1 Anti-Lock Brake System Control Using An Innovative Intelligent Tire-Vehicle Integrated Dynamic...

1

Anti-Lock Brake System Control Using An Innovative Anti-Lock Brake System Control Using An Innovative Intelligent Tire-Vehicle Integrated Dynamic Friction Intelligent Tire-Vehicle Integrated Dynamic Friction

Estimation TechniqueEstimation Technique

Kanwar Bharat Singh, Graduate StudentKanwar Bharat Singh, Graduate StudentSaied Taheri, Associate ProfessorSaied Taheri, Associate Professor

Mechanical Engineering DepartmentMechanical Engineering DepartmentCenter for Vehicle Systems and SafetyCenter for Vehicle Systems and SafetyIntelligent Transportation LaboratoryIntelligent Transportation LaboratoryVirginia TechVirginia Tech

2

State of the art - Modern Day Chassis Control SystemsState of the art - Modern Day Chassis Control Systems

IMU (6 axis)

Wheel Speed Sensors

Steering Wheel Angle Sensor

Vehicle With On-board Sensors

Vehicle State Estimator

Integrated Chassis

Controller

Control Inputs

Driver Input

Estimated StatesABS CMD

AFS CMD

CDC CMD

Estimated tire forces and tire–road friction coefficient

Controller Optimizes The Tire UsageController Optimizes The Tire Usage

On-line Measurements Of The State-of-the Vehicle

Knowledge Of Current Tire Knowledge Of Current Tire Force Utilization Level And Force Utilization Level And

Handling LimitsHandling Limits

Critical Critical InputInputForForTheThe

ControllerController

Masterpiece Of Both Technological Innovation And Impeccable DesignMasterpiece Of Both Technological Innovation And Impeccable Design

Ride and Handling CharacteristicsRide and Handling Characteristics

Advanced Chassis Control SystemAdvanced Chassis Control System

Increased Vehicle Safety

Increased Comfort

Better HandlingPerformance

What?What?

How?How?

Why?Why?

Host of Technological Innovations

Optimizing the interaction between the subsystems of a vehicleOptimizing the interaction between the subsystems of a vehicle

0 100 200 300 400 500-30

-20

-10

0

10

20

30

Yaw

rat

e [D

eg/s

ec]

Time[S]

3

Nonlinear Tire and Vehicle Model

Vehicle State Estimator ArchitectureVehicle State Estimator Architecture

Steering Wheel Angle

Vehicle State EstimatorVehicle State EstimatorDigital Signal Processing (DSP) Chip

Actual Vehicle

Actual Sensor Output

Estimated Sensor Output

Vehicle CAN BusVehicle CAN Bus

EstimatedStates

Vehicle ControllerVehicle Controller

+ -

FeedbackCorrection

0 100 200 300 400 500-30

-20

-10

0

10

20

30

Yaw

rat

e [D

eg/s

ec]

Time[S]

Actual Yaw Rate Estimated Yaw Rate

Fx, Fy, Fz

Estimate Of The Estimate Of The Tire ForcesTire Forces

Input To The Controller

Virtual Sensor Excited With Driver Input Model Based Outputs And Actual Sensors Measurements To Make Estimates Of Unknown Measurements

Implemented A Nonlinear State Estimator Using A High Fidelity Vehicle Dynamics Model

4

Tire-Force Estimator PerformanceTire-Force Estimator PerformanceTire Force Estimates

0 10 20 30 40 50 60 70 80 90-1.5

-1

-0.5

0

0.5

1

1.5x 10

4

Time [S]

[N]

Lateral Force - Front Axle

ActualEstimated

0 10 20 30 40 50 60 70 80 90-1

-0.5

0

0.5

1x 10

4

Time [S]

[N]

Lateral Force - Rear Axle

ActualEstimated

0 10 20 30 40 50 60 70 80 90 100-2

-1.5

-1

-0.5

0

0.5

1

1.5

2x 10

4

Time [S]

[N]

Longitudinal Force - Front Axle

ActualEstimated

Vehicle CAN Bus

Tire Force Estimator Architecture

4

What About The Performance Under Extreme Maneuvers? What About The Performance Under Extreme Maneuvers?

