TIRE WEAR STATE ESTIMATION SYSTEM UTILIZING CORNERING STIFFNESS AND METHOD
A tire wear state estimation system includes as inputs an axle force estimation, a measured tire inflation pressure; a tire load estimation; a tire cornering stiffness estimation; and a tire identification by which a specific tire-based correlation model correlates tire inflation pressure, the tire load estimation, the tire cornering stiffness estimation, and the vehicle-based sensor axle force estimation.
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The invention relates generally to tire monitoring systems for collecting measured tire parameter data during vehicle operation and, more particularly, to a system and method for estimating tire wear state based upon such measurements.
BACKGROUND OF THE INVENTIONVehicle-mounted tires may be monitored by tire pressure monitoring systems (TPMS) which measure tire parameters such as pressure and temperature during vehicle operation. Data from TPMS tire-equipped systems is used to ascertain the status of a tire based on measured tire parameters and alert the driver of conditions, such as low tire pressure or leakage, which may require remedial maintenance. Sensors within each tire are either installed at a pre-cure stage of tire manufacture or in a post-cure assembly to the tire.
Other factors such as tire wear state are important considerations for vehicle operation and safety. It is accordingly further desirable to measure tire wear state and communicate wear state to vehicle systems such as braking and stability control systems in conjunction with the measured tire parameters of pressure and temperature.
SUMMARY OF THE INVENTIONAccording to one aspect of the invention, a tire wear state estimation system includes as inputs to a tire-specific correlation model a measured tire inflation pressure; a tire load estimation; a tire cornering stiffness estimation; and a tire identification by which the correlation model makes a tire wear state estimation.
In another aspect, the tire cornering stiffness measuring means has inputs of vehicle operational measurements from one or more on-board vehicle-based sensor(s). The cornering stiffness estimator model may incorporate vehicle side slip angle estimations in a “Beta” inclusive embodiment, or exclude vehicle side slip angle in a “Beta-less” model embodiment. The cornering stiffness estimator in both the Beta and Beta-less alternative embodiments utilizes an estimation of axial force components from an axle force estimator.
According to another aspect, a road bank-grade angle estimation; bias compensation; vehicle longitudinal speed and slip ratio, and tire lateral force are inputs to a vehicle longitudinal and lateral velocity estimator. The vehicle side slip angle utilizes the vehicle longitudinal and lateral velocity estimation and the vehicle side slip angle estimation is then used in the Beta embodiment estimation of tire wear.
The tire wear state estimation system, in a further aspect, uses in the calculation of an estimated tire cornering stiffness a recursive least squares algorithm with forgetting factor based on a polynomial model capturing a dependency between axle force component estimation and the tire slip angle estimation.
DEFINITIONS“ANN” or “Artificial Neural Network” is an adaptive tool for non-linear statistical data modeling that changes its structure based on external or internal information that flows through a network during a learning phase. ANN neural networks are non-linear statistical data modeling tools used to model complex relationships between inputs and outputs or to find patterns in data.
“Aspect ratio” of the tire means the ratio of its section height (SH) to its section width (SW) multiplied by 100 percent for expression as a percentage.
“Asymmetric tread” means a tread that has a tread pattern not symmetrical about the center plane or equatorial plane EP of the tire.
“Axial” and “axially” means lines or directions that are parallel to the axis of rotation of the tire.
“CAN bus” is an abbreviation for controller area network.
“Chafer” is a narrow strip of material placed around the outside of a tire bead to protect the cord plies from wearing and cutting against the rim and distribute the flexing above the rim.
“Circumferential” means lines or directions extending along the perimeter of the surface of the annular tread perpendicular to the axial direction.
“Equatorial Centerplane (CP)” means the plane perpendicular to the tire's axis of rotation and passing through the center of the tread.
“Footprint” means the contact patch or area of contact created by the tire tread with a flat surface as the tire rotates or rolls.
“Groove” means an elongated void area in a tire wall that may extend circumferentially or laterally about the tire wall. The “groove width” is equal to its average width over its length. A grooves is sized to accommodate an air tube as described.
“Inboard side” means the side of the tire nearest the vehicle when the tire is mounted on a wheel and the wheel is mounted on the vehicle.
“Kalman Filter” is a set of mathematical equations that implement a predictor-corrector type estimator that is optimal in the sense that it minimizes the estimated error covariance when some presumed conditions are met.
“Lateral” means an axial direction.
“Lateral edges” means a line tangent to the axially outermost tread contact patch or footprint as measured under normal load and tire inflation, the lines being parallel to the equatorial centerplane.
“Luenberger Observer” is a state observer or estimation model. A “state observer” is a system that provide an estimate of the internal state of a given real system, from measurements of the input and output of the real system. It is typically computer-implemented, and provides the basis of many practical applications.
