System and method for indirect tire wear modeling and prediction from tire specifications
Indirect tire wear models using scaled tire parameters and empirical relationships address the inefficiencies of FEA-based methods, offering rapid and accurate tire wear predictions for improved fleet management.
Patent Information
- Authority / Receiving Office
- JP · JP
- Patent Type
- Patents
- Current Assignee / Owner
- BRIDGESTONE AMERICAS TIRE OPERATIONS LLC
- Filing Date
- 2023-10-02
- Publication Date
- 2026-06-03
AI Technical Summary
Existing tire wear prediction methods, particularly those relying on finite element analysis (FEA), are computationally expensive and time-consuming, making them impractical for real-time or frequent tire condition assessments, and there is a need for more efficient, accurate tire wear modeling when FEA models are unavailable.
Developing indirect tire wear models by scaling tire parameters from accessible FEA models to unmodeled tires, using publicly available specifications and empirical relationships, and integrating these models with vehicle data to predict tire wear conditions.
Provides reasonably accurate tire wear predictions with reduced computational effort, enabling timely tire replacement recommendations and improving fleet management efficiency.
Smart Images
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Abstract
Description
[Technical Field]
[0001] The present invention relates, in general, to the estimation and prediction of tire conditions for wheeled vehicles. More specifically, embodiments of the present invention disclosed herein relate to systems and methods for indirectly developing and implementing tire wear models from general tire specifications in characterizing and predicting tire conditions and conditions for wheeled vehicles, including but not limited to motorcycles, consumer vehicles (e.g., passenger cars and light trucks), commercial vehicles and off-road (OTR) vehicles. [Background technology]
[0002] Predicting tire wear is an important tool for those who own or operate vehicles, especially in the context of fleet management. It is important to pay attention to changes in tire condition over time, as insufficient tire tread at some point can lead to dangerous driving conditions. However, irregular tread wear can occur for a variety of reasons, sometimes leading 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 is undesirable, at the very least, because such measurements are difficult to obtain in real time and / or can be inaccurate, and furthermore, such measurements alone cannot predict future tire wear conditions.
[0003] Therefore, tire wear models have been developed for predictive implementation, enabling, for example, the prediction of tire wear throughout its lifecycle. However, tire wear is a complex phenomenon to model. While accurate models exist that utilize finite element analysis (FEA), these simulations can typically take several weeks to complete. If it is desired to simulate wear rates at several different tread depths, this would further increase the computational cost, potentially taking several months.
[0004] It is desirable to develop additional tire wear models that can indirectly model tires for which a corresponding complex FEA (or equivalent) model is not available, or to provide reasonably accurate tire wear modeling during periods when such complex models are not yet available. [Overview of the project]
[0005] Embodiments of the methods disclosed herein for indirect tire wear modeling and implementation construct or complement the existence of various accessible finite element models and corresponding direct tire wear models for each of a plurality of tire types. A control model is developed iteratively by scaling the values of multiple tire parameters of a control tire selected from the plurality of tire types having corresponding accessible finite element models to the values of each of the multiple tire parameters of any tire type lacking corresponding accessible finite element models. For a given first type of tire lacking corresponding accessible finite element models, corresponding values are obtained for the multiple 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 regarding the multiple tire parameters.
[0006] In one exemplary embodiment according to the above embodiment, the tire wear condition can be predicted at one or more future times for a first tire of the first type mounted on a vehicle, based at least in part on an indirect tire wear model of the first type of tire.
[0007] In another exemplary embodiment according to the above-described embodiment, the vehicle type and / or tire application 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 embodiment according to the above embodiment, the actual tire performance value of the first tire is monitored over time, and the monitored actual tire performance value may 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 embodiment according to the above-described embodiment, the determined current wear state of the first tire may be provided as feedback for iteratively developing a further tire wear model for the first type of tire.
[0010] In another exemplary embodiment according to the above embodiment, the replacement time for the first tire may be predicted based on the current wear state or a predicted tire wear state compared to a tire wear threshold associated with the first type of tire.