Situations in which the controllers should intervene to avoid a major mishap

5

Performance Under Extreme ConditionsPerformance Under Extreme Conditions

0 1 2 3 4-8000

-6000

-4000

-2000

0

Time [S]

[N]

Fxfl

Actual

Estimated

0 1 2 3 4-8000

-6000

-4000

-2000

0

Time [S]

[N]

Fxrl

Actual

Estimated

0 1 2 3 4-2500

-2000

-1500

-1000

-500

0

500

Time [S][N

]

Fxfr

Actual

Estimated

0 1 2 3 4-2500

-2000

-1500

-1000

-500

0

500

Time [S]

[N]

Fxrr

Actual

Estimated

Tire Force Estimates

What are the main sources of error?What are the main sources of error?

Significant Error In The Tire-Significant Error In The Tire-Force EstimatesForce Estimates

Could Be Detrimental To The Could Be Detrimental To The Performance Of Vehicle Stability Performance Of Vehicle Stability

Control Algorithms!!Control Algorithms!!

VVEEHHIICCLLEE

UUNNSSTTAABBLLEE

6

xF

yF

Tire Model( )

zF

Normal load

( )Slip ratio

( )Slipangle

( )Frictioncoefficient

Vehicle Model

Vehicle State Estimator

Inputs Variables For A Typical Tire Inputs Variables For A Typical Tire ModelModel

How exactly do we estimate these variables?How exactly do we estimate these variables?

Slip-ratio

Friction Coefficient

Sensor Info Vehicle observer Info

, , ', ,y xa a r , ,y xv v

11

* * ** *

2( )

*tan

*

using nonlinear observers

static

s r u a s rZ Z y x

y

x

e

m h m h m hF F a a

t a b

v l r

v

v r

v

Indirect Estimation Indirect Estimation TechniqueTechnique

Current Tire-Force Estimation MethodologyCurrent Tire-Force Estimation Methodology

Vehicle CAN busVehicle State Estimator

Load

Slipangle

Slip-ratio

Friction Coefficient

7

effects of payload parametric variations on the LWV states

Uncertainties Of Each Sensor And State Estimators Used In The Estimation Uncertainties Of Each Sensor And State Estimators Used In The Estimation Of These Variables Reduces The Accuracy And Reliability Of The Tire Force Of These Variables Reduces The Accuracy And Reliability Of The Tire Force

EstimatesEstimates

Incorrectly detected large banking angles when none existed e.g. when driving and side slipping

on a frozen lake.

Modeling error: Dynamics of the roll motion are different during normal operation (all

wheels on the ground) and in rollover phase (in two wheel lift off condition).

Challenge is to differentiate the bias induced by road bank disturbances from actual effect of vehicle lateral dynamics in current measurements

Effects of payload parametric variations on the vehicle model states

Indirect Estimation Techniques Have Several Inherent WeaknessesIndirect Estimation Techniques Have Several Inherent Weaknesses

Drawback…Drawback…

8

The Way ForwardThe Way ForwardDevelop a Direct Parameter Estimation TechniqueDevelop a Direct Parameter Estimation Technique

Robust And Prompt Information About Robust And Prompt Information About The Contact DynamicsThe Contact Dynamics

Measured Data Would Be Directly Measured Data Would Be Directly Available Without Any Uncertainty-adding Available Without Any Uncertainty-adding

ProceduresProcedures

Direct Estimation Technique

MethodologyMethodology

Attach Sensor Modules To The

Innerliner

Intelligent Tire System

Add “Intelligence” Add “Intelligence” To The Modern Day To The Modern Day

Passive Tire Passive Tire

9

Low Grip

On-board Vehicle On-board Vehicle ControllerController

Driver Assist SystemDriver Assist System

The Tire of The FutureThe Tire of The Future

(Improve the performance of current control systems like

ABS/VSC)

Vehicle Equipped With Intelligent TiresVehicle Equipped With Intelligent Tires

Tire Force Feedback Based Advanced Chassis Control Systems for Vehicle Handling and Active

Safety

““Tire- In -The Loop (TIL) System”Tire- In -The Loop (TIL) System”

(Drivers can adjust their driving style)

Feedback Feedback From From

The TireThe Tire

10

Project Roadmap - Paths of DevelopmentProject Roadmap - Paths of DevelopmentPath 1Path 1

