Kanwar Bharat Singh, Graduate Student Saied Taheri, Associate Professor

1. Anti-Lock Brake System Control Using An Innovative Intelligent Tire-Vehicle Integrated Dynamic Friction Estimation Technique Kanwar Bharat Singh, Graduate Student Saied Taheri, Associate Professor Mechanical Engineering Department Center for Vehicle Systems and Safety Intelligent Transportation Laboratory Virginia Tech

2. State of the art - Modern Day Chassis Control Systems Masterpiece Of Both Technological Innovation And Impeccable Design Vehicle With On-board Sensors Wheel Speed Sensors Driver Input Vehicle State Estimator Ride and Handling Characteristics IMU (6 axis) Host of Technological Innovations Control Inputs Steering Wheel Angle Sensor Advanced Chassis Control System On-line Measurements Of The State-of-the Vehicle Controller Optimizes The Tire Usage Estimated States ABS CMD Increased Vehicle Safety Increased Comfort Better Handling Performance Integrated Chassis Controller AFS CMD Estimated tire forces and tire–road friction coefficient Critical Input For The Controller Optimizing the interaction between the subsystems of a vehicle CDC CMD Knowledge Of Current Tire Force Utilization Level And Handling Limits

3. Vehicle State Estimator Architecture Virtual Sensor Excited With Driver Input Model Based Outputs And Actual Sensors Measurements To Make Estimates Of Unknown Measurements Actual Vehicle Estimate Of The Tire Forces Vehicle CAN Bus Vehicle State Estimator Digital Signal Processing (DSP) Chip Estimated States Steering Wheel Angle Fx, Fy, Fz Nonlinear Tire and Vehicle Model Actual Sensor Output Estimated Sensor Output Feedback Correction Input To The Controller + - Vehicle Controller Actual Yaw Rate Estimated Yaw Rate

4. Tire-Force Estimator Performance 4 What About The Performance Under Extreme Maneuvers? Tire Force Estimates Situations in which the controllers should intervene to avoid a major mishap Vehicle CAN Bus Tire Force Estimator Architecture Implemented A Nonlinear State Estimator Using A High Fidelity Vehicle Dynamics Model

5. Performance Under Extreme Conditions V E H I C L E U N S T A B L E Significant Error In The Tire-Force Estimates Tire Force Estimates Could Be Detrimental To The Performance Of Vehicle Stability Control Algorithms!! • What are the main sources of error?

6. Current Tire-Force Estimation Methodology Slip-ratio Friction Coefficient Vehicle State Estimator Vehicle State Estimator Vehicle CAN bus Tire Model Vehicle Model Load Slipangle Slip-ratio Friction Coefficient Inputs Variables For A Typical Tire Model How exactly do we estimate these variables? Indirect Estimation Technique

7. Drawback… Incorrectly detected large banking angles when none existed e.g. when driving and side slipping on a frozen lake. Effects of payload parametric variations on the vehicle model states 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 Uncertainties Of Each Sensor And State Estimators Used In The Estimation Of These Variables Reduces The Accuracy And Reliability Of The Tire Force Estimates effects of payload parametric variations on the LWV states Indirect Estimation Techniques Have Several Inherent Weaknesses

8. The Way Forward Develop a Direct Parameter Estimation Technique Methodology Attach Sensor Modules To The Innerliner Direct Estimation Technique Robust And Prompt Information About The Contact Dynamics Add “Intelligence” To The Modern Day Passive Tire Measured Data Would Be Directly Available Without Any Uncertainty-adding Procedures Intelligent Tire System

9. The Tire of The Future “Tire- In -The Loop (TIL) System” Vehicle Equipped With Intelligent Tires Low Grip Driver Assist System (Drivers can adjust their driving style) Feedback From The Tire On-board Vehicle Controller (Improve the performance of current control systems like ABS/VSC) Tire Force Feedback Based Advanced Chassis Control Systems for Vehicle Handling and Active Safety

