Road surface friction and surface type estimation system and method

Patent metadata

Field	Value
Publication number	US-9751533-B2
Application number	US-201414244273-A
Country	US
Kind code	B2
Filing date	Apr 3, 2014
Priority date	Apr 3, 2014
Publication date	Sep 5, 2017
Grant date	Sep 5, 2017

Abstract

A tire-based system and method for estimating road surface friction includes a model-based longitudinal stiffness estimation generator using tire-based parameter inputs and vehicle-based parameter inputs; an actual longitudinal stiffness estimation generator using real-time vehicle-based parameter inputs; and a tire road friction estimation generator for deriving a tire road friction estimation from a comparative analysis between the actual longitudinal stiffness estimation and the model-based longitudinal stiffness estimation. A road surface classifier algorithm is employed to generate a road surface type analysis from the road friction estimation, an ambient air temperature measurement, and an ambient air moisture measurement.

First claim

What is claimed is:

1. A tire-based system for estimating road surface friction comprising:
at least one tire mounted to a wheel hub and supporting a vehicle;
at least one tire sensor mounted to the at least one tire;
at least one vehicle sensor mounted to the vehicle;
a model-based longitudinal stiffness estimator producing a model-based longitudinal stiffness estimation for the one tire from a set of tire-based inputs generated from the at least one tire sensor and a first set of vehicle-based inputs generated from the at least one vehicle sensor;
an actual longitudinal stiffness estimator producing an actual longitudinal stiffness estimation from a second set of vehicle-based inputs generated from the at least one vehicle sensor;
a tire road friction estimator deriving a road friction estimation from a comparative analysis between the actual longitudinal stiffness estimation and the model-based longitudinal stiffness estimation.

2. The system of claim 1 , wherein the set of tire-based inputs include at least one input from the group:
a measured air cavity pressure of the one tire;
tire-specific construction characteristics of the one tire; and
a measured temperature of the one tire.

3. The system of claim 1 , wherein the second set of vehicle-based inputs include at least one input from the group:
a measured wheel speed of the vehicle;
a measured wheel torque; and
a measured wheel slip ratio.

4. The system of claim 3 , wherein the first set of vehicle-based inputs include a measured hub vertical acceleration, the system further comprising a tire wear state estimator for producing a wear state estimation for the one tire from the measured hub vertical acceleration.

5. The system of claim 4 , wherein the tire wear state estimator operably produces the tire wear state estimation from a detected a shift in a vertical mode of the one tire.

6. The system of claim 3 , wherein the model-based longitudinal stiffness estimator algorithmically calculates the longitudinal stiffness estimation from the first set of vehicle-based inputs and the set of tire based inputs including vehicle load, the measured air cavity pressure of the one tire and the measured temperature of the one tire compensated by the estimated tire wear state.

7. The system of claim 6 , wherein the actual longitudinal stiffness estimator comprises a longitudinal force estimator for generating a longitudinal force estimation from the measured wheel speed and the measured wheel torque.

8. The system of claim 7 , wherein the longitudinal force estimator comprises a sliding mode observer model.

9. The system of claim 8 , wherein the actual longitudinal stiffness estimator calculates the actual longitudinal stiffness estimation from the longitudinal force estimation and the measured wheel slip ratio.

10. The system of claim 9 , wherein the actual longitudinal stiffness estimator comprises a recursive least square algorithm with forgetting factor.

11. The system of claim 1 , further comprising a road surface classifier algorithm operably conducting a road surface type analysis based on the road friction estimation, an ambient air temperature measurement, and an ambient air moisture measurement.

12. The system of claim 11 , wherein the road surface classifier algorithm comprises a modified sensor fusion algorithm.

13. A tire-based method of estimating road surface friction based on at least one tire mounted to a wheel hub and supporting a vehicle, comprising:
generating a set of tire-based parameter inputs from at least one tire sensor mounted to the at least one tire;
generating a first set and a second set of vehicle-based parameter inputs from at least one vehicle sensor mounted to the vehicle;
generating a model-based longitudinal stiffness estimation from the set of tire-based parameter inputs and the first set of vehicle-based parameter inputs;
generating an actual longitudinal stiffness estimation from the second set of vehicle-based parameter inputs; and
deriving a tire road friction estimation from a comparative analysis between the actual longitudinal stiffness estimation and the model-based longitudinal stiffness estimation.

14. The method of claim 13 , further comprising:
using as the set of tire-based parameter inputs at least one parameter input from the group:
measured air cavity pressure of the one tire;
tire-specific construction specifications of the one tire; and
temperature of the one tire.

15. The method of claim 14 , further comprising:
using as the second set of vehicle-based parameter inputs at least one input from the group:
measured wheel speed of the vehicle;
measured wheel torque; and
measured wheel slip ratio.

16. The method of claim 15 , wherein further comprising:
using as one of the first set of vehicle-based inputs a measured hub vertical acceleration; and
generating a tire wear state estimation of the one tire from the measured hub vertical acceleration by detecting a shift in a vertical mode of the one tire.

17. The method of claim 16 , further comprising generating a longitudinal stiffness estimation from the first set of vehicle-based vehicle-based input parameters and the set of tire-based input parameters including a vehicle load estimation, the measured air cavity pressure of the one tire, and the measured temperature of the one tire compensated by the wear state estimation of the one tire.

18. The method of claim 17 , wherein further comprising making a longitudinal force estimation from the measured wheel speed and the measured wheel torque.

19. The method of claim 18 , wherein further comprising using a sliding mode observer model in making the longitudinal force estimation.

20. The method of claim 13 , further comprising utilizing a modified sensor fusion algorithm to determine a road surface type analysis using as parameter inputs the road friction estimation, an ambient air temperature measurement, and an ambient air moisture measurement.

Assignees

• Goodyear Tire & Rubber

Inventors

• Singh Kanwar Bharat

Classifications

• B60W40/068 Primary

  Road friction coefficient · CPC title

• B60T2270/86

  Optimizing braking by using ESP vehicle or tyre model · CPC title

• B60T2240/06

  Wheel load; Wheel lift · CPC title

• G01M17/02

  Tyres · CPC title

• B60C23/02

  Signalling devices actuated by tyre pressure {(hand-held tyre pressure gauges G01L17/00)} · CPC title