Tire wear prediction method, tire wear prediction system

Abstract

To propose tire wear amount prediction technique with improved prediction accuracy.SOLUTION: A tire wear amount prediction method comprises the steps of: setting a plurality of sections which are obtained by subdividing acceleration of a vehicle measured by an acceleration sensor during traveling of the vehicle, into two directions, that is, a front-back direction and a right-left direction; setting a plurality of driving modes comprised of combinations of the sections in the two directions; classifying each of a plurality of measurement data into any of the driving modes; setting some of the plurality of driving modes as a use mode; calculating a frequency of each use mode; acquiring a tire load satisfying the acceleration in the front-back direction and the right-left direction for each use mode; acquiring a cornering stiffness, an aligning stiffness, a driving stiffness, a ground-contact width, and a ground-contact length which satisfy the tire load for each use mode; calculating a tire wear amount for each use mode using formulas (1) to (6); and calculating the tire wear amount in all the use modes based on the tire wear amount in each use mode.SELECTED DRAWING: Figure 3

Description

Technical Field

[0001] The present disclosure relates to a tire wear amount prediction method and a tire wear amount prediction system.

Background Art

[0002] The actual vehicle wear test of a tire is performed by running an actual vehicle equipped with a test tire on an outdoor test course. However, the wear test by actual vehicle running has a cost problem. In Patent Document 1, when an actual vehicle equipped with a first test tire runs on a test course, the accelerations in the longitudinal and lateral directions are measured, and the measurement data is classified into a plurality of running modes in which the accelerations in the longitudinal and lateral directions are subdivided. The running conditions in each running mode are obtained, and for each running mode, a wear test of a second test tire is performed on a tire wear tester on a stand so as to match the running conditions.

[0003] Non-Patent Document 1 is a document that cites Non-Patent Document 2. In Non-Patent Documents 1 and 2, a wear equation of a tire has been proposed.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Non-Patent Documents

[0005]

Non-Patent Document 1

Non-Patent Document 2

Summary of the Invention

[0006] Patent Document 1 ultimately requires testing on a tire wear testing machine on a stand, and further reduction of testing is desired. Non-Patent Documents 1 and 2 seem to involve determining parameters that satisfy the average values ​​of longitudinal and lateral accelerations generated in an actual vehicle running on a test course, and the load in a stationary state, and then solving the tire wear equation. Therefore, they use the average values ​​of accelerations and representative values ​​of loads generated during driving, and it is considered necessary to improve the accuracy of predictions for the results of actual vehicle wear tests.

[0007] This disclosure proposes a tire wear prediction method and a tire wear prediction system that improve prediction accuracy. [Means for solving the problem]

[0008] The tire wear prediction method of this disclosure includes: acquiring multiple measurement data having acceleration in at least two directions, longitudinal and lateral, of the vehicle measured by an acceleration sensor while the vehicle is in motion; setting multiple subdivided sections for each of the at least two directions of acceleration, and setting multiple driving modes composed of combinations of the sections in at least two directions; classifying each of the acquired multiple measurement data into one of the multiple driving modes; setting at least some of the multiple driving modes as usable modes; calculating the frequency of each of the usable modes; acquiring a tire load that satisfies the longitudinal and lateral acceleration for each usable mode; acquiring cornering stiffness, aligning stiffness, driving stiffness, contact width, and contact length that satisfies the tire load for each usable mode from the results of a tire test or simulation; calculating the tire wear for each usable mode using equations (1) to (6); and calculating the tire wear for all usable modes based on the tire wear for each usable mode.