Tire Instrumentation & Testing

Path 2Path 2Sensor Signal Processing & Algorithm Development

Path 3Path 3 Vehicle Integration

(Sensor Fusion)

Path 4Path 4Development Of Chassis

Control System Algorithms

Sensor Glued To The Inner Liner

In-house Tire Test Trailer Based Testing

Outdoor Vehicle Based Testing

0 50 100 150 200 250 300 350-2000

-1500

-1000

-500

0

500

1000

1500

Wheel Turn [ ]

Accele

rati

on

[m

/s2 ]

Radial Acceleration

Fx, Fy, Fz

Raw Signal

Processing Algorithm

Fx, Fy, Fz,µ

Estimate Additional Vehicle States Required For Developing Integrated

Chassis Control Algorithms

Vehicle CAN bus

Vehicle Equipped With Intelligent Tires

Algorithm For Estimating Tire

Forces And Tire-road Friction Coefficient

CAN

Bus

ABS/VSC/EBD/AFS/

DYCCommand

ControllerNon – linear Vehicle Model

Tire Force Distribution Algorithm

Tire Sensor Signal

Identity Sensor PlatformsIdentity Sensor PlatformsFor Tire ApplicationsFor Tire Applications Convert To Valuable InformationConvert To Valuable Information Estimate Additional StatesEstimate Additional States Performance ImprovementPerformance Improvement

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Tire Instrumentation and TestingTire Instrumentation and Testing

Extensive Extensive OutdoorOutdoorTestingTesting

High Speed TestingHigh Speed Testing Wet TestingWet Testing

Outdoor Vehicle Based TestingOutdoor Vehicle Based Testing

Asphalt/Concrete TestingAsphalt/Concrete Testing Gravel TestingGravel Testing

Tri-axial accelerometerSensor placed in the crown

region

Sensor Location

Mounting: Adhesive

Evaluate the system performance in real world conditions

Goal: Examine Sensor Performance

Implement Implement Design Design Optimize OptimizePath1 Path2 Path3 Path4

0 50 100 150 200 250 300 350-50

0

50

100

150

Wheel Turn [ ]

Ac

ce

lera

tio

n [

g]

Lateral Acceleration

0 50 100 150 200 250 300 350-300

-200

-100

0

100

200

300

Wheel Turn [ ]

Ac

ce

lera

tio

n [

g]

Radial Acceleration

0 50 100 150 200 250 300 350-300

-200

-100

0

100

200

300

Wheel Turn [ ]

Ac

ce

lera

tio

n [

g]

Circumferential Acceleration

12

XX

YY

ZZ

Sensor Signal for One Tire RotationSensor Signal for One Tire Rotation

Sensor SignalSensor Signal

DDYYNNAAMMIICC

PPHHEENNMMEENNOONN

Linked to0 50 100 150 200 250 300 350

-300

-200

-100

0

100

200

300

Wheel Turn [ ]

Ac

ce

lera

tio

n [

g]

CircumferentialLateralRadial

Leading Edge Trailing Edge

0 1 2 3-800

-600

-400

-200

0

200

400

600

800Constant Speed Test

Acc

eler

atio

n[m

/s2 ]

Time [S]0 1 2 3 4 5

-300

-200

-100

0

100

200

300

400Acceleration (traction) Test

Acc

eler

atio

n[m

/s2 ]

Time [S]0 1 2 3 4

-600

-400

-200

0

200

400

600

Time [S]

Acc

eler

atio

n [

m/s

2 ]

Decceleration (braking) Test

9 10 11 12 13 14

-1000

-800

-600

-400

-200

0

200

400

600

800

1000

Time [S]

Acc

eler

atio

n [

m/s

2 ]

Steering TestTireTire

Engineering Engineering Dimensions & Dimensions &

CharacteristicsCharacteristics

Algorithm Development ProcessAlgorithm Development Process

Feature Feature Extraction Extraction AlgorithmAlgorithm

RRAAWW

SSIIGGNNAALL

Test Data Test Data From From

Extensive Extensive Outdoor Outdoor TestingTesting

Path1 Path2 Path3 Path4Goal: Derive a correlation between the signal and physical phenomenon under investigation

