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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
© MAS Kamal, Fukuoka IST
2010/5/19
2
Background
&
Motivation
© MAS Kamal, Fukuoka IST
Emission From Cars
© MAS Kamal, Fukuoka IST
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
© MAS Kamal, Fukuoka IST
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.
© MAS Kamal, Fukuoka IST
2010/5/19
4
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;
© MAS Kamal, Fukuoka IST
Eco-Driving Assistance
© MAS Kamal, Fukuoka IST
Driving TipsOn-board Assistance
for Driver
ECOECO indicator
ECOECO
2010/5/19
5
On-board Performance Indicator
© MAS Kamal, Fukuoka IST
Driving Efficiency
Support on Sloppy Road
© MAS Kamal, Fukuoka IST
2010/5/19
6
On-Board Assistance
© MAS Kamal, Fukuoka IST
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
© MAS Kamal, Fukuoka IST
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
7
EDAS
Ecological Driver Assistance System
© MAS Kamal, Fukuoka IST
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
© MAS Kamal, Fukuoka IST
2010/5/19
8
ITS Technologies
© MAS Kamal, Fukuoka IST
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
© MAS Kamal, Fukuoka IST
2010/5/19
9
� 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
© MAS Kamal, Fukuoka IST
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
10
Modeling
)( maxmax uuu ≥≥−
© MAS Kamal, Fukuoka IST
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&
© MAS Kamal, Fukuoka IST
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
11
Performance Index
( )∫+
=Tt
tdtpuxLJ ,,min
© MAS Kamal, Fukuoka IST
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 ++=
© MAS Kamal, Fukuoka IST
.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
12
Flow of Vehicle Control Process
© MAS Kamal, Fukuoka IST
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
© MAS Kamal, Fukuoka IST
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
13
AIMSUN NG
Host Vehicle
Model Predictive
Control
Other Traffic
Interactions
API
Control input
Measurement
EDASTraffic Signal
Interactions
Interactions
Simulation Interface
© MAS Kamal, Fukuoka IST
Case I
Eco-Driving on Flat Urban Road with Traffic
Signal at Junctions
© MAS Kamal, Fukuoka IST
2010/5/19
14
Test Route & Network Setting
© MAS Kamal, Fukuoka IST
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
© MAS Kamal, Fukuoka IST
=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
15
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
© MAS Kamal, Fukuoka IST
Average Fuel Consumption
© MAS Kamal, Fukuoka IST
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
16
Case II
Ecological Driving over Up-Down Slopes on a freeway
© MAS Kamal, Fukuoka IST
Fundamental Concept
© MAS Kamal, Fukuoka IST
Assumption: Vehicle is not interfered by other vehicle or traffic signals.
2010/5/19
17
Modeling
++
=0
)( 22 cPxxmFP
&
© MAS Kamal, Fukuoka IST
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
© MAS Kamal, Fukuoka IST
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
© MAS Kamal, Fukuoka IST
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
© MAS Kamal, Fukuoka IST
=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
© MAS Kamal, Fukuoka IST
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
© MAS Kamal, Fukuoka IST
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
20
Cumulative Fuel Consumption
0 400 800 1200x1 [m]
-0.3
0
0.3
0.6
u [
m/s
2]
MPC
FSD
ASCD
© MAS Kamal, Fukuoka IST
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
]
© MAS Kamal, Fukuoka IST
Fuel savings only on up-hill
Fuel savings by MPC over:
(a)FSD 9.32%(b)ASCD 10.55%
2010/5/19
21
Additional Results I
© MAS Kamal, Fukuoka IST
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
© MAS Kamal, Fukuoka IST
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
22
Additional Results II
© MAS Kamal, Fukuoka IST
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
© MAS Kamal, Fukuoka IST
2010/5/19
23
0 500 1000 1500 2000 2500 m-5
0
5
10
15
20
25
30
35
Road Elevation (from Digital Map)
北 南
© MAS Kamal, Fukuoka IST
Results: North to South
© MAS Kamal, Fukuoka IST
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
© MAS Kamal, Fukuoka IST
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
© MAS Kamal, Fukuoka IST
2010/5/19
25
Fundamental Concept
© MAS Kamal, Fukuoka IST
Test Route
© MAS Kamal, Fukuoka IST
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
© MAS Kamal, Fukuoka IST
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
© MAS Kamal, Fukuoka IST
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
27
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.
© MAS Kamal, Fukuoka IST