Comprehensive tire health modeling and a system for its development and implementation

Abstract

The tire health estimation system and method includes aggregating model generation data over time and iteratively generating a tire health model based thereon. The model generation data correlates various combinations of a first set of input values ​​for a given type of tire to each of the various tire health variables for each of the various tire components. A second set of input values ​​is measured and / or determined for the actual tire and an appropriate model is selected for the at least one tire health variable and the at least one tire component based on the second set. Each tire health variable is estimated for the tire component via the selected model and based on the second set of input values, and an output signal is generated corresponding to the tire health based on a comparison of the estimated tire health variables.

Description

[Technical Field]

[0001] The present invention relates generally to tire health estimation and prediction for wheeled vehicles. More specifically, one embodiment of the present invention disclosed herein relates to a system and method for developing, selecting, and implementing models for tire health characterization and prediction for tires for wheeled vehicles, including, but not limited to, motorcycles, consumer vehicles (e.g., passenger and light trucks), commercial and off-the-road (OTR) vehicles. [Background technology]

[0002] Tire health is a valuable insight to the end user, whether the end user is a fleet manager or individual vehicle owner. Health can be characterized as a measure of the remaining useful life (RUL) of the tire, and for purposes of this disclosure, can be divided into at least the categories of tread and carcass.

[0003] Tread health may be analogous to tread wear, including, for example, both uniform and irregular wear, and may be quantified as the number of miles until the tread reaches a threshold limit (such as a tread wear indicator) or until irregular wear progresses to the point where noise or vibrations are generated requiring tire replacement or retreading.

[0004] Carcass integrity takes into account normal aging, damage from impacts such as potholes and curbs, fatigue, and misuse / abuse such as under-inflated / overloaded running, too high speeds, improper application, etc. Carcass removal modes include (but are not limited to) belt edge separation (BES) or belt-detached belt (BLB), belt-detached carcass (BLC), and ply end separation (PES). Carcass integrity is also a major determinant of tire retread performance. Summary of the Invention

[0005] Physics-based and / or data science models can be used to estimate the health or remaining life of each tire component based on the history of conditions to which the tire has been exposed. This history may be determined either directly or indirectly via tire sensors, such as tire pressure monitoring system sensors and / or tire monitoring system sensors (TPMS / TMS), and vehicle sensors, such as accelerometers, wheel speed sensors, global positioning system (GPS) sensors, etc. Separate models may be required to predict different removal modes for various tire components, and a comprehensive tire health model can combine the results of these models into a single health index.

[0006] The present disclosure provides an enhancement to conventional systems, at least in part, by introducing a novel digital tire health model. The tire tread / wear may be covered by a digital twin of the wear model. Fatigue of various components can be estimated, for example, via structured learning of fracture mechanics. An underlying assumption may be, for example, the presence of microcracks or flaws that begin to grow slowly as the component undergoes deformation, with the crack growth rate being a function of the current crack length, strain, and temperature at the location of interest.

[0007] Knowing the tire load, speed, pressure, and contained air temperature can provide a method to estimate strains and temperatures at different locations of interest in the tire by creating a mapping of these different input conditions to different locations, for example, using finite element analysis (FEA).

[0008] In a first exemplary embodiment, a computer-implemented tire health estimation method disclosed herein includes aggregating model generation data in a data storage over time and iteratively generating a plurality of tire health models based on the aggregated model generation data, the model generation data correlating various combinations of a first set of input values ​​for a given type of tire to each of one or more tire health variables for each of a plurality of tire components. A second set of input values ​​is measured and / or determined via one or more sensors associated with a first tire of the given type of tire and / or associated with a vehicle on which the first tire is mounted. An appropriate model is selected for at least one of the one or more tire health variables for each of one or more of the plurality of tire components based on the measured second set of input values. A respective tire health variable is estimated for each of the at least one tire component via the one or more selected models and based on the measured second set of input values. An output signal corresponding to the health of the first tire is generated based on a comparison of the estimated tire health variables.

[0009] In a second embodiment, one exemplary aspect according to the above-referenced first embodiment may include at least one of the input values ​​being measured directly via one or more sensors and at least one of the input values ​​being determined indirectly via the at least one directly measured input value.

