Method for determining an estimate of the treadwear of a tire

Abstract

A method (400) for determining an estimate of treadwear of a tire, comprising: Receiving (402), by means of a control unit (26), sensor data from one or more vehicle sensors (22); Receiving (402), by means of the control unit (26), vehicle dynamics data from one or more vehicle systems (24); Receiving (402), by means of the controller (26), a direct tire tread wear measurement, if available; Carrying out (404), by means of the control unit (26), an indirect tire tread wear estimation using the sensor data and the driving dynamics data; Performing (414), by means of the controller (26), a data fusion of the indirect tire treadwear estimation with the direct tire treadwear measurement, if available; Estimating (418), by means of the control unit (26), the remaining service life of the tire in percent and the mileage until the end of the service life; Estimating, by means of the controller (26), a refined tire treadwear calibration coefficient to perform future indirect tire treadwear estimates; and Generating an initial notification to an operator of the remaining percentage of tire life and the mileage until the end of tire life if no direct tire treadwear measurement is available; or Generating, by means of the controller (26), a second notification to an operator of the unavailability of the tire treadwear estimate and an instruction to the operator to perform the direct tire treadwear measurement based on an estimated error in a tire treadwear distribution if a direct tire treadwear measurement is available.

Description

The present description relates generally to estimating tire tread wear at each corner of a vehicle in real-time, using indirect wear estimation and, if available, occasional direct wear measurement.The wear of the tire tread has a significant impact on vehicle safety and dynamic vehicle performance. The wear of the tire tread may be measured directly or estimated by a combination of sensor data and tire information. However, these direct methods may be costly due to the use of additional sensors.WO 2019 / 239 305 A2 describes a tread wear monitoring method comprising a tread wear model calibration step and a tread wear monitoring step, wherein the tread wear model calibration step comprises determining a calibrated tread wear model based on tread wear related quantities and first frictional energy related quantities. The tread wear monitoring step includes: acquiring drive-related quantities related to driving of the motor vehicle from a vehicle bus of a motor vehicle equipped with two or more wheels each provided with a tire; calculating second frictional energy-related quantities related to the frictional energy experienced during driving of a particular tire of the motor vehicle by providing a predefined vehicle dynamics model that mathematically relates the acquired drive-related quantities to the second frictional energy-related quantities, and calculating the second frictional energy-related quantities by inputting the acquired drive-related quantities into the predefined vehicle dynamics model; and performing a tread wear estimation and a prediction of the remaining tread material based on the calculated second frictional energy-related quantities and the calibrated tread wear model. The step of calibrating the tread wear model further comprises: performing tread wear tests on one or more tires; and measuring tread wear related quantities indicative of the reduction in tread depth resulting from the performed tread wear tests.EP 3 785 943 A1 describes a system for estimating tire wear. The system includes a vehicle, a tire supporting the vehicle, and a sensor unit mounted on the tire, the sensor unit including: a sensor for measuring the centerline length of the footprint to measure the centerline length of a footprint of the tire; a pressure sensor for measuring the pressure of the tire; a temperature sensor for measuring the temperature of the tire; and an electronic storage capacity for storing identification information for the tire. The system further comprises a processor in electronic communication with the sensor unit, the processor configured to receive the measured centerline length, the measured pressure, the measured temperature, and the identification information, a tire construction database storing tire construction data, the tire construction database in electronic communication with the processor, the identification information being correlated with the tire construction data, and an analysis module stored on the processor and configured to receive the measured centerline length, the measured pressure, the measured temperature, the identification information, and the tire construction data as inputs, wherein the analysis module includes a prediction model to generate an estimated wear condition for the tire from the inputs.EP 3 421 267 A1 describes a system and method for estimating tire wear. The system includes at least one tire supporting a vehicle, a CAN bus system disposed on the vehicle; and at least one sensor disposed on the vehicle and in electronic communication with the CAN bus system. The at least one sensor is used to measure selected parameters associated with the vehicle and to transmit data for the selected parameters via the CAN bus system, including a first data set, a second data set and a third data set. The system further comprises a rolling radius estimator operative to receive the first set of data and estimate a rolling radius for the at least one tire; an acceleration