Load estimation system for a tire

Abstract

A load estimation system for a tire is provided. The tire includes a pair of sidewalls extending to a circumferential tread and supports a vehicle. A sensor is mounted to the tire and measures an inflation pressure of the tire and a footprint length of the tread. A vehicle loading state estimator determines a loading state of the vehicle. An inflation correction factor is determined from the vehicle loading state. A pressure correction module receives the measured footprint length, the measured inflation pressure, and the inflation correction factor and determines an adjusted footprint length. A denoising module processor receives the adjusted footprint length to generate a filtered footprint length, and a wear correction module receives the filtered footprint length and corrects for tire wear to generate a wear corrected footprint length. A load determination module receives the wear corrected footprint length and determines an estimated load on the tire.

Description

TECHNICAL FIELD

[0001] The present invention relates generally to tire monitoring systems. More specifically, the present invention relates to systems that collect tire parameter data. The present invention relates to systems and methods for estimating tire load that employ tire parameter data to indirectly estimate tire load in an accurate and reliable manner. BACKGROUND

[0002] The load on each tire of a vehicle plays an important role in vehicle factors such as handling, safety, reliability, and performance. The measurement or estimation of the load on a tire during operation of a vehicle is often used by vehicle control systems such as braking, traction, stability, and suspension systems. For example, information about individual tire loads enables accurate estimation of the load distribution between the front and rear axles of a vehicle, which can then be used to optimize a braking control system. Alternatively, knowledge of tire loads and thus vehicle mass can enable more accurate estimation of the remaining range of an electric vehicle. As such, it is desirable to estimate the load on a tire in an accurate and reliable manner for input or use in such systems.

[0003] Prior art methods involve attempting to directly measure tire load with load or strain sensors. Such direct measurement techniques suffer from drawbacks, particularly during the life of a tire, as it is difficult to achieve sensors with a construction and placement on the tire that are capable of accurately and consistently measuring tire load.

[0004] Other prior art methods have been developed that involve estimation of tire load with fixed parameters. Such prior art methods suffer from drawbacks as techniques that rely on fixed parameters often result in less good predictions or estimations, which in turn reduces the accuracy and / or reliability of tire load predictions.

[0005] As a result, there is a need in the art for systems and methods that accurately and reliably estimate tire load. SUMMARY

[0006] According to an aspect of the exemplary embodiments of the present application, a load estimation system for a tire is provided. The tire includes a pair of sidewalls extending to a circumferential tread and supporting a vehicle. The system includes a sensor mounted to the tire and an inflation pressure of the tire is measured by the sensor. A footprint is formed by the tread and includes a footprint length measured by the sensor. A processor is in electronic communication with the sensor. A vehicle loading state estimator is in electronic communication with the processor and determines a loading state of the vehicle. An inflation correction factor is determined from the loading state of the vehicle and a pressure correction module is in electronic communication with the processor. The pressure correction module receives the measured footprint length, the measured inflation pressure and the inflation correction factor and determines an adjusted footprint length. A de-noising module is in electronic communication with the processor and receives the adjusted footprint length to generate a filtered footprint length. A wear correction module is in electronic communication with the processor, receives the filtered footprint length and corrects for tire wear to generate a wear corrected footprint length. A load determination model is in electronic communication with the processor, receives the wear corrected footprint length and determines an estimated load on the tire.

[0007] According to another aspect of the exemplary embodiments of the present application, a method for estimating a tire load is provided. The tire includes a pair of sidewalls extending to a circumferential tread and supporting a vehicle. In the method, a sensor is mounted to the tire and an inflation pressure of the tire is measured with the sensor. A length of a footprint formed by the tread is measured with the sensor and a processor is provided in electronic communication with the sensor. A loading state of the vehicle is determined with a vehicle loading state estimator in electronic communication with the processor. An inflation correction factor is determined from the loading state of the vehicle. An adjusted footprint length is determined with a pressure correction module in electronic communication with the processor, wherein the pressure correction module receives the measured footprint length, the measured inflation pressure and the inflation correction factor. A filtered footprint length is generated with a de-noising module in electronic communication with the processor, wherein the de-noising module receives the adjusted footprint length. A wear corrected footprint length is generated with a wear correction module in electronic communication with the processor, wherein the wear correction module receives the filtered footprint length. An estimated load on the tire is determined with a load determination model in electronic communication with the processor, wherein the load determination model receives the wear corrected footprint length.