[0010] In a third embodiment, one exemplary aspect according to any one of the above-referenced first or second embodiments may further include the plurality of selectable tire health models including a fatigue estimation model corresponding to associated failure variables for one or more of the plurality of tire components.

[0011] The fatigue estimation model may include, for example, a crack growth rate model for estimating a crack growth rate at each of a plurality of locations on the tire as a function of at least estimated strain and temperature at each of the plurality of locations.

[0012] In a fourth embodiment, one exemplary aspect according to any one of the above-referenced first through third embodiments may include aggregating the estimated respective tire health variables over time and predicting a remaining useful life of the tire based at least in part on the aggregated variables, wherein the output signal corresponds to the predicted remaining useful life of the tire.

[0013] In a fifth embodiment, one exemplary aspect according to at least the above-referenced fourth embodiment includes selecting an appropriate model for a subsequent iteration of the method based on a newly measured set of input values ​​and further based on a historical analysis of each of the aggregated estimated tire health variables over time.

[0014] In a sixth embodiment, one exemplary aspect of any one of the above-referenced first through fifth embodiments may include the plurality of selectable tire health models including an aging estimation model that accounts for tire series changes in relevant variables of one or more of the plurality of tire components for the first tire type.

[0015] In a seventh embodiment, one exemplary aspect according to any one of the above-referenced first through sixth embodiments may include the plurality of selectable tire health models including a damage estimation model that considers determined external influences related to the tire health of one or more of the plurality of tire components.

[0016] In an eighth embodiment, one exemplary aspect according to any one of the above-referenced first through seventh embodiments may include the plurality of selectable tire health models including a tire wear estimation model that takes into account the determined and / or predicted tread depth.

[0017] In a ninth embodiment, one exemplary aspect according to any one of the above-referenced first through eighth embodiments can include the plurality of selectable tire health models including one or more carcass health models for predicting a remaining useful life of the tire based at least in part on a predicted time before an occurrence of a condition selected from the group consisting of belt edge separation, belt belt separation, belt carcass separation, and ply end separation.

[0018] In a tenth embodiment, one exemplary aspect according to any one of the above-referenced first through ninth embodiments may include generating an output signal corresponding to a lowest predicted remaining life among the estimated tire health variables. Alternatively, the output signal may be generated corresponding to a predicted remaining life based on a combination of interrelated tire health variables as identified from a selected model.

[0019] In an eleventh embodiment, one exemplary aspect according to any one of the above-referenced first through tenth embodiments may include an output signal being selectively generated to a display unit associated with a user interface based on the determined passive intervention warning condition. Alternatively, the output signal may be selectively generated to one or more vehicle control units based on the determined active intervention warning condition.

[0020] In a twelfth embodiment, a tire health estimation system disclosed herein includes a data storage storing model generation data aggregated over time, and a plurality of tire health models iteratively generated based on the aggregated model generation data, the model generation data correlating various combinations of a first set of input values ​​for a given type of tire to each of one or more tire health variables for each of a plurality of tire components. The computer program product resides on a non-transitory computer-readable medium and is executable by a processor to direct the performance of operations according to at least one of the above-referenced first through eleventh embodiments. [Brief explanation of the drawings]

[0021] Hereinafter, embodiments of the present invention will be illustrated in more detail with reference to the drawings. [Figure 1] FIG. 1 is a block diagram depicting an exemplary embodiment of a system disclosed herein. [Figure 2] 1 is a graphical representation of exemplary components of a tire carcass. [Figure 3] FIG. 1 is a flow diagram depicting an exemplary embodiment of the method disclosed herein. [Figure 4] 3A-3C are graphical diagrams depicting certain exemplary embodiments of the methods disclosed herein with respect to the fatigue portion of the tire health model framework. [Figure 5] FIG. 10 is a graphical representation of an exemplary summary of results from a drum test for belt edge separation (BES) removal mode. [Figure 6a] FIG. 1 is a graphical representation of an exemplary plot of measured data against corresponding predicted data using finite element analysis (FEA) model output. [Figure 6b] FIG. 1 is a graphical representation of an exemplary plot of measured data against corresponding predicted data using finite element analysis (FEA) model output. [Figure 7] 1 is a flowchart illustrating an example process for predicting normal load on a tire from sensor measurements (e.g., speed, ambient temperature, inflation pressure, and CAT). [Figure 8] FIG. 1 is a graphical representation of an exemplary plot of measured data against corresponding predicted data using finite element analysis (FEA) model output. [Figure 9] FIG. 10 is a graphical representation of an exemplary comparison of measured versus predicted contained air temperature for a controlled indoor drum test with variations in both speed and vertical load. DETAILED DESCRIPTION OF THE INVENTION

[0022] Various exemplary embodiments of the invention may now be described in detail, generally with reference to Figures 1-9. Where various figures may illustrate embodiments that share various common elements and features with other embodiments, similar elements and features may be given the same reference numerals and redundant descriptions thereof may be omitted below.

