Patent Application
20220063347
The invention relates generally to tire monitoring systems. More particularly, the invention relates to systems that predict tire wear. Specifically, the invention is directed to a system for estimating the wear state of a tire by employing sub-models and determining a comprehensive wear state from the estimates generated by each sub-model.
Tire wear plays an important role in vehicle factors such as safety, reliability, and performance. Tread wear, which refers to the loss of material from the tread of the tire, directly affects such vehicle factors. As a result, it is desirable to monitor and/or measure the amount of tread wear experienced by a tire. For the purpose of convenience, the term “tread wear” may be used interchangeably herein with the term “tire wear”.
One approach to the monitoring and/or measurement of tread wear has been through the use of wear sensors disposed in the tire tread, which has been referred to a direct method or approach. The direct approach to measuring tire wear from tire mounted sensors has multiple challenges. Placing the sensors in an uncured or “green” tire to then be cured at high temperatures may cause damage to the wear sensors. In addition, sensor durability can prove to be an issue in meeting the millions of cycles requirement for tires. Moreover, wear sensors in a direct measurement approach must be small enough not to cause any uniformity problems as the tire rotates at high speeds. Finally, wear sensors can be costly and add significantly to the cost of the tire.
Due to such challenges, alternative approaches have been developed, which involve prediction of tread wear over the life of the tire, including indirect estimates of the tire wear state. These alternative approaches have experienced certain disadvantages in the prior art due to a lack of optimum prediction techniques, which in turn reduces the accuracy and/or reliability of the tread wear predictions.
Prior art indirect estimates of tire wear include statistical models that are based on determinations of particular tire behavior and/or characteristics. For example, indirect wear estimates have been based on: the rolling radius of the tire; the slip of the tire; the frictional energy of the tire; vibration of the tire; cornering stiffness of the tire; braking stiffness of the tire; footprint length of the tire; and analysis of parameter combinations such as tire mileage, weather, and tire construction.
Each of these techniques provides a specific estimate of the tire wear state. However, the reliability of each technique may be affected by a change in external parameters, such as weather, vehicle location, road surface and road roughness, as well as tire physical parameters, such as tire temperature, vehicle load state, and the like. In addition, any one of these techniques may outperform other techniques by providing a more accurate and/or reliable estimate of tire wear based on the tire operating environment and accompanying changes in external and physical parameters. In the prior art, there has been no manner of combining or evaluating the results of each separate technique in real time to arrive at an optimum wear state estimate.
As a result, there is a need in the art for a comprehensive tire wear state estimation system that provides a more accurate and reliable estimate of tire wear state than prior art systems.
According to an aspect of an exemplary embodiment of the invention, a tire wear state estimation system is provided. The system includes at least one tire that supports a vehicle. A sensor is mounted on the tire, and the tire mounted sensor measures tire parameters. At least one sensor is mounted on the vehicle, and the vehicle mounted sensor measures vehicle parameters. Each one of a plurality of sub-models receives selected tire parameters from the tire mounted sensor and selected vehicle parameters from the vehicle mounted sensor. Each one of the plurality of sub-models generates a respective sub-model wear state estimate. A reliability is determined for each one of the plurality of sub-models. A supervisory model receives the sub-model wear state estimates and the reliability for each one of the sub-models as inputs. The supervisory model generates a combined wear state estimate for the tire.
The invention will be described by way of example and with reference to the accompanying drawings, in which:
Similar numerals refer to similar parts throughout the drawings.
“Axial” and “axially” means lines or directions that are parallel to the axis of rotation of the tire.
“CAN” is an abbreviation for controller area network.
“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.
“GPS” is an abbreviation for global positioning system.
“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.
“Lateral” means an axial direction.
“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.
“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.
“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.
“TPMS” is an abbreviation for tire pressure monitoring system.
“Tread element” or “traction element” means a rib or a block element defined by a shape having adjacent grooves.