slip estimator operative to receive the second set of data and the estimated rolling radius to estimate the slip of the at least one tire during acceleration of the vehicle, a brake slip estimator operative to receive the third set of data and the estimated rolling radius to estimate the slip of the at least one tire during braking of the vehicle; and a tire slip analyzer operative to correlate the estimate of the slip of the at least one tire during acceleration of the vehicle and the slip of the at least one tire during braking of the vehicle and generate an estimated wear state of the at least one tire.Embodiments according to the present description provide a number of advantages. For example, embodiments according to the present description provide an algorithm framework that combines occasional direct measurement of tire tread depth with indirect estimation of tire tread depth to improve the accuracy of tire tread depth calculation.The method according to the invention for determining an estimate of the tread wear of a tire comprises receiving sensor data from one or more vehicle sensors by means of a control unit and receiving vehicle dynamics data from one or more vehicle systems by means of the control unit. The method also includes receiving, by the controller, a direct tire tread wear measurement, if available. The method further includes performing, by the controller, an indirect tire tread wear estimate using the sensor data and the vehicle dynamics data, performing, by the controller, a data fusion of the indirect tire tread wear estimate with the direct tire tread wear measurement, if available, estimating, by the controller, a remaining percent tire life and an mileage until the end of the tire life, and estimating, by the controller, a refined tire tread wear calibration coefficient for performing future indirect tire tread wear estimates. The method also includes generating, by the controller, a first notification to an operator of the remaining percent tire life and mileage until the end of the tire life if a direct tire tread wear measurement is not available, or generating, by the controller, a second notification to an operator of the unavailability of the tire tread wear estimate and an instruction to the operator to perform the direct tire tread wear measurement based on an estimated error in a tire tread wear distribution if a direct tire tread wear measurement is available.According to one embodiment, the data fusion of the indirect tire tread wear estimate with the direct tire tread wear measurement comprises performing, by the controller, a data fusion for a distribution of the indirect tire tread wear estimate with the direct tire tread wear measurements from the tread grooves of the tire having a minimum remaining tread depth when the direct tire tread wear measurements indicate non-uniform wear.According to a further embodiment, the data fusion of the indirect tire tread wear estimate with the direct tire tread wear measurement comprises carrying out a data fusion for a distribution of the indirect tire tread wear estimate with the direct tire tread wear measurements from all available direct measurements of the tire tread depths by means of the control unit if the direct tire tread wear measurements indicate uniform wear.According to another embodiment, the indirect estimate of tire tread wear uses recursive weighted tire slip, wherein the recursive weighted tire slip includes accumulated longitudinal and lateral slip and corner based tire slip estimate normalized by a surface of the tire and based on sensor data including one or more vehicle speeds, a yaw rate, a steering angle, and a wheel speed.According to another embodiment, the indirect estimate of tire tread wear is calculated using an effective tire radius, a pressure and temperature correction factor, a tire width, a tire type correction factor, a longitudinal slip weight factor, a lateral slip weight factor, a calibration coefficient, a relative longitudinal speed, and a relative lateral speed.According to another embodiment, the indirect estimate of tire tread wear includes a correction factor that is a function of tire tread depth.According to another embodiment, the indirect estimation of tire tread wear includes a correction factor that is a function of wheel alignment to estimate an effect of uneven tire tread wear.According to another embodiment, the indirect estimate of tire tread wear is dependent on relative longitudinal and lateral slip of the tire, represented by the calibration coefficient, and is not directly a function of the normal load on the tire.According to a further embodiment, the method further comprises combining indirect and direct tire tread wear measurement data of a plurality of vehicles by means of the control unit taking into account factors comprising one or more of geolocation, vehicle driving conditions, environment, vehicle type, tire type and direct measurement methods, in order to further improve an accuracy of the indirect tire tread wear estimation.In one application, a system is provided for determining an estimate of tire tread wear of a tire. The system includes at least one sensor configured to generate sensor data indicative of a vehicle speed, a yaw rate, a steering angle, a wheel speed, a tire pressure, and a tire temperature, and a controller electronically communicating with the at least one sensor. The control device is configured to carry out the method steps of the system according to the invention with its embodiments.The present description will be described in conjunction