[0023] Various embodiments of the systems disclosed herein may include a centralized computing node (e.g., a cloud server) that operatively communicates with multiple distributed data collectors and computing nodes (e.g., associated with individual vehicles) to effectively implement the models disclosed herein. Referring initially to FIG. 1 , an exemplary embodiment of a system 100 includes a computing device 102 onboard a vehicle and configured to at least acquire data, transmit the data to a remote server 130, and / or perform related calculations as disclosed herein. The computing device may be portable or otherwise modular as part of a distributed vehicle data collection and control system (as shown), or may otherwise be provided integrally to a central vehicle data collection and control system (not shown). The device may include a processor 104 and memory 106 upon which program logic 108 resides. Generally, the systems disclosed herein may implement multiple components distributed across one or more vehicles, but not necessarily associated with a fleet management entity, for example, and may further implement a central server or server network that operatively communicates with each of the vehicles via a communications network. The vehicle components may typically include one or more sensors, such as, for example, body accelerometers, gyroscopes, inertial measurement units (IMUs), position sensors such as global positioning system (GPS) transponders 112, tire pressure monitoring system (TPMS) sensor transmitters 118 and associated on-board receivers, that are linked, for example, to a controller area network (CAN) bus network, thereby providing signals to a local processing unit. The illustrated embodiment, for purposes of illustration and without limiting the scope of the invention, includes an ambient temperature sensor 116, an engine sensor 114 configured to provide, for example, a sensed air pressure signal, and a DC power supply 110.

[0024] Given the following discussion, other sensors for collecting and transmitting vehicle data related to speed, acceleration, braking characteristics, etc. will be readily apparent to those skilled in the art and will not be further discussed herein. Various bus interfaces, protocols, and associated networks are well known in the art for communicating vehicle dynamics data and the like between respective data sources and local computing devices, and those skilled in the art will recognize a wide range of such tools and implementation means for implementing the same.

[0025] In optional embodiments, the data sources disclosed herein are not necessarily limited to vehicle-specific sensors and / or gateway devices, but may also include third-party entities and associated networks, program applications resident on user computing devices 140 such as driver interfaces, fleet management interfaces, and any enterprise devices or other providers of raw streams of log data as may be deemed appropriate for the algorithms and models disclosed herein.

[0026] The system may include additional distributed program logic resident, for example, on a fleet management server or other user computing device 140, or a user interface on a device (not shown) resident on the vehicle or associated with its driver for real-time notification (e.g., via visual and / or audio indicators), which in some embodiments is operatively linked to on-board devices via a communications network. System programming information may be provided on-board, for example, by the driver or by the fleet manager.

[0027] The vehicle and tire sensors may, in one embodiment, be further provided with unique identifiers, allowing the on-board device processor 104 to identify signals provided by each sensor on the same vehicle; further, in certain embodiments, the central server 130 and / or fleet maintainer client device 140 may identify signals provided by tires and associated vehicle and / or tire sensors across multiple vehicles. In other words, sensor output values ​​may, in various embodiments, be associated with a particular tire, a particular vehicle, and / or a particular tire-vehicle system for purposes of on-board or remote / downstream data storage and implementation for the calculations disclosed herein. The on-board device processor may communicate directly with a hosted server, as shown in FIG. 1, or alternatively, the driver's mobile device or a truck-mounted computing device may be configured to receive and process / transmit on-board device output data to a hosted server and / or fleet management server / device.

