System and method for indirect tire wear modeling and prediction according to tire specifications

Abstract

A system and method for indirect tire wear modeling and implementation are disclosed. A data storage network stores an accessible finite element (FEA) model and a corresponding direct tire wear model for each of a variety of types of tires. A computing network is functionally linked to the data storage network and is configured to iteratively develop a contrast model that scales values of various tire parameters of a selected contrast tire of the variety of types of tires having a corresponding accessible FEA model to corresponding values of tire parameters of any type of tire lacking a corresponding accessible FEA model. For a first type of tire provided that lacks a corresponding accessible FEA model, corresponding values of tire parameters are obtained and an indirect tire wear model for the first type of tire is generated based on the first contrast model, the corresponding direct tire wear model, and the obtained tire parameter values.

Description

Technical Field

[0001] This invention relates generally to the estimation and prediction of tire conditions for wheeled vehicles. More specifically, embodiments of the invention disclosed herein relate to systems and methods for indirectly developing and implementing tire wear models based on general tire specifications, for characterizing and predicting the condition and state of tires of wheeled vehicles, including but not limited to motorcycles, consumer vehicles (e.g., buses and light trucks), commercial vehicles, and off-road (OTR) vehicles. Background Technology

[0002] Tire wear prediction is an important tool for anyone who owns or operates a vehicle, especially in the context of fleet management. At any given moment, understanding how tire condition changes over time becomes crucial, as insufficient tire tread can lead to unsafe driving conditions. However, irregular tread wear can occur for a variety of reasons, potentially causing the user to replace tires earlier than originally intended. Vehicle, driver, driving conditions, and any number of other factors can cause tires to wear at very different rates. That said, relying on measurements of tread depth and other such indicators of current tire wear is not advisable, at least because such measurements may be difficult to obtain in real time and / or inaccurate, and further because such measurements alone cannot predict future tire wear conditions.

[0003] Therefore, tire wear models have been developed to predict, for example, the wear condition of a tire throughout its corresponding life cycle. However, tire wear is a complex modeling phenomenon. Accurate models using finite element analysis (FEA) exist, but these simulations typically take weeks to complete. If it is desired to simulate wear rates at several different tread depths, this would further require computationally expensive simulations lasting several months.

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

[0005] The implementation scheme of the method for indirect tire wear modeling and implementation disclosed herein is based on or supplemented by the existence of various accessible finite element models and corresponding direct tire wear models for each of multiple tire types. A comparative model is iteratively developed, which scales the values ​​of multiple tire parameters of a selected comparative tire among multiple tire types having corresponding accessible finite element models to the corresponding values ​​of multiple tire parameters of any type of tire lacking a corresponding accessible finite element model. For a first type of tire lacking a corresponding accessible finite element model, the corresponding values ​​of multiple tire parameters are obtained, and an indirect tire wear model for the first type of tire is generated based on the iteratively developed comparative model, the corresponding direct tire wear model, and the obtained values ​​of the multiple tire parameters for the first type of tire.

[0006] In one exemplary aspect of the above implementation scheme, the tire wear status of a first type of tire mounted on a vehicle at one or more future times can be predicted, at least in part, based on an indirect tire wear model of the first type of tire.

[0007] In another exemplary aspect of the above implementation scheme, the type of vehicle and / or the application of tires can be provided as input to an indirect tire wear model to predict tire wear status at one or more future times.

[0008] In another exemplary aspect of the above implementation scheme, the actual tire performance value of the first tire can be monitored over time, and the monitored actual tire performance value can be applied to determine the current wear state of the first tire based on an indirect tire wear model of the first type of tire.

[0009] In another exemplary aspect of the above implementation scheme, the current wear state of the determined first tire can be provided as feedback for iteratively developing another tire wear model for the first type of tire.

[0010] In another exemplary aspect of the above implementation, the replacement time of the first tire can be predicted based on a comparison between the current wear condition or the predicted tire wear condition and a tire wear threshold associated with the first type of tire.