[0008] The present application provides the following technical solutions:

[0009] 1. A load estimation system for a tire, the tire including a pair of sidewalls extending to a circumferential tread and supporting a vehicle, the system comprising:

[0010] a sensor mounted to the tire;

[0011] an inflation pressure of the tire measured by the sensor;

[0012] a footprint formed by the tread, the footprint including a footprint length, wherein the footprint length is measured by the sensor;

[0013] a processor in electronic communication with the sensor;

[0014] a vehicle loading state estimator in electronic communication with the processor and determining a loading state of the vehicle;

[0015] an inflation correction factor determined from the loading state of the vehicle;

[0016] a pressure correction module in electronic communication with the processor, the pressure correction module receiving a measured footprint length, a measured inflation pressure, and the inflation correction factor, wherein the pressure correction module determines an adjusted footprint length;

[0017] a de-noising module in electronic communication with the processor, the de-noising module receiving the adjusted footprint length to generate a filtered footprint length;

[0018] a wear correction module in electronic communication with the processor, the wear correction module receiving the filtered footprint length and making a correction for wear of the tire to generate a wear-corrected footprint length; and

[0019] a load determination model in electronic communication with the processor, the load determination model receiving the wear-corrected footprint length and determining an estimated load on the tire.

[0020] 2. The load estimation system for a tire according to Scheme 1, wherein the tire is a front tire, the sensor is a front sensor mounted in the front tire, the inflation pressure is a front inflation pressure, and the footprint length is a front footprint length, the system further comprising:

[0021] a rear tire;

[0022] a rear sensor mounted to the rear tire;

[0023] a rear inflation pressure of the rear tire measured by the rear sensor; and

[0024] a rear footprint formed by a tread of the rear tire, the rear footprint comprising a rear footprint length, wherein the rear footprint length is measured by the rear sensor, wherein the vehicle loading state estimator receives the measured front and rear footprint lengths and the front and rear inflation pressures.

[0025] 3. The load estimation system for a tire according to Scheme 2, wherein the vehicle loading state estimator comprises a de-noising module that receives the measured front and rear footprint lengths, the de-noising module removing signal noise to generate a filtered front footprint length and a filtered rear footprint length.

[0026] 4. The load estimation system for tires according to Scheme 3, wherein the vehicle loading state estimator comprises a ratio estimator that divides the filtered front footprint length by the filtered rear footprint length to determine a footprint length ratio.

[0027] 5. The load estimation system for tires according to Scheme 4, wherein the vehicle loading state estimator comprises a vehicle loading state estimation classification model that receives the front air pressure, the rear air pressure, and the footprint length ratio to determine the loading state of the vehicle.

[0028] 6. The load estimation system for tires according to Scheme 5, wherein the vehicle loading state estimation classification model employs a multinomial logistic regression classification methodology.

[0029] 7. The load estimation system for tires according to Scheme 5, wherein the loading state of the vehicle comprises a classification of at least one of empty, half, and full.

[0030] 8. The load estimation system for tires according to Scheme 7, further comprising:

[0031] at least one of a lookup table and a database in electronic communication with the processor; and

[0032] an air sensitivity stored in the at least one of the lookup table and the database, the air sensitivity being related to the vehicle loading state classification, wherein the air correction factor is determined from the air sensitivity.

[0033] 9. The load estimation system for tires according to Scheme 1, wherein the denoising module comprises an event filter that receives a steering wheel angle of the vehicle from a controller area network bus of the vehicle to ensure that only footprint length measurements during straight driving of the vehicle are analyzed.

[0034] 10. The load estimation system for tires according to Scheme 9, wherein the denoising module comprises a denoising algorithm to filter the adjusted footprint length data.

[0035] 11. The load estimation system for tires according to Scheme 10, wherein the denoising algorithm comprises a recursive least squares algorithm with a forgetting factor.

[0036] 12. The load estimation system for tires according to Scheme 10, wherein the denoising module comprises a smoothing module that receives the adjusted footprint length from the denoising algorithm to generate the filtered footprint length.