The present invention provides a system that provides an indirect estimation of tire wear state using a supervisory model which determines a comprehensive tire wear state from tire wear state estimates generated by different sub-models.
A first exemplary embodiment of the of the tire wear state estimation system of the present invention is indicated at 10 and is shown in
Each tire 12 includes a pair of bead areas 16 (only one shown) and a bead core (not shown) embedded in each bead area. Each one of a pair of sidewalls 18 (only one shown) extends radially outward from a respective bead area 16 to a ground-contacting tread 20. The tire 12 is reinforced by a carcass 22 that toroidally extends from one bead area 16 to the other bead area, as known to those skilled in the art. An innerliner 24 is formed on the inside surface of the carcass 22. The tire 12 is mounted on a wheel 26 in a manner known to those skilled in the art and, when mounted, forms an internal cavity 28 that is filled with a pressurized fluid, such as air.
A sensor unit 30 may be attached to the innerliner 24 of each tire 12 by means such as an adhesive and measures certain parameters or conditions of the tire, as will be described in greater detail below. It is to be understood that the sensor unit 30 may be attached in such a manner, or to other components of the tire 12, such as between layers of the carcass 22, on or in one of the sidewalls 18, on or in the tread 20, and/or a combination thereof. For the purpose of convenience, reference herein shall be made to mounting of the sensor unit 30 on the tire 12, with the understanding that mounting includes all such attachment.
The sensor unit 30 is mounted on each tire 12 for the purpose of detecting certain real-time tire parameters inside the tire, such as tire pressure and temperature. Preferably the sensor unit 30 is a tire pressure monitoring system (TPMS) module or sensor, of a type that is commercially available, and may be of any known configuration. For the purpose of convenience, the sensor unit 30 shall be referred to as a TPMS sensor. Each TPMS sensor 30 preferably also includes electronic memory capacity for storing identification (ID) information for each tire 12, known as tire ID information. Alternatively, tire ID information may be included in another sensor unit, or in a separate tire ID storage medium, such as a tire ID tag 34.
The tire ID information may include manufacturing information for the tire 12, such as: the tire type; tire model; size information, such as rim size, width, and outer diameter; manufacturing location; manufacturing date; a treadcap code that includes or correlates to a compound identification; and a mold code that includes or correlates to a tread structure identification. The tire ID information may also include a service history or other information to identify specific features and parameters of each tire 12, as well as mechanical characteristics of the tire, such as cornering parameters, spring rate, load-inflation relationship, and the like. Such tire identification enables correlation of the measured tire parameters and the specific tire 12 to provide local or central tracking of the tire, its current condition, and/or its condition over time. In addition, global positioning system (GPS) capability may be included in the TPMS sensor 30 and/or the tire ID tag 34 to provide location tracking of the tire 12 during transport and/or location tracking of the vehicle 14 on which the tire is installed.
Turning now to
Aspects of the tire wear state estimation system 10 preferably are executed on the processor 38 or another processor that is accessible through the vehicle CAN bus 42, which enables input of data from the TMPS sensor 30 and the tire ID tag 34, as well as input of data from other sensors that are in electronic communication with the CAN bus. In this manner, the tire wear state estimation system 10 enables measurement of tire temperature and pressure with the TPMS sensor 30, which preferably is transmitted to the processor 38. Tire ID information preferably is transmitted from the tire ID tag 34 to the processor 38. The processor 38 preferably correlates the measured tire temperature, measured tire pressure, the measurement time, and ID information for each tire 12.
