Slide 1
Estimation of Road Load Parameters via On-road
g
Vehicle Testing
Dr. Rahul Ahlawata, Dr. Jürgen Bredenbeckb & Mr. Tatsuo Ichigec
a A&D
Technology, Michigan, USA
c A&D
b A&D
Europe GmbH, Griesheim, Germany
Company, Tokyo, Japan
Tire Technology Expo 2013
February 5-7,
5-7 Cologne,
Cologne Germany
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Slide 2
Energy
gy Loss in Vehicles
Standby
17%(4%)
Accessories
2%(2%)
Aerodynamic Drag
3%(11%)
Fuel Tank
100%(100%)
(
)
Rolling Resistance
4%(7%)
Braking
6%(2%)
Engine Loss
62%(69%)
Drivetrain Loss
6%(5%)
Losses of fuel energy in a vehicle in city usage (highway usage)
[U.S. National Academy of Science, 2006]
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Slide 3
Energy
gy Loss in Vehicles
Standby
17%(4%)
Accessories
2%(2%)
Aerodynamic Drag
3%(11%)
Fuel Tank
100%(100%)
(
)
Rolling Resistance
4%(7%)
Braking
6%(2%)
Engine Loss
Vehicle
62%(69%)
Drivetrain Loss
Road
Load
6%(5%)
Road Grade
Road load is define as the “…force
…force or torque which opposes the movement of
a vehicle…”
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[ISO 10521‐1, 2006]
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Slide 4
Significance of Road Load Determination
Vehicle platooning [1,2]
Roll‐over prevention [3]
EEnergy
management [4,5]
[SARTRE project test, Sweden]
Offline modeling &
control development [6]
[Toyota]
Engine certification [7]
[Mathworks]
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Slide 5
Objective
j
Estimate the road load parameters of a vehicle
 Focus on rolling resistance & aerodynamic drag
 Use on‐road testing of a production vehicle
 Use a novel force measurement method & compare
p
results
with traditional method(s)
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Slide 6
Outline
1. Introduction
2. Road load fundamentals
3. Instrumentation &
sample data
4. Coast down method
5. Force measurement
method
6. Summary
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Slide 7
Rolling Resistance
For a free rolling tire under no slip:
Mg: Vertical load on the tire due to sprung & unsprung mass
vx: Tire longitudinal velocity
ω: Tire angular speed
Rz: Ground reaction force
Rx: Rolling resistance force
Mrr: Rolling resistance moment
r: Loaded tire radius
vx
Mg
vx
ω
Mg
vx
ω
Mg
ω
Mrr
Rx
Tire normal force
distribution is asymmetric.
The resultant normal force
acts towards the leading
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edge.
Rx
a
Rz
Rz
a∙Rz = Mrr= r∙ Rx
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Rolling resistance
force
Slide 8
Aerodynamic
y
Dragg
[BMW]
Density of air
Frontal area of the vehicle
Coefficient of aerodynamic drag
Relative
l i velocity
l i off the
h vehicle
hi l wrt the
h wind
i d
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Slide 9
Total Road Load
The most commonly used form of road load equation is:
IIncludes
l d influence
i fl
Predominantly
P
d i
tl
Includes
l d
of road grade
includes the
aerodynamic drag
effect of rolling
resistance Includes dependence
of rolling resistance on
velocity & drivetrain
losses
• ‘b’ term is not always included
• A number of other formulations exist [8], including
• Influence of rotational inertias
• Correction factors
• Additional dependencies
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Slide 10
Road Load Measurement Methods
1. Individual component measurements
[Terrametrix]
[Nissan]
[Volvo]
Rolling resistance: Tire
testing
Aerodynamic drag:
Wind tunnel testing
[M t k]
[Maptek]
Grade: Road profiling
Pros: High repeatability, aids in parametric evaluation
Higher
cost, Inc.