[0037] 13. The load estimation system for a tire according to Scheme 12, wherein the smoothing module employs an exponentially weighted average filter.

[0038] 14. The load estimation system for a tire according to Scheme 1, wherein the wear correction module includes a direct current block filter that separates a signal for the filtered footprint length into a direct current component with load dependence and a drift component with wear dependence.

[0039] 15. The load estimation system for a tire according to Scheme 14, wherein the wear correction module removes the drift component from the filtered footprint length to generate the wear corrected footprint length.

[0040] 16. The load estimation system for a tire according to Scheme 1, wherein the wear determination model employs a regression model.

[0041] 17. The load estimation system for a tire according to Scheme 16, wherein the regression model includes a linear regression model.

[0042] 18. The load estimation system for a tire according to Scheme 1, further comprising a vehicle control system in electronic communication with the processor, the vehicle control system receiving the estimated load on the tire.

[0043] 19. The load estimation system for a tire according to Scheme 1, wherein the processor includes at least one of an on-board processor and a processor in a cloud-based computing system.

[0044] 20. A method for estimating a load of a tire, the tire including a pair of sidewalls extending to a circumferential tread and supporting a vehicle, the method comprising the steps of:

[0045] mounting a sensor to the tire;

[0046] measuring an inflation pressure of the tire with the sensor;

[0047] measuring a length of a footprint formed by the tread with the sensor;

[0048] providing a processor in electronic communication with the sensor;

[0049] determining a loading state of the vehicle with a vehicle loading state estimator in electronic communication with the processor;

[0050] determining an inflation correction factor from the loading state of the vehicle;

[0051] determining an adjusted footprint length with a pressure correction module in electronic communication with the processor, the pressure correction module receiving the measured footprint length, the measured inflation pressure, and the inflation correction factor;

[0052] generating a filtered footprint length with a de-noising module in electronic communication with the processor, the de-noising module receiving the adjusted footprint length;

[0053] generating a wear correction footprint length with a wear correction module in electronic communication with the processor, the wear correction module receiving the filtered footprint length; and

[0054] determining an estimated load on the tire with a load determination model in electronic communication with the processor, the load determination model receiving the wear correction footprint length. BRIEF DESCRIPTION OF DRAWINGS

[0055] The present application will be described by way of example, and with reference to the accompanying drawings, in which:

[0056] Figure 1 is a perspective view of a vehicle and sensor-equipped tire employing a tire load estimation system and method according to the present application;

[0057] Figure 2 is Figure 1 is a plan view of a footprint of the tire shown;

[0058] Figure 3 is a schematic illustration of an exemplary embodiment of a tire load estimation system of the present application;

[0059] Figure 4 is a schematic illustration of tire footprint length versus inflation pressure;

[0060] Figure 5 is a schematic illustration of tire footprint length versus tire load;

[0061] Figure 6 is a schematic illustration of tire load versus ratio of front tire footprint length to rear tire footprint length;

[0062] Figure 7 is Figure 4 is a schematic illustration of a vehicle loading state estimation module of an exemplary embodiment of a tire load estimation system and method shown;

[0063] Figure 8 is a schematic illustration of a classification model of the vehicle loading state estimation module that can be used Figure 7 is a schematic illustration of a classification model of the vehicle loading state estimation module shown; and

[0064] Figure 9 is a schematic illustration of the tire load estimation system and method shown with representations of data transfer to a cloud-based server and to a user device; Figure 1schematic view of a vehicle.

[0065] Like reference numerals refer to like parts throughout the several views of the drawings.

[0066] Definitions

[0067] "axial" and "axially" mean a line or direction parallel to the axis of rotation of the tire.

[0068] "CAN bus" is an abbreviation for Controller Area Network, which is a vehicle bus standard designed to allow microcontrollers and devices to communicate with each other in vehicles, without a host computer. CAN bus is a message-based protocol designed specifically for vehicle applications.

[0069] "circumferential" means a line or direction parallel to the circumference of the tire.

[0070] "equatorial center plane" means a plane perpendicular to the axis of rotation of the tire and passing through the center of the tread.

[0071] "footprint" means the contact patch or contact area made by the tire tread with a flat surface such as the ground when the tire is rotating or rolling.