Turning to
The sub-models or estimators analyzed by the supervisory model 60 include a rolling radius based wear state estimator 54, a slip based wear state estimator 56 and a frictional energy-based wear state estimator 58. Referring to
In the rolling radius based wear state estimator 54, tire parameters 68 obtained from the TPMS sensor 30, such as pressure, temperature and ID, are input into the rolling radius calculator 66. In addition, vehicle parameters 70 are measured by sensors that are mounted on the vehicle 14, and which are in electronic communication with the vehicle CAN bus system 42 (
The rolling radius calculator 66 calculates a change in the radius of the tire 12 based on the tire parameters 68 and the vehicle parameters 70, which is used by the rolling radius based wear state estimator 54 to generate the rolling radius wear estimate 64. An exemplary technique for determining the rolling radius wear estimate 64 is described in U.S. Pat. Nos. 9,663,115; 9,878,721; and 9,719,886, which owned by the same assignee as the present invention, The Goodyear Tire & Rubber Company, and which are hereby incorporated by reference.
An exemplary slip based wear state estimator 56 includes a tire slip calculator 72 that calculates slip of the tire 12 to generate a slip based wear state estimate 74. In the slip based wear state estimator 56, tire parameters 68 obtained from the TPMS sensor 30, such as pressure, temperature and ID, are input into the tire slip calculator 72. In addition, vehicle parameters 70, such as wheel speed, vehicle speed, and/or acceleration are obtained and input into the tire slip calculator 72.
The slip calculator 72 calculates slip of the tire 12 based on the tire parameters 68 and the vehicle parameters 70, which is used by the slip based wear state estimator 56 to generate the slip based wear state estimate 74. Exemplary techniques for determining the slip based wear state estimate 74 are described in U.S. Pat. Nos. 9,610,810; 9,821,611; and 10,603,962, which are owned by the same assignee as the present invention, The Goodyear Tire & Rubber Company, and which are hereby incorporated by reference.
An exemplary a frictional energy based wear state estimator 58 includes a tire frictional energy calculator 76 that calculates frictional energy of the tire 12 to generate a frictional energy based wear estimate 78. In the frictional energy based wear state estimator 58, tire parameters 68 obtained from the TPMS sensor 30, such as pressure, temperature and ID, are input into the frictional energy calculator 76. In addition, vehicle parameters 70, such as vehicle inertia and/or location are obtained and input into the frictional energy calculator 76.
The frictional energy calculator 76 calculates frictional energy of the tire 12 based on the tire parameters 68 and the vehicle parameters 70, which is used by the frictional energy based wear state estimator 58 to generate the frictional energy based wear estimate 78. An exemplary technique for determining the frictional energy based wear estimate 78 is described in U.S. Pat. No. 9,873,293, which is owned by the same assignee as the present invention, The Goodyear Tire & Rubber Company, and which is hereby incorporated by reference.
As described above, other sub-models that may be analyzed by the supervisory model 60. Exemplary techniques for determining a vibration based wear state estimate are described in U.S. Pat. Nos. 9,259,976 and 9,050,864, as well as U.S. Patent Application Publication Nos. 2018/0154707 and 2020/0182746, which are owned by the same assignee as the present invention, The Goodyear Tire & Rubber Company, and which are hereby incorporated by reference. An exemplary technique for determining a cornering stiffness based wear state estimate is described in U.S. Pat. No. 9,428,013, which is owned by the same assignee as the present invention, The Goodyear Tire & Rubber Company, and which is hereby incorporated by reference.
An exemplary technique for determining a braking stiffness based wear state estimate is described in U.S. Pat. No. 9,442,045, which is owned by the same assignee as the present invention, The Goodyear Tire & Rubber Company, and which is hereby incorporated by reference. Exemplary techniques for determining a footprint length based wear state estimator are described in U.S. Patent Application Ser. Nos. 62/893,862; 62/893,852; and 62/893,860, which are owned by the same assignee as the present invention, The Goodyear Tire & Rubber Company, and which are hereby incorporated by reference. An exemplary technique for determining a tire wear state estimate based on analysis of parameter combinations such as tire mileage, weather, and tire construction is described in U.S. Patent Application Publication No. 2018/0272813, which is owned by the same assignee as the present invention, The Goodyear Tire & Rubber Company, and which is hereby incorporated by reference.