[0011] In another exemplary embodiment according to the above embodiment, the step of generating an indirect tire wear model may include determining the friction energy associated with the first type of tire, at least in part, based on the first control model and values obtained for the first type of tire with respect to a plurality of tire parameters.
[0012] In another exemplary embodiment 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 embodiment according to the above embodiments, the step of developing a control model may further include determining an empirical relationship between wear energy at zero force and values of several tire parameters using one or more coefficients extrapolated from one or more of several accessible finite element models. The step of generating an indirect tire wear model may further include correlating friction energy associated with a first type of tire with wear energy, at least in part on the determined empirical relationship.
[0014] In another exemplary embodiment according to the above-described embodiment, the control model may include one or more scale factors for application to relevant tire parameters related to the 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, comprising a data storage network storing accessible finite element models and corresponding direct tire wear models for each of a plurality of tire types, and a computation network functionally linked to the data storage network. The computation network is configured to direct the execution of operations in the above embodiments and, optionally, in any one or more of the enumerated embodiments.
[0016] Many of the purposes, features, and advantages of the embodiments described herein will be readily apparent to those skilled in the art by reading the following disclosure in conjunction with the accompanying drawings. [Brief explanation of the drawing]
[0017] [Figure 1] Figure 1 is a block diagram representing an exemplary embodiment of the system disclosed herein. [Figure 2] Figure 2 is a flowchart illustrating an exemplary embodiment of the method disclosed herein. [Figure 3]FIG. 3 is a graph showing 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 the given tire based on the indirect model disclosed herein. [Figure 4] Includes four graphs showing the relationship between the lateral force results using the direct (e.g., FEA) model and the lateral force results using the indirect model disclosed herein.
Best Mode for Carrying Out the Invention
[0018] Generally, referring to FIGS. 1 - 4, various exemplary embodiments of the present invention can be described in detail here. When various figures may illustrate embodiments that share various common elements and features with other embodiments, the same elements and features are given the same reference numerals, and their redundant descriptions may be omitted hereinafter.
[0019] In various embodiments, the indirect tire wear model disclosed herein has, for example, relatively low accuracy but can still provide a reasonable wear prediction, while being able to be developed and implemented quickly and easily using only publicly available basic tire specification data.
[0020] Various embodiments of the system disclosed herein may include a centralized computing node (e.g., a cloud server) that functionally communicates with a plurality of 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] Referring initially to FIG. 1, an exemplary embodiment of system 100 includes at least server network 110 and data storage network 120, and may further include or be functionally linked to one or more public tire data sources 130, such as a tire monitoring network 140 including tire mounting sensors and intermediate devices, an in-vehicle computing device including a user interface 150 for each of a plurality of vehicles in a defined vehicle fleet, an endpoint computing device 160 for each of a plurality of users such as a fleet management administrator, etc. One or more of the foregoing components may be connected via a communication network (not shown) or otherwise functionally linked, and the communication network may include, in various embodiments, the Internet, a public network, a private network, or any other communication medium capable of transmitting electronic communications, in whole or in part.
[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, a server device, a desktop computer, a laptop computer, a smartphone, or any 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 as described above. In certain embodiments, server network 110, data storage network 120, and a plurality of in-vehicle computing devices or program modules resident therein may collectively define a host system for tire wear monitoring of tires mounted on a vehicle associated with in-vehicle computing device 150. In-vehicle computing device 150 may also be made portable or modular as part of a distributed vehicle data collection and control system, or may be provided integrally with a central vehicle data collection control system (not shown).
[0023] Other vehicle components that communicate with the on-board computer 150 may typically include one or more sensors, such as a vehicle accelerometer, gyroscope, inertial measurement unit (IMU), position sensor (e.g., a global positioning system (GPS) transponder), tire-mounted sensors, tire pressure monitoring system (TPMS) sensor transmitters, and associated on-board receivers, which are linked to, for example, a controller area network (CAN) bus network, thereby providing signals to a local processing unit.