[0028] Signals received from a particular vehicle and / or tire sensor may be stored in on-board device memory or an equivalent data storage unit operatively linked to an on-board device processor for selective retrieval as needed for calculations according to the methods disclosed herein. In some embodiments, raw data signals from various sensors may be communicated from the vehicle to a server in substantially real time. Alternatively, given the inherent inefficiencies in continuous data transmission, particularly of high frequency data, the data may be compiled, encoded, and / or summarized for more efficient (e.g., periodic time-based or alternatively defined event-based) transmission from the vehicle to a remote server, e.g., via a suitable communications network.

[0029] Once transmitted to the hosted server 130 via a communications network, the vehicle and / or tire data may be stored, for example, in a database 132 associated therewith. The server may include or be associated with a tire health model 134 and, optionally, related models, such as a tire traction model, for selectively acquiring and processing the vehicle and / or tire data as appropriate inputs. The models may be implemented, at least in part, through the execution of a processor that enables the selective acquisition of the vehicle and / or tire data, and further in electronic communication for the input of any additional data or algorithms from databases, look-up tables, etc. stored in association with the server.

[0030] For example, as depicted in FIG. 1 , a feedback signal corresponding to a predicted tire wear state (e.g., predicted tread depth at a given distance, time, etc.) may be provided via interface 120 to an on-board device 102 associated with the vehicle itself or to a mobile device 140 associated with a user, and may be integrated with a user interface configured to provide, for example, a warning or notification / recommendation that a tire should be changed or will soon need to be changed.

[0031] Referring next to FIG. 2, a typical pneumatic tire 201 comprises at least a carcass 222 consisting of one or more carcass plies in which cords extending toroidally between a pair of bead portions 221 are arranged radially, and a tread rubber 223 provided on the radial outside of the carcass 222.

[0032] More specifically, the exemplary tire 201 includes a tread portion 224, a pair of sidewall portions 225 extending continuously from both sides of the tread portion 224 radially inward in the tire, bead portions 221 continuing from the radially inner ends of each sidewall portion 225, and a carcass 222 consisting of one or more carcass plies extending toroidally between the pair of bead portions 221 to reinforce each portion. A bead core is embedded in each bead portion 221. A rubber chafer is provided on the outer surface of each bead portion 221 as a reinforcing member for the bead portion 221. A belt 226 consisting of one or more belt layers is provided in a crown portion of the carcass 222. A tread rubber 223 is located radially outward of the crown portion of the carcass 222.

[0033] In the tire 201 of this example, the tread rubber 223 includes a tread surface rubber layer 223a located on the outermost surface of the tread, and a tread inner rubber layer 223b located radially inward of the tread surface rubber layer 223a. The 100% modulus of the tread surface rubber layer 223a is higher than the 100% modulus of the tread inner rubber layer 223b. For example, as shown in FIG. 1, in the tire 201, the tread rubber 223 may be composed of two or more rubber layers. In other words, a plurality of tread inner rubber layers 223b may be provided.

[0034] Referring now to FIG. 3, an exemplary embodiment of the method 300 disclosed herein for determining tire health may be implemented as follows.

[0035] A preliminary model generation stage 310 may be performed, including, for example, mapping input conditions to different components (which may include, for example, positions at or on the tires) for each type of tire (step 312). Generally, model generation data may be aggregated over time in a data storage, and multiple tire health models may be iteratively generated based on the aggregated model generation data, correlating various combinations of a set of input values ​​for a given type of tire to each of one or more tire health variables for each of the tire components. In some cases, this process may include drum testing for a given tire specification, such as running a tire with an accelerometer attached to the inner liner to collect data under known parameters including load, speed, pressure, tread depth, etc. Physical testing may generally be replaced or at least supplemented in certain embodiments with finite element simulation and material testing of the tire (step 311). In certain embodiments, model development may further incorporate feedback data, for example, using machine learning techniques, to improve the initial correlation between the input data set and the associated output of a given model 314. Unless otherwise noted, the models disclosed herein may be initially generated and then implemented and / or modified by a given entity, or an entity may simply selectively retrieve one or more models for implementation generated by another entity.

[0036] Exemplary such models and their respective outputs may include (but are not limited to) a tire wear estimation model 314a that considers determined and / or predicted tread depth, a tire aging estimation model 314b that considers tire series changes in related variables of specific tire components for tire type, a tire fatigue estimation model 314c that corresponds to related failure variables of specific tire components, a time damage estimation model 314d that considers determined external impacts related to tire health of specific tire components, etc. In some embodiments, a carcass health model may be developed and selectable to predict the remaining useful life of a tire based at least in part on the predicted time before a condition occurs, such as, for example, belt edge separation, belt release, belt release carcass, ply end separation, etc.