Returning to
For example, the rolling radius model reliability score 82 is determined using a rolling radius reliability score function 88. Rolling radius sensitivity parameters 94 are factors that are unaccounted for in the rolling radius based wear state estimator 54 and are known to affect the reliability of the rolling radius wear estimate 64. The sensitivity parameters 94 include: the loading state of the vehicle 14, namely, the deviation of the current vehicle load from a nominal vehicle loading state; extreme high or low tire inflation pressure conditions, namely, the deviation of the tire inflation pressure from a nominal inflation pressure range; the road grade state, namely, the deviation of the grade of the road on which the vehicle is traveling from a flat road condition; and GPS status, namely, the deviation of the vehicle speed indicated by the vehicle GPS from non-driven wheel speeds. These sensitivity parameters 94 are input into the rolling radius reliability score function 88, which scores the parameters with a statistical modeling technique, such as a regression technique, a machine learning model, and/or a fuzzy logic technique or function, to generate the rolling radius model reliability score 82.
The slip based model reliability score 84 is determined using a slip based reliability score function 90. Slip based sensitivity parameters 96 are factors that are unaccounted for in the slip based wear state estimator 56 and are known to affect the reliability of the slip based wear state estimate 74. The sensitivity parameters 96 include: the loading state of the vehicle 14, namely, the deviation of the current vehicle load from a nominal vehicle loading state; extreme high or low tire inflation pressure conditions, namely, the deviation of the tire inflation pressure from a nominal inflation pressure range; GPS status, namely, the deviation of the vehicle speed indicated by the vehicle GPS from non-driven wheel speeds; the ambient temperature of the tire 12; and the road surface condition, namely, the surface characteristics of the road on which the vehicle is traveling as indicated by a frictional coefficient. These sensitivity parameters 96 are input into the slip based reliability score function 90, which scores the parameters with a statistical modeling technique, such as a regression technique, a machine learning model, and/or a fuzzy logic technique or function, to generate the slip based model reliability score 84.
The frictional energy based model reliability score 86 is determined using a frictional energy based reliability score function 92. Frictional energy based sensitivity parameters 98 are factors that are unaccounted for in the frictional energy based wear state estimator 58 and are known to affect the reliability of the frictional energy based wear estimate 78. The sensitivity parameters 98 include: the ambient temperature of the tire 12; the road surface condition, namely, the surface characteristics of the road on which the vehicle 14 is traveling as indicated by a frictional coefficient; and the road roughness condition, namely, the roughness of the road on which the vehicle is traveling as indicated by an international roughness index (IRI). These sensitivity parameters 98 are input into the frictional energy based reliability score function 92, which scores the parameters with a statistical modeling technique, such as a regression technique, a machine learning model, and/or a fuzzy logic technique or function, to generate the frictional energy based model reliability score 86.
The rolling radius wear estimate 64 generated by the rolling radius based wear state estimator 54 and the rolling radius model's reliability score 82 are input into the supervisory model 60. The slip based wear estimate 74 generated by the slip based wear state estimator 56 and the slip based model's reliability score 84 are also input into the supervisory model 60. Additionally, the frictional energy based wear estimate 78 generated by the frictional energy based wear state estimator 58 and the frictional energy based model's reliability score 86 are input into the supervisory model 60.
The tire wear state estimation system 10 preferably also includes an estimate of tire wear state at a previous time step 80, which may be referred to as the tire wear state at T−1. Because the tire 12 continues to wear as time progresses, the estimate of tire wear state at the previous time step 80 improves the current estimate of tire wear state 62. Thus, the estimate of tire wear state at the previous time step 80 preferably is also input into the supervisory model 60. When the estimate of tire wear state at the previous time step 80 is not available, a mileage 120 of the vehicle 14 may be input into the supervisory model 120 to enable an estimate of the tire wear state at a previous time step to be obtained.
