Bat Optimization for EV Energy Management

∗1∗2∗3
∗
1 2
3
µ
µ
µ
µ

C(Pfc +Psc) = C1Pc=C2Pi=C3Pm=Pv
Pfc Psc c PiPmPv
C C1C2C3
ECR
ECR =1.1×10−7·E
L
L E
×−7
ES
F1
F1
Fd
FrFc
F1=Fd+Fr+Fc
Fd=1
2ρ·Cd·S·v2
Cdρ
3 2
v
Fr=M·g·Cr
M g
Cr

Fc=M·g·sin φ
φ
PLoad
PLoad =v
3600 (1 2ρ·Cd·S·v2+M·g(Cr+ sin φ))
PES
PES (t) = 1
CPLoad(t)
Preq
Kbat(t)
Ksc(t)
Pfc(t) = Kf c(t)·PES (t)
Psc(t) = Ksc(t)·PES (t)
Kfc(t)Ksc(t)
Kfc(t)Ksc(t)
Pfc(t)Psc(t)
SOC
SO
SOC
SOCfcmin ≤SOCf c(t)≤SOCfcmax
SOCscmin ≤SOCsc(t)≤SOCscmax
SOC
SOC
Min
∈[0,T ]{ECR|Kbat(t),Ksc (t)}
PEX (t) = 1
CPLoad(t), t ∈[0, T ]
Kfc(t)
Ksc(t)
vixi
fmin λ
A0
r∈[0,1]
A0Amin
f
[fmin, fmax]
[λmin, λmax]
λ
f∈[0, fmax]
λ f λf =v
xi

vi
xt
ivt it
fi=fmin + (fmax −fmin)β
vt
i=vt−1
i+ (xt
i−x∗)fi
xt
i=xt−1
i+vt
i
β∈[0,1]
n
xnew =xold +ϵAt
ϵ∈[−1,+1] At=<
At
i>
At+1
i=αAt
i
rt+1
i=r0
i(1 −exp(−γt))
α γ
SOCfc SOCsc
Kfc
Kfc Pfc
Psc
ECR
ECR
A0
ir0 iα γ
Objectivefunction :M in
∈[0,T ]{ECR|Kbat(t),Ksc (t)}
which : 0.4≤SOCf c(t)≤0.8
0.3≤SOCsc(t)≤0.7
Objective function f (x), x = [x1, x2, ..., xn]t;
Initialize the bat population xi(i= 1,2, ..., n)and vi;
Def ine pulse f requency riat xi;
Initialize pulse rates riand the loudness Ai;
(t < Max number of iterations)
Generate new solutions by adjusting frequency,
And updating velocities and locations (13);
(rand > ri)
Select a solution among the best solutions,
Generate a local solution around the selected
best solution,
Generate a new solution by f lying randomly
(rand < Ai&f(xi)< f (x∗))
Accept the new solutions,
Increase riand reduce Ai,
Rank the bats and find the current best x∗,
P ost −process results and visualization.
n fmin fmin A0
ir0 iα γ
2
−5
× ×

×
±
SOCfc
SOCsc
ECR
ECR ECR
ECR
nd