System and method for indirect tire wear modeling and prediction from tire specifications

Abstract

A system and method for indirect tire wear modeling and implementation is disclosed. A data storage network stores accessible finite element analysis (FEA) models and corresponding direct tire wear models for each of various types of tires. A computational network is operatively linked to the data storage network and configured to iteratively develop control models that scale values ​​of various tire parameters of a control tire selected from a type of tire having a corresponding accessible FEA model to respective values ​​of the tire parameters of any type of tire lacking a corresponding accessible FEA model. For a provided first type of tire lacking a corresponding accessible FEA model, corresponding values ​​are obtained for the tire parameters, and an indirect tire wear model is generated for the first type of tire based on the first control model, the corresponding direct tire wear model, and the obtained tire parameter values.

Description

[Technical Field]

[0001] The present invention relates generally to tire condition estimation and prediction for wheeled vehicles. More specifically, embodiments of the invention disclosed herein relate to systems and methods for indirectly developing and implementing tire wear models from generic tire specifications in characterizing and predicting the state and condition of tires for wheeled vehicles, including, but not limited to, motorcycles, consumer vehicles (e.g., passenger cars and light trucks), commercial vehicles, and off-the-road (OTR) vehicles. [Background technology]

[0002] Tire wear prediction is an important tool for those who own or operate vehicles, especially in the context of fleet management. At some point, insufficient tire tread can lead to unsafe driving conditions, making it important to note changes in tire condition over time. However, irregular tread wear can occur for a variety of reasons and may lead users to replace tires sooner than necessary. The vehicle, driver, driving conditions, and any number of other factors can cause tires to wear at very different rates. That said, relying on tread depth measurements and other such indicators of current tire wear condition is undesirable, at least because such measurements can be difficult and / or inaccurate to obtain in real time, and further because such measurements alone cannot predict future tire wear conditions.

[0003] Therefore, tire wear models have been developed for predictive implementations, for example, allowing for prediction of the tire wear state of a tire throughout its respective lifecycle. However, tire wear is a complex phenomenon to model. While accurate models currently exist that utilize finite element analysis (FEA), these simulations can typically take weeks to complete. If it is desired to simulate wear rates at several different tread depths, this would further require several months of computationally expensive simulations.

[0004] It would be desirable to develop additional tire wear models that can indirectly model tires for which corresponding complex FEA (or equivalent) models are not available, or to provide reasonably accurate tire wear modeling during periods when such complex models are not yet available. Summary of the Invention

[0005] Embodiments of the method disclosed herein for indirect tire wear modeling and implementation build upon or complement the existence of various accessible finite element models and corresponding direct tire wear models for each of a plurality of types of tires. A control model is iteratively developed by scaling values ​​of a plurality of tire parameters of a control tire selected from the plurality of types of tires having corresponding accessible finite element models to respective values ​​of a plurality of tire parameters of any type of tire lacking a corresponding accessible finite element model. For a provided first type of tire lacking a corresponding accessible finite element model, corresponding values ​​are obtained for the plurality of tire parameters, and an indirect tire wear model is generated for the first type of tire based on the first control model, the corresponding direct tire wear model, and the values ​​obtained for the first type of tire for the plurality of tire parameters.

[0006] In one exemplary aspect according to the above embodiment, a tire wear condition may be predicted at one or more future times for a first tire of a first type mounted on a vehicle based at least in part on an indirect tire wear model for the first type of tire.

[0007] In another exemplary aspect according to the above embodiment, the vehicle type and / or tire usage may be provided as input to an indirect tire wear model to predict tire wear conditions at one or more future times.

[0008] In another exemplary aspect according to the above embodiment, actual tire performance values ​​of a first tire may be monitored over time, and the monitored actual tire performance values ​​may be applied to determine a current wear state of the first tire based on an indirect tire wear model for the first type of tire.

[0009] In another exemplary aspect according to the above-described embodiment, the determined current wear state of the first tire may be provided as feedback for iteratively developing further tire wear models for the first type of tire.

[0010] In another exemplary aspect according to the above embodiment, the time to replace a first tire may be predicted based on a current or predicted tire wear condition compared to a tire wear threshold associated with the first type of tire.

