Patent Description:
The load on each tire of a vehicle plays an important role in vehicle factors such as handling, safety, reliability, and performance. Measurement or estimation of the load on a tire during the operation of a vehicle is often used by vehicle control systems such as braking, traction, stability, and suspension systems. For instance, information about individual tire loads enables precise estimation of the load distribution between the front and the rear axle of the vehicle, which can then be used to optimize the brake control system. Alternatively, knowledge of tire loads and consequently the vehicle mass may enable more accurate estimation of the remaining range of an electric vehicle. Thus, it is desirable to estimate the load on a tire in an accurate and reliable manner for input or use in such systems.

Prior art approaches have involved attempts at directly measuring tire load using load or strain sensors. Such direct-measurement techniques have experienced disadvantages due to the difficulty in achieving a sensor with a construction and placement on the tire that enables accurate and consistent measurement of tire load, particularly over the life of a tire.

Other prior art approaches have been developed that involve estimation of tire load using fixed parameters. Such prior art approaches have experienced disadvantages since techniques relying upon fixed parameters often lead to less-than-optimum predictions or estimations, which in turn reduces the accuracy and/or reliability of the tire load predictions.

As a result, there is a need in the art for a system and method that accurately and reliably estimates tire load.

<CIT> describes a load estimation system and method for a tire. The system includes a sensor mounted to the tire for measuring inflation pressure of the tire and for measuring tread footprint length, a processor in electronic communication with the sensor, a vehicle loading state estimator in electronic communication with the processor for determining a loading state of a vehicle, a pressure correction module in electronic communication with the processor to determine an adjusted footprint length, a wear correction module in electronic communication with the processor and a load determination model in electronic communication with the processor.

<CIT> describes a tire wear state estimation system and method employing a footprint shape factor.

According to a preferred aspect of the invention, 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 that is 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, which is 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 wear of the tire 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.

According to another preferred aspect of the invention, a method for estimating the load of a tire 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 that is in electronic communication with the sensor is provided. A loading state of the vehicle is determined with a vehicle loading state estimator that is 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 that is in electronic communication with the processor, in which 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 that is in electronic communication with the processor, in which the de-noising module receives the adjusted footprint length. A wear-corrected footprint length is generated with a wear correction module that is in electronic communication with the processor, in which the wear correction module receives the filtered footprint length. An estimated load on the tire is determined with a load determination model that is in electronic communication with the processor, in which the load determination model receives the wear-corrected footprint length.

"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 within a vehicle without a host computer. CAN bus is a message-based protocol, designed specifically for vehicle applications.

"Equatorial Centerplane" 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, such as the ground, as the tire rotates or rolls.

"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.

"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.

"Tread element" or "traction element" means a rib or a block element defined by a shape having adjacent grooves.

An exemplary embodiment of the tire load estimation system <NUM> of the present invention is shown in <FIG>. With particular reference to <FIG>, the system <NUM> estimates the load on each tire <NUM> supporting a vehicle <NUM>. While the vehicle <NUM> is depicted as a passenger car, the invention is not to be so restricted. The principles of the invention find application in other vehicle categories such as commercial trucks in which vehicles may be supported by more or fewer tires than shown in <FIG>. For the purpose of convenience, analysis of a single tire <NUM> will be made except as specifically described below, with the understanding that a similar analysis is contemplated for each tire supporting the vehicle <NUM>.

The tire <NUM> is of conventional construction and is mounted on a respective wheel <NUM>. The tire <NUM> includes a pair of sidewalls <NUM> that extend to a circumferential tread <NUM>, which engages the ground during vehicle operation. The tire <NUM> preferably is equipped with a sensor <NUM> that is mounted to the tire for the purpose of detecting certain real-time tire parameters. For example, the sensor <NUM> may be a commercially-available tire pressure monitoring system (TPMS) module or sensor, which may be affixed to an inner liner <NUM> of the tire <NUM> by suitable means such as adhesive. The sensor <NUM> preferably includes a pressure sensor to sense the inflation pressure within a cavity <NUM> of the tire <NUM>, and a temperature sensor to sense the temperature of the tire and/or the temperature in the cavity.

