isit 2010 省燃費運転支援システム 1 省燃費運転支援システム ecological driver...

2010/5/19

1

省燃費運転支援省燃費運転支援省燃費運転支援省燃費運転支援システムシステムシステムシステムEcological Driver Assistance System

(EDAS)Presented byーーーー

M. A. S. Kamal

Researcher, Fukuoka Industry, Science & Technology Foundation

Email: [email protected];

URL: http://terra.ees.kyushu-u.ac.jp/~kamal/

© MAS Kamal, Fukuoka IST

ISIT 第６回カーエレクトロニクス研究会2010年5月14日・金曜日

Presentation Outline

• Background & Motivation

• Ecological Driver Assistance System

• Case Studies and Simulation

– Driving on a flat urban road with crowded traffic

– Driving on a freeway with up-down slope

– Driving on crowded road with up-down slope

• Conclusions


2010/5/19

2

Background

&

Motivation


Emission From Cars


Emission From CarsEmission From Cars

Emission of CO2 from Transportation is one of the major sources of Environment Pollution and Global Warming.

It is a demand of time to make Transportation

Systems more Environmentally friendly

Transportation

PSIndustry

2010/5/19

3

Fuel Efficient Vehicles


Progress for efficient Vehicles have been continuing

Realization of Eco-Driving

Major Factors influencing consumptions

• Vehicle maintenance,

• Route Selections,

• Driving Style.

Proper Driving or Vehicle Control Style may

save fuel consumption significantly.


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Ecological Driving

According to Recent Studies:

Eco-Driving may save fuel

By 10-25%.

Various Approaches are introduced to Motivate a

driver for Eco-Driving:

Example:

Driving Tips; ECO indicator; Fuel Ranking;


Eco-Driving Assistance


Driving TipsOn-board Assistance

for Driver

ECOECO indicator

ECOECO

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On-board Performance Indicator


Driving Efficiency

Support on Sloppy Road


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On-Board Assistance


CARWINGS and ECO Pedal

Limitation of Existing Assistance

• They only focus on fuel consumption characteristics of the Engine

• They do not analyze current road traffic situation

• They do not anticipate future traffic conditions

• They do not provide exact support


Therefore, A more Comprehensive EcoTherefore, A more Comprehensive Eco--Driving System is necessary for Optimum Driving System is necessary for Optimum AchievementAchievement

2010/5/19

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EDAS

Ecological Driver Assistance System


Proposed EDAS

An EDAS should Assist a Driver based

on

• Fuel Consumption Characteristics

of the Engine

• Road gradients, alignment and

Lanes

• Situation of current traffic

• Anticipation of Future Situation

• Traffic Signal ahead

• Safety


2010/5/19

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ITS Technologies


Necessary Information�Position, Speed, acceleration of surrounding vehicles.�Status of Signal (or Timing)�Road gradient and alignment

Possible Technologies�GPS, Camera, Laser, etc�Communication Among Vehicles�Communication with Infrastructure

Algorithm ?

Vehicle control problemVehicle control problem�� NonNon--linear linear � Requires AnticipationAnticipation of traffic� Discontinuous Events � Constraints

Model Predictive Control Model Predictive Control is the most suitable Options.

Selection of Algorithm


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� At t, using current state and initial inputs, states in the

prediction horizon t to t+T is predicted.

� Inputs are optimized using performance index.

� Only the first input is used to control the vehilce.

�The process is repeated in next steps.

Implmentation

t0∆t ∆tt1 t2 inputState

Prediction Horizon TPrediction Horizon T

t

Model Predictive Control

© MAS Kamal, Fukuoka IST11

Fuel Consumption Estimation

C

P

η

+++

++++

= −−)(ˆ1

1

012

2

012

23

3

)(cvcvca

bvbvbvb

eg

uV αβ


29.1%28.5%

27%

25.5%24%

Best efficiency points

Efficiency

Engine power

Efficiency (η) depends on torque and speed.

For a CVT Vehicle, it is assumed, the gear ratio is maintained at maximum efficiency point for any output power.

Therefore, the consumption [ml/s] can be obtained as: [ml/s]

Where C: calorific value of gasoline.

Using data of the engine Map, Fuel consumption is approximated as:

vWhere, is velocity, and

The sigmoid function indicates, no fuel consumption at input

θsinˆ gva += &

0≤u

2010/5/19

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Modeling

)( maxmax uuu ≥≥−


pAssumption： time dependent variable, at t, remains constant for a while.

