Patent Description:
Vehicle tires, and particularly pneumatic tires, typically have certain conditions or parameters that are beneficial to monitor during vehicle operation. For example, monitoring the pressure of a pneumatic tire may be helpful in assessing the condition and/or performance of the tire, as a low pressure may indicate that there is an issue with the tire.

To monitor tire pressure, techniques have been developed to measure the pressure inside the tire cavity using sensors that are attached to the tire. Such techniques obtain pressure data in real time from the sensors.

The measured tire pressure may be correlated to a specific tire and transmitted to an electronic control system of the vehicle. The measured tire pressure data may then be employed to improve the function of vehicle systems, such as an anti-lock brake system (ABS), electronic stability control system (ECS), and the like. The measured tire pressure data may also be sent to an operator of the vehicle.

In addition, for fleets of commercial vehicles or passenger vehicles, it is desirable for a manager of the fleet to be informed of tire pressure to make informed decisions about the tires and the vehicle. For example, in the event that a pressure measurement is below a threshold value, an alert may be sent to the fleet manager. The fleet manager may then instruct the vehicle operator to reduce the vehicle speed or direct the vehicle to a service center.

However, prior art techniques typically only compare the measured pressure to the threshold value and transmit an alert when the measured pressure drops below the threshold value. Such techniques lack precision, as they may generate an alert that is not needed. In addition, prior art techniques do not distinguish between a rapid leak condition and a slow leak condition. Detection of a slow leak detection is particularly advantageous for fleet managers, as preventive measures for the tire may be taken according to a fleet maintenance schedule, rather than unnecessarily removing the vehicle from immediate service.

As a result, there is a need in the art for a system that obtains tire pressure data, determines with precision if a rapid air pressure leak or a slow air pressure leak is present, and generates a corresponding notification.

<CIT> describes a system in accordance with the preamble of claim <NUM>.

A further method and system for processing information in a tire pressure monitoring system in known from <CIT>.

<CIT> describes a method and apparatus for determining a tires condition using ideal gas law.

According to an aspect of an exemplary embodiment of the invention, a tire pressuring monitoring system for monitoring the pressure in at least one tire supporting a vehicle is provided. The system includes at least one sensor mounted on the tire for measuring a pressure and a temperature of the tire. Transmission means transmit the measured pressure data and temperature data to a processor, and a tire pressure model is executed on the processor. The tire pressure model includes a driving event extractor to extract cold pressure data from the measured pressure data, and a temperature compensator to generate a compensated cold tire pressure from the cold pressure data. A noise filter filters sensor noise and generates a filtered cold tire pressure from the compensated cold tire pressure. A detection module receives the filtered cold tire pressure and determines an air pressure leak rate of the tire. A leak notification corresponding to the air pressure leak rate is generated by the tire pressure model.

"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 bus" is an abbreviation for controller area network.

"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.

"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.

Turning now to <FIG>, an exemplary embodiment of the tire pressure monitoring system <NUM> of the present invention is indicated. With particular reference to <FIG>, the system <NUM> monitors the pressure in 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, off-the-road vehicles, and the like, in which vehicles may be supported by more or fewer tires. In addition, the invention finds application in a single vehicle <NUM> or in fleets of vehicles.

Each tire <NUM> includes a pair of bead areas <NUM> (only one shown) and a bead core (not shown) embedded in each bead area. Each one of a pair of sidewalls <NUM> (only one shown) extends radially outward from a respective bead area <NUM> to a ground-contacting tread <NUM>. The tire <NUM> is reinforced by a carcass <NUM> that toroidally extends from one bead area <NUM> to the other bead area, as known to those skilled in the art. An innerliner <NUM> is formed on the inside surface of the carcass <NUM>. The tire <NUM> is mounted on a wheel <NUM> in a manner known to those skilled in the art and, when mounted, forms an internal cavity <NUM> that is filled with a pressurized fluid, such as air.

