Estimation of Road Load Parameters via On-road Vehicle ... · Slide 1 Estimation of Road Load...

Estimation of Road Load Parameters via On-road Vehicle Testingg

Dr. Rahul Ahlawata, Dr. Jürgen Bredenbeckb & Mr. Tatsuo Ichigec

a A&D Technology, Michigan, USA b A&D Europe GmbH, Griesheim, Germanyc A&D Company, Tokyo, Japan

Tire Technology Expo 2013February 5-7 Cologne Germany

Proprietary and Confidential, A&D Technology, Inc. www.aanddtech.com

February 5-7, Cologne, Germany

Energy Loss in VehiclesgyStandby17%(4%)

Accessories2%(2%)

Aerodynamic Drag3%(11%)

Fuel Tank100%(100%)

3%(11%)

Rolling Resistance4%(7%)( ) 4%(7%)

Braking6%(2%)

Engine Loss Drivetrain Loss

6%(2%)

62%(69%) 6%(5%)

Losses of fuel energy in a vehicle in city usage (highway usage)


[U.S. National Academy of Science, 2006]

Energy Loss in VehiclesgyStandby17%(4%)

Accessories2%(2%)

Aerodynamic Drag3%(11%)

Fuel Tank100%(100%)

3%(11%)

Rolling Resistance4%(7%)( ) 4%(7%)

Braking6%(2%)

Engine Loss Drivetrain Loss

6%(2%)

Vehicle Road Load Road Grade62%(69%) 6%(5%)

Road load is define as the “…force or torque which opposes the movement of

Vehicle Road Load Road Grade


Road load is define as the …force or torque which opposes the movement of a vehicle…”[ISO 10521‐1, 2006]

Significance of Road Load DeterminationE

Vehicle platooning [1,2] Roll‐over prevention [3]Energy

management [4,5]

[SARTRE project test, Sweden]

[Toyota]Offline modeling & control development [6] Engine certification [7]Engine certification [7]

[Mathworks]


[Mathworks]

Objectivej

Estimate the road load parameters of a vehicle Focus on rolling resistance & aerodynamic drag Focus on rolling resistance & aerodynamic drag Use on‐road testing of a production vehicle Use a novel force measurement method & compare results p

with traditional method(s)


Outline

1. Introduction

2. Road load fundamentals

3. Instrumentation & sample data

4. Coast down method

5. Force measurement method


6. Summary

Rolling Resistance

Mg: Vertical load on the tire due to sprung & unsprung massv : Tire longitudinal velocity ω: Tire angular speed R : Ground reaction force

For a free rolling tire under no slip:

vx vx vx

vx: Tire longitudinal velocity ω: Tire angular speed Rz: Ground reaction forceRx: Rolling resistance force Mrr: Rolling resistance moment r: Loaded tire radius

ω ω ωMg Mg MgMrr

RxRx

R R

aTire normal force distribution is asymmetric. The resultant normal force

xx


Rz RzThe resultant normal force acts towards the leading

edge.a∙Rz = Mrr= r∙ Rx

Rolling resistance force

Aerodynamic Dragy g

[BMW]

Density of airFrontal area of the vehicle

Coefficient of aerodynamic dragl i l i f h hi l h i d


Relative velocity of the vehicle wrt the wind

Total Road LoadThe most commonly used form of road load equation is:

P d i tl l d I l d i flPredominantly includes the

effect of rolling

Includes aerodynamic drag

Includes influence of road grade

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

Road Load Measurement Methods1. Individual component measurements

[Nissan][Terrametrix]

[M t k][Volvo]

[Maptek]Rolling resistance: Tire

testingAerodynamic drag: Wind tunnel testing Grade: Road profiling


Pros: High repeatability, aids in parametric evaluationCons: Higher cost, may not represent real driving conditions

Road Load Measurement Methods2. Coast‐down method [8,10] 3. Torque measurement [9]

[Timken][VTI Sweden] [Timken][VTI Sweden]

Pros: Less instrumentationCons: Time consuming tests include

[9]

Pros: Drivetrain losses are excludedCons: Difficult to install on


Cons: Time consuming tests, include drivetrain losses

Cons: Difficult to install on production vehicle

Road Load Measurement Methods4. Complete vehicle measurement system

[A&D][A&D]