Constraint :

Modeling

+−−−==

p

x

uxgxgxACm

x

puxfx vaD

3

1122

2

))(sin()(cos2

1),,(

θθµρ&


gµ

DC

ρ

A

θ

um

： Air density

： Drag Coefficient

： Frontal Area

： Slope angel

： Rolling Coefficient

： Gravitational force：Control input：Vehicle Mass

2010/5/19

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Performance Index

( )∫+

=Tt

tdtpuxLJ ,,min


Fuel Economy

Cost for acceleration/brakingand road gradient

Desired Speed*

Dynamic weights w1, w2, w3, w4 focus their relative contextual merits.

( )( ) ( )( ) .

2

1

201324

213

2222021

222

323

2

1

VdR

vaD

lxxxhwvxw

gxACm

uwbxbxbxbx

wL

−−−+−+

−−++++= µρ

Safe Clearance

Optimization Problem

),(),(),(:),,,( uxCuxfuxLuxH TT µλµλ ++=


.0

),(

),,,(

:

),(

),,,(

:),,(

11

111

00

0010

=

=

−−

−−−

NN

NNNNTu

Tu

uxC

uxH

uxC

uxH

txUF

µλ

µλ

Condition for Optimal solution with given initial values

Continuation/Generalized Minimum Residual (C/GMRES)Continuation/Generalized Minimum Residual (C/GMRES) [5] is used to finds the solutions of the above.

[9] T. Ohtsuka, “A Continuation/GMRES method for fast computation of nonlinear [9] T. Ohtsuka, “A Continuation/GMRES method for fast computation of nonlinear receding horizon control,” Automatica 40 (2004) 563receding horizon control,” Automatica 40 (2004) 563--574.574.

Hamiltonian:

2010/5/19

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Flow of Vehicle Control Process


Measure states of the vehicle,

At time t=kh

Using the model of vehicle dynamics, Information of road slope, Performance index and Constraint,

For a prediction horizon T, from t’=kh to, t’=kh+T

Optimize the current and future vehicle control inputs using C/GMRES

Implement best current input to control the vehicle

k=k+1

Test Environment


Functions can be Extended through API Routine to control a car in a special way

AIMSUNAIMSUN Microscopic Traffic SimulatorMicroscopic Traffic Simulator

Vehicles run as per Gipps model

2010/5/19

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AIMSUN NG

Host Vehicle

Model Predictive

Control

Other Traffic

Interactions

API

Control input

Measurement

EDASTraffic Signal

Interactions

Interactions

Simulation Interface


Case I

Eco-Driving on Flat Urban Road with Traffic

Signal at Junctions


2010/5/19

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Test Route & Network Setting


S1 S2 S3 S12 S13

J1 J2 J13

S14

J12J11

Test Route 4.00 km

Test Route 4.0 km14 sections13 junctions2-3 Lanes90 sec Signal cycle50 sec Green Timing

Vehicle flow3000+ vehicle/hourVehicle TypesTruck, Car, and Taxi

Simulations


=1200[kg]

=1.184[kg/m3]

=0.012

=34.5e+6[J/l]

=0.7[PS] =514.85[W]

=9.8[m/s2]

=2.5[m2]

=0.32

Modeling Parameters

M

DC

ρ

A

µ

g

C

cP

8

=12[s]

=50[km/h]

=110

=7.7

=0.39

= 0.1[s]

= 2.75[m/s2]

Algorithm’s Setting

Tdv

0w

maxu

h

1w

2w

Constraint is converted into inequality constraint as:

( ) 02

1),( 2

max22

2 =−+= uuuuxC

2010/5/19

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04.

Signal status

0 60 120 180 240 300 360 420 480 5400

4

8

12

16

.

Road sections

0 60 120 180 240 300 360 420 480 540

20

40

60

NV

No. of vehicles on the road section

0 60 120 180 240 300 360 420 480 5400

20

40

60

x4 [

km

/h]

Velocity of Preceding Vehicle

0 60 120 180 240 300 360 420 480 5400

20

40

60

80

x3-x

1 [m

]

Range clearance

0

0.8.

Change of Preceding Vehicles

0 60 120 180 240 300 360 420 480 5400

20

40

60

x2 [

km

/h]

Velocity of Host Car

0 60 120 180 240 300 360 420 480 540-4

-2

0

2

4

u

Control Input

0 60 120 180 240 300 360 420 480 540time [s]

0

60

120

180

Fu

el [

ml]

Cumulative Consumption

04.

Signal status

0 60 120 180 240 300 360 420 480 5400

4

8

12

16

.