“MSE” is an abbreviation for Mean square error, the error between and a measured signal and an estimated signal which the Kalman Filter minimizes.
“Net contact area” means the total area of ground contacting tread elements between the lateral edges around the entire circumference of the tread divided by the gross area of the entire tread between the lateral edges.
“Non-directional tread” means a tread that has no preferred direction of forward travel and is not required to be positioned on a vehicle in a specific wheel position or positions to ensure that the tread pattern is aligned with the preferred direction of travel. Conversely, a directional tread pattern has a preferred direction of travel requiring specific wheel positioning.
“Outboard side” means the side of the tire farthest away from the vehicle when the tire is mounted on a wheel and the wheel is mounted on the vehicle.
“Peristaltic” means operating by means of wave-like contractions that propel contained matter, such as air, along tubular pathways.
“Piezoelectric Film Sensor” a device in the form of a film body that uses the piezoelectric effect actuated by a bending of the film body to measure pressure, acceleration, strain or force by converting them to an electrical charge.
“PSD” is Power Spectral Density (a technical name synonymous with FFT (Fast Fourier Transform).
“Radial” and “radially” means directions radially toward or away from the axis of rotation of the tire.
“Rib” means a circumferentially extending strip of rubber on the tread which is defined by at least one circumferential groove and either a second such groove or a lateral edge, the strip being laterally undivided by full-depth grooves.
“Sipe” means small slots molded into the tread elements of the tire that subdivide the tread surface and improve traction, sipes are generally narrow in width and close in the tires footprint as opposed to grooves that remain open in the tire's footprint.
“SMC Observer” is a sliding mode observer for non-linear systems that brings the estimation error for all estimated states to zero in a finite (and arbitrarily small) time.
“Tread element” or “traction element” means a rib or a block element defined by having a shape adjacent grooves.
“Tread Arc Width” means the arc length of the tread as measured between the lateral edges of the tread.
The invention will be described by way of example and with reference to the accompanying drawings in which:
Referring to
z=p00+p10*x+p01*y+p20*x̂2+p11*x*y+p02*ŷ2+p21*x̂2*y+p12*x*ŷ2+p03*ŷ3
where x: tread depth, y: load, and z: cornering stiffness (Cy).
It is further verified experimentally that cornering stiffness and tire temperature are dependent. In
The graph 76 shows a representative plot of Cy axle [N/deg] vs. time. Actual and predicted results are compared for half non-skid tread depth, No non-skid tread depth, and full tread depth tire wear states. Simulation conditions were at a tire inflation of 37 psi. The graphs reflect that the estimates were accurate vs. actual.
As discussed previously, cornering stiffness is one variable input used in the ANN to estimate tire wear level. The method of estimating tire cornering stiffness on a vehicle for the purpose of estimating tire wear level is provided below. In
a=distance from CG to front axle
b=distance from CG to rear axle
u=longitudinal speed
v=lateral speed
r=yaw rate
αf=front axle slip angle
αr=rear axle slip angle
δf=road wheel angle
Treatise treatments of vehicle sideslip, cornering stiffness, and vehicle modeling, incorporated herein by reference, include:
- (1) “Estimation of vehicle sideslip, tire force, and wheel cornering stiffness”, Guillaume Baffet (a), Alip Charara (a), Daniel Lechner (b)
- (a) HEUDIASYC Laboratory, UMR CNRS 6599, Universite de Technologie de Complegne, Centr de recherché Royallie, BP20529, 60205 Compiegne, France
- (b) INRETS-MA Laboratory, Department of Accident Mechanism Analysis, Chemin de la Croix Blanche, 13300 Salon de Provence, France
- (2) “An enhanced generic single track vehicle model and its parameter identification for 15 different passenger cars”, Bart Niessen, Sven Jansen, Igo Besselink, Antoine Schmeitz, Henk Nijmeijer, Eindoven University of Technology
- (3) “Vehicle System Dynamics” International Journal of Vehicle Mechanics and Mobility”, http://www.tandfonline.com/loi/nvsd20
- (4) “Cornering stiffness estimation based on vehicle lateral dynamics”, C. Sierra (a), E. Tseng (b), A. Jain (a), H. Peng (a)
- (a) Department of Mechanical Engineering, University of Michigan, Ann Arbor, Mich.
- (b) Research/Advanced Engineering, Ford Motor Company, Published 4 Apr. 2007
In general, there are several methodologies for estimating cornering stiffness. The “ay method” is to eliminate reliance on the derivative of vehicle yaw rate. The “rdot-method” is a second method for cornering stiffness estimation. A third approach is the “beta-less method”, a simplified scheme which estimates cornering stiffness without consideration of beta, the vehicle sideslip angle, in its calculation. For the beta-less scheme, the following expression is utilized:
Ffront=front axle force
Frear=rear axle force
Cfront=front cornering stiffness
Crear=rear cornering stiffness
The above expression is in the standard parameter identification form as:
y=ψTθ
Hence the unknown parameters Cf and Cr are estimated using a recursive least square algorithm.