[A&D]


On-road Vehicle Testing

SStop

Test Vehicle: FWDMini Cooper S StartTest Vehicle: FWD Mini Cooper S


Test Track: Proving ground in Tochigi, Japan

Instrumentation

6‐Component Wheel Force Transducer

AnemometerMeasures wind velocity & direction

(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

o 0.1% resolution (6N or 1.8Nm)

Instrumentation

6‐Component Wheel Force Transducer

AnemometerMeasures wind velocity & direction

p(Fx, Fy, Fz, Mx, My, Mz)

I fl f d i i l i i l d d i hi


Influence of a and b terms is included in this measurement (ignoring bearing friction and wheel well aerodynamic losses)

Influence of drivetrain losses is included in this measurement

Vehicle Instrumentation IILaser Doppler SensorMeasures vehicle velocity & slip angleangleGPS Sensor & In‐vehicle Network

Measures vehicle longitude, latitude, altitude, and ECU CAN

communicationInertial SensorInertial SensorMeasures vehicle roll, pitch and yaw

Wheel Position SensorMeasures 6 degrees of freedom of the tireMeasures 6 degrees of freedom of the tire

Digital Signal Processing & A i iti


& Acquisition 100Hz sampling (max 100kHz)

Sample Data

FL FRRL RRRR


Sample DataAcceleration Coast‐down Acceleration Braking

FL FRRL RRRR


Sample DataAcceleration Coast‐down Acceleration Braking

Contribution of a and b terms

FL FRRL RRRR


Outline

1. Introduction


3. Instrumentation & sample datasample data


5. Force measurement method


6. Summary

Coast-down TestsProcedure:• Conduct tests on a flat road with low wind conditions• Accelerate the vehicle and put the transmission in N• 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

f i

30

35

of time

20

25

30

)

10

15

20

Vel

ocity

(m/s

)

Coast‐down

0

5


0 10 20 30 40 50 60 70 80 90-5

Time (sec)


f iof time

Flat road assumption6

Road grade (deg)

2

4

e (d

eg)

-2

0

Roa

d gr

ad

Proprietary and Confidential, A&D Technology, Inc. www.aanddtech.com20 40 60 80 100 120 140 160 180 200-6

-4

Time (sec)


f iof time

• Use linear regression to obtain coefficients a, b & cUse linear regression to obtain coefficients a, b & c• Use minimization of • Verify by Simulated Annealing

• SAE J1263 J2263 and ISO 10521 1 contain more detailed procedures


• SAE J1263, J2263 and ISO 10521‐1 contain more detailed procedures

Coast-down Test Results

800

1000

1200

Measured road load forceRegression estimates

Coefficient of • Front tires: Bridgestone

400

600

800

Forc

e (N

)

determination, R2=0.9087 Sneaker• Rear tires: Bridgestone

Sneaker

0

200• Estimated Values:a = 194.87, b = 3.87, c = 0.37Data‐sets augmented

35

Measured velocityCalculated velocity

0 10 20 30 40 50 60 70 80 90-200

Time (sec)

95% C fid b d(33m/s, 118km/hr, 73mph)

25

30

m/s

)

Calculated velocity95% Confidence bounds:

a: 184 – 204

15

20

Vel

ocity

(m

b: 2.7 – 5

c: 0.35 – 0.39

Proprietary and Confidential, A&D Technology, Inc. www.aanddtech.com0 10 20 30 40 50 60 70 80 905

10

Time (sec)

(7.5m/s, 27km/hr, 16mph)

Coast-down Test Results1000

1200

Measured road load forceRegression estimates

Coefficient of • Front tires: Bridgestone

400

600

800

Forc

e (N

)

Coefficient of determination, R2=0.9280 Blizzak

• Rear tires: Bridgestone Sneaker

0

200

400F

• Estimated Values:a = 191.69, b = 2.54, c = 0.41Data‐sets augmented

0 10 20 30 40 50 60 70 80-200

Time (sec)

95% C fid b d35

Measured velocityCalculated velocity95% Confidence bounds:

a: 183 – 200 25

30

m/s

)

