
Applicat ions
. 1
989; 25(2): 265-
273.
[4]
MA Rahman, R
Qin. A perman
ent magnet h
y
s
t
eresis
h
y
b
r
id s
y
n
c
hronous motor
for electr
i
c vehicles.
IE
EE T
ran
s
.
Ind. Electron
. 19
97; 44(1): 46- 53
.
[5]
A Nasri, A Hazzab, IK Bousserhane, S Hadjer
i, P Sicard.
T wo W
h
eel Speed Robust Sliding Mode Cont rol For
Ele
c
tri
c
Veh
i
cl
e
Drive.
S
e
rbian
Journal of
Elect rical Engin
eering
.
2008; 5(2):199-
216.
[6]
H Yoichi, T Yasushi, T Yoshim
asa. Tra
c
t ion Co
ntrol of El
ec
t
ric Ve
hi
c
l
e:
Ba
si
c
E
xpe
riment
al R
e
sult s using the
Test
EV. UOT
E
l
ec
t ri
c m
a
r
c
h.
I
EEE. Transactio
ns on I
ndustry Applicat
ions
. 199
8; 34(5): 1131
–
1138.
[7]
M Young. Th
e
Technical Writ
er
's Handbook. Mill Valley
, CA:
U
n
iversit
y
Science, 1989
.
[8]
H Il Song
Kim
A. Non Linear St ate of Charg
e
Es
t i- mator for Hy
brid Electric Vehicle Batt er
y
.
I
EEE T
r
ansactio
ns
.
2008; 23(4): 202
7-2034.
[9]
S Kandler, CY Wang. Power and t hermal
char
a
c
t eri
z
a
tion of Li
thium
-Ion batter
y
pa
ck for h
y
b
r
i
d
elec
tri
c
vehi
cl
es
,
Elservier,
Power
Sources
. 2006;
160: 662- 673
.
[10]
RE Coly
er et al.
Comparison of st eering geo
m
etries for mult
i-wheeled vehicles by
modelling and simulation.
Proceedings of
I
EEE
CDC'
98
. 19
98; 3:
3131-313
3.
[11]
Kada HARTANI,
Mohamed BO
URAHLA,
Yahia MILOUD,
Mohamed SEKOUR.
Electroni
c
Different ial wit h
Direct Torqu
e
Fuzzy
Con
t
rol for
Vehicle Propulsion Sy
stem.
T
urk J Elec Eng
&
C
o
mp
Sci
. 2009; 17(1): T
¨
U
B
˙
ITAK doi:10.39
06/elk-
0801-1.
0
1
2
3
4
5
0
20
40
60
80
te
m
p
s [s]
V
L
[K
m
/
h
]
F
i
g.
8
a
– l
e
f
t
W
h
eel
s
peed.
0
1
2
3
4
5
0
20
40
60
80
te
m
p
s
[s
]
V
R
[K
m
/
h
]
F
i
g.
8b –
R
i
ght
W
heel
s
p
e
ed.
0
1
2
3
4
5
0
20
40
60
80
te
m
p
s [s]
Te
L
[N
.m
]
F
i
g
.
8c
–
Lef
t
m
o
t
o
r
E
l
ec
t
r
om
agne
t
i
c
T
o
r
que.
0
1
2
3
4
5
0
20
40
60
80
te
m
p
s
[s
]
Te
R
[N
.m
]
F
i
g.
8d
– R
i
ght
m
o
t
o
r
E
l
ec
t
r
om
agn
et
i
c
T
o
r
q
ue.
0
1
2
3
4
5
0
20
40
60
80
t
e
m
p
s[
s]
V
h
[K
m
/
h
]
F
i
g.
8e – V
ehi
c
l
e s
peed i
n
r
i
ght
t
u
r
n
i
n
cu
r
v
e
d
w
a
y.
0
1
2
3
4
5
0
20
40
60
80
te
m
p
s
[s
]
v
ehi
c
l
e r
e
s
i
s
t
ant
t
o
r
que
[
N
.
m
]
F
i
g.
8f
– R
e
s
i
s
t
i
v
e T
o
r
ques
i
n
r
i
ght
t
u
r
n
i
n
cu
r
v
e
d
w
a
y.
0
1
2
3
4
5
0
20
40
60
80
te
m
p
s
[s
]
Te
R
L
[N
.m
]
F
i
g.
8g -
E
l
ec
t
r
om
agnet
i
c
T
o
rque
-0.
1
-0
.
0
5
0
0.
05
0.
1
-0
.
1
-0
.
0
5
0
0.
05
0.
1
F
l
ux
A
[
W
b]
F
l
ux
B
[
W
b]
F
i
g.
8h -
F
l
ux
VL
VR
VH
aer
o t
o
r
que
s
l
op t
o
r
que
ti
r
e
to
r
q
u
e
to
ta
l
t
o
r
q
u
e
Te
R
Te
L
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
SN
:
2
088
-86
94
I
J
PED
S
Vo
l.
4
,
No
.