[0024] Given the following considerations, 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 communication of vehicle dynamics data, etc., between their respective data sources and local computing devices, and those skilled in the art will recognize a wide range of such tools and means for implementation.
[0025] In one embodiment, the vehicle and tire sensors may be further provided with unique identifiers, allowing the on-board computer 150 to identify signals provided by each sensor on the same vehicle. Furthermore, in certain embodiments, the central server 110 and / or the fleet maintenance manager's client device 160 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 specific tire, a specific vehicle, and / or a specific tire-vehicle system for 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 Figure 1, or a driver's mobile device or truck on-board computer may be configured to receive and process on-board device output data and transmit it to a hosted server and / or fleet management server / device.
[0026] A data storage network 120, as shown in Figure 1, may include, for example, multiple databases or equivalent storage media for retrieving input data for models 122, 124, 126, 128 and their development. Vehicle data, sensed tire data, data from public tire data sources 130, etc., are transmitted to a hosted server network 110 via a communication network and may be stored accordingly in, for example, an associated database. 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 models for processing input.
[0027] The embodiment of System 100 shown in Figure 1 is not intended to limit the scope of the Systems or Methods 200 disclosed herein, and it should be noted that in alternative embodiments, one or more of the models disclosed herein may be implemented locally in an on-board computer 150 for the vehicle (e.g., an electronic control unit), or in another endpoint device 160 such as a fleet management device or server, rather than at the 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 downloaded to the on-board computer 150 and / or endpoint device 160 to locally perform one or more of the 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 shown 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 computer 150 associated with the vehicle itself, or to a mobile device 160 associated with the user, for example, by being integrated with a user interface configured to provide warnings, notifications / recommendations that the tires need to be replaced or will soon need to be replaced.
[0029] Referring now to Figure 2, an exemplary embodiment of Method 200 for developing and implementing a new indirect tire wear model for tires can be described as follows.
[0030] First, method 200 may include providing, or otherwise defining, access to multiple existing FEA models for each type of tire, and optionally access to corresponding direct tire wear models. In this context, “direct” tire wear models can generally refer to tire wear models for a particular tire developed based on FEA models for corresponding types of tires, and thus, as mentioned above, may be considered highly accurate but costly and time-consuming to develop. Subsequently, if a tire wear model for an existing type of tire is requested to system 100, 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 can 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 to system 100 for a new type of tire, or otherwise, if a new type of tire is selected or otherwise input / presented to the system in step 232, the “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., “no” in response to the query in step 230), and 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 an online data source, and generating a new “indirect” tire wear model (step 250) taking into further consideration the determined relationships, an example of which may be as follows:
[0032] For example, considering that the wear energy of a tire is related to the forces and slips seen at the tire / road contact interface, the average frictional energy seen by the tire can be calculated separately for the longitudinal and lateral forces / slips as follows. E γ , =F x s x =F x K (Equation 1) E fy =F y s y =F y α (Equation 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 slip / cornering stiffness of the tire. The offset due to ply steer in the lateral case and the offset due to rolling resistance in the longitudinal case can be further considered, and thus the equation for frictional energy becomes as follows.
[0034]
Number
[0037] The above equation suggests that when the lateral / longitudinal forces applied to the tire are zero, the wear energy is also zero. Those skilled in the art will understand that this is not true for certain areas of the tire footprint (contact patch) because they are in a "push" or "pull" state 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 aforementioned tire parameters can be determined or considered in other ways, which is given by the following equation.
[0038]
number
[0039] Using a simple model that relates various tire dimensions and stiffness parameters to the parameters in the above equation, one or more control (i.e., scaling) models can be developed that include scale factors for each selected control tire, which may have been previously modeled in a more accurate FEA method and defined, for example, using the following relationship (step 220).
[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 that two springs are in series, and therefore, as follows:
[0042]
number
[0043] Referring further to Figure 4, several tire models created using the FEA method are compared to similar tire models developed according to embodiments of Method 200 disclosed herein, and relevant tire parameters were obtained for the specific tire in question using publicly available online resources, e.g., www.tirerack.com.