[0037] An exemplary tire fatigue estimation model, such as that depicted in Figure 4, may include a crack growth rate model for estimating crack growth rates at each of a plurality of locations on the tire as a function of at least estimated strain and temperature at each of various locations of interest. The exemplary fatigue model was validated using various drum tests, as depicted in Figure 5 for a belt edge separation (BES) removal mode, summarizing actual results for a Bridgestone R284 Ecopia™ tire versus predicted results using measured built-in air temperature (CAT).

[0038] In another example, a physics-based tire model can be developed by two-dimensional modeling of the tire as a flexible ring on an elastic foundation (REF). The tire belt package is modeled as a flexible ring, the tread is modeled as a continuous radial spring, and the carcass is modeled as a foundation of radial springs. This model has several variables related to the tire structure (such as stiffness values ​​associated with these different springs) as well as the tire's condition (such as load, pressure, speed, tread depth, etc.). The model may be loaded against, for example, a flat or curved surface (e.g., a road or drum), and the radial deformation response of the ring may be calculated and further analyzed according to changes in one or more of load, pressure, speed, tread depth, etc. From the determined deformation, steady-state acceleration can be easily extracted if needed for further analysis.

[0039] Returning to FIG. 3 , the model implementation stage 320 may include measuring or otherwise determining (step 330) relevant input values ​​associated with actual vehicle-mounted tires during use, for example, via vehicle / tire sensors 332 and / or vehicle control unit 334, as described above. Exemplary sensors 332 may include tire sensors such as TPMS / TMS, and / or vehicle sensors such as accelerometers, wheel speed sensors, GPS, etc. In some cases, all input values ​​may be measured directly via their respective sensors, although it may be understood that in various embodiments, some input values ​​may be determined indirectly from other directly measured input values. It may also be understood that some input values ​​may be collected substantially continuously, while other input values ​​may be collected periodically when available, particularly, for example, inputs may change very slowly over time and may be reliably assumed if the most recent measurement was within a certain time window. Once an appropriate set of actual inputs (which may include, for example, values ​​of inputs corresponding to the set of inputs used for model generation) has been collected, one or more models 314 may be selectively retrieved for specific tire components for each type of tire (or vehicle-tire combination) in question (step 340).

[0040] Method 300 may continue in step 350 by using the selected model to estimate each tire health variable for each relevant tire component via a set of measured or otherwise determined input values. In one embodiment, some or all of the estimated tire health variables may be accumulated over time and, optionally, aggregated (step 360) to support analysis, such as trend analysis. Tire-specific aggregation and further analysis may further enable, for example, prediction of future tire health variables (step 370), where, for example, an output signal generated by the system (step 380) may correspond to the health of each tire based on the particular estimated tire health variable comparison and / or predicted future tire health variables.

[0041] In one embodiment, for subsequent iterations of method 300 as described above, the selection of the appropriate model may be based on a newly measured set of input values ​​and may further be based on the above-referenced historical analysis of each of the aggregated estimated tire health variables over time.

[0042] For example, an implementation of method 300 may include aggregating certain estimated tire health variables over time and predicting a remaining useful life of a tire based at least in part on the aggregated variables, with the output signal corresponding to the predicted remaining useful life of the tire. The output signal may be generated, for example, corresponding to a lowest predicted remaining life among the plurality of estimated tire health variables. The output signal may also or alternatively be generated corresponding to a predicted remaining life based on a combination of interrelated tire health variables as identified from a selected model.

[0043] In various embodiments, an output signal may be selectively generated to a display unit associated with the user interface based on the determined passive intervention warning condition (step 382). In other embodiments, an output signal may be selectively generated to one or more vehicle control units based on the determined active intervention warning condition (step 384). However, these embodiments are by no means exclusive, and it can be contemplated that an output signal may be generated to either or both the display unit and the vehicle control unit for a given system configuration, optionally depending on the type of warning condition. For example, a passive warning may be generated to indicate a predicted condition and may be converted to an active warning if the condition is not addressed or if a predetermined threshold / range is otherwise violated.