Raw Signal

Valuable Information

13Path1 Path2 Path3 Path4

Signal Processing and Feature ExtractionSignal Processing and Feature Extraction

0 50 100 150 200 250 300 350

-300

-200

-100

0

100

200

300

Wheel Turn [ ]

Accele

rati

on

[g

]

CircumferentialLateralRadial

Raw SignalRaw SignalPeak Peak

DetectionDetection

Digital Digital IntegratorIntegrator

Signal Signal AmplitudeAmplitude

Slope Slope EstimationEstimation

Power Spectral Power Spectral DensityDensity

Wavelet Wavelet TransformTransform

20 40 60 80 100 120 140 160 180 200 22026

28

30

32

34

36

38

40

42

44

46Total Signal Power per Revolution

No

rmal

ized

po

wer

[d

B]

Revolution Number

20 40 60 80 100 120 140 160 180 200 22026

28

30

32

34

36

38

40

42

44

46Total Signal Power per Revolution

No

rmal

ized

po

wer

[d

B]

Revolution Number

Contact Patch LengthContact Patch Length

Signal SlopeSignal Slope

Signal Power (Domain Extracted) Signal Power (Domain Extracted)

Multiresolution Signal Multiresolution Signal Decomposition (Signal Decomposition (Signal

Energy Content)Energy Content)

Locus Of Locus Of DeformationDeformation

Vibration Vibration RatioRatio

( )

( )

( )

( )

zF Normal load

Slipangle

Slip ratio

Frictioncoefficient

Develop Estimation Algorithms To Estimate Variables Of

Interest

Summary of Signal Feature Extraction Algorithms

14

Load (Fz) Estimation AlgorithmLoad (Fz) Estimation Algorithm

Inputs Output

Artificial Neural Network (ANN) Based Parameter Estimation AlgorithmArtificial Neural Network (ANN) Based Parameter Estimation Algorithm

FzFz

Features:Footprint lengthRadial Deformation

0 50 100 150 200800

900

1000

1100

1200

1300

1400

1500

1600

1700

1800

Tir

e L

oa

d [

lbs

]

Time[S]

ActualEstimated

Can we capture the load transfer effects using a single point sensor ? Can we capture the load transfer effects using a single point sensor ?

Longitudinal Load Longitudinal Load TransferTransfer

Lateral Load TransferLateral Load Transfer

AccelerationBraking

Steady State Axle Steady State Axle Load Variations Load Variations

Oscillations At Body Bounce And Wheel Hop Frequencies

zF Static load Lateral load transfer Longitudinal load transfer

Critical for any vehicle dynamics applicationCritical for any vehicle dynamics applicationPath1 Path2 Path3 Path4

Limitation: Working With A Single Point Limitation: Working With A Single Point SensorSensor

15

Dynamic Tire Load Estimation AlgorithmDynamic Tire Load Estimation Algorithm

CAN

BUS

xa

ya

Vehicle Vehicle Equipped Equipped

With With Intelligent Intelligent

TiresTires

, , ,FL FR RL RRz z z zF F F F

Load Transfer Ratio (LTR) Roll Angle

Estimate (bank angle compensated)

Parameter Adaptation

m

'

Information from an intelligent tire

Kalman Filter(Observer)

Roll angle

Roll rate

ya xa

Dynamic Tire Load Dynamic Tire Load Estimation Estimation AlgorithmAlgorithm

'

:

:

:

:

:

:ii

y

x

z

a Lateral acceleration

a Longitudinal acceleration

Roll rate

Roll angle

m Vehiclemass

F Tire load

Static normal load

AdaptiveLoad Transfer Ratio (ALTR)Estimation ya

xa(adaptive parameter

estimation)

'

Path1 Path2 Path3 Path4

Developed ADeveloped ASensor Fusion Sensor Fusion

ApproachApproach

Intelligent tireIntelligent tire++


15

2.6 2.8 3 3.2 3.4 3.6 3.8

x 104

0

1000

2000

3000

4000

5000

6000

7000

Time [S]

Fz F

L [

N]

Tire Load Estimate

Actual (Force Hub)Estimated (Intelligent Tire + Observer

Experimental ValidationExperimental Validation

-400 -300 -200 -100 0 100 200 300 400-800

-600

-400

-200

0

200

400

600

Xposition

[m]