The procedure for solving the RLS problem is as follows:
Step 0: Initialize the unknown parameter θ(0) and the covariance matrix P(0); set the forgetting factor λ.
Step 1: Measure the system output y(t) and compute the regression vector φ(t).
Step 2: Calculate the identification error e(t):
e(t)=y(t)−φT(t)·θ(t−1)
Step 3: Calculate the gain k(t):
k(t)=P(t−1)φ(t)[λ+φT(t)P(t−1)φ(t)]−1
Step 4: Calculate the covariance matrix:
P(t)=(1−k(t)φT(t))λ−1P(t−1)
Step 5: Update the unknown parameter:
θ(t)=θ(t−1)+k(t)e(t)
Step 6: Repeat Steps 1 through 5 for each time step.
The “beta-less method”, while representing one approach for estimating cornering stiffness, ignoring beta (vehicle sideslip angle) is not optimal. It will be appreciated that it is important to compensate the acceleration signals from on-board vehicle sensors for vehicle roll and pitch effects. Incorporating a beta (vehicle sideslip angle) estimation into the estimation of vehicle cornering stiffness provides for such a compensation. Accordingly, following is an alternative system and method, which takes into account Beta in its estimation scheme.
Referring to
may=Fyf+Fyr
IZψ=Fyf*lf−Fyr*lr
The axle force estimations are input into a vehicle sideslip angle estimator (extended Kalman filter) and vehicle sideslip angle beta (β) is obtained. The vehicle sideslip angle beta and CANBUS signal inputs Vx, ψ, and δf are inputs to a kinematics based tire slipangle estimator 104 to determine tire slip angle estimations αf, αr. The tire slip angle estimations αf, αr and axle force estimations Fyf, Fyr are inputs into an axle cornering stiffness estimator 106 consisting of a recursive least squares with forgetting factor algorithm that produces the desired axle cornering stiffness estimation 108. An expression for the model used in the axle cornering stiffness estimator 106 is provided below:
Fyi(αi)≈−Kiαi−Qiαi3
Fyi=front/rear axle force
αi=front/rear slip angle
K=Cofficient defining the shape of the tire force curve in the linear region
Q=Cofficient defining the shape of the tire force curve in the nonlinear region
The above expression is in the standard parameter identification form as:
y=ψTθ
Hence the unknown parameters K and Q are estimated using a recursive least square algorithm
In
The vehicle CAN bus 110 signals provide Tw, Tb and ω to a tire longitudinal force estimator 112 (for low slip conditions) from which vehicle mass and tire braking stiffness estimation 114 and tire longitudinal force estimation (for high slip conditions) are made.
Tire lateral force estimator 124 based on the sliding mode observer principal receives acceleration and yaw rate measurement signals from on-board vehicle sensors 110 and generates estimates of the front and rear axle forces. The measured lateral/longitudinal acceleration has both lateral/longitudinal dynamics components and gravity due to road bank/grade and chassis angles. Using the real-time vehicle roll and pitch angle estimates 118, the measured acceleration signals are compensated for the gravity components 120. An estimate of the vehicle longitudinal speed 122 is made based on the measurement of the four wheel rotational speed and longitudinal vehicle acceleration. Finally, an estimate of the vehicle lateral velocity and eventually vehicle side slip angle is made using a extended Kalman filtering (EKF) based observer formulated using vehicle dynamic equations based on a four wheel vehicle model 126.
Variations in the present invention are possible in light of the description of it provided herein. While certain representative embodiments and details have been shown for the purpose of illustrating the subject invention, it will be apparent to those skilled in this art that various changes and modifications can be made therein without departing from the scope of the subject invention. It is, therefore, to be understood that changes can be made in the particular embodiments described which will be within the full intended scope of the invention as defined by the following appended claims.
Claims
1. A tire wear state estimation system comprising:
- at least one tire supporting a vehicle;
- tire pressure measuring means affixed to the one tire for measuring tire inflation pressure;
- tire load measuring means for measuring tire load;
- tire cornering stiffness measuring means for measuring tire cornering stiffness;
- tire identification means for identifying the tire; and
- tire wear estimation means for calculating an estimation of a tire wear state based upon inputs comprising the tire inflation pressure, the tire load, the tire cornering stiffness, and the tire identification.
2. The tire wear state estimation system of claim 1, wherein the tire pressure measuring means comprises a tire-mounted pressure measuring device operative to measure by a pressure sensor a tire cavity pressure and transmit tire inflation pressure data from the tire cavity pressure measurement.
3. The tire wear state estimation system of claim 2, wherein tire identification data is stored within and accessible from the tire-mounted pressure measuring device.