[0011] In another exemplary aspect according to the above embodiment, generating the indirect tire wear model may include determining frictional energy associated with the first type of tire based at least in part on the first control model and values ​​obtained for the first type of tire for the plurality of tire parameters.

[0012] In another exemplary aspect according to the above embodiment, the frictional energy associated with the first type of tire may be related to the wear energy according to the determined elasticity of the corresponding tread compound.

[0013] In another exemplary aspect according to the above embodiment, developing the control model may further include determining an empirical relationship between the wear energy at zero force and values ​​of the plurality of tire parameters using one or more coefficients extrapolated from one or more of the plurality of accessible finite element models. Generating the indirect tire wear model may further include correlating friction energy associated with the first type of tire to the wear energy based at least in part on the determined empirical relationship.

[0014] In another exemplary aspect according to the above embodiment, the control model may include one or more scale factors to apply to relevant tire parameters related to tread stiffness and / or carcass stiffness of the selected control tire.

[0015] In another embodiment, a system for indirect tire wear modeling and implementation is disclosed herein that includes a data storage network storing accessible finite element models and corresponding direct tire wear models for each of a plurality of types of tires, and a computational network operatively linked to the data storage network, the computational network configured to direct the performance of operations in a method according to the above embodiments, and optionally any one or more of the enumerated aspects thereof.

[0016] Numerous objects, features, and advantages of the embodiments described herein will become readily apparent to those skilled in the art from a reading of the following disclosure in conjunction with the accompanying drawings. [Brief explanation of the drawings]

[0017] [Figure 1] FIG. 1 is a block diagram depicting an exemplary embodiment of the system disclosed herein. [Figure 2] FIG. 2 is a flow diagram depicting an exemplary embodiment of the method disclosed herein. [Figure 3]FIG. 3 is a graphical illustration of the relationship between the zero force wear strength determined for a given tire based on a direct (e.g., FEA) model and the zero force wear strength determined for a given tire based on the indirect model disclosed herein. [Figure 4] Included are four graphical illustrations depicting the relationship between lateral force results using a direct (e.g., FEA) model and lateral force results using the indirect model disclosed herein. DETAILED DESCRIPTION OF THE INVENTION

[0018] Various exemplary embodiments of the present invention may now be described in detail, generally with reference to Figures 1-4. 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.

[0019] In various embodiments, the indirect tire wear models disclosed herein, for example, have relatively low accuracy but can still provide reasonable wear predictions, while being quick and easy to develop and implement using only publicly available basic tire specification data.

[0020] 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 fleet management entities, end users, vehicles, tires, etc.) to effectively develop and implement the models disclosed herein.

[0021] 1 , an exemplary embodiment of system 100 includes at least a server network 110 and a data storage network 120, and further includes or is operatively linked to one or more public tire data sources 130, a tire monitoring network 140 including, for example, tire-mounted sensors and intermediate devices, an on-board computing device including a user interface 150 for each of a plurality of vehicles, for example, in a defined vehicle fleet, an endpoint computing device 160 for each of a plurality of users, for example, a fleet management administrator, etc. One or more of the foregoing components may be connected or otherwise operatively linked via a communications network (not shown), which in various embodiments may include, in whole or in part, the Internet, a public network, a private network, or any other communications medium capable of conveying electronic communications.

[0022] In various exemplary embodiments, any or all of computing devices 110, 150, 160 may be implemented as at least one of a server computer, server appliance, desktop computer, laptop computer, smartphone, or other equivalent electronic device capable of executing program instructions. The server network may include a processor 112, a memory 114 in which program logic resides, and a communication unit 116 for selectively linking one or more servers in the network to other components such as those described above. In certain embodiments, server network 110, data storage network 120, and multiple on-board computing devices or program modules resident thereon may collectively define a host system for tire wear monitoring of tires mounted on vehicles associated with on-board computing device 150. On-board computing device 150 may be portable or modular as part of a distributed vehicle data collection and control system, or may be integrally provided to a central vehicle data collection and control system (not shown).