Yp

osi

tio

n [

m]

Vehicle Path

-2 -1.5 -1 -0.5 0 0.5 1 1.5 2-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

Lateral Acceleration [g]

Lo

ng

itu

din

al

Ac

ce

lera

tio

n [

g]

g-g diagram


Extensive outdoor tests under severe handling maneuvers


0 50 100 150 200 250 300 350

-400

-200

0

200

400

600

Ac

ce

lera

tio

n [

m/s

2 ]

Lateral Acceleration- With Slip angle

0 50 100 150 200 250 300 350-500

0

500

Ac

ce

lera

tio

n [

m/s

2 ]

Lateral Acceleration - No Slip angle

Leading Edge Trailing Edge

Locus Of Locus Of DeformationDeformation

Multiresolution Multiresolution DecompositionDecomposition

Help us to Help us to recognize recognize

sliding sliding conditionsconditions

Direct tire slip angle estimationDirect tire slip angle estimation from the tire sensor measurementsfrom the tire sensor measurements

Tire Slip-angle Estimation AlgorithmTire Slip-angle Estimation Algorithm

1.21 1.22 1.23 1.24 1.25 1.26 1.27 1.28 1.29-12

-10

-8

-6

-4

-2

0

2

4

6x 10

-3

Lateral Deflection - Tire Centerline

Lat

eral

D

efle

ctio

n

Time

SA=0

SA=1

SA=3

Lateral Displacement Of The Contact PatchLateral Displacement Of The Contact Patch

1.21 1.22 1.23 1.24 1.25 1.26 1.27 1.28 1.29-12

-10

-8

-6

-4

-2

0

2

4

6x 10

-3

Lat

eral

Def

lect

ion

Time

SA=0

SA=1

SA=3

SA=5

Saturation EffectAt Higher Slip

Angles

0 500 1000 150020

25

30

35

40

45

50

55

60

65

Frequency [Hz]

Po

wer

[d

B]

Slip Angle Sweep Dependence Study- Frequency Domain

Straight Line Slip Angle SweepIdentify frequency

bands where vibrations rise due to sliding

Strain Will Saturate

17

Dynamic Tire Slip-angle Estimation AlgorithmDynamic Tire Slip-angle Estimation Algorithm

Tire Slip Angle ObserverTire Slip Angle Observer

2^ ^ ^1 1front rearf y y

x z x x z x

a abF F r

mv I v mv I v

, ,xv r ^ ^

,front reary yF F

Single-track model

Dynamics of slip angle

Vehicle CAN Bus

??

2^ ^ ^

The update equation for the front slip angle

1 1front rearf y y

x z x x z x

a abF F r

mv I v mv I v


^ ^ ^ ^

, ,front rear front righty y x xF F F F

(not available)(not available)availableavailable

Developed an online nonlinear axle-force estimatorDeveloped an online nonlinear axle-force estimator

Highlights: Observer uses

sensor information already available in modern cars equipped with VSC

No prior knowledge of tire characteristics, such as a Pacejka model, is required to implement the observer.

0 50 100-6000

-4000

-2000

0

2000

4000

6000

Time [S]

[N]

Fxfront

left

ActualEstimated

0 50 100-6000

-4000

-2000

0

2000

4000

6000

Time [S]

[N]

Fxfront

right

ActualEstimated

0 50 100-5000

0

5000

Time [S]

[N]

Fxrear

left

ActualEstimated

0 50 100-5000

0

5000

Time [S]

[N]

Fxrear

right

ActualEstimated

0 20 40 60 80 100-1.5

-1

-0.5

0

0.5

1x 10

4

Time [S]

[N]

Lateral Force - Front Axle

ActualEstimated

0 20 40 60 80 100-1

-0.5

0

0.5

1x 10

4

Time [S]

[N]