[0044] In one embodiment, the estimated or predicted tire health state may be provided as an output from the model to one or more downstream models or applications. For example, the predicted tread depth state may be generated as a feedback or feedforward signal to a vehicle control system, a traction model (either within the same system or as part of another system operatively linked thereto), and / or another predictive model related to fuel efficiency, durability, etc. Tire condition information (e.g., tread depth) may be provided as an input to the traction model, along with specific vehicle data, for example, and the traction model may be configured to provide an estimated traction state or one or more traction characteristics for each tire. Exemplary traction models may include a "digital twin" virtual representation of a physical part, process, or system, where digital and physical data are paired and combined with a learning system, such as an artificial neural network. Actual vehicle and / or tire data from specific tires, vehicles, or tire-vehicle systems throughout the lifecycle of the respective assets may be provided to generate virtual representations of vehicle tires for estimation of tire traction, and subsequent comparison of the estimated tire traction with corresponding measured or determined actual tire traction may be implemented as feedback for machine learning algorithms preferably executed at the server level.

[0045] The exemplary traction model may further utilize results from prior testing, including, for example, stopping distance test results, tire traction test results, etc., collected for associated combinations of tire condition values ​​for multiple tire-vehicle systems and input parameters (e.g., tire tread, inflation pressure, road surface characteristics, vehicle speed and acceleration, slip ratio and angle, normal force, braking pressure and load), such that tire traction output may be effectively predicted for a given setting of current vehicle data and tire data inputs.

[0046] In one embodiment, output from this traction model may be incorporated into active safety systems, autonomous fleet management systems, and the like. As previously mentioned, data may be collected from sensors on the vehicle to feed a tire wear model that predicts tread depth, which may further be fed into the traction model. As used herein, the term "active safety system" preferably encompasses systems such as those commonly known to those skilled in the art, including, but not limited to, collision avoidance systems, advanced driver assistance systems (ADAS), anti-lock braking systems (ABS), and the like, which may be configured to utilize traction model output information to achieve optimal performance. For example, collision avoidance systems are typically configured to take evasive action, such as automatically engaging the brakes of the host vehicle, to avoid or mitigate a potential collision with a target vehicle, and enhanced information regarding the traction capabilities of the tires, and thus the braking capabilities of the tire-vehicle system, is highly desirable.

[0047] As previously mentioned, the various inputs to the system may be measured directly or may be determined indirectly. Normal load on the tire may be a desired input to the system for implementation in various models and algorithms, but is frequently not available for direct measurement during use. Referring now to FIG. 7, an exemplary process 700 further provides a prediction of normal load on the tire from some of the commonly available sensor measurements (e.g., speed, ambient temperature, inflation pressure, and CAT) referenced above.

[0048] In one step 710, thermal properties (e.g., steady-state thermal properties) of the tire are determined as a function of various operating conditions. In various embodiments, this determination can be made via physical measurements, or alternatively via finite element analysis, other equivalent techniques, or a mixture thereof. In an exemplary embodiment, the steady-state contained air temperature is determined at several different normal loads, speeds, and inflation pressures. All of these conditions are then compiled into one parameter, which is the normal load multiplied by the speed (essentially the power input to the tire) divided by the inflation pressure.

[0049] 8 shows two example data curves, the first curve 801 being generated via field test results from physical measurements, and the second curve 802 being generated via finite element analysis simulation. The data represented in this embodiment is * (F Z * v / p)^b, where ΔT is the CAT (or T rim ) and ambient temperature (or T amb ), F is the normal load, v is the vehicle speed, p is the tire inflation pressure, and A and b are coefficients to be determined. For example curve 801, coefficient A is determined to be 25 and coefficient b is determined to be 0.65. For example curve 302, coefficient A is determined to be 35 and coefficient b is determined to be 0.65.

[0050] In another step 720, to predict transient temperatures, the tire may be treated as a lumped capacitance model, where, for example, the only parameter required is the time constant τ. This time constant τ may be different depending on whether the tire is heating up or cooling down. As an example, based on limited data collected from size 295 / 75R22.5 R283 truck and bus radial (TBR) tires, the cooling time constant τ cool is determined to be 2500 seconds, and the heating time constant τ heatis determined to be 1250 seconds. Those skilled in the art will appreciate that these time constants will likely vary from tire to tire, especially for tires of different sizes, and therefore these constants will need to be determined from empirical data.

