Abstract: A tire wear state estimation system includes a first sensor unit that is mounted on a tire and includes a footprint centerline length measurement sensor. A second sensor unit is mounted on the tire and includes a shoulder length measurement sensor. At least one of the first sensor unit and the second sensor unit includes a pressure sensor, a temperature sensor, and electronic memory capacity for storing tire identification information. A processor is in electronic communication with the sensor units and receives the measured centerline length, the measured pressure, the measured temperature, the identification information, and the measured shoulder length. The identification information is correlated to tire construction data. An analysis module stored on the processor receives the measured values, the identification information, and the tire construction data as inputs. The analysis module includes a prediction model that generates an estimated wear state for the tire from the inputs.

Abstract: A method for estimating a grip of a tire supporting a vehicle includes generating a first set of data from a tire-mounted sensor unit, generating a second set of data from the tire-mounted sensor unit and from data obtained from the vehicle, and generating a third set of data from data obtained from the vehicle and from the Internet. A grip estimation module is provided. The first, second and third sets of data are received in the grip estimation module. A friction probability distribution is calculated with the grip estimation module using the first, second and third sets of data, and the friction probability distribution is input into at least one vehicle system.

Abstract: A method for extracting changes in characteristics of a tire supporting a vehicle is provided. The method includes extracting selected tire characteristics from at least one sensor mounted on the tire. The selected tire characteristics are transmitted to a remote processor and are stored in a historical data log that is in communication with the remote processor. At least one tire characteristic of interest is selected, and a time series decomposition model is applied to data from the historical data log to delineate exogenous inputs from an underlying trend in the selected tire characteristic of interest. A learning model is applied to the underlying trend in the selected tire characteristic of interest to model a relationship between the selected tire characteristic of interest and a condition of the tire. A prediction value for a condition of the tire is output from the learning model.

Abstract: A vehicle sideslip estimation system includes sensors mounted on a vehicle and a kinematic model receiving signals from the sensors to estimate a lateral velocity of the vehicle. A compensated acceleration calculator calculates a compensated lateral acceleration as a measure of conditions that result in a deviation of a measured lateral acceleration. A lateral acceleration calculator determines, based on the compensated lateral acceleration and the measured lateral acceleration, if a lateral acceleration error is larger than a predefined threshold. A filter corrects the estimated lateral velocity of the vehicle when the lateral acceleration error is larger than the predefined threshold.

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 de-noising 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 wear of the tire to generate a wear-corrected footprint length. A load determination model receives the wear-corrected footprint length and determines an estimated load on the tire.

Abstract: A counter-deflection load estimation system for a tire is provided. The tire includes a pair of sidewalls extending to a circumferential tread and supporting a vehicle, and the vehicle includes a controlled area network bus. In the system, a sensor is mounted to the tire and measures a parameter of the tire. A counter-deflection of the tire is determined from the measured parameter, and a linear vehicle speed signal is received through the controlled area network bus. A processor is in electronic communication with the sensor and with the controlled area network bus. A load estimation module is in electronic communication with the processor, receives the linear vehicle speed signal and the counter-deflection of the tire, and determines a load on the tire.

Abstract: A tire wear state estimation system includes a tire that supports a vehicle. A sensor unit is mounted on the tire and includes a footprint centerline length measurement sensor, a pressure sensor, a temperature sensor, and electronic memory capacity for storing identification information for the tire. A processor is in electronic communication with the sensor unit and receives the measured centerline length, the measured pressure, the measured temperature and the identification information. A tire construction database stores tire construction data and is in electronic communication with the processor. The identification information is correlated to the tire construction data. An analysis module is stored on the processor and receives the measured centerline length, the measure pressure, the measured temperature, the identification information, and the tire construction data as inputs. The analysis module includes a prediction model that generates an estimated wear state for the tire from the inputs.

Abstract: A tire replacement forecasting system includes a tire supporting a vehicle. A sensor unit is mounted on the tire and includes a footprint centerline length measurement sensor and a pressure sensor. A processor is in electronic communication with the sensor unit and receives the measured centerline length and the measured pressure. An electronic vehicle network transmits selected vehicle parameters to the processor. A wear state predictor is stored on the processor and receives the measured centerline length, the measured pressure, and the selected vehicle parameters to generate an estimated wear state of the tire. A forecasting model is stored on the processor and receives multiple estimated wear states of the tire, and predicts future wear states of the tire. A forecast tire replacement date is generated by the forecasting model when the predicted future wear states of the tire are estimated to pass a predetermined wear threshold.

