Patent Description:
As is known in the art, a vehicle is supported by multiple tires. The stiffness of each tire affects the performance and characteristics of the tire during operation of the vehicle. For example, longitudinal stiffness of the tire, which is the stiffness of the tire in its longitudinal or travel direction, may be employed to distinguish between different road surface conditions and/or different wear states of the tire. In addition, the longitudinal stiffness may be employed to improve the operation of vehicle control systems, such as adaptive cruise control (ACC), anti-lock braking systems (ABS), electronic stability program (ESP), acceleration slip regulation (ASR), and the like.

Due to the usefulness of tire longitudinal stiffness, it is desirable to generate an accurate estimate of the longitudinal stiffness. In the prior art, systems were developed to provide such estimates. However, in order to arrive at accurate longitudinal stiffness estimates, such prior art systems have been complex, often employing data from multiple sources. For example, may be used data from the vehicle, from the tire, and from remote data servers.

The use of such complex systems, as well as data from such a variety of sources, may be undesirably difficult to implement. In addition, such complex systems require a significant amount of computing load. When a significant amount of computing load is involved, such systems may not be able to be executed on a vehicle-mounted processor, thereby undesirably requiring additional resources such as cloud computing, and undesirably taking significant time to generate a real-time estimate.

As a result, there is a need in the art for a system that estimates the longitudinal stiffness of a tire in real time which provides an accurate estimate based on data from limited sources, and which has a low computing load.

<CIT> describes a system and method in accordance with the preamble of claim <NUM> and <NUM>, respectively.

Further methods and systems for longitudinal stiffness estimation or determination of a tire are known from <CIT>, <CIT> and <CIT>.

<CIT> describes a tire wear state estimation system and method.

<CIT> describes a friction adaptive vehicle control.

According to an aspect of an exemplary embodiment of the invention, a longitudinal stiffness estimation system for at least one tire supporting a vehicle is provided. The system includes an electronic communication system that is disposed on the vehicle, and at least one sensor disposed on the vehicle which is in electronic communication with the electronic communication system. A processor is accessible through the electronic communication system. The sensor measures selected parameters associated with the vehicle and communicates data for the selected parameters through the electronic communication system to the processor. A µ-slip curve generator is in communication with the processor, receives the selected parameters, and generates a µ-slip curve in real time from the communicated data. An extraction module is in communication with the processor and extracts raw data from a linear portion of the µ-slip curve. A denoising module is in communication with the processor and de-noises the raw data from the µ-slip curve by determining a vector for the raw data, an orientation of the vector, and a heading of the vector. The denoising module generates de-noised data, and a stiffness calculator receives the de-noised data and generates a longitudinal stiffness estimate for the tire.

"ANN" or "Artificial Neural Network" is an adaptive tool for non-linear statistical data modeling that changes its structure based on external or internal information that flows through a network during a learning phase. ANN neural networks are non-linear statistical data modeling tools used to model complex relationships between inputs and outputs or to find patterns in data.

"CAN" is an abbreviation for controller area network, and is used in conjunction with CAN bus, which is an electronic communication system on a vehicle.

"Circumferential" means lines or directions extending along the perimeter of the surface of the annular tread of the tire perpendicular to the axial direction.

"Cloud computing" means computer processing involving computing power and/or data storage that is distributed across multiple data centers, which is typically facilitated by access and communication using the Internet.

"Kalman filter" is a set of mathematical equations that implement a predictor-corrector type estimator that is optimal in the sense that it minimizes the estimated error covariance when some presumed conditions are met.

"Luenberger observer" is a state observer or estimation model. A "state observer" is a system that provide an estimate of the internal state of a given real system, from measurements of the input and output of the real system. It is typically computer-implemented, and provides the basis of many practical applications.

"MSE" is an abbreviation for mean square error, the error between and a measured signal and an estimated signal which the Kalman filter minimizes.

"TPMS" means a tire pressure monitoring system.

An exemplary embodiment of the longitudinal stiffness estimation system <NUM> of the present invention is presented in <FIG>. The system <NUM> estimates a longitudinal stiffness of a tire <NUM> that supports a vehicle <NUM>, as shown in <FIG>. 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.