A sensor unit <NUM> may be attached to the innerliner <NUM> of each tire <NUM> by means such as an adhesive and measures certain parameters of the tire, as will be described in greater detail below. It is to be understood that the sensor unit <NUM> may be attached in such a manner, or to other components of the tire <NUM>, such as between layers of the carcass <NUM>, on or in one of the sidewalls <NUM>, on or in the tread <NUM>, and/or a combination thereof. For the purpose of convenience, reference herein shall be made to mounting of the sensor unit <NUM> on the tire <NUM>, with the understanding that mounting includes all such attachment.

The sensor unit <NUM> is mounted on each tire <NUM> for the purpose of detecting certain realtime tire parameters inside the tire, such as tire pressure and temperature. Preferably the sensor unit <NUM> 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 <NUM> shall be referred to as a TPMS sensor. Each TPMS sensor <NUM> preferably also includes electronic memory capacity for storing identification (ID) information for each tire <NUM>, 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 <NUM>.

The tire ID information may include manufacturing information for the tire <NUM>, 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 <NUM>, 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 <NUM> 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 <NUM> and/or the tire ID tag <NUM> to provide location tracking of the tire <NUM> during transport and/or location tracking of the vehicle <NUM> on which the tire is installed.

Turning now to <FIG>, the TMPS sensor <NUM> and the tire ID tag <NUM> each include an antenna for wireless transmission <NUM> of the measured tire pressure and temperature, as well as tire ID data, to a processor <NUM>. The processor <NUM> may be mounted on the vehicle <NUM> as shown, or may be integrated into the TPMS sensor <NUM>. For the purpose of convenience, the processor <NUM> will be described as being mounted on the vehicle <NUM>, with the understanding that the processor may alternatively be integrated into the TPMS sensor <NUM>. Preferably, the processor <NUM> is in electronic communication with or integrated into an electronic system of the vehicle <NUM>, such as the vehicle CAN bus system <NUM>, which is referred to as the CAN bus.

Aspects of the tire data information system <NUM> preferably are executed on the processor <NUM> or another processor that is accessible through the vehicle CAN bus <NUM>, which enables input of data from the TMPS sensor <NUM> and the tire ID tag <NUM>, as well as input of data from other sensors that are in electronic communication with the CAN bus. In this manner, the tire pressure monitoring system <NUM> enables direct measurement of tire pressure and temperature with the TPMS sensor <NUM>, which preferably is transmitted to the processor <NUM>. Tire ID information preferably is transmitted from the TPMS sensor <NUM> or the tire ID tag <NUM> to the processor <NUM>. The processor <NUM> preferably correlates the measured tire pressure, the measured tire temperature, the measurement time, and ID information for each tire <NUM>.

Referring to <FIG>, when the measured tire pressure, measured tire temperature, measurement time and ID information are correlated for each tire <NUM>, the data may be wirelessly transmitted <NUM> from the processor <NUM> (<FIG>) and/or the CAN-bus <NUM> on the vehicle <NUM> to a remote processor <NUM>, such as a processor in a cloud-based server <NUM>. The cloud-based server <NUM> preferably executes a model <NUM> of the tire pressure monitoring system <NUM>, which will be described in greater detail below. Output from the system <NUM> may be wirelessly transmitted <NUM> to a fleet management server <NUM> that includes a display <NUM> for showing output and/or notifications from the tire pressure monitoring system, as will be described in greater detail below.

Turning to <FIG>, the tire pressure monitoring system <NUM> includes a tire pressure model <NUM>, which receives tire data <NUM>. The tire data <NUM> includes the above-described measured tire pressure, measured tire temperature, measurement time and ID information for each tire <NUM>.

The tire pressure model <NUM> filters or extracts heat effects from the tire data <NUM> with a driving event extractor <NUM>. With additional reference to <FIG>, the driving event extractor <NUM> extracts cold pressure data <NUM> from the raw pressure data <NUM>. More particularly, due to heating effects of air in the tire cavity <NUM> (<FIG>) during operation of the vehicle <NUM>, the cold tire pressures <NUM> are shifted lower than the distribution of all of the raw pressure data <NUM>. As shown in <FIG>, the driving event extractor <NUM> thus removes heating effects of the raw pressure data <NUM> due to operation of the vehicle <NUM> to arrive at the cold pressure values <NUM>. Any suitable data filtering technique may be employed by the driving event extractor <NUM> to extract the cold pressure data <NUM>.