Road sections

0 60 120 180 240 300 360 420 480 540

20

40

60

NV

No. of vehicles on the road section

0 60 120 180 240 300 360 420 480 5400

20

40

60

x4 [

km

/h]

Preceding Car Velocity

0 60 120 180 240 300 360 420 480 5400

20

40

60

80

x3-x

1 [m

]

Range clearance

0

0.8.

Preceding Vehicle Changed at

0 60 120 180 240 300 360 420 480 5400

20

40

60

x2 [

km

/h]

Velocity of Host Car

0 60 120 180 240 300 360 420 480 540-4

-2

0

2

4

u

Control Input

0 60 120 180 240 300 360 420 480 540time [s]

0

60

120

180

Fu

el

[ml]

Cumulative Consumption

Results

GippsCar

EDASCar


Average Fuel Consumption


135

160

185

210

235

260

0 5 10 15 20 25 30 35

Vehicle on Observation

Fu

el [

ml]

MPCMeanMPC_Idle StopMeanGippsMeanGipps_Idle StopMean

23.92% Improvement by MPC (EDAS)

2010/5/19

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Case II

Ecological Driving over Up-Down Slopes on a freeway


Fundamental Concept


Assumption: Vehicle is not interfered by other vehicle or traffic signals.

2010/5/19

17

Modeling

++

=0

)( 22 cPxxmFP

&


θState equation of a vehicle:

,

))(),(()(

2

1

=

=

x

xx

tutxftx&

+−=

uM

F

xuxf

R

2

),(

)(sin2

11

22 xMgMgAxCF DR θµρ ++=

Air Rolling Slopes

Where forces

)0( >u

Power of the Engine:

5

)0( ≤u

： Air density

： Drag Coefficient

： Frontal Area

： Slope angel

： Rolling Coefficient

： Gravitational force： Power required at stand still.

gµ

DC

ρ

A

θ

： Location

： Velocity

：Control input (accel/brake) ：

: Motion resistance forces

: Vehicle Mass

2xu

MRF

cP

1x

And, )( maxmax uuu ≥≥−

Performance Index

( )∫+

′=Tt

ttdpuxLJ ,,min


Fuel Economy Desired Speed*

Acceleration/ braking

Dynamic weights w1, w2, w3 focus relative contextual merits of each terms.

Constraint is converted into inequality constraint as:

( ) 02

1),( 2

max22

2 =−+= uuuuxC

2010/5/19

18

Model Predictive Control


Measure states of the vehicle,

At time t=kh

Using the model of vehicle dynamics, Information of road slope, Performance index and Constraint,

For a prediction horizon T, from t’=kh to, t’=kh+T

Optimize the current and future vehicle control inputs using C/GMRES

Implement best current input to control the vehicle

k=k+1

Even at each timeOptimum inputs for the horizonis generated, the whole process is repeated in short interval.

Simulations


=1200[kg]

=1.184[kg/m3]

=0.012

=34.5e+6[J/l]

=0.7[PS] =514.85[W]

=9.8[m/s2]

=2.5[m2]

=0.32

Modeling Parameters

M

DC

ρ

A

µ

g

C

cP

The proposed algorithm is evaluated through simulation

8

=12[s]

=50[km/h]

=3

=34

=1

= 0.1[s]

= 2.75[m/s2]

Algorithm’s Setting

Tdv

1w

maxu

h

2w

3w

2010/5/19

19

Simulations Results


0

4

8

12

16

Ele

va

tion

[m

]

0 400 800 1200x1 [m]

-6

-3

0

3

6

Slo

pe

[%]

Road shape and Slope

Simulations Results


0 400 800 1200x1 [m]

44

46

48

50

52

54

56

x2

[k

m/h

]

MPC

FSD

ASCD

0 400 800 1200x1 [m]

-0.3

0

0.3

0.6

u [

m/s

2]

MPC

FSD

ASCD

• The Vehicle approaches at velocity 50 [km/h]• MPC is compared with two hypothetical Systems :

(a) FSD (Fixed Speed Drive)(b) ASCD (Automatic Speed Control Drive)

2010/5/19

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Cumulative Fuel Consumption

0 400 800 1200x1 [m]

-0.3

0

0.3

0.6

u [

m/s

2]

MPC

FSD

ASCD


0 400 800 1200x1 [m]

0

10

20

30

40

Fu

el C

onsu

med

[m

l]

MPC

FSD

ASCD

Fuel saving features:

(a) Avoid excessive input

(b) Avoid hard braking

(c) Speeding up before up slope

(d)Use down slope to speed up again

Results ： Comparison

29.75

32.5332.89

27

29

31

33

Fuel

[m

l]

MPC FSD ASCD

0

4

8

12

16

Ele

va

tion

[m

]