with the following figures, in which like numerals represent like elements. FIG. 1 is a schematic illustration of a system for estimating tire tread wear. FIG. 2 is a graph of a fusion of direct tire tread depth measurements with an indirect tire tread depth estimate. FIG. 3 is a graph of a fusion of distributions of direct tire tread depth measurements with an indirect estimation of tire tread depth. FIG. 4 is a flow diagram of a method for estimating tire tread wear.Embodiments of the present description will be described herein. It is to be understood, however, that the described embodiments are merely examples and other embodiments may take various and alternative forms. The figures are not necessarily to scale; some features could be exaggerated or minimized to show details of particular components. Therefore, specific structural and functional details described herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present description. As those skilled in the art will appreciate, various features illustrated and described with reference to one of the figures may be combined with features illustrated in one or more other figures to produce embodiments that are not expressly illustrated or described. The depicted combinations of features represent representative embodiments for typical applications. However, various combinations and modifications of the features consistent with the teachings of this specification could be desired for particular applications or implementations.Certain terms are used in the following description for reference purposes only and are therefore not to be considered limiting. For example, terms such as "top" and "bottom" refer to directions in the drawings to which reference is made. Terms such as "front," "rear," "left," "right," "rearward," and "side" describe the orientation and / or location of portions of the components or elements within a uniform, but arbitrary, frame of reference that will be apparent by reference to the text and the accompanying drawings in which the discussed components or elements are described. Moreover, terms such as "first", "second", "third" and so forth may be used to describe individual components. This terminology may include the terms mentioned above, their derivatives, and terms of similar meaning.For brevity, conventional techniques associated with signal processing, data transmission, signaling, control, and other functional aspects of the systems (and the individual operating components of the systems) will not be described in detail herein. Moreover, the connection lines depicted in the various figures are intended to represent example functional relationships and / or physical couplings between the various elements. It should be appreciated that many alternative or additional functional relationships or physical connections may be present in various embodiments.The embodiments discussed herein provide systems and methods for estimating tire wear on each vehicle curve in real-time. The method includes an indirect estimate of tire tread wear and a data fusion of the indirect estimate with an occasional direct measurement of the tire tread. The indirect estimate of tire tread wear calculates the accumulated weighted longitudinal and lateral slip of the tire (an accumulated longitudinal and lateral slip of the tire, which is derived from the curve-based slip estimate and vehicle dynamics) normalized by the tread of the tire. This estimation uses available vehicle data without the input of special tire mounted sensors. The effects of normal load were incorporated into the estimation based on tire slip. The methods include calibration factors that can be used to detect the effects of tire type, tire pressure, tire temperature, orientation, and other vehicle characteristics.To improve the accuracy of the indirect tire tread wear estimate, the indirect estimate of tire tread wear is fused to various direct measurement modalities, for example and without limitation by Bayesian filtering and weighted averaging. The data fusion framework corrects errors in the indirect estimate that may be due to inaccuracies in the calibration parameters as well as uneven wear of the tire tread. The occasional direct measurements may be used to adaptively adjust the calibration coefficients for tire tread wear used in the indirect tire tread wear estimate. The methods discussed herein for estimating tire tread wear enable correction of the estimation of tire tread wear for different types and sizes of tires (e.g., All Years, Winter, Summer, Various manufacturers of tires) as well as tire pressure, temperature, and orientation.Based on the improved accuracy and estimation of the indirect wear rate over the entire life of the tire, including the near end of the life, a percentage of the remaining life of the tire and / or mileage until the end of the life may be displayed to the user. The methods discussed herein, including merging the indirect tire tread wear estimate with the occasional direct measurements, may be performed in the vehicle, for example, in a vehicle controller, using edge computing, in a remote facility communicating with that vehicle and other vehicles, or any combination of the above methods.Referring to FIG. 1, a schematic diagram of a tire tread depth estimation system 100 including various components of the system 100 is shown, according to an embodiment. In various embodiments, the system 100 includes a controller or control module 26. The controller 26 includes an indirect tire tread wear estimation module 28, a tire tread wear fusion