Returning to <FIG>, the tire pressure model <NUM> filters ambient temperature effects from the cold pressure data <NUM> with a temperature compensator <NUM>. Ambient temperature affects the cold tire pressure <NUM>, as reflected by a sensitivity shown in the graph of <FIG>, which yields about a one pound per square inch (psi) change (<NUM>,<NUM> pascal change) in cold pressure <NUM> for a <NUM>-degree Fahrenheit change (<NUM>° C change) in a cold temperature <NUM> of the tire <NUM>. Therefore, the cold pressure data <NUM> is adjusted to arrive at a compensated or adjusted cold tire pressure <NUM>, as shown in <FIG>. An exemplary adjustment executed by the temperature compensator <NUM> includes calculating the compensated or adjusted cold tire pressure <NUM> as equal to the cold tire pressure <NUM> multiplied by a ratio of an adjusted tire temperature to the measured tire temperature <NUM>: <MAT> In this manner, the temperature compensator <NUM> generates the compensated cold tire pressure <NUM>. It is to be understood that any suitable data compensation technique may be employed by the temperature compensator <NUM> to generate the compensated cold tire pressure <NUM>.

Returning to <FIG>, the tire pressure model <NUM> filters sensor noise from the compensated cold tire pressure <NUM> with a noise filter <NUM>. More particularly, there may be unwanted variations, known as noise, in the data signal transmitted by the TPMS sensor <NUM>. To improve the accuracy of the tire pressure data, and specifically the compensated cold tire pressure data <NUM>, the variations or noise are filtered out of the data using the noise filter <NUM>, which preferably includes a linear quadratic estimation or a Kalman filter. As shown in <FIG>, the noise filter <NUM> processes the compensated cold tire pressure data <NUM> using the Kalman filter and generates a filtered cold tire pressure <NUM>.

Returning again to <FIG>, the filtered cold tire pressure data <NUM> may optionally be stored in an electronic storage means <NUM>, such as a data buffer. The storage means <NUM> enables the filtered cold tire pressure data <NUM> to be stored for further analysis and/or historical archiving. Once the tire pressure model <NUM> extracts heat effects from the tire data <NUM> with the driving event extractor <NUM>, filters ambient temperature effects from the cold pressure data <NUM> with the temperature compensator <NUM>, and filters sensor noise from the compensated cold tire pressure <NUM> with the noise filter <NUM>, a detection module <NUM> analyzes the filtered cold tire pressure <NUM> for data indicative of inflation or deflation of the tire <NUM>.

More particularly, the detection module <NUM> includes a comparator <NUM>, which analyzes the filtered cold tire pressure data <NUM>. As shown in <FIG>, the comparator <NUM> detects an inflation <NUM> of the tire <NUM> by comparing neighboring filtered cold tire pressure data values <NUM> to find local maxima. When an inflation <NUM> is detected, the tire pressure model <NUM> generates an inflation notification <NUM>, as will be described in greater detail below.

The detection module <NUM> also determines whether a specific tire <NUM> has an air leak, and if so, the rate of the leak. More particularly, the detection module <NUM> converts time scale observations into an absolute scale, and executes a regression analysis to fit the filtered cold tire pressure data <NUM> with a robust regression. A slope <NUM> as the median of all slopes between paired values at a <NUM> percent confidence interval is determined, and equates to the air pressure leak rate of the tire <NUM>.