[0023] Other vehicle components in communication with the on-board computing device 150 may typically include one or more sensors, such as body accelerometers, gyroscopes, inertial measurement units (IMUs), position sensors (such as global positioning system (GPS) transponders), tire-mounted sensors, tire pressure monitoring system (TPMS) sensor transmitters, and associated on-board receivers, which may be linked, for example, to a controller area network (CAN) bus network, thereby providing signals to a local processing unit.

[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 means for implementing the same.

[0025] The vehicle and tire sensors may, in one embodiment, further be provided with unique identifiers, allowing the on-board computing device 150 to distinguish between signals provided by respective sensors on the same vehicle; further, in certain embodiments, the central server 110 and / or the fleet maintainer's client device 160 may distinguish between 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, vehicle, and / or 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 the host server network 110, as shown in FIG. 1, or the driver's mobile device or truck's on-board computing device may be configured to receive, process, and transmit on-board device output data to a hosted server and / or fleet management server / device.

[0026] 1 may include, for example, multiple databases or equivalent storage media for retrievably storing models 122, 124, 126, 128 and input data for their development. Vehicle data, sensed tire data, data from public tire data sources 130, etc., may be transmitted to hosted server network 110 via a communications network and stored accordingly, for example, in databases associated therewith. System 100 may include or otherwise selectively retrieve at least FEA model 122, direct tire wear model 124, scaling model 126, and / or new tire (indirect) wear model for processing inputs.

[0027] 1 does not limit the scope of the system or method 200 disclosed herein, and in alternative embodiments, one or more of the models disclosed herein may be implemented locally on an on-board computing device 150 (e.g., an electronic control unit) for a vehicle or another endpoint device 160, such as a fleet management device or server, rather than at a central (host) server level 110. For example, one or more of the models disclosed herein may be generated and trained over time at the host server level 110 and then downloaded to the on-board computing device 150 and / or endpoint device 160 for locally performing one or more steps or operations disclosed herein.

[0028] In one embodiment, the estimated or predicted tire condition may be provided as an output from the model to one or more downstream models or applications. For example, as depicted in Figure 1, a feedback signal corresponding to the predicted tire wear condition (e.g., predicted tread depth at a given distance, time, etc.) may be provided to an on-board computing device 150 associated with the vehicle itself, or may be provided to a mobile device 160 associated with a user, e.g., integrated with a user interface configured to provide a warning or notification / recommendation that a tire needs or will soon need to be replaced.

[0029] Referring now to FIG. 2, an exemplary embodiment of a method 200 for developing and implementing an indirect tire wear model for a new tire can be described as follows.

[0030] Initially, method 200 may include providing access to or otherwise defining multiple existing FEA models for each type of tire, and optionally a corresponding direct tire wear model. A "direct" tire wear model in this context may generally refer to a tire wear model for a particular tire developed based on an FEA model for the corresponding type of tire, and thus, as previously described, may be considered highly accurate, but costly and time-consuming to develop. Thereafter, when a tire wear model is requested from system 100 for an existing type of tire, for example, via tire selection or input 232, "existing" type of tire in this context means a type of tire for which an existing or otherwise accessible FEA model is available (i.e., "yes" in response to the query in step 230), and thus system 100 may use conventional techniques to retrieve or otherwise develop a tire wear model for the tire based on the corresponding FEA model.

[0031] If a tire wear model is requested from the system 100 for a new type of tire, or if a new type of tire is otherwise selected or otherwise input / presented to the system in step 232, where a "new" type of tire in this context means a type of tire for which an existing or otherwise accessible FEA model is not available (i.e., a "No" in response to the query in step 230), the method 200 of the present disclosure further involves obtaining various tire parameters for the tire (step 240) based at least in part on publicly available specifications 242 for the tire, such as from online data sources, and generating a new "indirect" tire wear model (step 250) further taking into account the determined relationships, examples of which may be as follows:

[0032] For example, considering that the wear energy of a tire is related to the forces and slip seen at the tire / road contact interface, the average frictional energy seen by the tire can be calculated separately for longitudinal and lateral forces / slip as follows: E fx =F x s x =F x K (formula 1) E fy =F y s y =F y α (Formula 2) Here, K is the slip ratio and α is the slip angle seen from the tire.