[0044] Exemplary tire parameters as input to the developed model include the 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 in question.
[0045] Additional exemplary tire parameters as inputs to the developed model may include predicted operating load and inflation pressure, which may be determined indirectly based on, for example, the vehicle type and / or application type (e.g., mid-size SUV, collection and delivery, etc.) or predicted in other ways.
[0046] Further exemplary tire parameters as input to the developed model may include, as described above, tread compound parameters such as elasticity, which is a function of the tangent delta of the compound, and may be determined or otherwise predicted based on, for example, the tire type and / or evaluation (e.g., standard touring all-season, high-performance summer, etc., and / or uniform tire quality grading (UTQG), tread wear guarantee, etc.).
[0047] In some embodiments, method 200, more specifically step 250, may include a tire wear model selection step, which may depend on application-related factors such as the wheel mounting position of the tire in question, and may take into account any known or predicted relevant dependencies of the load applied based on such wheel mounting distinctions.
[0048] As shown in Figure 4, the relative accuracy of the indirectly developed tire model, combined with the relative ease of model development, demonstrates the potential usefulness of Method 200 disclosed herein.
[0049] The new tire wear model generated according to step 250 may, in some embodiments, be a general tire wear model for a particular type of tire, or a tire wear model for a particular tire developed from a general model for the type of tire in question, and method 200 may continue by predicting the tire wear condition at one or more future times for that particular tire and / or 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, to further develop an indirect tire model of the tire type itself, at least in part on a comparison of the actual tire wear state at a given time point with a previously predicted tire wear state at the same time point. For example, a model related to tire wear prediction can be updated over time using actual measurements, and the system can selectively "correct" the model prediction for each measurement obtained from a particular tire element and / or vehicle-tire system. As long as the tire wear model can be at least partially probabilistic in nature, a feedback loop including actual tire wear values or corresponding inputs can enable potential time series or similar time-series progression curves, and by attempting to blend or otherwise consider all such possibilities and associated uncertainties when predicting future tire wear and associated events, the system can therefore effectively exclude or minimize certain such model components in relation to a given tire, or even to a tire type, based on the aggregation of such inputs.
[0051] In one embodiment, the comparison may further consider one or more factors that are specific to the tire in question and contribute to wear that were not considered (or at least not fully considered) at the start of the prediction. Such factors may include, for example, driving style, vehicle positioning settings, driving route, road surface, environmental conditions, tire manufacturing variability, etc., and may represent known causes of variability in tire wear life between otherwise equivalent tires.
[0052] During operation of the vehicle on which the problematic tire is installed, method 200 may further include step 270 of determining or otherwise predicting tire intervention and recommending it to the relevant user of system 100. For example, a feedback signal corresponding to the predicted tire wear condition may be provided to an on-board device 150 associated with the vehicle itself via an interface, or to a mobile device associated with user 160, such as being integrated with a user interface configured to provide warnings or notifications / recommendations of intervention events, such as needing to replace, reposition, align, or inflate one or more tires.
[0053] Throughout this specification and the claims, unless the context indicates otherwise, the following terms have at least the meanings expressly related herein. The meanings identified below are not necessarily limiting and are merely examples of the use of the terms. The meanings of “a,” “an,” and “the” may include multiple references, and the meaning of “in” may include “in” and “on.” As used herein, the phrase “in one embodiment” may, but does not necessarily, refer to the same embodiment.
[0054] Various illustrative logic blocks, modules, and algorithmic 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 hardware and software compatibility, various illustrative components, blocks, modules, and steps are 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 overall system. The described functionality can be implemented in various ways for each specific application, but such implementation decisions should not be construed as causing a departure from the scope of this disclosure.
[0055] Various illustrative logic blocks and modules described in connection with the embodiments disclosed herein can be implemented or executed by machines, such as general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, 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, or a combination thereof. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors working in conjunction with a DSP core, or any other such configuration.