Referring to <FIG>, the air pressure leak rate <NUM> below a target air pressure <NUM> for the tire <NUM> dictates the type of a leak notification <NUM> that is generated by the system <NUM>. For example, a first leak rate 82a includes an air pressure rate loss of more than <NUM> percent of the air in the tire <NUM> within a <NUM>-hour or one day time period. The first leak rate 82a is considered to be a rapid leak rate and dictates a first leak notification 86a, which is to immediately stop the vehicle <NUM> for repair or replacement of the tire <NUM>. A second leak rate 82b includes an air pressure rate loss of more than <NUM> percent of the air in the tire <NUM> during a time period that is between one day and one week. The second leak rate 82b dictates a second leak notification 86b to repair the tire <NUM> "now" or within the next day, enabling the vehicle <NUM> to be directed to a service center in a safe manner during an appropriate time window.

A third leak rate 82c includes an air pressure rate loss of more than <NUM> percent of the air in the tire <NUM> during a time period that is between one week and one month. The third leak rate 82c dictates a third leak notification 86c to repair the tire <NUM> "soon" or within the next week, enabling the vehicle <NUM> to be directed to a service center in a safe and convenient manner during an appropriate time window. A fourth leak rate 82d includes an air pressure rate loss of more than <NUM> percent of the air in the tire <NUM> during a time period that is between one month and two months. The fourth leak rate 82d dictates a fourth leak notification 86d to check and/or repair the tire <NUM> "when possible" or within the next several weeks, enabling the vehicle <NUM> to be scheduled for service during an appropriate time window.

A fifth third leak rate 82e includes an air pressure rate loss of more than <NUM> percent of the air in the tire <NUM> during a time period that is between two months and six months. The fifth leak rate 82e dictates a fifth leak notification 86e to check and/or repair the tire <NUM> "in next routine maintenance" or within the next month, enabling the tire to be checked during the next scheduled maintenance of the vehicle <NUM>. The leak rate <NUM> and corresponding leak notification <NUM> may be adjusted depending on particular operating conditions for the tire <NUM> and/or service conditions for the vehicle <NUM>, and thus may be different from the foregoing examples, without affecting the concept or operation of the invention.

Turning to <FIG>, the air pressure leak rate <NUM> may also dictate the particular analysis technique to be employed, further increasing the accuracy of the tire pressure monitoring system <NUM>. For example, for a rapid leak rate such as the first leak rate 82a, which may be an air pressure rate loss of more than <NUM> percent of the air in the tire <NUM> within a <NUM>-hour time period, a rapid leak model <NUM> is employed. A preferred rapid leak model <NUM> is a short-time window deviation model. For a leak rate <NUM> that may be considered a slow leak, such as the second leak rate 82b, third leak rate 82c, fourth leak rate 82d and fifth leak rate 82e, which may be an air pressure rate loss of more than <NUM> percent of the air in the tire <NUM> during a time period that is between one day and several weeks or months, a slow leak model <NUM> is employed. A preferred slow leak model <NUM> includes a regression slope analysis technique.

With reference to <FIG>, an exemplary rapid leak model <NUM> is shown. The rapid leak model <NUM> receives tire data <NUM>. The driving event extractor <NUM> extracts heat effects from the tire data <NUM>, the temperature compensator <NUM> filters ambient temperature effects, the noise filter <NUM> filters sensor noise, and the data buffer <NUM> stores the resulting filtered cold tire pressure data <NUM>. The comparator <NUM> detects inflation <NUM> of the tire <NUM> and uses this information to continuously retrain the model <NUM>. In addition, historical data <NUM> is retrieved from the data buffer <NUM>.

According to the ideal gas law, the volume of the tire cavity <NUM> remains within a relatively small interval during operation of the tire <NUM>, which may be modeled as a constant with a predetermined error. Once enough tire data <NUM> for the tire <NUM> has been gathered, the rapid leak model <NUM> executes a linear regression of the tire pressure data and compares a set of consecutive tire data to the model output <NUM>. If a difference between the prediction from the linear regression and the tire data <NUM> is greater than a predetermined threshold amount <NUM>, a rapid leak rate 82a is detected, and the first leak notification 86a to immediately stop the vehicle <NUM> is generated.