[0072] "inside face" means the tire side closest to the vehicle when the tire is mounted on a wheel and the wheel is mounted on a vehicle.

[0073] "lateral" means an axial direction.

[0074] "lateral edge" means a line tangent to the axially outermost tread contact patch or footprint, as measured under standard load and tire inflation conditions, these lines being parallel to the equatorial center plane.

[0075] "net contact area" means the total area of the ground contacting tread elements between the lateral edges around the entire circumference of the tread divided by the total area of the entire tread between the lateral edges.

[0076] "outside face" means the tire side farthest from the vehicle when the tire is mounted on a wheel and the wheel is mounted on a vehicle.

[0077] "radial" and "radially" mean directions radially toward or away from the axis of rotation of the tire.

[0078] "tread element" or "traction element" means a rib or block element defined by a shape having adjacent grooves. DETAILED DESCRIPTION

[0079] An exemplary embodiment of the tire load estimation system of the present application is indicated generally at 10 in Figures 1 to 9 Figure 1 ​System 10 estimates the load on each tire 12 supporting vehicle 14. Although vehicle 14 is depicted as a passenger car, the invention is not so limited. The principles of the invention apply to vehicles that can be constructed by means of a vehicle... Figure 1 Other vehicle types, such as commercial trucks, are shown with more or fewer tires supporting their components. For convenience, except as specifically described below, an analysis will be performed on a single tire 12; it should be understood that a similar analysis is envisioned for each tire supporting the vehicle 14.

[0080] Tire 12 has a conventional construction and is mounted on a corresponding wheel 16. Tire 12 includes a pair of sidewalls 18 extending to a circumferential tread 20 that engages the ground during vehicle operation. Tire 12 is preferably equipped with sensors 26 mounted to the tire for the purpose of detecting certain real-time tire parameters. For example, sensor 26 may be a commercially available tire pressure monitoring system (TPMS) module or sensor that can be attached to the inner liner 22 of tire 12 by a suitable method such as adhesive. Sensor 26 preferably includes: a pressure sensor to sense the inflation pressure within the cavity 24 of tire 12; and a temperature sensor to sense the temperature of the tire and / or the temperature within the cavity.

[0081] Sensor 26 preferably also includes a processor and a memory for storing tire identification (tire ID) information for each specific tire 12. For example, the tire ID may include manufacturing information for the tire 12, including: tire model; size information such as rim size, width, and outer diameter; manufacturing location; manufacturing date; tread crown code, which includes or is related to a compound identifier; and mold code, which includes or is related to a tread structure identifier. The tire ID may also include maintenance history or other information to identify specific characteristics and parameters of each tire 12. Sensor 26 preferably also includes an antenna for analyzing data used to transmit the measured parameters and tire ID data to a remote processor 28, which may be a processor integrated into the vehicle's CAN bus 30.

[0082] The tire load estimation system 10 and its accompanying method attempt to overcome the aforementioned challenges posed by existing systems and methods that seek to measure tire load through direct sensor measurements. Therefore, the system and method of this subject are referred to herein as an "indirect" load estimation system and method.

[0083] The tire load estimation system 10 is preferably implemented in a processor 28 accessible via the vehicle CAN bus 30. Figure 3 The processor 28 can be an in-vehicle processor, or a remote Internet or cloud-based processor. Figure 9). The use of such a processor 28 and accompanying methodology enables data from the tire-mounted sensors 26, data from certain vehicle-based sensors, and data from lookup tables or databases stored in suitable storage media and in electronic communication with the processor to be input into the system 10. The CAN bus 30 enables the tire load estimation system 10 to interact with other electronic components and systems of the vehicle 14.

[0084] Turning now to Figure 2 , the footprint 32 of the tread 20 of the tire 12( Figure 1 ) is shown. The footprint 32 is the area created or formed as the tread 20 contacts the ground as the tire 12 rotates. The footprint 32 includes a width 34 extending in a lateral direction across the tread 20. The footprint 32 also includes a centerline 36 extending in a circumferential direction, that is, perpendicular to the axial or lateral direction. The centerline 36 is disposed midway through the width 34 of the footprint 32 and includes a length 38 referred to as the footprint centerline length or footprint length.