The sensor <NUM> preferably also includes a processor and memory to store tire identification (tire ID) information for each specific tire <NUM>. For example, the tire ID may include manufacturing information for the tire <NUM>, including: the 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 may also include a service history or other information to identify specific features and parameters of each tire <NUM>. The sensor <NUM> preferably further includes an antenna for transmitting measured parameters and tire ID data to a remote processor <NUM>, which may be a processor that is integrated into a vehicle CAN bus <NUM>, for analysis.

The tire load estimation system <NUM> and accompanying method attempts to overcome the above-described challenges posed by prior art systems and methods that seek to measure the tire load through direct sensor measurements. As such, the subject system and method is referred herein as an "indirect" load estimation system and method.

Aspects of the tire load estimation system <NUM> preferably are executed on a processor <NUM> (<FIG>) that is accessible through the vehicle CAN bus <NUM>. The processor <NUM> may be a vehicle-mounted processor, or may be a remote Internet or cloud-based processor (<FIG>). Use of such a processor <NUM>, and accompanying memory, enables input of data into the system <NUM> from the tire-based sensor <NUM>, data from certain vehicle-based sensors, and data from a lookup table or a database that is stored in a suitable storage medium and is in electronic communication with the processor. The CAN bus <NUM> enables the tire load estimation system <NUM> to interface with other electronic components and systems of the vehicle <NUM>.

Turning now to <FIG>, a footprint <NUM> of the tread <NUM> of the tire <NUM> (<FIG>) is shown. The footprint <NUM> is the area that is created or formed as the tread <NUM> contacts the ground as the tire <NUM> rotates. The footprint <NUM> includes a width <NUM> that extends in a lateral direction across the tread <NUM>. The footprint <NUM> also includes a centerline <NUM> that extends in a circumferential direction, that is, perpendicular to an axial or lateral direction. The centerline <NUM> is disposed at the middle of the width <NUM> of the footprint <NUM>, and includes a length <NUM> that is referred to as the footprint centerline length or the footprint length.

The footprint length <NUM> may be sensed by the tire-mounted sensor <NUM> (<FIG>) or by another suitable sensor. For example, the sensor <NUM> may include a strain sensor or piezoelectric sensor that measures deformation of the tread <NUM> and thus indicates the footprint length <NUM>.

With reference to <FIG>, the tire load estimation system <NUM> employs the measured footprint length <NUM> to estimate tire load. The system <NUM> provides a compensation or correction of the measured footprint length <NUM> to account for inflation pressure effects, while also compensating for a loading state by comparing a footprint length 38F of a front tire 12F to a footprint length 38R of a rear tire 12R. The system <NUM> also provides a compensation or correction of the footprint length <NUM> that accounts for wear of the tire <NUM>.

The tire-mounted sensor <NUM> preferably wirelessly transmits the measured footprint length <NUM> and a measured inflation pressure <NUM> of the tire <NUM> to the processor <NUM>. A pressure correction module <NUM> is stored on or is in electronic communication with the processor <NUM> and receives the measured footprint length <NUM> and the measured inflation pressure <NUM> for each tire <NUM>. The pressure correction module <NUM> provides a compensation or correction of the measured footprint length <NUM> to account for inflation pressure effects.

More particularly, with additional reference to <FIG>, a plot <NUM> of the footprint length <NUM> versus the inflation pressure <NUM> of the tire <NUM> shows how the inflation pressure of the tire affects the footprint length. Specifically, a higher inflation pressure <NUM> corresponds to a shorter footprint length <NUM>. In order to remove the effect of inflation pressure <NUM> on footprint length <NUM> and thus normalize the footprint length, the pressure correction module <NUM> receives the measured footprint length and the measured inflation pressure.