module 30, and a notification module 32. Although shown as a single unit for purposes of illustration, controller 26 may additionally include one or more other controllers, collectively referred to as "controllers.". The controller 26 may include a microprocessor or central processing unit (CPU) that communicates with various types of computer readable storage devices or media. The computer readable storage devices or media may include volatile and nonvolatile memories, for example, read only memory (ROM), random access memory (RAM), and keep alive memory (KAM). KAM is a persistent or non-volatile memory that can be used to store various operating variables while the CPU is off. Computer readable storage devices or media may be implemented using any number of known storage devices, such as programmable read only memory (PROM), electrically erasable programmable read only memory (EPROM), electrically erasable programmable read only memory (PROM), EEPROM (electrically erasable programmable read only memory), flash memory, or other electrical, magnetic, optical, or combination storage devices capable of storing data, some of which represent executable instructions, used by the controller 26 in controlling the vehicle.In various embodiments, the inputs to the controller 26 are determined by one or more sensors 22, direct tire tread wear measurement data 23, one or more vehicle systems 24, and / or other sub-modules (not shown) of the controller 26. The one or more sensors 22 are configured to sense observable conditions of the vehicle and generate sensor signals based thereon, and may include inertial measurement unit (IMU), vehicle speed, steering wheel angle, tire pressure monitoring system (TPMS), GPS, RADAR, LIDAR, optical cameras, thermal cameras, ultrasonic sensors, and / or additional sensors, as appropriate. The direct tire tread wear measurement data 23 is determined by a direct physical measurement, image data, and / or other direct real-time measurement means, in some embodiments. The one or more vehicle systems 24 determine a condition associated with the vehicle and generate signals and / or messages based thereon. In various embodiments, the one or more vehicle systems 24 generate signals and / or messages indicative of conditions of the vehicle. The vehicle systems 24 provide the signals and / or messages directly or indirectly via a communication bus (not shown) or other communication means.The controller 26 receives the signals from the sensors 22, the direct tire tread wear measurement data 23, and the signals and / or messages from the vehicle systems 24, and determines an indirect tire tread wear estimate. Using the indirect tire tread wear estimate and the direct tire tread wear measurement data 23, the controller 26 performs calculations to merge the direct tire tread wear measurement data 23 with the indirect tire tread wear estimate to produce an improved tire tread wear estimate, the percentage of remaining tire tread wear, and / or the mileage until the end of life. The controller 26 selectively informs the user of the percentage of remaining tire tread wear and / or the mileage until the end of life and / or when the tires should be changed. The controller 26 notifies the user through visual, audible, and / or haptic feedback in the vehicle and / or messages sent to remote devices (e.g., email messages, text messages, and so forth).The wear of the tire tread is the result of relative motion, referred to as slip, between the tire and the road surface. The indirect tire tread wear estimation module 28 receives sensor data from the one or more sensors 22 and vehicle system data from the vehicle systems 24. The indirect tire tread wear estimation module 28 uses a low computational, indirect real-time method to estimate tire tread wear using recursive weighted tire slip (an accumulated longitudinal and lateral slip of the tire, obtained from the curve-based slip estimation and vehicle dynamics normalized by the tire tread).The indirect distribution of tread wear for each tire at each corner is estimated using Equation 1 below:Wherein:i is the axis and j is the position of the tire on the vehicleR effij= tire effective radius (effective tire radius)W tij= Tire width (tire width)K x= Longitudinal Slide Weight Factor (Longitudinal Slip Weight Factor)K y= Lateral Slippage weight factor (cross-slip weight factor)V rxij= Relative longitudinal velocity (relative longitudinal velocity)V ryij= Relative lateral velocity (relative lateral velocity) y = Calibration coefficient (calibration coefficient)K PTij= Pressure and temperature correction factor (pressure and temperature correction factor)K tij= tire type correction factor (tire type correction factor)The indirect distribution of tire tread wear (where μT tread wear average is the tire tread wear distribution and σT tread wear distribution covariance) uses available vehicle data (e.g., speed, wheel speed, steering angle, yaw rate, tire pressure, and so forth) derived from the sensors 22 and / or the vehicle systems 24. In some embodiments, the tire type correction factor (K tij) may be normalized based on test data at the tread block row level. In various embodiments, the tire type correction factor (K tij) is a function of the tread depth of the tire. In various embodiments, the tire type correction factor (K tij) is a function of wheel alignment. The alignment information may be based on initial factory settings and alignment monitoring and / or regular alignment inspection.Since the effect of normal load on tire tread wear is taken into account in