research: simulate operating ev-taxi fleets
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Operating Electric Taxi Fleets: a New Dispatching Strategy with Charging Plans
@2012 IEEE International Electric Vehicle Conference (IEVC2012)
Jun-Li Lu Mi-Yen Yeh Ming-Syan Chen
Yu-Ching Hsu Shun-Neng Yang Chai-Hien Gan
Outline
• Introduction to EV-taxi fleets
• System
• Experiment results & conclusion
2
Background
• Environment• In Tokyo, taxis occupied 2% of cars but produced 20% emissions, 2011/2
• Electricity’s energy efficiency is high• In Taiwan (20.1% > 14.6%)
• Promotion on EV-taxis• Trails at Tokyo, San Francisco, Hanover, Beijing etc.
• Fit green policy
3
Observation on EV-taxi
• Long driving distance• a taxi runs 186 miles per operation day, Taipei
• Long charging time• Quick-charging: 80% power about 30 mins; Slow charging: 6-8 hours
• Battery switch: 5-10 mins
• A commercial EV taxi need recharge 1-2 times a day by referencing [1]
To reduce time on charging,i.e. to reduce EV charging by battery.
4
Related work
• EV’s reachability• Relating to remaining power, power consumption rate, and traffic conditions,…
• Traditional taxi fleets• Minimize communication between taxi driver and center
• Automatical process to reduce mistakes
• Reduce the dispatching time by distributed computing
• Select taxi based on first response or shortest distance to client location
=> Mainly considering on client-request
5
System
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Charging station 1
Charging station 2…
Battery switch station 1
Battery switch station 2…
Clien
t 1
Clien
t 2
…
Clients
Dispatching center
Taxi
dispatching
decision
module
Availability analysis of
battery charging/ switching
stations module
Reachability
analysis module
E-tax
i 1
E-tax
i 2
…
Electric taxis
Data exchange interface
Phone/
Internet
/etc.
Radio/G
PS/etc.
Taxi demand
analysis module
Electric taxis
and drivers
info.
Dispatching flow7
Accept taxi requests
from the client
Select the taxis close to the
client as candidates, and
apply reachability analysis
Confirm the task. If necessary, make
charging plans to the dispatched taxi
End
ModulesStart
Type of charging
stations?
battery switch
Both battery
charging/switch
battery
charging
A B C
Taxi dispatching
decision module
Reachability analysis module
Electric taxis and drivers info.
Taxi dispatching decision module
Electric taxis and drivers info.
Availability analysis of battery charging/ switching stations
module
Taxi dispatching
decision module
Dispatching policy
• To reduce the affect of battery charging on available working time of taxi drivers
• To meet client requests as many as possible
• We consider two indexes, future taxi demand and availability of battery charging or switching stations, in the dispatching process
8
Dispatching strategy (1/3)
• When only batter charging stations exist
Taxi demand at destination 𝐿𝑑
at time 𝑡𝑑?
Dispatch the taxi with lowest
remaining power
High Low
Availability of battery charging stations
near destination 𝐿𝑑at time 𝑡𝑑?
Dispatch the taxi with highest
remaining power
High
Low
A
9
Dispatching strategy (2/3)
• When only batter switching stations exist
Dispatch the taxi with lowest
remaining power
High Low
Availability of battery-switch stations near
destination 𝐿𝑑at time 𝑡𝑑 ?
Dispatch the taxi with highest
remaining power
B
10
Dispatching strategy (3/3)
• When both batter charging
and switching stations exist
11
Taxi demand at destination 𝐿𝑑
at time 𝑡𝑑?
Dispatch the taxi with lowest
remaining power
Dispatch the taxi with highest
remaining power
High Low
High Low
Availability of battery-switch stations near
destination 𝐿𝑑at time 𝑡𝑑?
Availability of battery charging
stations near destination 𝐿𝑑
at time 𝑡𝑑?
Dispatch the taxi with highest
remaining power
LowHigh
Dispatch the taxi with lowest
remaining power
C
Taxi demand analysis
• Taxi demand is predicted by compiling historical taxi requests• Weekday and weekend pattern
• Taxi request reservations is also an indicator
0
20
40
60
80
100
1207 8 9
10
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20
21
Taxi
dem
an
d
hour
threshold
Fig. an example distribution of taxi demands at a region
12
Availability analysis of battery charging or switching stations• Availability is decided by the operation modes of charging stations
• By appointment• The waiting time is computed by checking the reservation schedule in a
station
• On-site queuing• Get average waiting time by analyzing historical info.