Calculated velocity

b: 1.5 – 3.5

c: 0.39 – 0.4315

20

Vel

ocity

(m

Proprietary and Confidential, A&D Technology, Inc. www.aanddtech.com0 10 20 30 40 50 60 70 805

10

Time (sec)

Validation Procedure

During coast‐down:

Generalized equation under all conditions (including coast‐down):

Tire traction/braking force

Wheel force


Wheel force sensor

measurements

Validation Procedure

35

20

25

30

m/s

)

5

10

15

Vel

ocity

(m

Parameter identification

0 10 20 30 40 50 60 70 80 90-5

0

Time (sec)

Parameter Validation


Estimates are poor outside the coast‐down region

Residual Analysisy

Analyze cross‐correlation coefficient of residuals:

An example:

0.05

0.1

elat

ion

coef

ficie

nt

0 6

0.8

1

1.2

latio

n co

effic

ient

-0.05

0

rmal

ized

cro

ss c

orre

0

0.2

0.4

0.6rm

aliz

ed c

ross

cor

re


-1000 -800 -600 -400 -200 0 200 400 600 800 1000-0.1

No

Time-1000 -800 -600 -400 -200 0 200 400 600 800 1000

-0.2

Nor

Time

Residual Analysisy

Analyze cross‐correlation coefficient of residuals:

0.4

0.5

n co

effic

ient

posi

tion)

0.4

0.5

0.6

n co

effic

ient

g an

gle) Residuals & steering angle Residuals & accelerator

pedal position

0 1

0.2

0.3

mal

ized

cro

ss-c

orre

latio

nR

esid

uals

& A

cc p

edal

0.1

0.2

0.3

mal

zed

cros

s-co

rrela

tion

(Res

idua

ls &

Ste

erin

g p p

0 10 20 30 40 50 600

0.1

Time (sec)

Nor

m (

0 10 20 30 40 50 60-0.1

0

Time (sec)

Nor

m


Limitations of Coast-down TestsProcedure:1. A long straight flat track is needed

• SAE procedures requires a minimum speed band of 70 to 15 mph2. Results include drivetrain losses and may not be suitable for some

applicationspp3. Results are not consistent for all driving conditions, especially outside the

coast‐down region• Residuals are correlated with accelerator pedal position, steeringResiduals are correlated with accelerator pedal position, steering

angle,…4. Predictor basis is not orthogonal giving rise to the mathematical

complications due to multicollinearitycomplications due to multicollinearity• Estimates of a, b, c might be biased or have high variance


Outline

1. Introduction


3. Instrumentation & sample datasample data


5. Force measurement th dmethod


6. Summary

Identification using Force Methodg

Use regression to identify c c = 0.5371

30

35 IDID

20

25

(m/s

)

5

10

15

Vel

ocity

5

0

5

Validation


0 50 100 150 200 250-5

Time (sec)

Validation Test

R2 0 9926


R2 = 0.9926

95% confidence limit for c: 0.53‐ 0.55

Comparison of Coast-down & Force Method


• Front tires: Bridgestone Blizzak• Rear tires: Bridgestone Sneaker

Comparison of Coast-down & Force Method


Cross-correlation of Residuals0.4

0.5

icie

nt)

New residualsCoastdown residualsResiduals & steering angle

0.2

0.3

corre

latio

n co

effi

& S

teer

ing

angl

e)

0 1

0

0.1

Nor

mal

zed

cros

s-(R

esid

uals

&

0 50 100 150 200 250-0.2

-0.1

Time (sec)

N

0.5

0.6

ent

New residualsCoastdown residuals

Residuals & accelerator d l iti

0 2

0.3

0.4

orre

latio

n co

effic

iec

peda

l pos

ition

) pedal position

0

0.1

0.2

mal

ized

cro

ss-c

o(R

esid

uals

& A

cc

Proprietary and Confidential, A&D Technology, Inc. www.aanddtech.com0 50 100 150 200 250-0.2

-0.1

Time (sec)

Nor

Traction & Braking ScenariosAssumptions:• Small inclination and side‐slip angles• No vertical displacement of the tirevx Mg

m: Mass of tire wheel assemblyJ: Polar moment of inertia of tire‐wheel assembly about the center of wheel hub