may not represent real
driving conditions
Cons:
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Slide 11
Road Load Measurement Methods
2. Coast‐down method [8,10]
3. Torque measurement [9]
[VTI Sweden]
[Timken]
[9]
Pros: Less instrumentation
Cons: Time consuming tests,
tests include
drivetrain losses
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Pros: Drivetrain losses are excluded
Cons: Difficult to install on
production vehicle
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Slide 12
Road Load Measurement Methods
4. Complete vehicle measurement system
[A&D]
[A&D]
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Slide 13
On-road Vehicle Testing
Stop
Test Vehicle: FWD Mini Cooper S
S
Start
Test Track: Proving ground in Tochigi, Japan
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Slide 14
Instrumentation
Anemometer
Measures wind velocity & direction
6‐Component Wheel Force Transducer
(Fx, Fy, Fz, Mx, My, Mz)
o Distributed force bridges with model based decomposition to get
orthogonal force components
o Very low cross sensitivity, interference & temperature sensitivity and
high sampling rate
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o 0.1% resolution (6N or 1.8Nm) www.aanddtech.com
Slide 15
Instrumentation
Anemometer
Measures wind velocity & direction
6‐Component
p
Wheel Force Transducer
(Fx, Fy, Fz, Mx, My, Mz)
I fl
Influence
off d
drivetrain
i
i losses
l
iis included
i l d d iin this
hi measurement
Influence of a and b terms is included in this measurement (ignoring bearing
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friction and wheel well aerodynamic losses) www.aanddtech.com
Slide 16
Vehicle Instrumentation II
Laser Doppler Sensor
Measures vehicle velocity & slip
angle
GPS Sensor & In‐vehicle Network
Measures vehicle longitude,
latitude, altitude, and ECU CAN
communication
Inertial Sensor
Measures vehicle roll,
pitch and yaw
Wheel Position Sensor
Measures 6 degrees of freedom of the tire
Digital Signal Processing
&A
Acquisition
i iti
100Hz sampling
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(max 100kHz)
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Slide 17
Sample Data
FL
RL
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FR
RR
Slide 18
Sample Data
Acceleration
FL
RL
Coast‐down
Acceleration
FR
RR
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Braking
Slide 19
Sample Data
Acceleration
Coast‐down
Acceleration
Braking
Contribution of a and b terms
FL
RL
FR
RR
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Slide 20
Outline
1. Introduction
2. Road load fundamentals
3. Instrumentation &
sample data
4. Coast down method
5. Force measurement
method
6. Summary
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Slide 21
Coast-down Tests
Procedure:
• Conduct tests on a flat road with low wind conditions
• Accelerate the vehicle and put the transmission in N
• Begin coast down in a straight line
• Record vehicle velocity and vehicle velocity relative to wind as a function
off time
i
35
30
Velocity (m/s))
25
20
Coast‐down
15
10
5
0
-5
0
10
20
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30
40
50
Time (sec)
60
70
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80
90
Slide 22
Coast-down Tests
Procedure:
• Conduct tests on a flat road with low wind conditions
• Accelerate the vehicle and put the transmission in N
• Begin coast down in a straight line
• Record vehicle velocity and vehicle velocity relative to wind as a function
off time
i
Flat road assumption
6
Road grade (deg)
Road grade (deg)
4
2
0
-2
-4
-6
20 Technology,
40
60
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80
100
120
Time (sec)
140
160
180
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200
Slide 23
Coast-down Tests
Procedure:
• Conduct tests on a flat road with low wind conditions
• Accelerate the vehicle and put the transmission in N
• Begin coast down in a straight line
• Record vehicle velocity and vehicle velocity relative to wind as a function
off time
i
•
•
Use linear regression to obtain coefficients a, b & c
• Use minimization of
• Verify by Simulated Annealing
SAE J1263,
J1263 J2263 and ISO 10521‐1
10521 1 contain more detailed procedures
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Slide 24
Coast-down Test Results
1200
Measured road load force
Regression estimates
1000
• Front tires: Bridgestone
Coefficient of
determination, R2=0.9087 Sneaker
Force (N)
800
Rear tires: Bridgestone
Sneaker
• Estimated Values:
a = 194.87, b = 3.87, c = 0.37
•
600
400
200
0
-200
0
Data‐sets augmented
10
20
30
40
50
Time (sec)
60
35
30
a: 184 – 204
25
b: 2.7 – 5
c: 0.35 – 0.39
Velocity (m
m/s)
95% Confidence
C fid
b
bounds
d:
70
80
90
(33m/s, 118km/hr, 73mph)
Measured velocity
Calculated velocity
20
15
10
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5
0
(7.5m/s, 27km/hr, 16mph)
10
20
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30
40
50
Time (sec)
60
70
80
90
Slide 25
Coast-down Test Results
1200
Measured road load force
Regression estimates
1000
• Front tires: Bridgestone
Coefficient of
Blizzak
2
determination, R =0.9280 • Rear tires: Bridgestone
600
Sneaker
• Estimated Values:
a = 191.69, b = 2.54, c = 0.41
400
200
0
-200
0
Data‐sets augmented
10
20
30
40
50
35
Time (sec)
95% C
Confidence
fid
b
bounds
d:
30
a: 183 – 200
25
b: 1.5 – 3.5
c: 0.39 – 0.43
60
70
80
Measured velocity
Calculated velocity
Velocity (m
m/s)
Force (N)
F
800
20
15
10
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5
0
10
20
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30
40
Time (sec)
50
60
70
80
Slide 26
Validation Procedure
During coast‐down:
Generalized equation under all conditions (including coast‐down):
Tire traction/braking
force
Wheel force
sensor
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measurements
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Slide 27
Validation Procedure
35
30
Velocity (m
m/s)
25
20
15
Parameter
identification
10
5
0
-5
0
Parameter Validation
10
20
30
40
50
60
70
80
90
Time (sec)
Estimates are poor outside the coast‐down region
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Slide 28
Residual Analysis
y
Analyze cross‐correlation coefficient of residuals:
0.1
Norrmalized cross correlation coefficient
Normalized cross corre
elation coefficient
An example:
0.05
0
-0.05
-0.1
-1000
-800
-600
-400
-200
0
Time
200
400
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600
800
1000
1.2
1
0.8
06
0.6
0.4
0.2
0
-0.2
-1000
-800
-600
-400
-200
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0
Time
200
400
600
800
1000
Slide 29
Residual Analysis
y
Analyze cross‐correlation coefficient of residuals:
0.5
0.4
0.5
Residuals & steering angle
0.3
0.2
0.1
0
-0.1
0
10
20
30
40
50
Norm
malized cross-correlation
n coefficient
(Residuals & Acc pedal position)
Norm
malzed cross-correlation
n coefficient
(Residuals & Steering
g angle)
0.6
60
Residuals & accelerator
pedal p
p
position
0.4
0.3
0.2
01
0.1
0
0
10
20
Time (sec)
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30
Time (sec)
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40
50
60
Slide 30
Limitations of Coast-down Tests
Procedure:
1. A long straight flat track is needed
• SAE procedures requires a minimum speed band of 70 to 15 mph
2. Results include drivetrain losses and may not be suitable for some
applications
pp
3. Results are not consistent for all driving conditions, especially outside the
coast‐down region
• Residuals are correlated with accelerator pedal position, steering
angle,…
4. Predictor basis is not orthogonal giving rise to the mathematical
complications due to multicollinearity
• Estimates of a, b, c might be biased or have high variance
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Slide 31
Outline
1. Introduction
2. Road load fundamentals
3. Instrumentation &
sample data
4. Coast down method
5. Force measurement
method
th d
6. Summary
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Slide 32
Identification usingg Force Method
Use regression to identify c
ID
35
c = 0.5371
ID
30
Velocity (m/s)
25
20
15
10
5
0
-5
5
0
Validation
50
100
150
Time (sec)
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200
250
Slide 33
Validation Test
R2 = 0.9926
0 9926
95% confidence limit for
c: 0.53‐ 0.55
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Slide 34
Comparison of Coast-down & Force Method
• Front tires: Bridgestone Blizzak
• Confidential,
Rear tires:
Bridgestone
Sneaker
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Slide 35
Comparison of Coast-down & Force Method
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Slide 36
Comparison of Coast-down & Force Method
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Slide 37
Cross-correlation of Residuals
Normalzed cross-correlation coeffiicient
N
(Residuals & Steering angle))
0.5
Normalized cross-co
orrelation coefficie
ent
(Residuals & Acc
c pedal position)
0.6
0.5
0.4
0.4
Residuals & steering angle
0.3
0.2
0.1
0
-0.1
01
-0.2
0
Residuals & accelerator