13
Experiment
• We simulated that operating electric taxi fleets in Taipei, Taiwan
• Compare our dispatching (ETD) with Random dispatching
• ER = |𝑉𝑒−𝑉𝑟|𝑉𝑟 ∗ 100%,
• 𝑉𝑒, 𝑉𝑟: the value of the same index for ETD and Random, respectively
14
Region A geographic space with 5.4*5.4 square kilometer in Taipei, and the number of regions is 20
Number of regions, N𝒓20
Time period (14 hours) 7:00 ~ 21:00
Number of total taxis, N𝒕 2000
Taxi demand,
Number of taxi demands per
hour
Low: (N𝑡/N𝑟) * {0.4,0.7,1.0,1.3}
High: (N𝑡/N𝑟) * 2.7
Probability of high (low) taxi
demand of a region per hour50%
Experiment15
Availability of charging stations
The set waiting time of using battery charging or
switching in a region per hour
high: {30,50,70,90} mins
Low: 10 mins
Probability of high (low) waiting time of using
battery charging or switching in a region per hour
50%
Time to consume in charging battery 30 mins to charge a battery of full power
5 mins of switching a full power battery
Taxi info.
Travel speed of a taxi 20 ~ 60 km/h
Income of taking clients per km 20 NT/km
Electric vehicle
Model Nissan leaf
Capacity of full power battery 24 kWh
Power consumption rate 21.131 Kw-hr/100 km
Initial capacity of battery The value generated by
normal distri. (u=Full power*0.5,std=Full
power*0.17)
Experiment
16
0
10
20
30
40
50
60
70
0
10
20
30
40
50
60
20 40 60 80
%
av
era
ge
wa
itin
g t
ime
per
ch
arg
e (m
in)
Dw(min.)
Random
ETD
ER
0
10
20
30
40
50
60
70
0
10
20
30
40
50
60
20 40 60 80
%
av
era
ge
wa
itin
g t
ime
per
ch
arg
e (m
in)
Dw(min.)
Random
ETD
ER
0
10
20
30
40
50
60
70
0
5
10
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30
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45
50
20 40 60 80
%
av
era
ge
wa
itin
g t
ime
per
ch
arg
e (m
in)
Dw(min.)
Random
ETD
ER
Dw: the difference between the set of high waiting time (30-90 mins) and the low waiting time (10 min)
Left top: only battery charging stations exist Right top: only battery switching stations exist Left down: both exist
Experiment
17
0
2
4
6
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13.5
14
14.5
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15.5
20 40 60 80
%
nu
mb
er o
f ta
sks
per
dri
ver
Dw(min.)
Random
ETD
ER
0
1
2
3
4
5
6
7
13
13.5
14
14.5
15
15.5
16
20 40 60 80
%
nu
mb
er o
f ta
sks
per
dri
ver
Dw(min.)
Random
ETD
ER
0
1
2
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8
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10
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13.5
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14.5
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15.5
16
20 40 60 80%
nu
mb
er o
f ta
sks
per
dri
ver
Dw(min.)
Random
ETD
ER
Experiment
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50
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60
0
5
10
15
20
25
30
35
40
1.4 1.7 2 2.3
%
av
era
ge
wa
itin
g t
ime
per
ch
arg
e(m
in)
Dr
Random
ETD
ER
50
51
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60
0
5
10
15
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25
30
35
1.4 1.7 2 2.3
%
av
era
ge
wa
itin
g t
ime
per
ch
arg
e(m
in)
Dr
Random
ETD
ER
50
51
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60
0
5
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1.4 1.7 2 2.3
%
av
era
ge
wa
itin
g t
ime
per
ch
arg
e(m
in)
Dr
Random
ETD
ER
Dr: the difference between the high demand (2.7 times) and the set of low demand (0.4 – 1.3 times)
Experiment
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0
1
2
3
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1.4 1.7 2 2.3
%
nu
mb
er o
f ta
sks
per
dri
ver
Dr
Random
ETD
ER
0
0.5
1
1.5
2
2.5
3
3.5
4
13
15
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25
1.4 1.7 2 2.3
%
nu
mb
er o
f ta
sks
per
dri
ver
Dr
Random
ETD
ER
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1.4 1.7 2 2.3
%
nu
mb
er o
f ta
sks
per
dri
ver
Dr
Random
ETD
ER
Experiment
• The percentage of an electric taxi complete power recharging when the station availability is high
0
10
20
30
40
50
60
70
80
90
100
20 40 60 80
H (
%)
Dw(min.)
C
S
CS
20
Conclusion
• We propose a system to operate electric taxi fleets and design a new dispatching strategy to reduce the effect of the long battery charging time
• In simulation, our system can efficiently reduced the waiting time for charging (the ER: 33% - 64%) and thus increased the available working time
• Also, the number of tasks completed was higher (the ER: 2.6% -10.62%)
21
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