• No slipω

Mg

MZ

yT: Applied torque at wheel hubFa: Tire‐road friction force

Mrr

TX

Z

Fr

Let WFS measurements be represented as Fx, Fz and MyThen, From laser Doppler sensor

Rz

Fa From encoder

RzFa

Proprietary and Confidential, A&D Technology, Inc. www.aanddtech.comDetermined before the experiment

Calculation of ‘a’ from Force Method6 constant speed tests for each speed, 18 tests total

RL TireRL Tire

Assuming that wheel well aerodynamic losses are negligible at low g y g gspeeds, calculate

a = 271

Note: Measured value of rolling resistance is much higher than what


standardized tests predict [11]

Calculation of ‘b’ from Force Method

Mean RRF=85.4381 NσRRF= 1.7629 N Mean RRF=84.5861 N

σ = 1 9318 NσRRF= 1.9318 N

Mean RRF=84.8949 NσRRF= 2.2992 N

Very slight reduction in rolling resistance as speed increases


b ≈ 0

Summaryy

a = 191 69 b = 2 54 c = 0 41Coast‐down Method Force Method

a 271 b 0 c 0 5371a = 191.69, b = 2.54, c = 0.41• R2 = 0.9280• Estimate variance is higher

a = 271, b = 0, c = 0.5371• R2 = 0.9926• Estimation based on physics; g

• Estimation only over coast‐down; road load is under‐estimated for non coast down conditions

p y ;variance is very low

• Estimation can be carried out d i ll ditinon‐coast‐down conditions

• Residuals are correlated with driver inputs

during all conditions• Correlation is significantly

reduced• Changing the tire changes ‘c’

substantially• Influence of drivetrain losses is

• Changing the tires preserves ‘c’ very closely

• Influence of drivetrain losses is• Influence of drivetrain losses is included

• Less instrumentation is needed

• Influence of drivetrain losses is NOT included

• More instrumentation required


• Less distortion of vehicle aerodynamic

• Vehicle aerodynamics are modified to a greater extent

Acknowledgementsg

O d d t i iti t• On-road data acquisition team: – Takayasu Sasaki – Yuuki Sakurai– Masaaki Banno– Hiroki Yamaguchi

• Kenji Sato A&D Technology Ann Arbor• Kenji Sato, A&D Technology, Ann Arbor • Dr. Michael Smith, A&D Technology, Ann Arbor


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Heavy-Duty Vehicles”, Vehicle System Dynamics, Vol. 25, No. 4, 19962. D.Yanakiev & I.Kanellakopoulos, “Nonlinear spacing policies for automated heavy-duty2. D.Yanakiev & I.Kanellakopoulos, Nonlinear spacing policies for automated heavy duty

vehicles”, IEEE Transactions on Vehicular Technology, Vol. 47, No. 43. Bae, Ryu & Gerdes, “Road grade vehicle parameter estimation for longitudinal control using

GPS”, 2001 IEEE Intelligent Transportation Systems 4. C. Musardo, G. Rizzoni & B.P. Staccia, “A-ECMS: An Adaptive Algorithm for Hybrid Electric

Vehicle Energy Management” 2005 European Control Conference5. Hong Yang & Joel Maguire, “Predictive Energy Management Control Scheme for a Hybrid

Powertrain System” US Patent 2011/0066308 A1Powertrain System , US Patent 2011/0066308 A1 6. www.Carsim.com 7. 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

8. Karlsson, Hammarström, Sörensen & Eriksson, “Road surface influence on rolling resistance - Coastdown measurements for a car and an HGV”, VTI, 2011

9 J Ż b ki “T ti ffi i f h l d t t i t ti ti ” A t ti9. J.Żebrowski, “Traction efficiency of a wheeled tractor in construction operations”, Automation in Construction, Vol. 19, No. 2, 2010

10. Sandburg & Ejsmont, “Noise emission, friction and rolling resistance of car tires – Summary of an experimental study”, National conference on noise control engineering, Dec 3-5, 2000,


p y , g g, , ,Newport beach, California

11. S.K. Clark, “A handbook for the rolling resistance of pneumatic tires”, 1979

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