[0033] This can be further simplified by assuming a small amount of slip (i.e., a linear force-slip relationship) by including the tire slip / cornering stiffness. We can further account for offsets due to plysteer in the lateral case and rolling resistance in the longitudinal case, so the friction energy equation becomes:

[0034]

number

[0035] Additional friction energy may also be caused by the tilt angle, which is taken into account by the following formula:

[0036]

number

[0037] The above equation suggests that when the lateral / longitudinal force applied to the tire is zero, the wear energy will also be zero. Those skilled in the art will understand that this is not the case because certain areas of the tire footprint (contact patch) are in a "push" or "pull" condition where the net result is zero force. To account for this, an empirical relationship between the wear energy at zero force and some of the tire parameters mentioned above can be determined or otherwise considered, which is given by the following equation:

[0038]

number

[0039] Using a simple model relating various tire dimensional and stiffness parameters to the parameters in the above equation, one or more control (i.e., scaling) models can be developed (step 220) that include scale factors for each selected control tire that may have been previously modeled in a more accurate FEA method and defined, for example, using the following relationship:

[0040]

number

[0041] The slip stiffness of a tire can be assumed to be equal to the tread stiffness, while the cornering stiffness is related to the carcass and tread by assuming two springs in series, thus:

[0042]

number

[0043] With further exemplary reference to FIG. 4 , several tire models created using FEA methods were compared to similar tire models developed in accordance with embodiments of the method 200 disclosed herein, with relevant tire parameters obtained for the particular tires of interest using online publicly available resources, such as www.tirerack.com.

[0044] Exemplary tire parameters as inputs to the developed model include original tread depth, tread width, section width, outer diameter, and rim diameter, each of which can be obtained directly from publicly available specifications for the tire of interest.

[0045] Additional exemplary tire parameters as inputs to the developed model may include predicted operating loads and inflation pressures, which may be indirectly determined or otherwise predicted based on, for example, vehicle type and / or use type (e.g., mid-size SUV, pickup and delivery, etc.).

[0046] Further exemplary tire parameters as inputs to the developed model may include tread compound parameters, such as elasticity, which is a function of the compound's tangent delta, as described above, and may be determined or otherwise predicted based on, for example, tire type and / or rating (e.g., standard touring all-season, high performance summer, etc., and / or uniform tire quality grading (UTQG), treadwear warranty, etc.).

[0047] In some embodiments, method 200, and more particularly step 250, may include a tire wear model selection step that depends on application-related factors such as, for example, the wheel mounting location of the tire in question, and may take into account any known or predicted relevant dependencies of applied load based on such wheel mounting distinctions.

[0048] As shown in FIG. 4, the relative accuracy of the indirectly developed tire models, combined with the relative ease of model development, further demonstrates the potential utility of the method 200 disclosed herein.

[0049] The new tire wear model generated according to step 250 may, in some embodiments, be a generic tire wear model for a particular type of tire, or a tire wear model for a particular tire developed from the generic model for the type of tire in question, and method 200 may continue by predicting tire wear conditions at one or more times in the future for that particular tire and / or the determined tire-vehicle combination and / or tire application (step 260).

[0050] System 100 can collect inputs related to tire use over time and further process the inputs to further develop a tire wear model for a particular tire, or in some embodiments, an indirect tire model for the type of tire itself, based at least in part on a comparison of actual tire wear conditions at a specified time with previously predicted tire wear conditions at the same time. For example, models related to tire wear prediction can be updated over time using actual measurements, and the system can selectively “correct” model predictions for each measurement obtained from a particular tire element and / or vehicle-tire system. To the extent that tire wear models can be at least partially probabilistic in nature, allowing for potential time series or similar progression curves over time, and by blending or otherwise attempting to consider all such possibilities and associated uncertainties when predicting future tire wear and related events, a feedback loop involving actual tire wear values ​​or corresponding inputs can thus enable system 100 to effectively eliminate or minimize the relevance of certain such model components for a given tire, or even for a type of tire, based on the aggregation of such inputs.

