research: simulate operating ev-taxi fleets

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

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System

6

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

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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

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• When only batter switching stations exist


remaining power

High Low

Availability of battery-switch stations near

destination 𝐿𝑑at time 𝑡𝑑 ?


remaining power

B

10


• When both batter charging

and switching stations exist

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Taxi demand at destination 𝐿𝑑

at time 𝑡𝑑?


remaining power


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?


remaining power

LowHigh


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

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80

100

1207 8 9

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Taxi

dem

an

d

hour

threshold

Fig. an example distribution of taxi demands at a region

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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.

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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

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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

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av

era

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in)

Dw(min.)

Random

ETD

ER

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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

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Experiment

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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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Experiment

• The percentage of an electric taxi complete power recharging when the station availability is high

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H (

%)

Dw(min.)

C

S

CS

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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%)

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research: simulate operating ev-taxi fleets

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