Each tire <NUM> is preferably of conventional construction, and is mounted on a wheel <NUM>. Each tire <NUM> preferably includes a pair of sidewalls <NUM> that extend to a circumferential tread <NUM>. Each tire <NUM> is preferably equipped with a sensor or transducer <NUM>, which may be a tire pressure monitoring (TPMS) module or sensor, and detects tire parameters such as pressure within a tire cavity <NUM> and tire temperature. The sensor <NUM> preferably is affixed to an inner liner <NUM> of the tire <NUM> by suitable means such as adhesive.

The tire <NUM> includes a longitudinal stiffness, which is its stiffness in a longitudinal or travel direction. Turning to <FIG>, the longitudinal stiffness estimation system <NUM> calculates the longitudinal stiffness of the tire <NUM> by providing a longitudinal stiffness estimate <NUM>. Aspects of the longitudinal stiffness estimation system <NUM> preferably are executed on a processor <NUM> that is accessible through an electronic communication system on the vehicle, which enables central communication between multiple vehicle sensors, such as a CAN bus system <NUM>. The processor <NUM> may be a local processor that is mounted on the vehicle <NUM>, or may be a remote processor, such as a cloud computing processor.

The longitudinal stiffness estimation system <NUM> preferably provides a longitudinal stiffness estimate <NUM> for each tire <NUM> mounted on a driven wheel <NUM> of the vehicle <NUM>. For example, in a front wheel drive vehicle <NUM>, the system <NUM> generates a stiffness estimate <NUM> for each one of the front tires <NUM>. For the purpose of convenience, the system <NUM> is described with reference to one tire <NUM>, with the understanding that an estimate <NUM> preferably is provided for each tire <NUM> mounted on a driven wheel <NUM> of the vehicle <NUM>.

The longitudinal stiffness estimation system <NUM> receives as inputs certain parameters measured by sensors that are mounted on the vehicle <NUM> and which are in electronic communication with the vehicle CAN bus system <NUM>. Specifically, the CAN bus <NUM> electronically communicates a longitudinal acceleration (Ax or ax) <NUM> of the vehicle <NUM>, a wheel speed <NUM>, a throttle or gas pedal position <NUM>, a brake pedal position <NUM>, and a vehicle reference speed <NUM>, to an acceleration module <NUM> and a µ-slip curve generator <NUM>. The vehicle reference speed <NUM> may be obtained from a global positioning system (GPS) or other reliable source of the vehicle reference speed.

In the acceleration module <NUM>, the gas pedal position <NUM> and brake pedal position <NUM> are employed to confirm that the vehicle <NUM> is accelerating. For example, if the gas pedal position <NUM> is below a predetermined throttle threshold, or if the brake pedal position <NUM> is above a predetermined brake threshold, the system <NUM> determines that the vehicle <NUM> is not accelerating. When the vehicle <NUM> is not accelerating, the system <NUM> does not proceed to the µ-slip curve generator <NUM>. If the gas pedal position <NUM> is greater than a predetermined throttle threshold and/or the brake pedal position <NUM> is below a predetermined brake threshold, the system <NUM> determines that the vehicle <NUM> is accelerating and proceeds to the µ-slip curve generator <NUM>.

With additional reference to <FIG>, the µ-slip curve generator <NUM> generates a µ-slip curve <NUM> in real time. On the vertical axis, the µ-slip curve <NUM> plots the frictional force between the tire <NUM> and the surface on which the tire is traveling, represented by the coefficient of friction mu (µ). The µ-slip curve generator <NUM> employs the longitudinal acceleration <NUM> of the vehicle <NUM> to approximate the coefficient of friction µ during vehicle acceleration. On the horizontal axis, the µ-slip curve <NUM> plots tire slip <NUM>, which is the relative motion between the tire <NUM> and the surface on which the tire is traveling. The µ-slip curve generator <NUM> employs the wheel speed <NUM> and the vehicle reference speed <NUM> to calculate slip <NUM>: <MAT>.

In this manner, the µ-slip curve generator <NUM> of the longitudinal stiffness estimation system <NUM> generates a µ-slip curve <NUM> in real time using input signals from the vehicle CAN bus system <NUM>.