10. Project Roadmap - Paths of Development Path 1 Tire Instrumentation & Testing Path 2 Sensor Signal Processing & Algorithm Development Path 3 Vehicle Integration (Sensor Fusion) Path 4 Development Of Chassis Control System Algorithms Sensor Glued To The Inner Liner Vehicle Equipped With Intelligent Tires Raw Signal C A N B u s Tire Sensor Signal In-house Tire Test Trailer Based Testing Vehicle CAN bus Algorithm For Estimating Tire Forces And Tire-road Friction Coefficient Processing Algorithm ABS/VSC /EBD/AFS/ DYC Command Fx, Fy, Fz,µ Controller Non – linear Vehicle Model Estimate Additional Vehicle States Required For Developing Integrated Chassis Control Algorithms Fx, Fy, Fz Outdoor Vehicle Based Testing Tire Force Distribution Algorithm Identity Sensor Platforms For Tire Applications Convert To Valuable Information Estimate Additional States Performance Improvement

11. Tire Instrumentation and Testing Sensor Location Goal: Examine Sensor Performance Tri-axial accelerometer Sensor placed in the crown region Mounting: Adhesive Asphalt/Concrete Testing Gravel Testing Extensive Outdoor Testing High Speed Testing Wet Testing Outdoor Vehicle Based Testing Implement  Design Optimize Evaluate the system performance in real world conditions Path1 Path2 Path3 Path4

12. Algorithm Development Process Sensor Signal Linked to R AW S I G N A L Sensor Signal for One Tire Rotation D Y N A M I C P H E N M E N O N Leading Edge Trailing Edge Test Data From Extensive Outdoor Testing Raw Signal Feature Extraction Algorithm X Valuable Information Y Tire Engineering Dimensions & Characteristics Z • Goal: Derive a correlation between the signal and physical phenomenon under investigation Path1  Path2 Path3 Path4

13. Signal Processing and Feature Extraction Contact Patch Length Signal Power (Domain Extracted) Locus Of Deformation Digital Integrator Peak Detection Signal Amplitude Raw Signal Develop Estimation Algorithms To Estimate Variables Of Interest Multiresolution Signal Decomposition (Signal Energy Content) Wavelet Transform Slope Estimation Signal Slope Power Spectral Density Vibration Ratio Summary of Signal Feature Extraction Algorithms Path1  Path2 Path3 Path4

14. Can we capture the load transfer effects using a single point sensor ? Load (Fz) Estimation Algorithm Fz Features: Footprint length Radial Deformation • Longitudinal Load Transfer Inputs Output • Acceleration • Braking Limitation: Working With A Single Point Sensor Artificial Neural Network (ANN) Based Parameter Estimation Algorithm • Steady State Axle Load Variations • Oscillations At Body Bounce And Wheel Hop Frequencies • Lateral Load Transfer Critical for any vehicle dynamics application Path1  Path2 Path3 Path4

15. Experimental Validation Dynamic Tire Load Estimation Algorithm Extensive outdoor tests under severe handling maneuvers Developed A Sensor Fusion Approach Intelligent tire+ Vehicle CAN Bus C A N B U S Vehicle Equipped With Intelligent Tires Information from an intelligent tire Roll angle Load Transfer Ratio (LTR) Roll Angle Estimate (bank angle compensated) Kalman Filter (Observer) Dynamic Tire Load Estimation Algorithm Roll rate Adaptive Load Transfer Ratio (ALTR) Estimation Static normal load (adaptive parameter estimation) Parameter Adaptation 15 Path1  Path2 Path3 Path4 Path1  Path2 Path3 Path4

16. Tire Slip-angle Estimation Algorithm Direct tire slip angle estimation from the tire sensor measurements Lateral Displacement Of The Contact Patch Help us to recognize sliding conditions Saturation Effect At Higher Slip Angles Multiresolution Decomposition Locus Of Deformation Strain Will Saturate Identify frequency bands where vibrations rise due to sliding Leading Edge Trailing Edge Path1  Path2 Path3 Path4

17. Dynamic Tire Slip-angle Estimation Algorithm Tire Slip Angle Observer • 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. Single-track model Dynamics of slip angle Developed an online nonlinear axle-force estimator ? available (not available) Vehicle CAN Bus Path1  Path2 Path3 Path4

18. Tire-Axle Force Estimator Performance Dynamic Tire Slip-angle Estimation Algorithm *Evaluated performance using the commercial software CARSIM Feedback Term Estimation Results Vehicle CAN Bus Low Frequency High Frequency High Frequency Improved Performance Specially In The Nonlinear Region Of Handling Nonlinear Observer –Tire-Axle Force Estimator Intelligent Tire Vehicle CAN bus Vehicle State Estimator Validation Results Longitudinal Force Estimator – Per Wheel 18 Vehicle equipped with VSC controller Path1  Path2 Path3 Path4