Returning to <FIG>, the pressure correction module <NUM> also compensates for a loading state <NUM> of the tire <NUM>. More particularly, to accurately adjust the measured footprint length <NUM> for changes in inflation pressure <NUM>, the loading state <NUM> of the tire <NUM> needs to be accounted for. With additional reference to <FIG>, a plot <NUM> of the footprint length <NUM> versus the loading state <NUM> for the tire <NUM> shows how the loading state of the tire affects the footprint length. Specifically, a higher loading state <NUM> corresponds to a longer footprint length <NUM>.

It has been determined that, for a certain type of vehicle <NUM>, such as a light commercial vehicle, the load on a front vehicle tire 12F (<FIG>) does not significantly change when the vehicle is fully laden. In such a case, a footprint length 38F of the front tire 12F does not significantly change. In contrast, the load on a rear vehicle tire 12R significantly changes when the vehicle <NUM> is fully laden, and a footprint length 38R of the rear tire significantly changes. Based on this, the footprint length 38F of the front tire 12F may be used as a reference and compared to the footprint length 38R of the rear tire 12R to estimate the loading state of the vehicle <NUM>, which may then be used to account for the loading state <NUM> of the tire <NUM>.

As shown in <FIG>, a plot <NUM> or comparison of the tire loading state <NUM> to a ratio <NUM> of the footprint length 38F of the front tire 12F to the footprint length 38R of the rear tire 12R under cruising conditions for the vehicle <NUM> shows that the vehicle loading state <NUM> may be determined. It is to be understood that a cruising condition is when the vehicle <NUM> is driven at a constant speed on a straight road. The vehicle loading state <NUM> may be categorized as empty <NUM>, half laden <NUM>, or fully laden <NUM>.

Turning now to <FIG>, the determination of the vehicle loading state <NUM> preferably is made by a vehicle loading state estimator <NUM>. The tire-mounted sensor <NUM> preferably wirelessly transmits the measured footprint length 38F and an inflation pressure 40F of a front tire 12F, and the measured footprint length 38R and an inflation pressure 40R of a rear tire 12R, to the processor <NUM>. The vehicle loading state estimator <NUM> is stored on or is in electronic communication with the processor <NUM> and receives the measured footprint lengths 38F and 38R and the inflation pressures 40F and 40R.

Each measured footprint length 38F and 38R is filtered to remove signal noise from the measured data with a de-noising module <NUM>. An example of a de-noising module <NUM> is described in greater detail below. The de-noising module <NUM> 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 <NUM> compares the filtered front footprint length 66F to the filtered rear footprint length 66R to determine the footprint length ratio <NUM>.

With additional reference to <FIG>, 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 <NUM> are input into a vehicle loading state estimation classification model <NUM> of the vehicle loading state estimator <NUM>. The classification model <NUM> preferably identifies the vehicle loading state <NUM> from a multiclass classification of empty <NUM>, half laden <NUM>, or fully laden <NUM> using the front inflation pressure 40F, the rear inflation pressure 40R, and the footprint length ratio <NUM>. Preferably, the classifier <NUM> employs a multinomial logistic regression classification methodology, such as a softmax regression, to identify the vehicle loading state <NUM>. The multinomial logistic regression classification methodology is preferred based on its capability to predict the probabilities of different outcomes of a categorically distributed dependent variable when given a set of independent variables. The vehicle loading state estimation classification model <NUM> determines the specific loading state <NUM> of the vehicle <NUM>, which is described by way of example as empty <NUM>, half laden <NUM>, or fully laden <NUM>.

Returning to <FIG>, once the vehicle loading state estimator <NUM> determines the loading state <NUM> of the vehicle <NUM>, the loading state is correlated to an inflation sensitivity <NUM> for the tire <NUM>. The inflation sensitivity may be stored in a lookup table or database <NUM> that is stored on or is in electronic communication with the processor <NUM>. The inflation sensitivity <NUM> that corresponds to the specific loading state <NUM> enables a predetermined inflation correction factor <NUM> for the tire <NUM> to be determined.