[0009] The tire wear prediction system of this disclosure includes: a measurement data acquisition unit that acquires multiple measurement data having acceleration in at least two directions, longitudinal and lateral, of the vehicle measured by an acceleration sensor while the vehicle is in motion; a driving mode setting unit that sets multiple subdivided sections for each of the at least two directions of acceleration and sets multiple driving modes composed of combinations of the sections of at least two directions; a classification unit that classifies each of the acquired multiple measurement data into one of the driving modes of the multiple driving modes; a usage mode setting unit that sets at least some of the multiple driving modes of the multiple driving modes into usage modes; and each of the above The system includes: a frequency calculation unit that calculates the frequency of each usage mode; a tire load acquisition unit that acquires tire loads that satisfy the longitudinal and lateral accelerations for each usage mode; a parameter acquisition unit that acquires cornering stiffness, aligning stiffness, driving stiffness, contact width, and contact length that satisfy the tire loads for each usage mode from the results of tire tests or simulations; a first wear amount calculation unit that calculates the amount of tire wear for each usage mode using equations (1) to (6); and a second wear amount calculation unit that calculates the amount of tire wear for all usage modes based on the amount of tire wear for each usage mode. [Brief explanation of the drawing]

[0010] [Figure 1] A block diagram showing the entire system used in the tire wear prediction method of the first embodiment. [Figure 2] A block diagram showing a tire wear prediction system according to the first embodiment. [Figure 3] A flowchart illustrating a method for predicting tire wear. [Figure 4] An explanatory diagram regarding acceleration frequency distribution data. [Figure 5] An explanatory diagram showing, with diagonal lines, the modes used among the multiple driving modes that constitute the acceleration frequency distribution data of the first embodiment. [Figure 6] An explanatory diagram showing the usage modes for modified examples, indicated by diagonal lines. [Figure 7]An explanatory diagram showing the usage modes for modified examples, indicated by diagonal lines. [Figure 8] An explanatory diagram showing the usage modes for modified examples, indicated by diagonal lines. [Modes for carrying out the invention]

[0011] <First Embodiment> Hereinafter, a first embodiment of this disclosure will be described with reference to the drawings.

[0012] [Tire wear prediction system] The tire wear prediction system 2 of the first embodiment predicts (calculates) tire wear. As shown in Figure 1, the tire wear prediction system 2 can acquire measurement data D1 measured by an acceleration sensor 10 attached to an actual vehicle running on a test course. The tire wear prediction system 2 can acquire tire behavior data from the tire behavior prediction system 11. The tire wear prediction system 2 can acquire cornering stiffness (CS) and aligning stiffness (AS) from a known cornering test machine 12. The tire wear prediction system 2 can acquire driving stiffness (DS) from a known drive braking test machine 13. The tire wear prediction system 2 can acquire contact surface shape data from a known contact surface observation machine 14. The tire wear prediction system 2 can acquire side stiffness (axial elastic modulus ks) from a known tire side stiffness test machine 15.

[0013] As shown in Figure 2, the tire wear prediction system 2 includes a measurement data acquisition unit 20, a driving mode setting unit 21, a classification unit 22, a usage mode setting unit 23, a frequency calculation unit 24, a tire load acquisition unit 25, a parameter acquisition unit 26, a first wear amount calculation unit 27, and a second wear amount calculation unit 28. Each of these units (20-28) is realized through the collaborative action of software and hardware, with the processor 2a executing the processing routines shown in Figure 3, which are pre-stored in a computer equipped with a processor 2a, memory 2b, various interfaces, etc. In this embodiment, each unit is realized by a processor 2a in a single device, but this is not limited to this. For example, it may be configured so that multiple processors 2a execute the processing of each unit by distributing them using a network. That is, one or more processors 2a execute the processing. The memory 2b stores data necessary for calculating tire wear, such as measurement data D1, acceleration frequency distribution data D2, tire load data D3, and parameters D4.