[0085] The footprint length 38 can be sensed by the tire-mounted sensors 26( Figure 1 ) or by another suitable sensor. For example, the sensors 26 can include strain sensors or piezoelectric sensors that measure deformation of the tread 20 and thus indicate the footprint length 38.

[0086] Referring to Figure 3 , the tire load estimation system 10 employs the measured footprint length 38 to estimate the tire load. The system 10 provides compensation or correction of the measured footprint length 38 to account for the effect of inflation pressure while also compensating for the loading condition by comparing the footprint length 38F of the front tire 12F to the footprint length 38R of the rear tire 12R. The system 10 also provides compensation or correction of the footprint length 38 to account for wear of the tire 12.

[0087] The tire-mounted sensors 26 preferably wirelessly transmit the measured footprint length 38 and the measured inflation pressure 40 of the tire 12 to the processor 28. A pressure correction module 42 is stored on or in electronic communication with the processor 28 and receives the measured footprint length 38 and the measured inflation pressure 40 of each tire 12. The pressure correction module 42 provides compensation or correction of the measured footprint length 38 to account for the effect of inflation pressure.

[0088] More particularly, referring additionally to Figure 4 , a plot 44 of the footprint length 38 of the tire 12 versus the inflation pressure 40 shows how the inflation pressure of the tire affects the footprint length. Specifically, higher inflation pressures 40 correspond to shorter footprint lengths 38. To remove the effect of inflation pressure 40 on the footprint length 38 and thus normalize the footprint length, the pressure correction module 42 receives the measured footprint length and the measured inflation pressure.

[0089] Returning to Figure 3 , the pressure correction module 42 also compensates for a loading condition 46 of the tire 12. More specifically, to accurately adjust the measured footprint length 38 for changes in the inflation pressure 40, the loading condition 46 of the tire 12 needs to be accounted for. Additional reference is made to Figure 5 A graph 48 of the footprint length 38 of the tire 12 versus the loading condition 46 of the tire shows how the loading condition of the tire affects the footprint length. Specifically, a higher loading condition 46 corresponds to a longer footprint length 38.

[0090] It has been determined that for certain types of vehicles 14, such as light commercial vehicles, the load on the front tires 12F( Figure 7 ) of the vehicle does not significantly change when the vehicle is fully loaded. In this case, the footprint length 38F of the front tires 12F does not significantly change. In contrast, the load on the rear tires 12R of the vehicle significantly changes when the vehicle 14 is fully loaded, and the footprint length 38R of the rear tires significantly changes. Based on this, the footprint length 38F of the front tires 12F can be used as a reference and compared to the footprint length 38R of the rear tires 12R to estimate the loading condition of the vehicle 14, which can then be used to account for the loading condition 46 of the tire 12.

[0091] As Figure 6 shown, a graph 50 or comparison of the ratio 52 of the footprint length 38F of the front tires 12F and the footprint length 38R of the rear tires 12R versus the loading condition 46 of the vehicle 14 under cruising conditions shows that the vehicle loading condition 54 can be determined. It should be understood that the cruising conditions are when the vehicle 14 is driven on a straight road at a constant speed. The vehicle loading condition 54 can be categorized as empty 56, half 58, or full 60.

[0092] Turning now to Figure 7 , the determination of the vehicle loading condition 54 is preferably made by a vehicle loading condition estimator 62. The tire mounted sensor 26 preferably wirelessly transmits the measured footprint length 38F and inflation pressure 40F of the front tires 12F and the measured footprint length 38R and inflation pressure 40R of the rear tires 12R to the processor 28. The vehicle loading condition estimator 62 is stored on the processor 28 or in electronic communication with the processor 28 and receives the measured footprint lengths 38F and 38R and the inflation pressures 40F and 40R.

[0093] Each measured footprint length 38F and 38R is filtered with a de-noising module 64 to remove signal noise from the measured data. Examples of the de-noising module 64 are described in more detail below. The de-noising module 64 outputs a filtered front footprint length 66F for the front tire 12F and a filtered footprint length 66R for the rear tire 12R. A ratio estimator 68 compares the filtered front footprint length 66F with the filtered rear footprint length 66R to determine the footprint length ratio 52.