[0051] FIG. 9 shows a comparison of measured contained air temperature 902 versus predicted contained air temperature 901 for a controlled indoor drum test with variations in both speed and vertical load. In the first block of test run 910, the speed is 40 miles per hour (mph) and the load is 6,173 pounds of force (lbf). In the second block of test run 920, the speed is 50 mph and the load is 6,173 lbf. In the third block of test run 930, the speed is 60 mph and the load is 6,173 lbf. In the fourth block of test run 940, the speed is 40 mph and the load is 6,614 lbf. In the fifth block of test run 950, the speed is 50 mph and the load is 6,614 lbf. In the sixth block of test run 960, the speed is 60 mph and the load is 6,614 lbf.

[0052] When the TPMS device 118 is implemented as described above, the contained air temperature measurement 902 may typically be obtained directly therefrom. This allows the aforementioned model to be used to predict the unknown variable, i.e., vertical load. When this method is applied for the embodiment shown in FIG. 9, the vertical load is predicted to within 120 N of the actual value. Those skilled in the art will appreciate that this data can be very sparse and / or noisy when viewed instantaneously with respect to vehicle conditions, but the noise can be filtered or otherwise minimized via smoothing analysis over a longer period of time, such as over a 24-hour period for a long-distance truck route. This can significantly impact the ability of a typical system to more accurately predict wear and durability.

[0053] Throughout this specification and claims, unless context dictates otherwise, the following terms take on at least the meanings explicitly associated therewith. The meanings identified below do not necessarily limit the terms but merely provide illustrative examples of the terms. The meanings of "a," "an," and "the" may include plural references, and the meaning of "in" may include "in" and "on." As used herein, the phrase "in one embodiment" does not necessarily refer to the same embodiment, but may.

[0054] The various illustrative logical blocks, modules, and algorithm steps described in connection with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this interchangeability of hardware and software, the various illustrative components, blocks, modules, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the particular application and design constraints imposed on the overall system. The described functionality can be implemented in various ways for each particular application, and such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

[0055] The various illustrative logical blocks and modules described in connection with the embodiments disclosed herein may be implemented or performed by a machine, such as a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but alternatively, the processor may be a controller, microcontroller, or state machine, combinations thereof, etc. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

[0056] The steps of a method, process, or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, a hard disk, a removable disk, a CD-ROM, or any other form of computer-readable medium known in the art. An exemplary computer-readable medium may be coupled to the processor such that the processor can read information from, and write information to, the memory / storage medium. Alternatively, the medium may be integral to the processor. The processor and the medium may reside in an ASIC. The ASIC may reside in a user terminal. Alternatively, the processor and the medium may reside as separate components in a user terminal.

[0057] As used herein, conditional language such as, among others, "can," "might," "may," "e.g.," and the like, is generally intended to convey that certain embodiments include particular features, elements, and / or conditions, while other embodiments do not include those particular features, elements, and / or conditions, unless specifically stated otherwise or understood within the context in which it is used. Thus, such conditional language is generally not intended to suggest that features, elements, and / or conditions are in any way required for one or more embodiments, nor is it generally intended to suggest that one or more embodiments necessarily include logic for determining, with or without author input or prompting, whether those features, elements, and / or conditions should be included in or implemented in any particular embodiment.

[0058] While certain preferred embodiments of the present invention may be described herein typically with respect to tire wear estimation for fleet management systems, and more specifically for autonomous vehicle fleet or commercial truck applications, the present invention is in no way expressly limited thereto, and the term "vehicle," as used herein, unless otherwise stated, refers to an automobile, truck, or any equivalent thereof, whether self-propelled or not, which may include one or more tires and therefore may require accurate estimation or prediction of tire wear, and potential disabling, replacement, or intervention, for example in the form of direct vehicle control adjustments.

[0059] As used herein, unless otherwise specified, the term "user" may refer to, for example, a driver, passenger, mechanic, technician, fleet management personnel, or any other person or entity that may be associated with a device having a user interface for providing the features and steps disclosed herein.

[0060] The foregoing detailed description has been provided for purposes of illustration and description. Thus, while specific embodiments of a novel and useful invention have been set forth, it is not intended that such references be construed as limitations on the scope of the invention, except as set forth in the following claims.