[0014] The measurement data acquisition unit 20 acquires multiple measurement data points D1. The measurement data D1 includes acceleration measured by the acceleration sensor 10 installed on the vehicle while it is in motion. The measurement data D1 includes acceleration in two directions of the vehicle, namely, longitudinal acceleration Ax in the longitudinal direction of the vehicle and lateral acceleration Ay in the lateral direction (see Figure 4). The unit of acceleration is [m / s²] 2]. When the acceleration sensor 10 detects acceleration in three axes, the measurement data D1 may also include vertical acceleration. As a prerequisite for the measurement data acquisition unit 20 to acquire the measurement data D1, an acceleration measurement process is performed, and multiple pieces of measurement data D1 are generated by the acceleration measurement process. In the acceleration measurement process, an actual vehicle equipped with an acceleration sensor 10 at a predetermined position (e.g., the center of gravity) is driven on a predetermined test course, and the acceleration sensor 10 measures acceleration while the vehicle is running. For example, one example is that the vehicle accelerates from a standstill at the starting point of the course, decelerates before a curve and turns the curve, then accelerates again, and after repeating acceleration, deceleration, and turning, the acceleration sensor 10 measures acceleration until the vehicle stops at the end point of the course. The measurement data D1 measured on an actual vehicle equipped with the first test tire can be used to predict the amount of tire wear of the second test tire other than the first test tire. In other words, as long as the test course is the same, it is sufficient to perform a single driving test with a real vehicle equipped with one test tire (the first test tire), and the measurement data D1 obtained at that time can be used to predict the tire wear of the other tires.

[0015] The measurement data acquisition unit 20 may acquire the measurement data D1 from the acceleration sensor 10 by any means necessary, as long as it can acquire the measurement data D1. For example, the measurement data D1 may be stored on a storage medium of a computer mounted on the vehicle, and after the vehicle has completed its run, the storage medium may be attached to a reading device of the tire wear prediction system 2, and the measurement data D1 may be acquired from the reading device. Alternatively, the measurement data acquisition unit 20 may receive the measurement data D1 wirelessly from a computer including the vehicle's acceleration sensor 10.

[0016] The travel mode setting unit 21 sets a plurality of travel modes. As schematically shown in FIG. 4, the travel mode setting unit 21 sets a plurality of sections subdivided for each of at least two directions of acceleration. In FIG. 4, one side of a box corresponds to one section. One travel mode can be represented by one box shown in the figure. One box (travel mode) is composed of a combination of sections (sides) in two directions. In the present embodiment, the two directions are each divided by 0.05 m / s 2 each, and therefore, the acceleration range (width of the side) of one travel mode (box) is 0.05 m / s 2 . Since the width of the section is 0.05 m / s 2 , the representative value of each travel mode changes as +0.10 [m / s 2 , +0.05 [m / s 2 , 0 [m / s 2 , -0.05 [m / s 2 , -0.10 [m / s 2 . In order to avoid setting a travel mode in which the measurement data D1 is not classified, the travel mode setting unit 21 preferably sets the travel mode by referring to a plurality of measurement data D1.

[0017] The classification unit 22 classifies each of the plurality of measurement data acquired by the measurement data acquisition unit 20 into any one of the plurality of travel modes. The measurement data is classified into a travel mode in which the acceleration in each direction matches. For example, when the longitudinal acceleration Ax is 0.12 m / s 2 , the representative value is 0.10 m / s 2 and the acceleration range is 0.075 to 0.125 m / s 2The data is classified into one of several driving modes. As a result, the measured data is always classified into one of several driving modes. There may be cases where no driving modes are classified, and the number of measured data that are classified will also vary. By classifying the measured data D1 into one of several driving modes, it is possible to generate acceleration frequency distribution data D2 which has multiple driving modes and the frequency of each driving mode. The frequency represents how often that driving mode appears in all the measured data. As illustrated in Figure 4, a certain driving mode M1 has a representative longitudinal acceleration Ax and a representative lateral acceleration Ay, and is associated with a frequency (frequency value: 0.1) described later. Similarly, a certain driving mode M2 ​​has a representative longitudinal acceleration Ax and a representative lateral acceleration Ay, and is associated with a frequency (frequency value: 0.01). Note that it is sufficient to classify multiple measured data D1 into driving modes, and it is not necessary to calculate the frequency before setting the usage mode described later, or it may be necessary to calculate the frequency. The acceleration frequency distribution data D2 shown in Figure 4 has 11 segments in the longitudinal direction and 11 segments in the lateral direction, and an example of 11 × 11 = 121 driving modes. However, the number of segments is the resolution and can be changed arbitrarily.