4
,
D
ecem
b
er
2
014
:
55
7 – 566
56
6
[12]
Cao, Xianq
i
ng,
Zang, Chunhu
a, Fan,
Lip
i
ng. Direct
Torque Con
t
rolled
Drive for
Permanent Mag
n
et S
y
n
c
hronou
s
Mot or Based o
n
Neural Net w
o
rks and Mult i
Fuzz
y
Con
t
roll
ers
. IEEE Int
ernat ional Conference on
Robot ics
andBiomimetics
. ROBIO ’06. PD
CA12-70 data sh
eet. Opt o Spee
d
SA, Mezzovico
, Swit zerland. 20
06; 197-201.
[13]
M Vasudevan,
R Arumugam.
New direct
torque contro
l
s
c
heme
of indu
ction
mot
o
r
for
el
ectr
i
c
v
e
hic
l
es
. 5
th
Asian
Control Conf
erence. 2004; 2: 13
77-1383.
[14]
Ali Jafari
an Abianeh
and Hew Wooi Ping.
Simulation Studies of Optimized
Classic
a
l Direc
t
Torque
Fuzzy
Controlled Drive for Permanen
t
Magnet Synchr
onous Motor.
2n
d Int ernat ional
Conference on Indust rial Mechat
ronics and
Auto
mation.
978- 1
-
4244-7656- 5/10
/$26.00 ©2010
I
EEE.
[15]
A Haddoun, ME
H Benbouzid, D Diallo, R
Abdessem
e
d, J
Ghouili, K Srairi
. Slid
ing Mode Contro
l of EV Electric
Different
ial
Syst
em
.
author manu
script pub
lished
in ICEM’06,
chaina greece. 2006; 00527546, version2- 11-2010
[16]
R Arulmozhiy
al, K Baskaran
. I
m
plementation
of a Fuzzy
PI
Controller
for Speed Contro
l of
Induction Motors
Using FPGA.
Journal
of Power Electronics.
201
0; 10: 65-71.
[17]
D Zhang
, et al
.
Common
Mode
Circulating Curr
ent Control of I
n
terl
eaved
Three
-
P
h
as
e Two-Lev
e
l Voltag
e
-S
ource
Converters with
Discontinuous Space-Ve
ctor Mo
dulation
.
I
E
EE
E
n
ergy Conversio
n
Congress and Exposition
.
2009
;
1-6
:
3906-3912.
[18]
Z Yinhai
, et
al.
A Novel SVPW
M Modulation S
c
heme.
Appl
ied
Power Electron
i
cs Conference and Exposition, 2
009.
APEC. Twenty
-
F
ourth A
nnual I
EEE. 2009: 128-
131.
BIOGRAP
HI
ES OF
AUTH
ORS
M
e
djdoub
K
h
e
ssam
was born in 1984 at Naama-Algeri
a he’s receiv
e
d the electrical
engineering d
i
ploma from Bechar University
, A
l
geria in 2009
, and the Master d
e
gree from the
University
Sid
i
bel abbes Alge
ria in 2011. He’s currently
prep
ar
ing his Ph.d. degree in electr
ic
vehicles s
y
s
t
em
control.
H
a
zzab A
bde
ld
je
bar
receiv
e
d the state eng
i
neer degree
in
electrical eng
i
neer
ing
in 1995 from
the University
o
f
Sciences and Technolog
y
of
Oran (USTO), Alg
e
ria th
e M.Sc. degree from the
Electrical Engin
eering Institute
of the USTO in
1999, and
the P
h
.D. degr
ee fro
m the Electr
i
cal
Engineering Institute of th
e USTO in 2006. H
e
is
curren
t
ly
prof
essor of electr
i
cal
engineering
at
Univers
i
t
y
of B
echar
, Bech
ar,
Algeria
,
where
he
is
Director
of the Res
earch
Laborator
y of
Control Anal
ys
i
s
and Optim
izat
ion of the
El
ec
tro-Energ
e
ti
c S
y
s
t
em
s
.
His
res
earch
int
e
res
t
s
includ
e power elec
troni
cs, e
l
ec
tric driv
es
control
,
and a
r
tifi
c
ia
l inte
llig
ence and
thei
r
applications.
Bouc
hiba Bou
s
maha
was bor
n in 1977 at
Bechar-Alg
eri
a
, he’s received
the elec
tri
c
a
l
engineering d
i
ploma from Bechar University
,-Algeria in
1999, an
d the Master
deg
r
ee
from the Univ
er
sity
Alex
andria
Eg
y
p
t
in 2006
and the Ph.D. deg
r
ee
f
r
om the Electrical
Engineering I
n
stitute of
the SDB in 2011. Curren
t
l
y
, h
e
is an
assist
ant prof
essor at
Bechar
Univers
i
t
y
.
wher
e he
is
m
e
m
b
er
of the
Re
search
Laborator
y
of
C
ontrol Analy
s
is
and
Optim
izatio
n of
the
Ele
c
tro-
Ener
geti
c S
y
s
t
em
s
.
H
i
s
res
ear
ch
inte
re
s
t
s
includ
e powe
r
el
ect
ronics
,
ele
c
tri
c
dr
ives
c
ontrol,
and
art
i
fi
cia
l
in
tel
ligen
ce
and th
eir
appl
ic
a
tions
.
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