the calculation of longitudinal and lateral slip, the indirect estimation of tire tread wear is independent of normal load. To clarify this, in an embodiment in which the vehicle is modeled as a single-lane vehicle, the relative lateral speed V ryij may be calculated using the lateral speed that is proportional to the ratio of the lateral force to the slip angle of the tire. This proportionality was detected by the calibration coefficient y in the above equation 1. As the normal load increases, the above ratio increases, resulting in an increase in the lateral speed, thereby increasing the relative lateral speed V ryij or vice versa.The tire tread wear fusion module 30 receives as inputs the indirect estimate of tire tread wear calculated for each tire as above and, if available, regular direct tire tread wear measurements. The indirect estimate of tire tread wear is susceptible to accumulated errors due to inaccuracies in the calibration parameters, such as, but not limited to, correction factors for the tire type and uneven wear of the tire tread. As the useful life increases, i.e. as the service life of the tire increases and / or the running performance of the tire increases, the uncertainty of the estimate increases. A direct measurement of the tire tread, such as a measurement with a tire tread meter (e.g., by direct contact, laser, or by an image processing system), is used to increase the accuracy of the indirect estimation of tire tread wear. Merging of the direct tire tread wear measurement with the indirect tire tread wear estimate is done, for example and without limitation, by Bayesian filtering and weighted averaging to reduce cumulative inaccuracies in the indirect tire tread wear estimate.With continued reference to FIG. 1, the notification determination module 32 receives as input the percentage of tire life remaining and / or the mileage to the end of tire life, both determined from merging the indirect tire tread wear estimate with the direct tire tread wear measurement, if available. The notification determination module 32 generates notification signals 70 and / or notification messages 72 to inform the user of the estimated remaining life of the individual tires of the vehicle. In various embodiments, the notification signals 70 and / or messages 72 include a message or other indication (e.g., audible or haptic) that it is time to change one or more of the tires.In various embodiments, the notification determination module 32 generates the notification signals 70 and / or messages 72 at a time that may be more beneficial to the user. For example, the notification determination module 32 may receive as input data on the health state of the vehicle and / or behavioral data. The notification determination module 32 coordinates the transmission and / or content of the notification signals 70 and / or messages 72 based on the vehicle state data and / or the behavior data.In Figures 2 and 3, the tread depth of the tire is plotted as a function of useful life or running performance. A new tire has a tread depth represented by line 202. A worn tire, i.e., a tire at the end of its life, has a tread depth represented by line 204. The actual or true tread depth of the tire is represented by line 206. The indirect estimate of tire tread wear calculated from the above equation is represented by line 208. As shown in FIG. 2, the indirect estimate of tire tread wear (line 208) over time or mileage deviates from actual tire tread wear (line 206), resulting in inaccurate estimation of tire tread wear. To correct for the imprecision, a direct tire tread wear measurement may be made, represented by line 210. The association of the direct tire tread wear measurement with the indirect tire tread wear estimate results in an estimate that is much closer to the actual tread depth. FIG. 3 shows the fusion of the direct tire tread wear measurement to the indirect tire tread wear estimate. In an exemplary embodiment, the merging of these two distributions is performed using the equation:As shown in FIG. 2, both the slope (indicating the calibration coefficients) and the distribution of the indirect tire tread wear estimate are corrected using the data fusion framework shown in Equation 2.Referring to FIG. 3, the indirect estimate of tire tread wear is shown, bel(xt) is shown as line 302. The direct measurement of tire wear, p(z t| x t) is shown as line 304. Bayesian filtering uses the distribution of the estimate (mean and standard distribution) and that of the direct measurement and merges them into the line 30 representing the merged direct tire tread wear measurement data with the less uncertainty bel(x t). η is the normalization factor. In various embodiments, the indirect estimate of tire tread wear is merged with the minimum of the distribution of the direct tire tread depth sensation.If the distribution of the indirect tire tread wear estimate and the direct measurements of tire tread wear is Gaussian, that is, p(z t| x t) = N 1( μ 1, σ 1) and bel(xt)=N 0( μ 0, σ 0) then bel(xt)=N(μ́, σ́). The resulting distribution is expressed as follows:The covariance of the indirect estimation is an indication of its distribution uncertainty. As this uncertainty increases over time, it could be used not only to provide the user with a context in terms of accuracy, but also to indicate the need for additional discrete direct measurements.FIG. 4 shows a method 400 for estimating tire wear, according to an embodiment. The method 400 may be performed for each tire of a vehicle. The method 400 may be used in connection with