[0011] In another exemplary aspect of the above implementation, the step of generating an indirect tire wear model may include determining the frictional energy associated with the first type of tire based at least in part on a comparative model developed iteratively and values ​​of a plurality of tire parameters obtained for a first type of tire.

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

[0013] In another exemplary aspect of the above-described implementation, the step of developing a comparative model may further include determining an empirical relationship between the wear energy at zero force and the values ​​of a plurality of tire parameters using one or more coefficients extrapolated from one or more accessible finite element models. The step of generating an indirect tire wear model may further include associating the friction energy associated with the first type of tire with the wear energy, at least in part, based on the determined empirical relationship.

[0014] In another exemplary aspect of the above-described embodiments, the comparison model may include one or more scaling factors for application to associated tire parameters related to the tread stiffness and / or carcass stiffness of the selected comparison tire.

[0015] In another embodiment, this document discloses 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 multiple tire types; and a computational network functionally linked to the data storage network. The computational network is configured to guide the execution of operations according to the above embodiments and optionally according to any one or more of the aspects described herein.

[0016] Many of the objects, features and advantages of the embodiments described herein will become apparent to those skilled in the art when they read the following disclosure in conjunction with the accompanying drawings. Attached Figure Description

[0017] Figure 1 This is a block diagram illustrating an exemplary implementation of the system as disclosed herein.

[0018] Figure 2 This is a flowchart illustrating an exemplary implementation of the method disclosed herein.

[0019] Figure 3 It is a graphical representation of the relationship between the zero-force wear intensity of a given tire determined based on a direct (e.g., FEA) model and the zero-force wear intensity of a given tire determined based on an indirect model as disclosed herein.

[0020] Figure 4 This includes four graphical representations showing the relationship between lateral force results using a direct (e.g., FEA) model and an indirect model as disclosed herein. Detailed Implementation

[0021] General Reference Figures 1 to 4Various exemplary embodiments of the invention will now be described in detail. Where various figures may depict embodiments that share various common elements and features with other embodiments, similar elements and features are given the same reference numerals, and their redundant descriptions may be omitted below.

[0022] In various implementation schemes, such as the indirect tire wear model disclosed herein, the accuracy may be relatively low, but it still provides reasonable wear predictions, while being able to be developed and implemented quickly and easily using only publicly available basic tire specification data.

[0023] Various implementations of the systems disclosed herein may include a centralized computing node (e.g., a cloud server) that can effectively develop and implement the model disclosed herein by communicating functionally with multiple distributed data collectors and computing nodes (e.g., associated with various fleet management entities, end users, vehicles, tires, etc.).

[0024] First refer to Figure 1 An exemplary embodiment of system 100 includes at least a server network 110 and a data storage network 120, and also includes, or is functionally linked to, one or more public tire data sources 130, a tire monitoring network 140 (including, for example, sensors and intermediate devices mounted on tires), an on-board computing device (including a user interface 150 for each of a plurality of vehicles in a defined fleet of vehicles, for example), an endpoint computing device 160 for each of a plurality of users (such as a fleet manager), etc. One or more of the foregoing components may be connected or otherwise functionally linked via a communication 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 communication medium capable of transmitting electronic communications.

[0025] In various exemplary embodiments, any or all of the computing devices 110, 150, and 160 may be implemented as at least one of a server computer, server device, 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 on 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 some embodiments, the server network 110, the data storage network 120, and multiple onboard computing devices or program modules residing thereon may collectively define a host system for tire wear monitoring of tires mounted on a vehicle associated with the onboard computing device 150. The onboard computing device 150 may be portable or otherwise modular as part of a distributed vehicle data collection and control system, or may otherwise be provided integrally with respect to a central vehicle data collection and control system (not shown).