A slope <NUM> of a linear portion <NUM> of the µ-slip curve <NUM> corresponds to the longitudinal stiffness of the tire <NUM>. As will be described in greater detail below, the longitudinal stiffness estimation system <NUM> extracts the longitudinal stiffness of the tire <NUM> and provides a longitudinal stiffness estimate <NUM> in an accurate manner.

Referring to <FIG> and <FIG>, an extraction module <NUM> extracts raw data <NUM> from the linear portion <NUM> of the µ-slip curve <NUM>. The raw data <NUM> includes signal noise, that is, unwanted modifications in the data that occur during capture, storage, transmission, and/or processing. As a result, the raw data <NUM> must be de-noised or cleaned to improve its accuracy. It is to be understood that the data shown in <FIG> may include hypothetical representations by way of example for the purpose of illustrating the principles of the invention.

Turning now to <FIG> and <FIG>, a denoising module <NUM> de-noises or cleans the raw data <NUM>. Denoising module <NUM> preferably applies principal component analysis (PCA) to determine patterns from the raw data <NUM> for prediction analysis. For example, PCA identifies a principal component or eigenvector, which is a characteristic vector of a linear transformation of the raw data <NUM>. In this manner, a pattern that is visualized or represented by a vector <NUM> is determined from the raw data <NUM>.

The PCA of the denoising module <NUM> predicts an orientation <NUM> of the vector <NUM>, which corresponds to the data variance, rather than predicting each value of the raw data <NUM>. The orientation of the vector <NUM> and the raw data <NUM> is determined using the first principal component of the raw data. In addition to the orientation <NUM>, another parameter referred to as a heading of the vector <NUM>, which is indicated at theta (θ), is determined. The heading θ enables an accurate fit of the vector <NUM> to be obtained that covers the variance of the data. The heading θ is the angle between a horizontal line <NUM> extending from an origin of µ and the orientation <NUM> of the vector <NUM>.

For accuracy, the heading θ must reflect a proper alignment of the vector <NUM> with the data. Since the heading θ is initially an unknown value, a density module <NUM> is employed to determine the heading. In the density module <NUM>, the determination of an optimum value for the heading θ is driven by the data.

With reference to <FIG> and <FIG>, in the density module <NUM>, the data are transformed to polar coordinates <NUM> and a density versus heading θ plot <NUM> is generated. Density is indicated by rho (ρ). Because the data are symmetric, positive values may be used for simplicity. In addition, because the data are not continuous, the first ten percent (<NUM>%) of the standard deviation of the data is used to select a data range <NUM>. The density ρ of the data range <NUM> is calculated based on a median of the data: <MAT>.

A density center of the data range <NUM> is determined using the median of the headings θ, which corresponds to the optimum value for the heading.

With additional reference to <FIG>, once the heading θ is determined, the data range <NUM> is transformed back to the µ versus slip <NUM> plot and the orientation <NUM> of the vector <NUM> is calculated using the polar coordinates. Specifically, the coefficient of a second polar axis PC2 is divided by the coefficient of a first polar axis PC1 to determine the orientation <NUM>. In this manner, the orientation <NUM> and the heading θ for the vector <NUM> are determined, thus de-noising the data <NUM>.

Once the data has been de-noised <NUM> by determining the orientation <NUM> and the heading θ for the vector <NUM>, a stiffness calculator <NUM> ascertains a slope of the vector. The slope of the vector <NUM> is the longitudinal stiffness estimate <NUM> for the tire <NUM>. Preferably, the longitudinal stiffness estimate <NUM> is communicated by the CAN bus system <NUM> to other vehicle control systems for use in such systems and/or to determine certain conditions of the tire <NUM>.

For example, turning to <FIG>, the longitudinal stiffness estimate <NUM> may be used in a road state monitor <NUM> to monitor the condition of a road surface over which the tire <NUM> travels by distinguishing between different road surface conditions <NUM>. The stiffness <NUM> of the tire <NUM> exhibits fast time-varying characteristics on different road surface conditions <NUM>. More particularly, in a non-temperature compensated plot <NUM>, each tire stiffness estimate <NUM> is different for a summer tire <NUM>, an all-season tire <NUM>, and a winter tire <NUM> on a dry surface <NUM>, a wet surface <NUM>, a snow-covered surface <NUM>, and an icy surface <NUM>, respectively.