pedal
d l position
iti
50
New residuals
100
150
(sec)
Coastdown Time
residuals
0.3
02
0.2
0.1
0
-0.1
-0.2
0
50
New residuals
Coastdown residuals
100
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150
Time (sec)
200
250
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200
250
Slide 38
Traction & Braking Scenarios
vx
Mg
Assumptions:
• Small inclination and side‐slip angles
• No vertical displacement of the tire
• No slip
ω
Mrr
Z
T
X
r
Fa
Let WFS measurements be represented as Fx, Fz and My
Then,
From laser Doppler sensor
Rz
Fa
m: Mass of tire wheel assembly
J: Polar moment of inertia of tire‐wheel
assemblyy about the center of wheel hub
T: Applied torque at wheel hub
Fa: Tire‐road friction force
From encoder
Rz
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Determined before
the experiment
Slide 39
Calculation of ‘a’ from Force Method
6 constant speed tests for each speed, 18 tests total
RL Tire
Assumingg that wheel well aerodynamic
y
losses are negligible
g g
at low
speeds, calculate
a = 271
Note: Measured value of rolling resistance is much higher than what
standardized tests predict [11]
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Slide 40
Calculation of ‘b’ from Force Method
Mean RRF=85.4381 N
σRRF= 1.7629 N
Mean RRF=84.5861 N
σRRF= 1.9318
1 9318 N
Mean RRF=84.8949 N
σRRF= 2.2992 N
Very slight reduction in rolling resistance as speed increases
b≈0
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Slide 41
Summaryy
Coast‐down Method
Force Method
a = 191.69,
191 69 b = 2.54,
2 54 c = 0.41
0 41
• R2 = 0.9280
a = 271,
271 b = 0
0, c = 0.5371
0 5371
• R2 = 0.9926
• Estimate variance is higher
g
• Estimation only over coast‐down;
road load is under‐estimated for
non coast down conditions
non‐coast‐down
• Residuals are correlated with
driver inputs
• Changing the tire changes ‘c’
substantially
• Influence of drivetrain losses is
included
• Less instrumentation is needed
• Less distortion of vehicle
aerodynamic
• Estimation based on p
physics;
y ;
variance is very low
• Estimation can be carried out
d i allll conditions
during
diti
• Correlation is significantly
reduced
• Changing the tires preserves ‘c’
very closely
• Influence of drivetrain losses is
NOT included
• More instrumentation required
• Vehicle aerodynamics are
modified
to a greater extent
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Slide 42
Acknowledgements
g
•
•
•
On-road
O
dd
data
t acquisition
i iti team:
t
– Takayasu Sasaki
– Yuuki Sakurai
– Masaaki Banno
– Hiroki Yamaguchi
Kenji Sato
Sato, A&D Technology,
Technology Ann Arbor
Dr. Michael Smith, A&D Technology, Ann Arbor
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Slide 43
References
1.
2.
3.
4.
5.
6.
7.
8.
9
9.
10.
11.
D.Yanakiev & I.Kanellakopoulos, “Speed Tracking and Vehicle Follower Control Design for
Heavy-Duty Vehicles”, Vehicle System Dynamics, Vol. 25, No. 4, 1996
D.Yanakiev & I.Kanellakopoulos, “Nonlinear
Nonlinear spacing policies for automated heavy-duty
heavy duty
vehicles”, IEEE Transactions on Vehicular Technology, Vol. 47, No. 4
Bae, Ryu & Gerdes, “Road grade vehicle parameter estimation for longitudinal control using
GPS”, 2001 IEEE Intelligent Transportation Systems
C. Musardo, G. Rizzoni & B.P. Staccia, “A-ECMS: An Adaptive Algorithm for Hybrid Electric
Vehicle Energy Management” 2005 European Control Conference
Hong Yang & Joel Maguire, “Predictive Energy Management Control Scheme for a Hybrid
Powertrain System”
System , US Patent 2011/0066308 A1
www.Carsim.com
J Fredriksson, E Gelso, M Åsbogard, M Hygrell, O Sponton, NG Vagstedt, “On emission
certification of heavy-duty hybrid electric vehicles using hardware-in-the-loop simulation”,
Chalmers University of Technology, 2011
Karlsson, Hammarström, Sörensen & Eriksson, “Road surface influence on rolling resistance
- Coastdown measurements for a car and an HGV”, VTI, 2011
JŻ b
J.Żebrowski,
ki “Traction
“T ti efficiency
ffi i
off a wheeled
h l d tractor
t t in
i construction
t ti operations”,
ti
” Automation
A t
ti
in Construction, Vol. 19, No. 2, 2010
Sandburg & Ejsmont, “Noise emission, friction and rolling resistance of car tires – Summary
p
study”,
y , National conference on noise control engineering,
g
g, Dec 3-5,, 2000,,
of an experimental
Newport beach, California
S.K. Clark, “A handbook for the rolling resistance of pneumatic tires”, 1979
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Slide 44
More Information
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