[0026] Other vehicle components that communicate with the onboard computing device 150 may typically include one or more sensors, such as vehicle accelerometers, gyroscopes, inertial measurement units (IMUs), position sensors such as GPS transponders, tire-mounted sensors, tire pressure monitoring system (TPMS) sensor transmitters, and associated onboard receivers.

[0027] Based on the following discussion, other sensors used for collecting and transmitting vehicle data, such as those relating to speed, acceleration, and braking characteristics, will become sufficiently obvious to those skilled in the art and will not be discussed further herein. Various bus interfaces, protocols, and associated networks are well known in the art for transmitting vehicle dynamics data between appropriate data sources and local computing devices, and those skilled in the art will recognize a wide range of such tools and apparatuses for implementing them.

[0028] In implementations, vehicle and tire sensors are also provided with unique identifiers, wherein the on-board computing device 150 can distinguish between signals provided from corresponding sensors on the same vehicle, and further, in some implementations, wherein the central server 110 and / or fleet maintenance monitor client device 160 can distinguish between signals provided from tires and associated vehicle and / or tire sensors on multiple vehicles. In other words, in various implementations, sensor output values ​​may be associated with a specific tire, a specific vehicle, and / or a specific tire-vehicle system for the purpose of on-board or remote / downstream data storage and for specific implementations of computations as disclosed herein. The on-board device processor may communicate directly with the hosting server network 110, such as... Figure 1 As shown, or alternatively, the driver's mobile device or computing device installed on the truck may be configured to receive data output from the onboard equipment and process / transmit it to a hosting server and / or a fleet management server / device.

[0029] like Figure 1 The data storage network 120 shown may include, for example, multiple databases or equivalent storage media for retrievably storing the input data for models 122, 124, 126, 128 and their development. Vehicle data, sensed tire data, data from public tire data source 130, etc., once transmitted via a communication network to the hosting server network 110, can be stored accordingly in, for example, the associated database. System 100 may include, or otherwise selectively retrieve at least the FEA model 122, the direct tire wear model 124, the scaling model 126, and / or the new tire (indirect) wear model for processing input.

[0030] It should be noted that, such as Figure 1 The embodiments of system 100 shown do not limit the scope of system or method 200 as disclosed herein, and in alternative embodiments, one or more models of the models disclosed herein may be implemented locally at an onboard computing device 150 (e.g., an electronic control unit) of a vehicle or at 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 models of the models disclosed herein may be generated and trained over time at the host server level 110 and downloaded to the onboard computing device 150 and / or endpoint device 160 for local execution of one or more steps or operations as disclosed herein.

[0031] In the implementation scheme, the estimated or predicted tire condition can be provided as output from the model to one or more downstream models or applications. For example, in... Figure 1As indicated in the diagram, the feedback signal corresponding to the predicted tire wear condition (e.g., the predicted tread depth at a given distance, time, etc.) can be provided to an onboard computing device 150 associated with the vehicle itself, or to a mobile device 160 associated with the user, such as one integrated with a user interface configured to provide an alert or notification / recommendation that the tire should or will soon need to be replaced.

[0032] Next reference Figure 2 An exemplary implementation of a method 200 for developing and implementing an indirect tire wear model for a new tire can be described as follows.

[0033] Initially, method 200 may include providing or otherwise defining access to multiple existing FEA models for the corresponding type of tire, and optionally access to the corresponding direct tire wear model. In this context, a “direct” tire wear model can generally refer to a tire wear model for a specific tire developed based on the FEA model for the corresponding type of tire, and it can be accordingly considered very accurate, but is costly and time-consuming to develop, as previously described. If a tire wear model for an existing type of tire is subsequently requested from system 100 via, for example, tire selection or input 232, where “existing” type of tire in this context means the type of tire for which an existing or otherwise accessible FEA model is available (i.e., in response to a “yes” query in step 230), system 100 may accordingly use conventional techniques to retrieve or otherwise develop a tire wear model for the tire based on the corresponding FEA model.