Because tire stiffness is sensitive to temperature, it is important to correct the tire stiffness estimate <NUM> for the influence of temperature, as shown in temperature-compensated plot <NUM>. From the temperature-compensated plot <NUM>, it can be seen that temperature compensation may exaggerate the differences in the stiffness estimate <NUM>, particularly for the summer tire <NUM> and the all-season tire <NUM>. In addition, it can be seen that the stiffness estimate <NUM> for the winter tire <NUM> generally exhibits a lower dependence on the type of road surface <NUM>, while the stiffness estimate for the summer tire <NUM> and the all-season tire <NUM> is higher on an icy surface <NUM> than on a snow-covered surface <NUM>. Based on this information, the longitudinal stiffness estimate <NUM> may thus be used by the road surface monitor <NUM> to distinguish between a dry road surface <NUM>, a wet road surface <NUM>, a snow-covered road surface <NUM>, and an icy road surface <NUM>.

With reference to <FIG>, the longitudinal stiffness estimate <NUM> may be used in a wear state monitor <NUM> to monitor the wear state of the tire <NUM> by distinguishing between different wear states. The stiffness <NUM> of the tire <NUM> exhibits slow time-varying characteristics with wear. More particularly, in µ versus slip plots <NUM> for worn tires <NUM>, the stiffness estimates <NUM> were at least thirty percent (<NUM>%) higher than the stiffness estimates in µ versus slip plots <NUM> for worn tires <NUM>. Based on this information, the longitudinal stiffness estimate <NUM> may thus be used by the wear state monitor <NUM> to distinguish between different wear states of the tire <NUM>.

In this manner, the longitudinal stiffness estimation system <NUM> of the present invention provides a stiffness estimate <NUM> for a tire <NUM> in real time based on input signals from a standard vehicle system, such as the CAN bus system <NUM>. The longitudinal stiffness estimation system <NUM> of the present invention thus provides an accurate stiffness estimate <NUM> based on minimal data sources. In addition, the use of the above-described denoising module <NUM> in the longitudinal stiffness estimation system <NUM> involves a low computing load and may thus be executed on a vehicle-based processor <NUM>, as opposed to prior art systems that involve high computing loads and must be executed remotely.

The present invention also includes a method of estimating the longitudinal stiffness of a tire <NUM>. The method includes steps in accordance with the description that is presented above and shown in <FIG>. The longitudinal stiffness estimation system <NUM> of the present invention and the accompanying method may be referred to as a Nouri technique.

Claim 1:
A longitudinal stiffness estimation system for at least one tire (<NUM>) supporting a vehicle (<NUM>), the longitudinal stiffness estimation system (<NUM>) comprising:
an electronic communication system (<NUM>) disposed on the vehicle (<NUM>);
at least one sensor (<NUM>) disposed on the vehicle (<NUM>) and in electronic communication with the electronic communication system (<NUM>); and
a processor (<NUM>) accessible through the electronic communication system (<NUM>);
the at least one sensor (<NUM>) being configured for measuring selected parameters associated with the vehicle (<NUM>) and communicating data for the selected parameters through the electronic communication system (<NUM>) to the processor (<NUM>);
characterized in that the system further comprises:
a µ-slip curve generator (<NUM>) in communication with the processor (<NUM>) and configured for receiving the selected parameters and generating a µ-slip curve (<NUM>) in real time from the communicated data;
an extraction module (<NUM>) in communication with the processor (<NUM>) and configured for extracting raw data (<NUM>) from a linear portion (<NUM>) of the µ-slip curve (<NUM>);
a denoising module (<NUM>) in communication with the processor (<NUM>) and configured for de-noising the raw data (<NUM>) from the µ-slip curve (<NUM>) by determining a pattern from the raw data (<NUM>) represented by a vector (<NUM>), an orientation of the vector (<NUM>), and a heading of the vector (<NUM>), wherein the denoising module (<NUM>) is configured to generate de-noised data (<NUM>); and
a stiffness calculator (<NUM>) configured for receiving the de-noised data (<NUM>) and generating a longitudinal stiffness estimate (<NUM>) for the at least one tire (<NUM>).