MODEL-BASED ANALYSIS OF WHEEL SPEED VIBRATIONS FOR ROAD FRICTION CLASSIFICATION USING MF-SWIFT. Antoine Schmeitz, Mohsen Alirezaei

Transcription

1 MODEL-BASED ANALYSIS OF WHEEL SPEED VIBRATIONS FOR ROAD FRICTION CLASSIFICATION USING MF-SWIFT Antoine Schmeitz, Mohsen Alirezaei

2 CONTENTS Introduction Road friction classification from wheel speed vibrations Tyre modelling Simulation environment Evaluation of the estimation method Concluding remarks

3 INTRODUCTION Consider an Autonomous Emergency Braking (AEB) test:

4 INTRODUCTION Now imagine that it is winter and the road is icy? What would happen with the cyclist if the controller does not know the friction coefficient between tyre and road? Probably this:

5 INTRODUCTION Basic problem: identifying friction potential during normal driving conditions Solution becomes more important with higher levels of vehicle automation when task are taken over from the human driver Interesting possible solutions was presented by Toyota (Umeno): Wheel speed vibrations are used for friction estimation T. Umeno: Estimation of Tire-Road Friction by Tire Rotational Vibration Model, Review of Toyota CRDL, vol. 37 No. 3, 2002.

6 INTRODUCTION In 2006 this method was evaluated by Pavković et al. together with the slipslope method (SAE paper ) Able to distinguish different road friction conditions from both methods Identified slip stiffnesses from both methods did however not agree Contribution of this paper: Review method and tyre modelling Developing a simulation environment to study the estimation method in terms of accuracy and robustness Simulation environment to develop Integrated Friction Estimator using multiple methods

7 ROAD FRICTION CLASSIFICATION FROM WHEEL SPEED VIBRATIONS ABS wheel speed sensor Signal conditioning Bandpass filter Slip stiffness Friction Tyre model Estimation method Estimation method: identify parameters of a second order linear time-invariant (LTI) system use discrete time autoregressive (AR) model in combination with instrumental variable (IV) method

8 TYRE MODELLING Basic idea: Slip stiffness is measure for friction Slip stiffness drops at low friction surfaces Magic Formula model: Slip stiffness is changed by scaling factor LKX

9 TYRE MODELLING Simple tyre model of Umeno 2 nd order model for describing the 1 st tyre in-plane resonance f n ζ = = π C J θ a C J θ a ( J + J ) C a Fκ R b 2 Natural frequency affected by sidewall stiffness (e.g. inflation pressure) Damping ratio increases with decreasing slip stiffness (i.e. friction)

10 TYRE MODELLING MF-Swift rigid ring Magic Formula slip model contact patch model enveloping model Well-validated Accurately represents primary tyre modes (rigid ring modes)

11 SIMULATION ENVIRONMENT Quarter vehicle model rigid car body, wheel carrier and rim bodies suspension stiffness and damping linear elements, uncoupled in X, Z TNO s MF-Swift tyre model Measured road profiles

12 SIMULATION ENVIRONMENT Power spectral densities of wheel speed for different slip stiffness (i.e. friction) Tyre resonance disappears for low friction surfaces (i.e. small LKX)

13 SIMULATION ENVIRONMENT Natural frequencies and damping ratios from eigenvalue analysis Conclusions: Damping increases with decreasing slip stiffness (i.e. friction) (similar conclusion as Umeno et al.) However: natural and peak frequencies (f n, f p ) also decrease

14 EVALUATION OF ESTIMATION METHOD Algorithms have been implemented Results can be compared LKX = 0.5 Model Estimation f p [Hz] ζ [-]

15 EVALUATION OF ESTIMATION METHOD LKX = 1 LKX = 0.5 LKX = 0.2 Model Estimation Model Estimation Model Estimation f p [Hz] ζ [-] ζ ref / ζ [-] similar trends as found by others generally resonance frequency is rather accurately estimated LKX = 0.2 (~ice): no excitation inaccurate results increased damping is still good measure for low friction Inaccurate simple tyre model: f n = 1 2π C J θ a, ζ = 1 2 C J θ a ( J a + J b ) 1 Cθ ( J a + J b ) = * 2 2 J 2 C R ( LKX C ) R Fκ a Fκ

16 EVALUATION OF ESTIMATION METHOD Influence of road roughness Model Estimation Smooth road Rough road 1 Rough road 2 Rough road 3 f p [Hz] ζ [-] ζ ref / ζ [-] as expected influence of road roughness deviations are relatively small estimation method is quite robust

17 CONCLUDING REMARKS First tyre resonance as indicator for monitoring tyre-road friction is studied Simulation environment to study this application has been developed: results qualitatively agree well with results found in literature reference for algorithm design and testing, i.e.: real values are known frequencies and damping ratios can be estimated with reasonable accuracy trends observed with changing road friction are consistent Review of method: Simple tyre model (Umeno) is insufficiently accurate to identify slip stiffness MF-Swift model can be used to translate identified frequencies and damping ratios to correct slip stiffness values

18 CONCLUDING REMARKS Next steps: Test the method using real car data identify operating range, accuracy and limitations Implement method in an Integrated Friction Estimator sensor / estimator fusion basic idea: several algorithms should give the same tyre characteristic values (i.e. comparing apples with apples) one source (tyre model) to relate tyre characteristic values to friction estimate

19 THANK YOU FOR YOUR ATTENTION