[0094] With additional reference to Figure 8 The measured inflation pressure 40F for the front tire 12F, the measured inflation pressure 40R for the rear tire 12R, and the footprint length ratio 52 are input into a vehicle loading state estimation classification model 70 of the vehicle loading state estimator 62. The classification model 70 preferably utilizes the front inflation pressure 40F, the rear inflation pressure 40R, and the footprint length ratio 52 to identify the vehicle loading state 54 from a multi-class classification of an empty load 56, a half load 58, or a full load 60. Preferably, the classifier 72 employs a multinomial logistic regression classification methodology, such as a Softmax regression, to identify the vehicle loading state 54. The multinomial logistic regression classification methodology is preferably based on its ability to predict the probabilities of different outcomes of the dependent variable of its classification distribution when given a set of independent variables. The vehicle loading state estimation classification model 70 determines the specific loading state 54 of the vehicle 14, which is described by way of example as an empty load 56, a half load 58, or a full load 60.

[0095] Returning to Figure 3 Once the vehicle loading state estimator 62 determines the loading state 54 of the vehicle 14, the loading state is correlated with an inflation sensitivity 72 for the tires 12. The inflation sensitivity can be stored in a look-up table or database 74, which is stored on the processor 28 or in electronic communication therewith. The inflation sensitivity 72 corresponding to the specific loading state 54 enables a determination of a predetermined inflation correction factor 76 for the tires 12.

[0096] The inflation correction factor 76 is input into the pressure correction module 42 along with the measured footprint length 38 and the measured inflation pressure 40 for the tires 12. The pressure correction module 42 adjusts the measured footprint length 38 according to the measured inflation pressure 40 and the inflation correction factor 76 to account for variations in the inflation pressure and loading state of the tires to determine an adjusted footprint length 78. The pressure correction module 42 preferably includes a regression model, which can be a linear regression model or a non-linear regression model, to determine the adjusted footprint length 78.

[0097] For example, the relationship between the measured footprint length 38 and the measured inflation pressure 40 can be implemented by a linear regression model, which can be based on data from testing of the vehicle 14. Once the regression model coefficients are determined, the slope can be utilized to adjust the measured footprint length 38 with the following equation:

[0098] Adjusted FPL = Measured FPL - (Measured P - Predetermined P) * SC

[0099] where Adjusted FPL is the adjusted footprint length 78, Measured FPL is the measured footprint length 38, Measured P is the measured inflation pressure 40, Predetermined P is a predetermined target inflation pressure for the tire 12, and SC is a slope.

[0100] The adjusted footprint length 78 is filtered with a denoising module 64, stored on or in electronic communication with the processor 28, to remove signal noise from the measured data. As an example, the denoising module 64 can receive a steering wheel angle 80 of the vehicle 14 as an input from the vehicle CAN bus system 30. The steering wheel angle 80 is input into an event filter 82, which screens the measured footprint length data 38 to ensure that only footprint length measurements during straight line travel of the vehicle 14 are analyzed. In this way, the event filter 82 ensures that consistent footprint length measurements 38 from straight line travel are employed.

[0101] While the event filter 82 ensures that the vehicle 14 is traveling in a straight line, a denoising algorithm 84 filters the adjusted footprint length data 78. The preferred denoising algorithm 84 is an adaptive filtering algorithm, such as a recursive least squares algorithm with a forgetting factor, which gives less weight to earlier data samples to ensure that the most recent data receives a higher priority. After the denoising algorithm 84, the adjusted footprint length data 78 is smoothed in a smoothing module 86 to capture significant patterns in the data. The smoothing module 86 employs techniques useful for time series data, such as the adjusted footprint length data 78. The preferred technique in the smoothing module 86 is an exponential weighted average filter.

[0102] When the adjusted footprint length data 78 has been filtered through the denoising module 64, a filtered footprint length 88 for the tire 12 is generated. As the tire 12 wears, the measured footprint length 38 and the filtered footprint length 88 typically decrease in length. Thus, as the tire 12 wears, the decreasing footprint length can create an inaccurate assumption that the tire load is changing. To account for such an assumption, the tire load estimation system 10 corrects for wear of the tire 12 with a wear correction model 90.