[0034] If a tire wear model for a new type of tire is requested from system 100, or in other words, if a new type of tire is selected or otherwise input / presented to the system in step 232, where “new” type of tire in this context means a tire type that is not available in existing or otherwise accessible FEA models (i.e., “no” in response to the query in step 230), then method 200 of this disclosure also involves obtaining various tire parameters of the tire based at least in part on publicly available tire specifications 242 (such as from online data sources) (step 240), and further generating a new and “indirect” tire wear model based on determined relationships (step 250), examples of which may be as follows.

[0035] Considering that, for example, tire wear energy is related to the forces and slippage experienced at the tire / road contact interface, the average frictional energy experienced by the tire can be calculated separately for front / rear and lateral forces / slippage:

[0036] E fx =F x s x =F xK (Equation 1)

[0037] E fy =F y s y =F y α (Equation 2)

[0038] Where K is the slip ratio and α is the slip angle experienced by the tire.

[0039] This can be further simplified by assuming a small amount of slip (i.e., a linear force-slip relationship) by including the tire's slip / lateral stiffness. Offsets due to ply steering in lateral cases and rolling resistance in front / rear cases can be further considered, where the equation for frictional energy becomes:

[0040]

[0041] Where k κ It is the slip stiffness, k α It is the lateral stiffness, C RR It is the rolling resistance coefficient, and α0 is the slip angle caused by the turning of the fabric layer.

[0042] The tilt angle may also cause additional frictional energy, as shown in the equation:

[0043]

[0044] Where γ is the tilt angle, k γ This refers to the tire's camber thrust stiffness. Then, the wear energy can be correlated with the frictional energy by multiplying it by the elasticity of the tread compound, which is a function of the tangent increment of the compound.

[0045] The above equation suggests that when zero lateral / forward / rear force is applied to the tire, 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 track (contact surface) are in a "push" or "pull" state, where the net result is zero force. To explain this, an empirical relationship between the wear energy at zero force and some previously mentioned tire parameters can be determined or otherwise interpreted, given by the following equation:

[0046]

[0047] In this illustrative case, by fitting several FEA models to tires of different sizes and types, the coefficients c1, c2, and c3 were found, and exemplary results are shown in [the provided text]. Figure 3 The Chinese side indicated that...

[0048] Using a simple model that correlates various tire size and stiffness parameters with the parameters in the above equation, one or more contrast (i.e., scaling) models (step 220) can be developed. These models include scaling factors for the corresponding selected contrast tires that have previously been modeled using a more accurate FEA method, and can be defined, for example, using the following relationship:

[0049]

[0050] Where R is the outer radius, h is the tread height, E is the tread rubber modulus, kr is the radial stiffness, ks is the lateral stiffness, and b is the tire width. The subscript 0 refers to the corresponding comparison tire, while 1 refers to the tire of interest in a given application of the corresponding comparison model. Rt is the scaling factor for the tread stiffness applied to the comparison tire, and Rc is the scaling factor for the carcass stiffness applied to the corresponding comparison tire.

[0051] The slip stiffness of a tire can be assumed to be equal to the tread stiffness, while the lateral stiffness is related to the tire carcass and tread by assuming two springs connected in series, such that:

[0052]

[0053] Further illustrative reference Figure 4 Several tire models created using the FEA method are compared with similar tire models developed according to an embodiment of method 200 disclosed herein, using online and publicly available resources (e.g. www.tirerack.com You can obtain the relevant tire parameters for a specific tire of interest.

[0054] Exemplary tire parameters that serve as input to the developed model include the original tread depth, tread width, cross-section width, outer diameter, and rim diameter. Each of these parameters can be obtained directly from the publicly available specifications of the tire of interest.

[0055] Additional exemplary tire parameters that can be used as input to the developed model may include predicted operating loads and inflation pressures, which may be determined indirectly or otherwise predicted, for example, based on vehicle type and / or application type (e.g., mid-size SUVs, pickup trucks, and delivery vehicles).