[0051] In one embodiment, the comparison may further consider one or more factors contributing to wear that are specific to the tire in question and that were not considered (or at least not fully considered) at the beginning of the prediction. Such factors may include, for example, driving style, vehicle alignment settings, route driven, road surface, environmental conditions, tire manufacturing variations, etc., and may represent known causes of variation in tire wear life between otherwise comparable tires.

[0052] During operation of a vehicle equipped with the tire in question, method 200 may further include step 270 of determining or otherwise predicting and recommending tire intervention to an associated user of system 100. For example, a feedback signal corresponding to the predicted tire wear state may be provided via an interface to an on-board device 150 associated with the vehicle itself, or to a mobile device associated with user 160, such as integrated with a user interface configured to provide an alert or notification / recommendation of an intervention event, such as one or more tires needing or soon to be replaced, repositioned, aligned, inflated, etc.

[0053] Throughout this specification and claims, unless context dictates otherwise, the following terms have at least the meanings explicitly associated with them herein. The meanings identified below do not necessarily limit the terms, but merely provide examples of their usage. 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 logic 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 in the alternative, the processor may be a controller, microcontroller, or state machine, combinations thereof, or the like. 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 discrete components in a user terminal.

[0057] As used herein, conditional language such as "can," "might," "may," "eg," and the like, among others, is generally intended to convey that certain embodiments include certain features, elements, and / or conditions, while other embodiments do not include certain 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, or 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] 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 described, 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 method for indirect tire wear modeling and implementation, comprising: providing an accessible finite element model and a corresponding direct tire wear model for each of a plurality of types of tires; iteratively developing a control model that scales values ​​of a plurality of tire parameters of a control tire selected from the plurality of types of tires having corresponding accessible finite element models to respective values ​​of the plurality of tire parameters of any type of tire lacking a corresponding accessible finite element model; obtaining corresponding values ​​of the plurality of tire parameters for a provided first type tire lacking a corresponding accessible finite element model; generating an indirect tire wear model for the first type of tire based on a first control model, the corresponding direct tire wear model, and the obtained values ​​for the first type of tire for the plurality of tire parameters.

2. 2. The method of claim 1, further comprising predicting a tire wear state at one or more future times for a first tire of the first type mounted on a vehicle based at least in part on the indirect tire wear model for the first tire type.

3. 3. The method of claim 2, wherein the vehicle type and / or the tire usage are provided as inputs to the indirect tire wear model to predict the tire wear state at the one or more future times.

4. 3. The method of claim 2, further comprising: monitoring actual tire performance values ​​of the first tire over time; and applying the monitored actual tire performance values ​​to determine a current wear state of the first tire based on the indirect tire wear model for the first type of tire.

5. 5. The method of claim 4, comprising providing the determined current wear state of the first tire as feedback for iteratively developing further tire wear models for the first type of tire.

6. 5. The method of claim 4, further comprising predicting when to replace the first tire based on the current or predicted tire wear state compared to a tire wear threshold associated with the first type of tire.

7. 2. The method of claim 1, wherein the step of generating an indirect tire wear model includes determining a frictional energy associated with the first type of tire based at least in part on the first control model and the obtained values ​​for the first type of tire for the plurality of tire parameters.

8. The method of claim 7 , wherein the frictional energy associated with the first type of tire is related to a wear energy according to a determined elasticity of a corresponding tread compound.

9. the step of developing the control model further includes determining an empirical relationship between zero-force wear energy and values ​​of the plurality of tire parameters using one or more coefficients extrapolated from one or more of the plurality of accessible finite element models; 8. The method of claim 7, wherein the step of generating an indirect tire wear model further comprises correlating the friction energy associated with the first type of tire to a wear energy based at least in part on the determined empirical relationship.

10. The method of claim 1 , wherein the control model includes one or more scale factors to apply to relevant tire parameters related to tread stiffness and / or carcass stiffness of the selected control tire.

11. 1. A system for indirect tire wear modeling and implementation, comprising: a data storage network storing accessible finite element models and corresponding direct tire wear models for each of a plurality of types of tires; A computing network operatively linked to said data storage network and configured to direct the execution of the steps of the method of any one of claims 1 to 10.

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