Lateral Force - Rear Axle

ActualEstimated

Tire-Axle Force Estimator PerformanceTire-Axle Force Estimator Performance

Vehicle CAN Bus

Nonlinear Observer –Tire-Axle Force

Estimator

Longitudinal Force Estimator

– Per Wheel

frontFy

rearFy

right right left leftf r f rFx Fx Fx Fx

, ,x ya a r

xa BrakeCMD

Signal

, , ,right right left leftf r f rFx Fx Fx Fx

, , ,fl fr rl rrzF

18

Estimation ResultsEstimation Results

Vehicle equipped with VSC controller

*Evaluated performance using the commercial software CARSIM

180 1 2 3 4 5 6 7 8 9 10

-8

-6

-4

-2

0

2

4

6

8

10

12

Time [S]

Sli

p a

ng

le [ ]

Slip Angle Estimator- Performance

ActualEstimated Without FeedbackEstimated With FeedbackImproved Performance Improved Performance

Specially In The Nonlinear Specially In The Nonlinear Region Of HandlingRegion Of Handling

Dynamic Tire Slip-angle Estimation AlgorithmDynamic Tire Slip-angle Estimation Algorithm

Validation ResultsValidation Results

intelligent tire

2^ ^ ^ ^ ^1 1( )

front rearf y y f fx z x x z x

a abF F r k

mv I v mv I v

Feedback Term

Intelligent Tire


Low Frequency

Vehicle CAN bus Vehicle State Estimator

High Frequency High Frequency

19

Dynamic Tire Slip-ratio Estimation AlgorithmDynamic Tire Slip-ratio Estimation Algorithm


ABS Module

High Frequency Vibrations Appear In The Acceleration Data In The Radial Direction

Of The Tire.

Slip-ratio estimator

Slip state in the contact patch

ABS slip-ratio estimator –During a hard braking event- significant error in our estimates of slip-ratio.

Get a measure of the slip state of the tire by identifying high frequency vibrations in the acceleration data-Feedback for our slip-ratio estimator.

Combination of slip-ratio + tire slip state estimatorCombination of slip-ratio + tire slip state estimator

20

Dynamic Tire Force EstimationDynamic Tire Force Estimation

Intelligent TireIntelligent Tire


Vehicle Vehicle &&

Tire ModelTire Model

SSEENNSSOORR

FFUUSSIIOONN

, ,zF xF

yF


, ,zF

High High FrequencyFrequency(reliable)(reliable)

Low Low FrequencyFrequency

xF v/s λ

yF v/s

Self Aligning Torque ObserverSelf Aligning Torque Observer

0 2 4 6 8 10 12 14 16-20

-15

-10

-5

0

5

10

15

20

Time [S]S

elf

Alig

nin

g T

orq

ue

[Nm

]

a simulation

a estimated

0 2 4 6 8 10 12 14 16-40

-30

-20

-10

0

10

20

30

40

Time [S]

Ste

erin

g W

hee

l An

gle

[ ]

Input steering wheel angle

Observer Performance

Electric power steering (EPS) is becoming common in modern day cars. . Linear disturbance observer enables us to extract self aligning torque from steering torque measurements.

Observer

0 2 4 6 8 10 12-100

-50

0

50

100

150

200

Slip Angle [ ]

Mz[N

m]

Adaptation of the brush model towards experimental data

Cy = 65985.6898 = 0.28891

Cy = 95528.1811 = 0.95648

Experimental Data-Low

Brush Model- Low

Experimental Data-High

Brush Model- High

M v/sz

, , , , ,x y z zF F F M

‘‘Effect-based Approach”

MeasureThe Effects That Friction

Has On TheTires During Driving.

Attempt To ExtrapolateWhat The Limit Friction Will Be Based On This

Data

21

Friction Coefficient (µ )Estimation – Pure Slip ConditionsFriction Coefficient (µ )Estimation – Pure Slip Conditions

2 3 3

2

tan( ) tan( ) tan ( )1 1tan( )

3 27y y

y yz z

C CF C

F F

3tan( ) tan( )

13 3

y ya

z

C a C

F

Tire Model Used: Brush Model

Estimation Algorithm: NLLS

Tire Model Used : Brush Model

Estimation Algorithm: NLLS

Estimation Algorithm: NLLS0 20 40 60 80 100 120 140

0

1000

2000

3000

4000

5000

6000

7000Gough Plot

Self Aligning Moment (-Mz) [Nm]

Lat

eral

Fo

rce

(Fy)

[N

]