the system 100 described herein. Method 400 may be used in connection with controller 26 discussed herein, or by other systems connected to or disconnected from the vehicle, according to example embodiments. The order of execution of the method 400 is not limited to the sequential execution illustrated in FIG. 4, but may be performed in one or more different orders, or the steps may be performed simultaneously, as is applicable in accordance with the present description.At 402, the controller 26 receives sensor data from the plurality of sensors 22 and vehicle dynamics data from the vehicle systems 24, including, for example and without limitation, speed, lateral and longitudinal acceleration at each corner of the vehicle, wheel speed, tire pressure, tire temperature, and tire orientation. Controller 26 also receives direct data for measuring tire wear, if available.Next, at 404, the indirect tire tread wear estimation module 28 of the controller 26 calculates the average and covariance estimates of the indirect tire tread wear using the sensor data and the vehicle dynamics data as described herein. In various embodiments, the indirect estimate of tire tread wear is calculated using an effective tire radius, a pressure and temperature correction factor, a tire width, a tire type correction factor, a longitudinal slip weight factor, a lateral slip weight factor, a calibration coefficient, a relative longitudinal speed, and a relative lateral speed.Method 400 proceeds to 406 where controller 26 determines whether a direct tire tread wear measurement is available. If the determination at 406 is "no", i.e., no direct tire tread wear measurement is available, method 400 proceeds to 418, and continues as described below. If the determination at 406 is positive or yes, i.e., a direct tire tread wear measurement is available, method 400 proceeds to 408.At 408, the controller 26 determines whether an indication of uneven wear is included in the measurement data of direct tire tread wear. If this determination is positive, i.e., the direct tire tread wear measurement indicates non-uniform tire tread wear, method 400 proceeds to 410. At 410, method 400 selects the minimum distribution of the direct measurement data from the tire tread grooves with the maximum tire tread wear and continues to 414. If the determination at 408 is negative, i.e., the direct tire tread wear measurement does not indicate uneven tire tread wear (i.e., the direct tire tread wear measurement indicates even wear), method 400 proceeds to 412. At 412, method 400 selects the direct measurement data from all available direct measurements of tire tread depth and continues to 414.At 414, the tire tread wear fusion module 30 of the controller 26 performs data fusion using the direct tire tread wear measurement and the indirect tire tread wear estimate distribution of block 404. This data fusion process merges the direct tire tread wear measurement distributions from blocks 410 or 412 as determined with the indirect tire tread wear estimation distribution using the fusion methods discussed above.At 416, the controller 26 compares the direct tire tread wear measurement data to the indirect tire tread wear estimate to adaptively adjust or refine the tire tread wear compensation coefficients used to perform future indirect tire tread wear estimates.Next, at 418, controller 26 estimates the percent remaining life of the tires and the mileage until the end of the life of the tires. If the covariance of the indirect and / or fused distribution is less than a threshold (i.e., the errors are below a threshold), the notification module 32 of the controller 26 generates a notification signal and / or notification including the percentage of tire life remaining and the mileage until the end of tire life. If the covariance of the indirect and / or fused distribution is greater than or equal to a threshold (i.e., the errors are equal to or greater than a threshold), the notification module 32 of the controller 26 generates a notification signal and / or a notification that the wear condition of the tire or tires is not available. In various embodiments, the notification module 32 of the controller 26 generates a notification signal and / or message when the covariance of the indirect and / or fused distribution is greater than or equal to a threshold to instruct or trigger the operator to initiate a direct measurement to reduce errors in the wear estimates of the tire tread. Method 400 then returns to 402 and continues as described herein.While method 400 is described as being performed by a controller 26 of the vehicle, in various embodiments method 400 is performed either partially or fully through edge computing, back office cloud, or any combination of onboard and offboard computing resources or controllers. Additionally, method 400 may be performed partially or fully through edge computing or back office cloud computing, and the indirect and direct tire tread wear data from multiple vehicles may be combined in consideration of factors such as geolocation, driving behavior classification, vehicle driving conditions, environment, vehicle type, tire type, and direct measurement methods to further improve the accuracy of the tire tread wear estimates. In various embodiments, the data may be combined in the backoffice cloud, by edge computing, or on board, or in any combination thereof. In various embodiments, the controller 26 is further configured to receive modified correction factors based on indirect and direct tire tread wear measurement data from multiple vehicles