19. Dynamic Tire Slip-ratio Estimation Algorithm Combination of slip-ratio + tire slip state estimator High Frequency Vibrations Appear In The Acceleration Data In The Radial Direction Of The Tire. • 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. Slip state in the contact patch Slip-ratio estimator ABS Module Path1  Path2 Path3 Path4

20. Dynamic Tire Force Estimation Self Aligning Torque Observer Vehicle CAN Bus ‘‘Effect-based Approach” Measure The Effects That Friction Has On The Tires During Driving. Attempt To Extrapolate What The Limit Friction Will Be Based On This Data S E N S O R F U S I O N Input steering wheel angle Observer Performance Observer Vehicle & Tire Model High Frequency (reliable) Low Frequency • 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. Intelligent Tire Path1  Path2Path3 Path4

21. Friction Coefficient (µ )Estimation – Pure Slip Conditions Tire Model Used: Brush Model Fy v/s Slip angle Estimation Algorithm: NLLS “Force-Slip Method” Tire Model Used : Brush Model Mz v/s Slip angle Estimation Algorithm: NLLS “Moment-Slip Method” Tire Model Used : Brush Model Fy v/s Mz Estimation Algorithm: NLLS “Force-Moment Method” Tire Model Used : Linear Model Fx v/s Slip ratio Estimation Algorithm: RLS “Force-Slip Method” Tire Model Used : Brush Model Fx v/s Slip ratio 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 Estimation Algorithm: RLS “Force-Slip Method”

22. Coverage Of The Presented Estimation Method In The Friction Circle Acceleration -- Lateral Dynamics Based Large Excitation Medium Excitation Small Excitation Friction Limit -- Longitudinal Dynamics Based Large Excitation Small Excitation Right Turn Left Turn Typically, during a severe handling maneuver, vehicle experiences combined slip conditions!! • Pure slip methods cover almost all of the range of pure excitation Way Forward: Develop A Friction Estimator With Increased Coverage • All these methods based on pure-slip assumption might not handle combined slip conditions. Deceleration

23. Increase Coverage Model Based µ Estimation Nonlinear Least Squares Parameter Estimation (Unknown parameters being estimated)

24. Integrated Friction Estimation Algorithm – Flow Diagram Intelligent Tire No No Yes Yes Pure Longitudinal Slip Pure Lateral Slip Combined Slip Force-Slip Method Small Slip-ratio Method Yes Yes Force- Moment Method No No Combined-slip Tire Model Based Nonlinear Least Square Parameter Estimation Algorithm Moment-SlipMethod Yes Force-Slip Method Large Slip-ratio Method Yes No Yes No Force-SlipMethod Hold No Hold µ Path1  Path2 Path3 Path4

25. Motivation to Develop Advanced Chassis Control Systems for Vehicle Handling and Active Safety Front Left Front Right Rear Left Rear Right Path1  Path2 Path3Path4

26. Anti-Lock Brake System (ABS) Background (Optimal Brake Force Distribution) 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 Braking Force Magnitudes Depend On The Tire Load ABS Module Target Slip To Maximize The Brake Force Is Dependent On Road Surface Condition!! (Optimal Slip Control) Path1  Path2 Path3Path4

27. Present ABS Control Strategy Road Surface Condition Based Target Slip Selection Payload 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. v Reduces Effectiveness Of The Controller Tire Load Based Optimal Force Distribution Unladen v Initial instants of a braking maneuver are often used by the ABS controller to detect weight distribution. Laden Path1  Path2 Path3Path4

28. An Intelligent Tire Based Adaptive ABS Algorithm Brake Command Driver ABS Module Fuzzy/Sliding mode/Proportional Integral (Fuzzy-SMC-PI (FSP)) control + Optimal Slip Selector - Intelligent Tire Emulator Brake Preconditioning Module Optimal Force Distribution Algorithm DYC Braking Command Vehicle Yaw Moment And Lateral Force Tracking Controller A F S MO D U L E AFS Command Path1  Path2 Path3Path4

29. Controller Performance Test Condition: Jump-μ Straightline Braking Test Friction Estimator Performance Baseline ABS Strategy Proposed Modified ABS Strategy 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%) Path1  Path2 Path3Path4

30. Conclusion • 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 • Validate controller performance via hardware-in-the-loop (HIL) simulations. HIL Setup, Intelligent Transportation Laboratory Virginia Tech

31. Thank You… Questions?