Claims

1. 1. A computer-implemented tire health estimation method (300), comprising: aggregating model generation data in a data storage over time; and iteratively generating a plurality of tire health models based on the aggregated model generation data, the model generation data correlating various combinations of a first set of input values ​​for a given type of tire to each of one or more tire health variables for each of a plurality of tire components (310, 311, 312, 314); measuring or determining (330) the second set of input values ​​via one or more sensors associated with a first tire of the given type of tire and / or associated with a vehicle on which the first tire is mounted; selecting (340) an appropriate tire health model for at least one of the one or more tire health variables for each of one or more of the plurality of tire components based on the measured or determined second set of input values; estimating (350) a respective tire health variable for each of the at least one tire component via the one or more selected tire health models and based on the measured or determined second set of input values; generating (380) an output signal corresponding to the health of the first tire based on a comparison of the estimated tire health variables; a fatigue estimation model (314c) corresponding to a failure variable as one type of the tire health variables associated with one or more of the plurality of tire components, the fatigue estimation model including a crack growth rate model for estimating a crack growth rate at each of a plurality of locations on the tire as a function of at least estimated strain and temperature at each of the plurality of locations.

2. A computer-implemented tire health estimation method (300), comprising: aggregating model generation data in a data storage over time; and iteratively generating a plurality of tire health models based on the aggregated model generation data, the model generation data correlating various combinations of a first set of input values ​​for a given type of tire to each of one or more tire health variables for each of a plurality of tire components (310, 311, 312, 314); measuring or determining (330) the second set of input values ​​via one or more sensors associated with a first tire of the given type of tire and / or associated with a vehicle on which the first tire is mounted; selecting (340) an appropriate tire health model for at least one of the one or more tire health variables for each of one or more of the plurality of tire components based on the measured or determined second set of input values; estimating (350) a respective tire health variable for each of the at least one tire component via the one or more selected tire health models and based on the measured or determined second set of input values; generating (380) an output signal corresponding to the health of the first tire based on a comparison of the estimated tire health variables; aggregating (360) the estimated respective tire health variables over time; and predicting (370) a remaining useful life of the tire based at least in part on the aggregated variables, wherein the output signal corresponds to the predicted remaining useful life of the tire; selecting an appropriate tire health model for subsequent iterations of the method based on the newly measured or determined set of input values ​​and further based on a historical analysis of each of the aggregated estimated tire health variables over time.

3. A computer-implemented tire health estimation method (300), comprising: aggregating model generation data in a data storage over time; and iteratively generating a plurality of tire health models based on the aggregated model generation data, the model generation data correlating various combinations of a first set of input values ​​for a given type of tire to each of one or more tire health variables for each of a plurality of tire components (310, 311, 312, 314); measuring or determining (330) the second set of input values ​​via one or more sensors associated with a first tire of the given type of tire and / or associated with a vehicle on which the first tire is mounted; selecting (340) an appropriate tire health model for at least one of the one or more tire health variables for each of one or more of the plurality of tire components based on the measured or determined second set of input values; estimating (350) a respective tire health variable for each of the at least one tire component via the one or more selected tire health models and based on the measured or determined second set of input values; generating (380) an output signal corresponding to the health of the first tire based on a comparison of the estimated tire health variables; The plurality of selectable tire health models are an aging estimation model (314b) that considers tire series variation as one of the tire health variables of one or more of the plurality of tire components for the type of the first tire; a damage estimation model (314d) that considers the determined external impact as one of the tire health variables for one or more of the plurality of tire components; a tire wear estimation model (314a) that takes into account the determined and / or predicted tread depth as one of the tire health variables; and one or more carcass health models for predicting the remaining useful life of the tire based at least in part on a predicted time before an occurrence of a condition selected from the group consisting of belt edge separation, belt detachment, belt detachment carcass, and ply end separation as one of the tire health variables.

4. A tire health estimation system (100), a data storage (106, 132) storing model generation data aggregated over time; and a plurality of tire health models (134) iteratively generated based on the aggregated model generation data, the model generation data correlating various combinations of a first set of input values ​​for a given type of tire to each of one or more tire health variables for each of a plurality of tire components; and a computer program product residing on a non-transitory computer-readable medium and executable by a processor to direct the execution of steps in the computer-implemented tire health estimation method (300) of any one of claims 1 to 3.

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