The supervisory model 60 thus receives the rolling radius model's wear estimate 64, the rolling radius model's reliability score 82, the slip based model's wear estimate 74, the slip based model's reliability score 84, the frictional energy based model's wear estimate 78, the frictional energy based model's reliability score 86 and the estimate of tire wear state at the previous time step 80 as inputs. The supervisory model 60 then executes a statistical inference to determine a probability distribution over the tire wear states, indicating the single most likely combined wear estimate 62. When a Bayesian Network is employed as the supervisory model 60, the wear estimate 62 is generated by performing a Bayesian inference.
In this manner, the first embodiment of the tire wear state estimation system 10 of the present invention provides an accurate and reliable estimate of tire wear state 62 using a supervisory model 60. The supervisory model determines the comprehensive wear state 62 from estimates generated by multiple sub-models 54, 56 and 58.
Referring now to
In the second embodiment of the tire wear estimation system 100, the rolling radius model's reliability 82 is inferred using multiple correlations. For example, a first rolling radius correlation 102 includes correlating the rolling radius of the tire 12 to the mileage of the vehicle 14. A second rolling radius correlation 104 includes correlating the global positioning system (GPS) speed to the wheel speeds of the vehicle 14. A third rolling radius correlation 106 includes correlating the rolling radius of the tire 12 to the vehicle load. A fourth rolling radius correlation 108 is related to the grade of the road on which the vehicle 14 is travelling. These correlations 102, 104, 106 and 108 are used by the supervisory model to infer the reliability 82 of the rolling radius model. When a Bayesian Network is employed as the supervisory model 60, the reliability 82 is inferred by performing a Bayesian inference.
The slip based model's reliability 84 is also inferred using multiple correlations. A first slip based correlation 110 includes a correlation between the slip of the tire 12 and the mileage of the vehicle 14. A second slip based correlation 112 includes a correlation between the global positioning system (GPS) speed to the wheel speeds of the vehicle 14. A third slip based correlation 114 includes correlating the slip of the tire 12 to the temperature of the tire. A fourth slip based correlation 116 is related to the surface characteristics of the road on which the vehicle 14 is travelling. A fifth correlation 118 is related to the roughness of the road on which the vehicle 14 is traveling. These correlations 110, 112, 114, 116 and 118 are used by the supervisory model to infer the reliability 84 of the slip based model . When a Bayesian Network is employed as the supervisory model 60, the reliability 84 is inferred by performing a Bayesian inference.
As with the first embodiment of the tire wear state estimation system 10, in the second embodiment of the tire wear state estimation system 100, the supervisory model 60 receives the rolling radius model's wear estimate 64, the rolling radius model's reliability 82, the slip based model's wear state estimate 74, the slip based model's reliability 84, the frictional energy based model's wear estimate 78, the frictional energy based model's reliability score 86 and the estimate of tire wear state at the previous time step 80 as inputs. The supervisory model 60 then executes a statistical inference to determine a probability distribution over the tire wear states, this helps indicate the single most likely combined wear estimate 62. When a Bayesian Network is employed as the supervisory model 60, the wear estimate 62 is generated by performing a Bayesian inference.
In this manner, the second embodiment of the tire wear state estimation system 100 of the present invention provides an accurate and reliable estimate of tire wear state 62 using a supervisory model 60. The supervisory model 60 determines the comprehensive wear state 62 from estimates generated by multiple sub-models 54, 56 and 58.
As shown in
The present invention also includes a method of estimating the wear state 62 of a tire 12. The method includes steps in accordance with the description that is presented above and shown in
It is to be understood that the structure and method of the above-described tire wear state estimation system 10, 100 may be altered or rearranged, or components or steps known to those skilled in the art omitted or added, without affecting the overall concept or operation of the invention.
The invention has been described with reference to preferred embodiments. Potential modifications and alterations will occur to others upon a reading and understanding of this description. It is to be understood that all such modifications and alterations are included in the scope of the invention as set forth in the appended claims, or the equivalents thereof.
| Number | Date | Country | |
|---|---|---|---|
| 63070506 | Aug 2020 | US |