via electronic communication with the controller 26 to further improve the accuracy of the tire tread wear estimation.Modalities for direct tire tread wear measurement include, but are not limited to, 3D laser scans, calipers, image data, or other optical gauges including a handy camera or other camera. Data fusion methods and corrections include, among other things, Bayesian filter approaches such as Kalman filters, information filters, histogram filters, particle filters and weight averages. Direct measurement data may be input to the fusion algorithm, including by cloud-based data synchronization or manual input methods.Advantages of the fusion concept discussed herein include higher prediction accuracy in the operating range adjacent to the direct measurement modalities. For example, the end-of-life prediction accuracy is improved by direct measurements toward the end of tire life.It should be emphasized that many variations and modifications may be made to the embodiments described herein, with the elements of these embodiments being understood as further acceptable examples. All such modifications and variations are intended to be included herein within the scope of this specification and protected by the following claims. Moreover, each of the steps described herein may be performed simultaneously or in a different order than that given herein. Moreover, as should be appreciated, the features and characteristics of the specific embodiments described herein may be combined in different ways to form additional embodiments, all of which fall within the scope of the present description.As used herein, the conditional language, such as "may", "could", "for example", and the like, is generally intended to convey that certain embodiments include certain features, elements, and / or states, while other embodiments do not include these, unless expressly stated otherwise or otherwise understood in context. Therefore, such conditional language is generally not intended to imply that features, elements and / or states are in any way required for one or more embodiments or that one or more embodiments necessarily include logic to decide, with or without the author's input or prompt, whether these features, elements and / or states are included in or are to be executed in a particular embodiment.In addition, the following terminology may have been used herein. The singular forms "a", "an", and "the" include plural references unless the context clearly dictates otherwise. For example, reference to an item includes reference to one or more items. The term "one" refers to one, two, or more, and generally applies to the selection of a portion or the entirety of a set. The term "multiple" refers to two or more copies of an article. The term "about" or "approximately" means that amounts, dimensions, sizes, formulations, parameters, shapes, and other features need not be exact, but may be approximated and / or larger or smaller as desired, taking into account acceptable tolerances, conversion factors, rounding offs, measurement errors, and the like, as well as other factors known to those skilled in the art. The term "substantially" means that the specified feature, parameter, or value need not be achieved exactly, but that deviations or variations, for example, tolerances, measurement errors, measurement accuracy constraints, and other factors known to those skilled in the art, may occur to an extent that does not exclude the intended effect of the feature.For simplicity, a plurality of elements may be listed in a common list. However, these lists should be designed as if each member of the list is individually identified as a separate and unique member. Therefore, no single member of such a list should be construed as a factual equivalent of another member of the same list solely because of its presentation in a common group without any sign on the contrary. When the terms "and" and "or" are used in connection with a list of items, they are to be broadly construed so that each of the listed items may be used alone or in combination with other listed items. The term "alternative" refers to the selection of one of two or more alternatives, and is not intended to limit the selection to the listed alternatives or to only one of the listed alternatives at a time, unless the context clearly indicates otherwise.The processes, methods, or algorithms described herein may be provided to or executed by a processing device, controller, or computer, which may include any existing programmable electronic control unit or dedicated electronic control unit. Likewise, the processes, methods, or algorithms can be stored as data and instructions executable by a controller or computer in many forms including, but not limited to, information permanently stored on non-writable storage media such as ROM devices and information alterably stored on writeable storage media such as floppy disks, magnetic tapes, CDs, RAM devices, and other magnetic and optical media. The processes, methods, or algorithms can also be implemented in an executable software object. Alternatively, the processes, methods, or algorithms can be embodied in whole or in part using suitable hardware components, such as application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), state machines, controllers or other hardware components or devices, or a combination of hardware, software, and firmware components. Such example devices may be onboard or off-board as part of a vehicle computing system and may perform remote communication with devices in one or more vehicles.