The inflation correction factor <NUM> is input into the pressure correction module <NUM> along with the measured footprint length <NUM> and the measured inflation pressure <NUM> for the tire <NUM>. The pressure correction module <NUM> adjusts the measured footprint length <NUM> according to the measured inflation pressure <NUM> and the inflation correction factor <NUM>, thereby accounting for changes in inflation pressure and the loading state of the tire, to determine an adjusted footprint length <NUM>. The pressure correction module <NUM> preferably includes a regression model, which may be a linear regression model or a nonlinear regression model, to determine the adjusted footprint length <NUM>.

For example, the relationship between the measured footprint length <NUM> and the measured inflation pressure <NUM> may be accomplished with a linear regression model, which may be based on data from testing of the vehicle <NUM>. Once the regression model coefficients have been determined, a slope coefficient may be employed to adjust the measured footprint length <NUM> using the following equation: <MAT> where Adjusted FPL is the adjusted footprint length <NUM>, Measured FPL is the measured footprint length <NUM>, Measured P is the measured inflation pressure <NUM>, Predetermined P is a predetermined target inflation pressure for the tire <NUM>, and SC is the slope coefficient.

The adjusted footprint length <NUM> is filtered to remove signal noise from the measured data with a de-noising module <NUM>, which is stored on or is in electronic communication with the processor <NUM>. By way of example, the de-noising module <NUM> may receive a steering wheel angle <NUM> of the vehicle <NUM> as an input from the vehicle CAN bus system <NUM>. The steering wheel angle <NUM> is input into an event filter <NUM>, which screens the measured footprint length data <NUM> to ensure that only footprint length measurements during straight-line travel of the vehicle <NUM> are analyzed. In this manner, the event filter <NUM> ensures that consistent footprint length measurements <NUM> from straight-line travel are employed.

When the event filter <NUM> ensures that the vehicle <NUM> is traveling in a straight line, a de-noising algorithm <NUM> filters the adjusted footprint length data <NUM>. A preferred de-noising algorithm <NUM> is an adaptive filter algorithm, such as a recursive least square algorithm with a forgetting factor, which gives less weight to older data samples to ensure that the most recent data receives a higher priority. After the de-noising algorithm <NUM>, the adjusted footprint length data <NUM> is smoothed in a smoothing module <NUM> to capture significant patterns in the data. The smoothing module <NUM> employs a technique that is useful for time series data such as the adjusted footprint length data <NUM>. A preferred technique in the smoothing module <NUM> is an exponential weighted average filter.

When the adjusted footprint length data <NUM> has been filtered by the de-noising module <NUM>, a filtered footprint length <NUM> for the tire <NUM> is yielded. As the tire <NUM> wears, the measured footprint length <NUM> and the filtered footprint length <NUM> typically decrease. Thus, as the tire <NUM> wears, the shortened footprint length may create an inaccurate presumption that the tire load is changing. To account for such a presumption, the tire load estimation system <NUM> corrects for wear of the tire <NUM> with a wear correction model <NUM>.

The wear correction module <NUM> receives the filtered footprint length <NUM> and is stored on or is in electronic communication with the processor <NUM>. It has been determined that wear appears as a slow-moving drift in the filtered footprint length data <NUM>. The wear correction module <NUM> removes the drift in the filtered footprint length data <NUM> to correct for wear of the tire <NUM>. To remove the drift, the wear correction module <NUM> applies a direct current (DC) block filter to the filtered footprint length data <NUM>. The DC block filter separates the signal for the filtered footprint length data <NUM> into two components. The first component is a DC component, which carries a load dependency, and the second component is a drift component, which carries a wear dependency. The wear correction module <NUM> identifies and removes the drift component from the filtered footprint length data <NUM> to generate a wear-corrected footprint length <NUM>.