[0056] Other exemplary tire parameters that can be used as input to the developed model may include tread rubber parameters (such as elasticity, which, as mentioned above, is a function of the tangential increment of the tread rubber) and may be determined or otherwise predicted, for example, based on tire type and / or rating (e.g., standard all-season touring, high-performance summer, etc., and / or uniform tire quality rating (UTQG), tread wear warranty, etc.).

[0057] In some implementations, method 200 and more specifically step 250 may include a tire wear model selection step, which may depend, for example, on application-related factors such as the wheel mount position of the tire in question, and take into account any known or predicted dependencies of the applied load based on such wheel mount differences.

[0058] like Figure 4 As shown, the relative accuracy of the indirectly developed tire model, combined with the relative ease of model development, demonstrates the potential utility of the method 200 disclosed in this paper.

[0059] Using the new tire wear model generated according to step 250 (in some embodiments, the new tire wear model may be a general tire wear model for a specific type of tire or a tire wear model for a specific tire developed from a general model of the tire type in question), method 200 may continue, i.e., predict the tire wear status of the specific tire and / or the determined tire-vehicle combination and / or tire application at one or more future times (step 260).

[0060] System 100 can collect inputs associated with tire use over time and, at least in part, further process the inputs to further develop a tire wear model for a specific tire, or, in some embodiments, to further develop an indirect tire model for that type of tire itself, based at least in part on a comparison of the actual tire wear at a specified point in time with the previously predicted tire wear at the same point in time. For example, the model associated with tire wear predictions can be updated over time using actual measurements, where the system can selectively “correct” model predictions using each measurement made to a specific tire element and / or vehicle tire system. Since the tire wear model can be at least partially probabilistic in nature, allowing for potential time series or similar progression curves over time, and attempting to mix or otherwise account for all such possibilities and associated uncertainties when predicting future tire wear and associated events, including feedback loops of actual tire wear values ​​or corresponding inputs, system 100 can accordingly allow system 100 to effectively exclude or minimize the correlation of certain such model components with respect to a given tire or even with respect to that type of tire based on the aggregation of such inputs.

[0061] In the implementation, the comparison may further consider one or more wear-causing factors specific to the tire in question and not considered (or at least not fully considered) at the start of the prediction. These factors may include, for example, driving style, vehicle alignment settings, driving route, road surface, environmental conditions, tire manufacturing variability, etc., to represent known causes of tire wear life variations in other equivalent tires.

[0062] During operation of a vehicle on which the tires in question are mounted, method 200 may further include step 270: determining or otherwise predicting a tire intervention and recommending such intervention to a relevant user of system 100. For example, a feedback signal corresponding to the predicted tire wear condition may be provided via an interface to an onboard device 150 associated with the vehicle itself, or to a mobile device 160 associated with the user, such as, for example, integrated with a user interface configured to provide warnings or notifications / recommendations for intervention events, such as, for example, one or more tires that should or will soon need to be replaced, rotated, aligned, inflated, etc.

[0063] Throughout the specification and claims, unless the context otherwise requires, the following terms have at least the meaning explicitly associated herein. The meanings indicated below are not necessarily limiting of the terms, but rather provide illustrative examples only. The meanings of “an,” “a,” 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, although it may refer to the same embodiment.

[0064] The various exemplary logic blocks, modules, and algorithmic steps described in conjunction with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this interchangeability between hardware and software, the various exemplary components, blocks, modules, and steps have been generally described above in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the system as a whole. The described functionality may be implemented differently for each specific application, but such implementation decisions should not be construed as departing from the scope of this disclosure.

[0065] The various exemplary logic blocks and modules described in conjunction with the embodiments disclosed herein can be implemented or executed 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 alternative embodiments, the processor may be a controller, a microcontroller, or a state machine, a combination thereof, etc. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, a combination of multiple microprocessors, a combination of one or more microprocessors combined with a DSP core, or any other combination of such configurations.