Claims

A method (400) of determining an estimate of tread wear of a tire, comprising: receiving (402), by a controller (26), sensor data from one or more vehicle sensors (22); receiving (402), by the controller (26), vehicle dynamics data from one or more vehicle systems (24); receiving (402), by the controller (26), a direct tire tread wear measurement, if available; performing (404), by the controller (26), an indirect tire tread wear estimate using the sensor data and the vehicle dynamics data; performing (414), by the controller (26), a data fusion of the indirect tire tread wear estimate with the direct tire tread wear measurement, if available; estimating (418), by the controller (26), the percent remaining life of the tire and the mileage until the end of the life; estimating, by the controller (26), a refined tire tread wear calibration coefficient to make future indirect estimates of tire tread wear; and generating a first notification to an operator of the percent remaining tire life and the mileage until the end of the tire life if a direct tire tread wear measurement is not available; or generating a second notification to an operator of the unavailability of the tire tread wear estimate and an instruction to the operator to perform the direct tire tread wear measurement by the controller (26) based on an estimated error in a tire tread wear distribution if a direct tire tread wear measurement is available.The method (400) of claim 1, wherein the data fusion of the indirect tire tread wear estimate with the direct tire tread wear measurement comprises performing, by the controller (26), data fusion for distribution of the indirect tire tread wear estimate with the direct tire tread wear measurements from the tread grooves of the tire having a minimum remaining tread depth when the direct tire tread wear measurements indicate non-uniform wear.The method (400) of claim 1, wherein the data fusion of the indirect tire tread wear estimate with the direct tire tread wear measurement comprises performing, by the controller (26), data fusion for a distribution of the indirect tire tread wear estimate with the direct tire tread wear measurements from all available direct tire tread depth measurements when the direct tire tread wear measurements indicate uniform wear.The method (400) of claim 1, wherein the indirect estimate of tire tread wear uses recursive weighted tire slip, wherein the recursive weighted tire slip comprises an accumulated longitudinal and lateral slip of the tire and a corner-based tire slip estimate normalized by a surface of the tire and based on sensor data comprising one or more of a vehicle speed, a yaw rate, a steering angle, and a wheel speed.The method (400) of claim 4, wherein the indirect estimate of tire tread wear is calculated using an effective tire radius, a pressure and temperature correction factor, a tire width, a tire type correction factor, a longitudinal slip weight factor, a lateral slip weight factor, a calibration coefficient, a relative longitudinal speed, and a relative lateral speed.The method (400) of claim 5, wherein the indirect estimate of tire tread wear comprises a correction factor that is a function of tire tread depth.The method (400) of claim 5, wherein the indirect estimate of tire tread wear comprises a correction factor that is a function of wheel alignment to estimate the effect of uneven tire tread wear.The method (400) of claim 5, wherein the indirect estimate of tire tread wear is dependent on a relative longitudinal and lateral slip of the tire as represented by the calibration coefficient and is not directly dependent on a normal load of the tire.The method (400) of claim 1, further comprising combining indirect and direct tire tread wear measurement data from multiple vehicles via the controller (26) in consideration of factors such as geolocation, classification of driving behavior, driving conditions of the vehicle, environment, vehicle type, tire type, and direct measurement method to further improve the accuracy of the tire tread wear estimation.

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