The wear-corrected footprint length <NUM> is input into a load determination model <NUM>, which is stored on or is in electronic communication with the processor <NUM>. The load determination model <NUM> preferably employs a regression model to calculate the load on the tire <NUM> that corresponds to the wear-corrected footprint length <NUM>. The regression model may be a linear regression model, or a nonlinear regression model. The load determination model <NUM> thus determines and outputs an estimated load <NUM> on the tire <NUM>. The estimated load <NUM> may be communicated through the vehicle CAN bus system <NUM> from the tire load estimation system <NUM> for use by a vehicle control system, such as a braking, traction, stability, and/or suspension system.

Turning to <FIG>, the tire load estimation system <NUM> preferably is executed on a processor <NUM> that is accessible through the vehicle CAN bus <NUM>, which may be mounted on the vehicle <NUM>, or which may be in an Internet or cloud-based computing system <NUM>, referred to herein as a cloud-based computing system. The tire load estimation system <NUM> preferably employs wireless data transmission <NUM> between the vehicle <NUM> and the cloud-based computing system <NUM>. The tire load estimation system <NUM> may also employ wireless data transmission <NUM> between the cloud-based computing system <NUM> and a display device <NUM> that is accessible to a user of the vehicle <NUM>, such as a smartphone, or to a fleet manager. Alternatively, the system <NUM> may also employ wireless data transmission <NUM> between the vehicle CAN bus <NUM> and the display device <NUM>.

In this manner, the tire load estimation system <NUM> of the present invention indirectly estimates tire load in an accurate and reliable manner using the measured footprint length <NUM> of the tire <NUM>. The tire load estimation system <NUM> provides compensation of the measured footprint length <NUM> to account for inflation pressure effects, and also compensates for a loading state by comparing a footprint length 38F of a front tire 12F to a footprint length 38R of a rear tire 12R. The system <NUM> also provides a compensation or correction of the footprint length <NUM> that accounts for wear of the tire <NUM>.

Claim 1:
A load estimation system for a tire (<NUM>), the tire (<NUM>) including a pair of sidewalls (<NUM>) extending to a circumferential tread (<NUM>) and supporting a vehicle (<NUM>), the system (<NUM>) comprising:
a sensor (<NUM>) being mounted to the tire (<NUM>) and being configured for measuring an inflation pressure (<NUM>) of the tire (<NUM>);
a footprint (<NUM>) formed by the tread (<NUM>), the footprint (<NUM>) including a footprint length (<NUM>), wherein sensor (<NUM>) if configured for measuring the footprint length (<NUM>);
a processor (<NUM>) in electronic communication with the sensor (<NUM>);
a vehicle loading state estimator (<NUM>) in electronic communication with the processor (<NUM>) for determining a loading state (<NUM>) of the vehicle (<NUM>);
a pressure correction module (<NUM>) in electronic communication with the processor (<NUM>), wherein the pressure correction module (<NUM>) is configured for receiving a measured footprint length (<NUM>), a measured inflation pressure of the tire (<NUM>), and an inflation correction factor (<NUM>) as determined from the loading state (<NUM>) of the vehicle (<NUM>), wherein the pressure correction module (<NUM>) is configured to determine an adjusted footprint length (<NUM>);
a de-noising module (<NUM>) in electronic communication with the processor (<NUM>), the de-noising module (<NUM>) being configured for receiving the adjusted footprint length (<NUM>) to generate a filtered footprint length (<NUM>);
a wear correction module (<NUM>) in electronic communication with the processor (<NUM>), the wear correction module (<NUM>) being configured for receiving the filtered footprint length (<NUM>) and for correcting for wear of the tire (<NUM>) to generate a wear-corrected footprint length (<NUM>); and
a load determination model (<NUM>) in electronic communication with the processor (<NUM>), the load determination model (<NUM>) being configured for receiving the wear-corrected footprint length (<NUM>) and for determining an estimated load (<NUM>) on the tire (<NUM>).