Fuel savings only on up-hill

Fuel savings by MPC over:

(a)FSD 9.32%(b)ASCD 10.55%

2010/5/19

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Additional Results I


0 400 800 1200

-8

-4

0

4

Ele

va

tion

[m

]

0 400 800 1200x1 [m]

44

46

48

50

52

54

56

x2 [

km

/h]

MPC

FSD

ASCD

0 400 800 1200x1 [m]

-0.3

0

0.3

0.6

u [

m/s

2]

MPC

FSD

ASCD

29.68

32.5232.73

27

29

31

33

Fu

el [m

l]

MPC FSD ASCD

MPC has similar benefit over FSD and ASCD

Additional Results II


0 400 800 1200

0

2

4

6

8

Ele

va

tion

[m

]

0 400 800 1200x1 [m]

46

48

50

52

54

x2 [

km

/h]

MPC

FSD

ASCD

0 400 800 1200x1 [m]

0

0.25

0.5

0.75

1

u [

m/s

2]

MPC

FSD

ASCD

(c)

31.7731.8 31.79

31.2

31.4

31.6

31.8

Fue

l [m

l]

MPC FSD ASCD

Almost the same fuel consumption by MPC, FSD and ASCD

2010/5/19

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Additional Results II


0 400 800 1200

-6

-4

-2

0

2

4

Ele

va

tion

[m

]

0 400 800 1200x1 [m]

46

48

50

52

54

x2 [

km

/h]

MPC

FSD

ASCD

0 400 800 1200x1 [m]

-0.5

-0.25

0

0.25

0.5

u [

m/s

2]

MPC

FSD

ASCD

18.71

19.819.91

16

17

18

19

20

Fue

l [m

l]

MPC FSD ASCD

About 6% Fuel savings

Test

Route


2010/5/19

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0 500 1000 1500 2000 2500 m-5

0

5

10

15

20

25

30

35

Road Elevation （from Digital Map）

北南


Results: North to South


0 450 900 1350 1800 2250 2700x1 [m]

5101520253035

Ele

vatio

n [m

]

-6

-3

0

3

6

Slo

pe

[%]

44

48

52

56

Velo

city

, x2 [k

m/h

]

EcoDFSDASCD

-0.4

0

0.4

0.8

1.2

Inpu

t, u

[m/s

2 ]

EcoDFSDASCD

118.06

123.55124.27

113

116

119

122

125

Fu

el [

ml]

EcoD FSD ASCD

Fuel savings about 5.0%

2010/5/19

24

Results: South to North


0 450 900 1350 1800 2250 2700x1 [m]

5101520253035

Ele

vatio

n [m

]

-9

-6

-3

0

3

6

Slo

pe

[%]

44

48

52

56

Velo

city

, x2 [k

m/h

]

EcoDFSDASCD

-0.8

-0.4

0

0.4

0.8

Input

, u [m

/s2 ]

EcoDFSDASCD

78.15

82.87

84.07

74

77

80

83

86

Fu

el [

ml]

EcoD FSD ASCD

Fuel savings about 7.04%

Case III

Driving over Up-Down Slopes on a Urban road


2010/5/19

25

Fundamental Concept


Test Route


Test Route 1.7km

Elevation of the road segment

20

2

20

2

54

Nishi Ward,

Fukuoka City,

Japan

Test Route onthe Map

2010/5/19

26

Simulation Result


0

20

40

60

x 2 [

km/h

]

-3

0

3

u [m

/s2 ]

0

20

40

60

x 4 [

km/h

]

-404

Slo

pe [%

]

0 50 100 150 200 250t [sec]

0

50

100

Fue

l [m

l]

0Sig

nal

0

20

40

60

x 2 [

km/h

]

-3

0

3

u [m

/s2 ]

0

20

40

60

x 4 [

km/h

]

-404

Slo

pe [%

]

0 50 100 150 200 250t [sec]

0

50

100

Fue

l [m

l]

0Sig

nal

Gipps Vehicle EDAS Vehicle

Comparison


27.6

78.7

24.6

68.9

0

20

40

60

80

100

120

Gipps Eco Drive

Other sectons

Sloppy section

Test Route 1.7km

Elevation of the road segment

20

2

20

2

54

Nishi Ward,

Fukuoka City,

Japan

Fuel Consumption [ml]

2010/5/19

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Conclusions

• A novel concept of EDAS has been presented.

• Vehicle is controlled based on Fuel consumption, anticipation of future state and information of road shape.

• Simulation Results reveal the significant improvement in fuel consumption compared to other methods.

• Further fine tuning of EDAS will be followed by real experiments.


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