[0066] The steps of the methods, processes, or algorithms described in conjunction with the embodiments disclosed herein may be directly embodied in hardware, in a software module executed by a processor, or a combination of both. The software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM, or any other form of computer-readable medium known in the art. An exemplary computer-readable medium may be coupled to a processor such that the processor can read information from and write information to the memory / storage medium. Alternatively, the medium may be integrated into the processor. The processor and medium may reside in an ASIC. The ASIC may reside in a user terminal. Alternatively, the processor and medium may reside as discrete components in the user terminal.

[0067] Unless otherwise specifically stated or otherwise understood in the context in which they are used, the conditional language used herein (such as "may," "may," "can," "for example," etc.) is generally intended to convey that certain embodiments include certain features, elements, and / or states, while other embodiments do not include certain features, elements, and / or states. Therefore, such conditional language is not generally intended to imply that features, elements, and / or states are necessary in any way for one or more embodiments, or that one or more embodiments need to include logic for determining, with or without author input or prompting, whether such features, elements, and / or states are included in any particular embodiment or whether they will be performed in any particular embodiment.

[0068] The preceding detailed description has been provided for purposes of illustration and description. Therefore, although specific embodiments of the new and useful invention have been described, these references are not intended to be construed as limiting the scope of the invention, except as set forth in the following claims.

Claims

1. A method of indirect tire wear modeling and implementation, the method comprising: providing, for each of a plurality of types of tires, an accessible finite element model and a corresponding direct tire wear model; iteratively developing a correlation model that scales values of a plurality of tire parameters of a selected correlation tire of the plurality of types of tires having a corresponding accessible finite element model to corresponding values of the plurality of tire parameters of any type of tire lacking a corresponding accessible finite element model; for a first type of tire provided that lacks a corresponding accessible finite element model, obtaining corresponding values of the plurality of tire parameters; and based on the iteratively developed correlation model, the corresponding direct tire wear model, and the obtained values of the first type of tire with respect to the plurality of tire parameters, generating an indirect tire wear model of the first type of tire.

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

3. The method of claim 2, wherein a type of the vehicle and / or an application of the tire is provided as an input to the indirect tire wear model for predicting the tire wear state at the one or more future times.

4. 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 of the first type of tire.

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

6. The method of claim 4, further comprising predicting a replacement time of the first tire based on a comparison of the current wear state or the predicted tire wear state to a tire wear threshold associated with the first type of tire.

7. The method of claim 1, wherein the step of generating an indirect tire wear model comprises determining a friction energy associated with the first type of tire based at least in part on the iteratively developed correlation model and the obtained values of the first type of tire with respect to the plurality of tire parameters.

8. The method of claim 7, wherein the friction energy associated with the first type of tire is related to an energy of wear determined from an elasticity of a corresponding tread band.

9. The method of claim 7, wherein: the step of developing the correlation model further comprises determining an empirical relationship between an energy of wear 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; and the step of generating an indirect tire wear model of the first type of tire further comprises determining a tread band wear rate of the first type of tire based at least in part on the determined empirical relationship and the obtained values of the first type of tire with respect to the plurality of tire parameters. The step of generating an indirect tire wear model further comprises associating the friction energy associated with the first type of tire with a wear energy based at least partly on the determined empirical relationship.

10. The method of claim 1, wherein the contrast model comprises one or more scale factors for application to an associated tire parameter related to the tread stiffness and / or the carcass stiffness of the selected contrast tire.

11. A system for indirect tire wear modeling and implementation, the system comprising: a data storage network having stored thereon an accessible finite element model and a corresponding direct tire wear model for each of a plurality of types of tires; and a computing network functionally linked to the data storage network and configured to direct performance of the steps of the method of one of claims 1 to 10.

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