[0018] The usage mode setting unit 23 sets at least some of the multiple driving modes shown in Figure 4 as usage modes. It is sufficient that multiple driving modes are set as usage modes; all driving modes may be set as usage modes, or some of the multiple driving modes may be set as usage modes. In the first embodiment, multiple driving modes set as usage modes are shown with diagonal lines. In the example shown in Figure 5, there are usage modes in which the acceleration interval in either the longitudinal or lateral direction contains 0. Specifically, the multiple usage modes include a first usage mode (M) in which the acceleration interval in the longitudinal direction contains 0. 0,5 M 0,4 M 0,3 M 0,2 M 0,1 M 0,0 M 0,-1 M 0,-2 M 0,-3 M 0,-4 M 0,-5) includes a second operating mode (M) in which the interval of acceleration in the left-right direction includes 0. 5,0 M 4,0 M 3,0 M 2,0 M 1,0 M 0,0 M -1,0 M -2,0 M -3,0 M -4,0 M -5,0 It includes ).

[0019] The frequency calculation unit 24 calculates the frequency of each usage mode. Specifically, the frequency calculation unit 24 calculates the frequency of each usage mode based on the number of measurement data classified into the usage mode for which the frequency is to be calculated and the total number of measurement data classified into any of the usage modes. In the example in Figure 5, the frequency of each of the 21 usage modes is calculated. In the example in Figure 5, for example, usage mode (M 0,0 When calculating the frequency of the target usage mode (M) for which the frequency is to be calculated, 0,0 The frequency can be calculated by dividing the number of measurement data classified under ( ) by the total number of measurement data classified under one of the 21 usage modes (first usage mode and second usage mode). Here, the frequency is calculated using only the driving modes that were set as usage modes, without considering driving modes that were not set as usage modes.

[0020] The tire load acquisition unit 25 acquires tire loads that satisfy the longitudinal and lateral accelerations for each usage mode. The tire behavior prediction system 11 shown in Figure 1 calculates tire behavior data based on vehicle specifications data, tire specifications data, and longitudinal and lateral accelerations in the usage mode. The tire behavior data includes data on longitudinal force (Fx), lateral force (Fy), and vertical force (Fz; tire load) in the tire. The tire load acquisition unit 25 acquires the tire load (Fz) included in the tire behavior data. In the first embodiment, the vehicle motion simulation software "CarSIM®" manufactured by Mechanical Simulation, Inc., USA is used as the tire behavior prediction system 11. Based on the input data, CarSIM® drives a virtual vehicle so that the specified longitudinal and lateral accelerations are obtained. For example, 5 m / s in the right direction. 2 If set to steady-state driving, the acceleration to the right will be 5 m / s². 2 The vehicle continues turning so that the acceleration (deceleration) during braking is 5 m / s². 2 In that case, the vehicle will be 5 m / s from its initial speed. 2 The brake pressure is controlled to decelerate the vehicle. This allows the vehicle to be driven in a way that achieves a specified driving mode (acceleration in the longitudinal and lateral directions), and tire behavior data for each of the four wheels can be obtained at that time.

[0021] Vehicle specifications may be pre-stored in the tire behavior prediction system 11, or they may be pre-stored in the tire wear prediction system 2 and transmitted to the tire load acquisition unit 25 by the tire behavior prediction system 11. Specific examples of vehicle specifications include the vehicle's overall length [m], overall width [m], overall height [m], front axle load mass [kg], rear axle load mass [kg], wheelbase [m], distance between the contact points of the tires mounted on the left and right of the front wheels [m], distance between the contact points of the tires mounted on the left and right of the rear wheels [m], horizontal distance between the front axle and the center of gravity, front overhang or rear overhang, roll inertia moment, pitch inertia moment, yaw inertia moment, front wheel camber angle, rear wheel camber angle, front wheel toe angle, and rear wheel toe angle.

[0022] The tire specifications may be pre-stored in the tire behavior prediction system 11, or they may be pre-stored in the tire wear prediction system 2 and transmitted to the tire behavior prediction system 11 by the tire load acquisition unit 25. Specific examples of the tire specifications include tire mass, longitudinal stiffness, rolling radius, radius under no load, rolling resistance, μ-S characteristics, SA-CF characteristics, SA-SAT characteristics, and relaxation length.