4. The tire wear state estimation system of claim 2, wherein the tire cornering stiffness measuring means comprises:
- at least one on-board vehicle based sensor;
- a cornering stiffness estimator model operatively using vehicle information input from the at least one on-board vehicle based sensor.
5. The tire wear state estimation system of claim 4, wherein the cornering stiffness estimator model comprises:
- an axle force estimator receiving the vehicle information input and operative to generate an axial force component estimation;
- a tire slipangle estimator for generating a tire slipangle estimation based on the vehicle information input and the vehicle sideslip angle estimation; and
- an axle cornering stiffness estimator for generating an axle cornering stiffness estimation based on the tire slip angle estimation and the axial force component estimation.
6. The tire wear state estimation system of claim 5, wherein the axle cornering stiffness estimator comprises a recursive least squares algorithm with forgetting factor based on a polynomial model capturing a dependency between axle force component estimation and the tire slip angle estimation.
7. The tire wear state estimation system of claim 5, further comprising a vehicle sideslip angle estimator operative to generate a vehicle sideslip angle estimation from the axial force component estimation, the tire slip angle estimator generating the tire slip angle estimation based on the vehicle slip angle estimation and the axial force component estimation.
8. The tire wear state estimation system of claim 5, wherein the tire slip angle estimator comprises a sliding mode observer for non-linear systems.
9. The tire wear state estimation system of claim 1, wherein the tire wear estimation means comprises a correlation model based on the tire identification between the tire wear state, the tire inflation, the tire load, and the tire cornering stiffness.
10. The tire wear state estimation system of claim 8, wherein the correlation model comprises a recursive least squares algorithm based on a polynomial model with forgetting factor capturing a dependency between the tire wear state, the tire inflation, and the tire cornering stiffness for a tire-specific tire identification.
11. A tire wear state estimation system comprising:
- at least one tire for supporting a vehicle;
- a tire-mounted pressure measuring device affixed to the one tire operative to measure a tire pressure within a tire cavity;
- tire identification stored within and accessible from a tire-mounted data storage device;
- tire load measuring means for measuring tire load;
- tire cornering stiffness measuring means for measuring tire cornering stiffness; and
- tire wear estimation means for calculating an estimation of a tire wear state based upon inputs comprising the tire inflation, the tire load, the tire cornering stiffness, and the tire identification.
12. The tire wear state estimation system of claim 11, wherein the tire cornering stiffness measuring means comprises a cornering stiffness estimator model operatively using vehicle information input from at least one on-board vehicle based sensor generating vehicle operational information; and the cornering stiffness estimator model comprises an axle force estimator receiving the vehicle operational information and operative to generate an axial force component estimation based on the vehicle operational information.
13. The tire wear estimation system of claim 11, wherein further comprising:
- a vehicle side slip angle estimator for generating a vehicle side slip angle estimation;
- a tire slip angle estimator for generating a tire slip angle estimation based on the vehicle information input and the vehicle side slip angle estimation; and
- an axle cornering stiffness estimator for generating an axle cornering stiffness estimation based on the tire slip angle estimation and the axial force component estimation.
14. The tire wear state estimation system of claim 12, wherein the axle cornering stiffness estimator comprises a recursive least squares algorithm with forgetting factor based on a polynomial model capturing a dependency between axle force component estimation and the tire slip angle estimation.
15. A method of tire wear state estimation comprising:
- affixing a tire pressure measuring device to a vehicle-supporting tire, the pressure measuring device having at least one pressure sensor measuring a tire inflation pressure within a tire cavity;
- measuring tire load;
- measuring tire cornering stiffness;
- determining a tire identification; and
- estimating a tire wear state based upon inputs comprising the tire inflation pressure, the tire load, the tire cornering stiffness, and the tire identification.
16. The method of claim 15, wherein measuring the tire cornering stiffness comprises:
- utilizing an axle force estimator receiving vehicle information input from at least one vehicle-based sensor and operative to generate an axial force component estimation;
- utilizing a vehicle side slip angle estimator to generate a vehicle side slip angle estimation;
- utilizing a tire slip angle estimator for generating a tire slip angle estimation based on the vehicle information input and a vehicle side slip angle estimation; and
- an axle cornering stiffness estimator for generating an axle cornering stiffness estimation based on the tire slip angle estimation and the axial force component estimation.
17. The method of claim 16, wherein further comprising utilizing in the axle cornering stiffness estimator a recursive least squares algorithm with forgetting factor based on a polynomial model capturing a dependency between axle force component estimation and the tire slip angle estimation.
Type: Application
Filed: Aug 22, 2013
Publication Date: Feb 26, 2015
Applicant: The Goodyear Tire & Rubber Company (Akron, OH)
Inventor: Kanwar Bharat Singh (Stow, OH)
Application Number: 13/973,262
International Classification: B60C 11/24 (20060101);