Abstract: A tire load estimation system includes at least one tire supporting a vehicle, in which the at least one tire includes a pair of sidewalls extending to a circumferential tread. A sensor is mounted to the at least one tire. A footprint is formed by the tread and includes a centerline with a footprint centerline length. The footprint centerline length is measured by the sensor. A tire load estimator receives a precalibrated sensitivity, the footprint centerline length during straight-line driving conditions, a reference footprint value, and a reference load value as inputs. The tire load estimator determines an estimation of tire load and outputs the estimation to at least one of a vehicle control system and a vehicle electronic control unit. A method for estimating the load of a tire is also provided.

Abstract: A tire wear state estimation system includes at least one tire that supports a vehicle. A sensor is mounted on the tire and measures tire parameters. At least one sensor is mounted on the vehicle and measures vehicle parameters. Each one of a plurality of sub-models receives selected tire parameters from the tire mounted sensor and selected vehicle parameters from the vehicle mounted sensor. Each one of the sub-models generates a sub-model wear state estimate, and a model reliability is determined for each one of the sub-models. A supervisory model receives the wear state estimate from each sub-model and the model reliability for each sub-model, and generates a combined wear state estimate for the tire.

Abstract: A method for estimating a grip of a tire supporting a vehicle includes generating a first set of data from a tire-mounted sensor unit, generating a second set of data from the tire-mounted sensor unit and from data obtained from the vehicle, and generating a third set of data from data obtained from the vehicle and from the Internet. A grip estimation module is provided. The first, second and third sets of data are received in the grip estimation module. A friction probability distribution is calculated with the grip estimation module using the first, second and third sets of data, and the friction probability distribution is input into at least one vehicle system.

Abstract: A vehicle sideslip estimation system includes sensors mounted on a vehicle and a kinematic model receiving signals from the sensors to estimate a lateral velocity of the vehicle. A compensated acceleration calculator calculates a compensated lateral acceleration as a measure of conditions that result in a deviation of a measured lateral acceleration. A lateral acceleration calculator determines, based on the compensated lateral acceleration and the measured lateral acceleration, if a lateral acceleration error is larger than a predefined threshold. A filter corrects the estimated lateral velocity of the vehicle when the lateral acceleration error is larger than the predefined threshold.

Abstract: A tire wear state estimation system includes a tire that supports a vehicle. A sensor unit is mounted on the tire and includes a footprint centerline length measurement sensor, a pressure sensor, a temperature sensor, and electronic memory capacity for storing identification information for the tire. A processor is in electronic communication with the sensor unit and receives the measured centerline length, the measured pressure, the measured temperature and the identification information. A tire construction database stores tire construction data and is in electronic communication with the processor. The identification information is correlated to the tire construction data. An analysis module is stored on the processor and receives the measured centerline length, the measure pressure, the measured temperature, the identification information, and the tire construction data as inputs. The analysis module includes a prediction model that generates an estimated wear state for the tire from the inputs.

Abstract: A method for extracting changes in characteristics of a tire supporting a vehicle is provided. The method includes extracting selected tire characteristics from at least one sensor mounted on the tire. The selected tire characteristics are transmitted to a remote processor and are stored in a historical data log that is in communication with the remote processor. At least one tire characteristic of interest is selected, and a time series decomposition model is applied to data from the historical data log to delineate exogenous inputs from an underlying trend in the selected tire characteristic of interest. A learning model is applied to the underlying trend in the selected tire characteristic of interest to model a relationship between the selected tire characteristic of interest and a condition of the tire. A prediction value for a condition of the tire is output from the learning model.

Abstract: A tire wear state estimation system includes a first sensor unit that is mounted on a tire and includes a footprint centerline length measurement sensor. A second sensor unit is mounted on the tire and includes a shoulder length measurement sensor. At least one of the first sensor unit and the second sensor unit includes a pressure sensor, a temperature sensor, and electronic memory capacity for storing tire identification information. A processor is in electronic communication with the sensor units and receives the measured centerline length, the measured pressure, the measured temperature, the identification information, and the measured shoulder length. The identification information is correlated to tire construction data. An analysis module stored on the processor receives the measured values, the identification information, and the tire construction data as inputs. The analysis module includes a prediction model that generates an estimated wear state for the tire from the inputs.

Abstract: A tire load estimation system includes at least one tire supporting a vehicle, in which the at least one tire includes a pair of sidewalls extending to a circumferential tread. A sensor is mounted to the at least one tire. A footprint is formed by the tread and includes a centerline with a footprint centerline length. The footprint centerline length is measured by the sensor. A tire load estimator receives a precalibrated sensitivity, the footprint centerline length during straight-line driving conditions, a reference footprint value, and a reference load value as inputs. The tire load estimator determines an estimation of tire load and outputs the estimation to at least one of a vehicle control system and a vehicle electronic control unit. A method for estimating the load of a tire is also provided.