=1=0.6=0.2=1=2=3=4=5=6=7

0 2 4 6 8 10 12-100

-50

0

50

100

150

200

Slip Angle [ ]

Mz[N

m]

Adaptation of the brush model towards experimental data

Cy = 65985.6898 = 0.28891

Cy = 95528.1811 = 0.95648

Experimental Data-Low

Brush Model- Low

Experimental Data-High

Brush Model- High

2 3 3

2

3

tan( ) tan( ) tan ( )1 1tan( )

3 27

tan( ) tan( )1

3 3

y yy y

z z

y ya

z

C CF C

F F

C a C

F


Tire Model Used : Linear Model

x xF C

Estimation Algorithm: RLS


Estimation Algorithm: RLS 0 2 4 6 8 10 12 14 16 18 200

500

1000

1500

2000

2500

3000

3500

Slip Ratio []

Fx

Adaptation of the brush model towards MF tire model

= 1.1

= 0.95

= 0.35

= 0.2

Magic Formula- (Dry Asphalt)Brush Model- (Dry Asphalt)Magic Formula- (Wet Asphalt)Brush-Model- (Wet Asphalt)Magic Formula- (Snow)Brush-Model- (Snow)Magic Formula- (Ice)Brush-Model- (Ice)

32 3

2

1 11 1 11 3 27

x x

x xz z

C CF C

F F

Fy v/s

Slip angle

Mz v/s

Slip angle

Fy v/s Mz

Fx v/s

Slip ratio

Fx v/s

Slip ratio

“Force-Slip Method”

“Moment-Slip Method”

“Force-Moment Method”



ExcitationExcitation EstimatorEstimator Underlying PrincipleUnderlying Principle

Large Lateral Large Lateral ExcitationExcitation

(80 -100%)

Medium Lateral Medium Lateral ExcitationExcitation

(50 -80%)

Small Lateral Small Lateral ExcitationExcitation

(30 -50%)

Small Longitudinal Small Longitudinal ExcitationExcitation

(0 -2%)

Large Longitudinal Large Longitudinal ExcitationExcitation

(30 -100%)Looked at a number of different algorithms and did a parametric analysis to study the performance of each of these methods under different levels of excitation

22

LeftLeftTurnTurn

Coverage Of The Presented Estimation Method In The Friction CircleCoverage Of The Presented Estimation Method In The Friction Circle

RightRightTurnTurn

AccelerationAcceleration

DecelerationDeceleration

Friction Limit

-- -- Lateral Dynamics BasedLateral Dynamics Based

-- -- Longitudinal Dynamics BasedLongitudinal Dynamics Based

Large Excitation

Medium Excitation

Small Excitation

Large Excitation

Small Excitation

Typically, during a severe handling

maneuver, vehicle experiences combined

slip conditions!! Way Forward: Develop A Friction Estimator With

Increased Coverage

Pure slip methods cover almost all of the range of pure excitation

All these methods based on pure-slip assumption might not handle combined slip conditions.

23

2 2 2 2 2

_ _2 2 2 2

( ) ( ) (1 ) cos ( ) ( )* *sin *cos

( ) tan ( ) ( )

x y xfront right front right

y x

F F C FFy Fx

CF F

2 2 2 2 2

_ _2 2 2 2

(1 ) cos ( ) ( ) sin ( )( ) ( ) tan( )* *cos *sin

sin( )( ) tan ( ) ( )

yx yfront right front right

y x

F CF FFy Fx

CF F

Increase Coverage Model Based µ EstimationIncrease Coverage Model Based µ EstimationNonlinear Least Squares Parameter EstimationNonlinear Least Squares Parameter Estimation

, ,C C

The required parameters for the estimation algorithm include:

L.H.SL.H.S R.H.SR.H.S

Tire load (Fz)Slip ratio (λ)Slip angle ()

(Unknown parameters being estimated)

_front rightFy

_front rightFy

, , ,right right left leftf r f rFx Fx Fx Fx

24

Integrated Friction Estimation Algorithm – Flow DiagramIntegrated Friction Estimation Algorithm – Flow Diagram