[0056] Steps of methods, processes, or algorithms described in relation to embodiments disclosed herein may be implemented directly in hardware, in software modules executed by a processor, or in combination of both. The software modules may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disks, removable disks, CD-ROMs, or any other form of computer-readable medium known in the art. An exemplary computer-readable medium may be coupled to a processor so that the processor can read information from and write information to the memory / storage medium. Alternatively, the medium may be integrated with the processor. The processor and medium may reside within an ASIC. The ASIC may reside within a user terminal. Alternatively, the processor and medium may reside as separate components within a user terminal.
[0057] Conditional statements used herein, in particular, such as “can,” “might,” “may,” and “e.g.,” are generally intended to convey that certain embodiments include certain features, elements, and / or states, while other embodiments do not, unless otherwise specifically stated or understood in the context in which they are used. Accordingly, such conditional statements are generally not intended to suggest that features, elements, and / or states are required in any way for one or more embodiments, nor are they generally intended to suggest that one or more embodiments necessarily include logic for determining, with or without author input or prompting, whether such features, elements, and / or states are included in or should be performed in any particular embodiment.
[0058] The detailed explanations provided above are for illustrative and illustrative purposes only. Therefore, while specific embodiments of the novel and useful invention have been described, such references are not intended to be construed as limiting the scope of the invention, except as stated in the following claims.
Claims
1. A method for generating and / or using an indirect tire wear model that predicts the wear state of a target tire without using a finite element model that utilizes finite element analysis, Computers For each of several types of existing tires, obtain an accessible finite element model and a direct tire wear model obtained using the finite element model, which predicts the tire wear state based on tire parameters. Obtaining values for multiple tire parameters for the aforementioned target tire, To generate a control model that scales the values of known tire parameters of a control tire having an accessible finite element model and a direct tire wear model, arbitrarily selected from the multiple types of existing tires, to the values of the corresponding tire parameters of the target tire, Based on the control model, the direct tire wear model corresponding to the control tire, and the values of the multiple tire parameters of the target tire, an indirect tire wear model is generated to predict the wear state of the target tire. How to do it.
2. The tire parameters include dimensional parameters and stiffness parameters representing tire stiffness, The stiffness parameter is expressed as a function of multiple dimensional parameters, Generating the control model includes determining the ratio of the dimensional parameter of the control tire to the corresponding dimensional parameter of the target tire for each of the plurality of dimensional parameters, and using the ratio of each dimensional parameter to determine a scale factor that represents the relationship between the value of the unknown stiffness parameter of the target tire and the value of the stiffness parameter of the control tire. The method according to claim 1.
3. Further includes predicting the tire wear state of one or more future times of the target tire mounted on the vehicle, based at least partially on the indirect tire wear model of the target tire, The method according to claim 1, wherein the type of vehicle and / or the application of the tire is provided as input to the indirect tire wear model to predict the tire wear condition at one or more future times.
4. Predicting the tire wear state of one or more future times of the target tire mounted on the vehicle, based at least partially on the indirect tire wear model of the target tire, The actual tire performance values of the target tire are monitored over time, and the current wear state of the target tire is determined based on the indirect tire wear model of the target tire by applying the monitored actual tire performance values. The current wear state of the target tire, as determined above, is provided as feedback for iteratively developing a further tire wear model for the target tire. The method according to claim 1, further comprising:
5. Generating the indirect tire wear model includes determining the friction energy associated with the target tire, at least in part, based on the control model and the values of the plurality of tire parameters obtained for the target tire. Generating the control model further includes determining an empirical relationship between the wear energy at zero force and the 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, The method according to claim 1, wherein generating the indirect tire wear model further comprises correlating the frictional energy associated with the tire in question with wear energy, at least in part, based on the determined empirical relationship.
6. A system for generating and using an indirect tire wear model that predicts the wear state of a target tire without using a finite element model that utilizes finite element analysis, A data storage network that stores, for each of several types of existing tires, an accessible finite element model and a direct tire wear model obtained using the finite element model, which predicts the tire wear state based on tire parameters, A computing network functionally linked to the data storage network and configured to instruct the execution of the steps in any one of claims 1 to 5, A system equipped with these features.