Turning to <FIG>, an exemplary slow leak model <NUM> is shown. The slow leak model <NUM> receives tire data <NUM>. The driving event extractor <NUM> extracts heat effects from the tire data <NUM>, the temperature compensator <NUM> filters ambient temperature effects, the noise filter <NUM> filters sensor noise, and the data buffer <NUM> stores the resulting filtered cold tire pressure data <NUM>. The comparator <NUM> detects inflation <NUM> of the tire <NUM>, and when inflation is detected, the inflation notification <NUM> is generated. When inflation <NUM> is not detected, the slow leak model <NUM> analyzes the filtered cold tire pressure data <NUM> with a regression slope analysis technique to determine a rate of pressure change <NUM> in the tire <NUM>. The slow leak model <NUM> executes a verification <NUM> of the rate of pressure change <NUM>, and an appropriate slow leak notification 86b, 86c. 86d or 86e for the rate of pressure change is generated.

Returning to <FIG> and <FIG>, the tire pressure model <NUM> generates an inflation notification <NUM> when inflation <NUM> is detected. When an air pressure leak in the tire <NUM> is detected, the tire pressure model <NUM> generates an appropriate leak notification <NUM> that corresponds to the leak rate <NUM>. When a notification <NUM> or <NUM> is generated, the tire pressure monitoring system <NUM> preferably wirelessly transmits <NUM> the notification from the cloud-based server <NUM> to the fleet management server <NUM>, which is shown on the display <NUM>. Display of the notifications <NUM> and <NUM> enables a fleet manager viewing the display <NUM> to take preventative measures, such as instructing a vehicle operator to slow the vehicle <NUM> down, direct the vehicle to a service center, and/or schedule the vehicle for maintenance. The notifications <NUM> and <NUM> may also be transmitted to a device that is visible to the operator of the vehicle <NUM>, thereby enabling the operator to take action based on the notification. In addition, as shown in <FIG> and <FIG>, the notifications <NUM> and <NUM> may be recorded <NUM> in the cloud-based server <NUM> for future analysis.

In this manner, the tire pressure monitoring system <NUM> obtains tire pressure data <NUM>, extracts heat effects from the tire data, filters ambient temperature effects, filters sensor noise, detects inflation of the tire <NUM>, and detects an air pressure leak in the tire. When the system <NUM> detects inflation <NUM> of the tire <NUM>, an inflation notification <NUM> is generated. When the system <NUM> detects an air pressure leak in the tire <NUM>, the system determines with precision if a rapid air pressure leak or a slow air pressure leak in each tire <NUM> is present and generates a corresponding leak notification <NUM>. By distinguishing between a rapid leak condition 82a and slow leak conditions 82b, 82c, 82d and 82e, the tire pressure monitoring system <NUM> enables a fleet manager to take appropriate action based upon the condition according to a fleet maintenance schedule, rather than unnecessarily removing the vehicle <NUM> from immediate service.

Claim 1:
A tire pressure monitoring system for monitoring the pressure in at least one tire (<NUM>) supporting a vehicle (<NUM>), the system (<NUM>) comprising:
at least one sensor (<NUM>) mounted on the at least one tire (<NUM>) for measuring a pressure and a temperature of the at least one tire (<NUM>);
means for transmitting the measured pressure data (<NUM>) and temperature data to a processor (<NUM>, <NUM>); characterized in that
a tire pressure model (<NUM>) being executed on the processor (<NUM>, <NUM>), the tire pressure model (<NUM>) including:
a driving event extractor (<NUM>) to extract cold pressure data (<NUM>) from the measured pressure data (<NUM>);
a temperature compensator (<NUM>) to generate a compensated cold tire pressure (<NUM>) from the cold pressure data (<NUM>);
a noise filter (<NUM>) to filter sensor noise and generate a filtered cold tire pressure (<NUM>) from the compensated cold tire pressure (<NUM>);
a detection module (<NUM>) receiving the filtered cold tire pressure (<NUM>) and determining an air pressure leak rate of the at least one tire (<NUM>); and
a leak notification (<NUM>) corresponding to the air pressure leak rate generated by the tire pressure model (<NUM>).