Pure Longitudinal Slip Pure Lateral Slip Combined Slip

µ

2%

30%

Force-Slip Method

Small Slip-ratio Method

Force-Slip Method

Large Slip-ratio Method

Yes

Yes

Yes

Hold

limlower it nonlinear Force- Moment

Method

No

nonlinear sat

Yes

Moment-SlipMethod

No

sat Force-SlipMethod

Yes

Yes

NoHold

No

Combined-slip Tire Model Based Nonlinear Least Square Parameter Estimation Algorithm

No

No

No

Yes

2% lower threshold

Intelligent Tire

,



Front Left

Rear Left

Front Right

Rear Right

Motivation to Motivation to Develop Advanced Develop Advanced

Chassis Control Chassis Control Systems for Vehicle Systems for Vehicle Handling and Active Handling and Active

SafetySafety

x

y

z

F

F

F

26

Anti-Lock Brake System (ABS)Anti-Lock Brake System (ABS)


The control target of ABS: The control target of ABS:

Keep the wheels from locking, thus guaranteeing good controllability of the vehicle and exploiting maximally the coefficient of friction between the tire and the road

Target Slip To Maximize The Brake Force Is Target Slip To Maximize The Brake Force Is Dependent On Road Surface Condition!!Dependent On Road Surface Condition!!

BackgroundBackground

Braking Force Braking Force Magnitudes Magnitudes

Depend On The Depend On The Tire Load Tire Load

ABS Module

(Optimal Slip Control)(Optimal Slip Control)

(Optimal Brake Force (Optimal Brake Force Distribution)Distribution)

27

Present ABS Control StrategyPresent ABS Control Strategy


First part of the maneuver (about 1.5 s) is used by the control system to adjust braking pressure according to tire–road adherence conditions.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.80

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Normalized Front Axle Brake Force [Fxfront

/Vehicle Weight]No

rmal

ized

Rea

r A

xle

Bra

ke F

orc

e [F

xre

ar/V

ehic

le W

eig

ht]

ax=0.1g

0.2g

0.3g

0.4g

0.5g

0.6g

0.7g

0.8g

0.9g

1g

*ax= Vehicle Deceleration

Optimal Brake Force Distribution

UnladenLaden (payload increased by 25%)

Payload

Unladen

Laden

Road Surface Condition Based Target Slip SelectionRoad Surface Condition Based Target Slip Selection

Tire Load Based Optimal Force DistributionTire Load Based Optimal Force Distribution

Initial instants of a braking maneuver are often used by the ABS controller to detect weight distribution.

Reduces Reduces Effectiveness Of Effectiveness Of The ControllerThe Controller

v

v

28

An Intelligent Tire Based Adaptive ABS AlgorithmAn Intelligent Tire Based Adaptive ABS Algorithm


, ,x y zF F F

Intelligent TireEmulator

Optimal Slip Selector

desired

current+

-

Brake Preconditioning

Module

,desired desiredx yF F

e

AFS

MODULE

AFS Command

DYC Braking Command

ABS ModuleFuzzy/Sliding mode/Proportional

Integral (Fuzzy-SMC-PI (FSP)) control

bT

Optimal Force Distribution Algorithm

Brake Command

Driver

Vehicle states

Vehicle Yaw Moment And Lateral Force

Tracking Controller

29

Controller PerformanceController Performance


Using the surface condition information from the intelligent tire makes it possible to apply a brake preconditioning algorithm & allows for a considerable decrease in the stopping distance (reduction in the stopping distance scales up to 4.1%)

Baseline ABS StrategyBaseline ABS Strategy Proposed Modified ABS StrategyProposed Modified ABS Strategy

Test Condition: Test Condition: Jump-Jump-μ μ Straightline Braking TestStraightline Braking Test

Friction Friction Estimator Estimator

PerformancePerformance

30

ConclusionConclusion This work focuses on the possibility of enhancing the performance of the ABS (Antilock Braking System)/EBD (electronic braking distribution) control system by using the additional information provided by intelligent tires.

We expect the intelligent tire system to stimulate the development of a new generation of traction, braking and stability control systems for improving vehicle safety and performances.

Major challenge: Meeting the power supply needs of all the electronic components of an intelligent tire system.

Future Work Future Work Validate controller performance via hardware-in-the-loop (HIL) simulations.

HIL Setup, Intelligent Transportation LaboratoryVirginia Tech

31

Thank You…Thank You…

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