[0103] The wear correction module 90 receives the filtered footprint length 88 and is stored on or in electronic communication with the processor 28. It has been determined that wear occurs with a slow drift in the filtered footprint length data 88. The wear correction module 90 removes the drift in the filtered footprint length data 88 to correct for wear of the tire 12. To remove the drift, the wear correction module 90 applies a direct current (DC) block filter to the filtered footprint length data 88. The DC block filter separates the signal for the filtered footprint length data 88 into two components. The first component is a DC component, which is load dependent, and the second component is a drift component, which is wear dependent. The wear correction module 90 identifies and removes the drift component from the filtered footprint length data 88 to generate a wear corrected footprint length 106.

[0104] The wear corrected footprint length 106 is input to a load determination model 92, which is stored on or in electronic communication with the processor 28. The load determination model 92 preferably employs a regression model to calculate the load on the tire 12 corresponding to the wear corrected footprint length 106. The regression model can be a linear regression model or a non-linear regression model. The load determination model 92 thus determines and outputs an estimated load 94 on the tire 12. The estimated load 94 can be communicated from the tire load estimation system 10 over the vehicle CAN bus system 30 for use by vehicle control systems such as braking, traction, stability, and / or suspension systems.

[0105] Turning Figure 9 The tire load estimation system 10 is preferably executed on a processor 28 accessible through the vehicle CAN bus 30, which can be installed on the vehicle 14, or it can be in an internet or cloud-based computing system 96, referred to herein as a cloud-based computing system. The tire load estimation system 10 preferably employs wireless data transmission 98 between the vehicle 14 and the cloud-based computing system 96. The tire load estimation system 10 can also employ wireless data transmission 100 between the cloud-based computing system 96 and a display device 102 accessible to a user of the vehicle 14, such as a smartphone, or to a fleet manager. Alternatively, the system 10 can also employ wireless data transmission 104 between the vehicle CAN bus 30 and the display device 102.

[0106] In this manner, the tire load estimation system 10 of the present application utilizes the measured footprint length 38 of the tire 12 to indirectly estimate the tire load in an accurate and reliable manner. The tire load estimation system 10 provides compensation for the measured footprint length 38 to account for the effect of inflation pressure, and also compensates for the loading condition by comparing the footprint length 38F of the front tire 12F with the footprint length 38R of the rear tire 12R. The system 10 also provides compensation or correction for the footprint length 38 to account for wear of the tire 12.

[0107] The present application also includes a method for estimating the load of the tire 12. The method includes the steps of accounting for the effect of inflation pressure as described above and illustrated in Figures 1 to 9

[0108] It should be understood that the structure and method of the tire load estimation system described above can be changed or rearranged, or components or steps can be omitted or added, without affecting the overall concept or operation of the present application, as would be known to those skilled in the art.

[0109] The present application has been described with reference to the preferred embodiments. Persons skilled in the art will recognize upon reading and understanding the foregoing specification and the attached claims that various modifications and changes can be made thereto without departing from the spirit and scope of the present application. It is intended to cover in the appended claims all such modifications and changes as fall within the scope of the present application, or the scope of equivalents thereof.​

Claims

1. A load estimation system for a tire, the tire comprising a pair of sidewalls extending to a circumferential tread and supporting a vehicle, the system comprising: a sensor mounted to the tire; an inflation pressure of the tire measured by the sensor; a footprint formed by the tread, the footprint comprising a footprint length, wherein the footprint length is measured by the sensor; a processor in electronic communication with the sensor; a vehicle loading state estimator in electronic communication with the processor and determining a loading state of the vehicle, wherein the loading state of the vehicle distinguishes between an empty, half, and full vehicle state; an inflation correction factor determined from the loading state of the vehicle; a pressure correction module in electronic communication with the processor, the pressure correction module receiving the measured footprint length, the measured inflation pressure, and the inflation correction factor, wherein the pressure correction module determines an adjusted footprint length; a denoising module in electronic communication with the processor, the denoising module receiving the adjusted footprint length to generate a filtered footprint length; a wear correction module in electronic communication with the processor, the wear correction module receiving the filtered footprint length and making a correction for wear of the tire to generate a wear corrected footprint length; and a load determination model in electronic communication with the processor, the load determination model receiving the wear corrected footprint length and determining an estimated load on the tire.

2. The load estimation system for a tire according to claim 1, wherein the tire is a front tire, the sensor is a front sensor mounted in the front tire, the inflation pressure is a front inflation pressure, and the footprint length is a front footprint length, the system further comprising: a rear tire; a rear sensor mounted to the rear tire; a rear inflation pressure of the rear tire measured by the rear sensor; and a rear footprint formed by a tread of the rear tire, the rear footprint comprising a rear footprint length, wherein the rear footprint length is measured by the rear sensor, wherein the vehicle loading state estimator receives the measured front and rear footprint lengths and the front and rear inflation pressures.

3. The load estimation system for a tire according to claim 2, wherein the vehicle loading state estimator comprises a denoising module receiving the measured front and rear footprint lengths, the denoising module removing signal noise to generate a filtered front footprint length and a filtered rear footprint length.

4. The load estimation system for a tire according to claim 3, wherein the vehicle loading state estimator comprises a ratio estimator comparing the filtered front footprint length to the filtered rear footprint length to determine a footprint length ratio.

5. The load estimation system for a tire according to claim 4, wherein the vehicle loading state estimator comprises a vehicle loading state estimation classification model receiving the front inflation pressure, the rear inflation pressure, and the footprint length ratio to determine the loading state of the vehicle.

6. The load estimation system for a tire according to claim 5, wherein the vehicle loading state estimation classification model employs a multinomial logistic regression classification methodology.

7. The load estimation system for a tire of claim 1, further comprising: at least one of a lookup table and a database in electronic communication with the processor; and and an inflation sensitivity stored in at least one of the lookup table and the database, the inflation sensitivity being related to the vehicle loading condition classification, wherein the inflation correction factor is determined from the inflation sensitivity.

8. The load estimation system for a tire according to claim 1, wherein, The denoising module includes an event filter that receives a steering wheel angle of the vehicle from a controller area network bus of the vehicle to ensure that only track lengths during straight driving of the vehicle are analyzed.

9. The load estimation system for a tire according to claim 8, wherein, The denoising module includes a denoising algorithm to filter the adjusted track length data.

10. The load estimation system for a tire according to claim 9, wherein, The denoising algorithm includes a recursive least squares algorithm with a forgetting factor.

11. The load estimation system for a tire according to claim 9, wherein, The denoising module includes a smoothing module that receives the adjusted track length from the denoising algorithm to generate the filtered track length.

12. The load estimation system for a tire according to claim 11, wherein, The smoothing module employs an exponentially weighted average filter.

13. The load estimation system for a tire according to claim 1, wherein, The wear correction module includes a direct current block filter that separates a signal for the filtered track length into a direct current component with a load dependency and a drift component with a wear dependency.

14. The load estimation system for a tire according to claim 13, wherein, The wear correction module removes the drift component from the filtered track length to generate the wear corrected track length.

15. The load estimation system for a tire according to claim 1, wherein, The load determination model employs a regression model.

16. The load estimation system for a tire according to claim 15, wherein, The regression model includes a linear regression model.

17. The load estimation system for a tire of claim 1, further comprising a vehicle control system in electronic communication with the processor, the vehicle control system receiving the estimated load on the tire.

18. The load estimation system for a tire according to claim 1, wherein, The processor includes at least one of an on-board vehicle processor and a processor in a cloud-based computing system.

19. A method for estimating a load of a tire, the tire including a pair of sidewalls extending to a circumferential tread and supporting a vehicle, the method comprising the steps of: installing a sensor to the tire; measuring an inflation pressure of the tire with the sensor; measuring a length of a track formed by the tread with the sensor; providing a processor in electronic communication with the sensor; determining a loading condition of the vehicle with a vehicle loading condition estimator in electronic communication with the processor, wherein the loading condition of the vehicle distinguishes between an empty, half-loaded, and fully loaded vehicle condition; determining an inflation correction factor from the loading condition of the vehicle; determining an adjusted track length with a pressure correction module in electronic communication with the processor, the pressure correction module receiving the measured track length, the measured inflation pressure, and the inflation correction factor; generating a filtered track length with a denoising module in electronic communication with the processor, the denoising module receiving the adjusted track length; generating a wear corrected track length with a wear correction module in electronic communication with the processor, the wear correction module receiving the filtered track length; and determining an estimated load on the tire with a load determination model in electronic communication with the processor, the load determination model receiving the wear corrected track length. ​

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