[0023] The parameter acquisition unit 26 reads the tire load (Fz) acquired by the tire load acquisition unit 25 for each usage mode. i Cornering stiffness (CS) that satisfies ) i ), aligning stiffness (AS i ), driving stiffness (DS i ), contact surface shape data (contact width w i , ground length l i The tire load (Fz) is obtained from the tire test results. Each parameter is a value that represents the characteristics of the test tire for which the amount of tire wear is to be predicted. The cornering test machine 12, the drive braking test machine 13, and the contact surface observation machine 14 measure the tire load (Fz). i The test can be conducted under the following conditions to obtain each parameter. zi represents the tire load in the i-th usage mode, and w iThis represents the ground contact width in the i-th usage mode, and l i This represents the grounding length in the i-th usage mode, CS i This represents the cornering stiffness in the i-th usage mode, AS i This represents the aligning stiffness in the i-th usage mode, DS i This represents the driving stiffness in the i-th usage mode. The parameter acquisition unit 26 also acquires the side stiffness (axial elastic modulus ks) and tire radius r from the tire side stiffness testing machine 15.

[0024] The first wear amount calculation unit 27 calculates the tire wear amount for each usage mode using equations (1) to (6).

number

[0025] The second wear amount calculation unit 28 calculates the tire wear amount h for each usage mode calculated by the first wear amount calculation unit 27. i Based on this, the tire wear amount h in all usage modes. total The following formula (7) can be used to calculate the tire wear amount h. N represents the number of usage modes. total This is the value to be predicted.

number

[0026] [Method for predicting tire wear] The tire wear prediction method will be explained using Figure 3. As shown in Figure 3, the tire wear prediction method first involves step ST1, in which the acceleration sensor 10 measures the acceleration of the vehicle in two directions, longitudinal and lateral, while the vehicle is in motion. In the next step ST2, the measurement data acquisition unit 20 acquires multiple measurement data points having the measured acceleration. In the next step ST3, the driving mode setting unit 21 sets multiple subdivided sections for each of the two acceleration directions and sets multiple driving modes consisting of combinations of at least two sections. In the next step ST4, the classification unit 22 classifies each of the acquired multiple measurement data points into one of the multiple driving modes. In the next step ST5, the usage mode setting unit 23 sets at least some of the multiple driving modes into usage modes. In the next step ST6, the frequency calculation unit 24 calculates the frequency of each usage mode. In the next step ST7, the tire load acquisition unit 25 acquires tire loads that satisfy the longitudinal and lateral accelerations for each usage mode. In the next step, ST8, the parameter acquisition unit 26 acquires the cornering stiffness, aligning stiffness, driving stiffness, contact width, and contact length that satisfy the tire load for each usage mode from the tire test results. In the next step, ST9, the first wear amount calculation unit 27 calculates the tire wear amount for each usage mode using equations (1) to (6). In the next step, ST10, the second wear amount calculation unit 28 calculates the tire wear amount for all usage modes based on the tire wear amount for each usage mode.

[0027] [1] As described above in the first embodiment, the tire wear prediction method may include: acquiring multiple measurement data having acceleration in at least two directions, longitudinal and lateral, of the vehicle measured by the acceleration sensor 10 while the vehicle is in motion; setting multiple subdivided sections for each of the at least two directions of acceleration, and setting multiple driving modes composed of combinations of the sections in at least two directions; classifying each of the acquired multiple measurement data into one of the multiple driving modes; setting at least some of the multiple driving modes as usable modes; calculating the frequency of each usable mode; acquiring tire loads that satisfy the longitudinal and lateral acceleration for each usable mode; acquiring cornering stiffness, aligning stiffness, driving stiffness, contact width, and contact length that satisfy the tire load for each usable mode from the results of tire tests or simulations; calculating the tire wear amount for each usable mode using equations (1) to (6); and calculating the tire wear amount for all usable modes based on the tire wear amount for each usable mode.

[0028] In this way, the amount of tire wear for each usage mode is calculated using a formula based on the acceleration for each usage mode, and the total amount of tire wear for all usage modes is calculated. As a result, the prediction accuracy can be brought closer to that of actual vehicle wear tests compared to the amount of wear calculated using the same formula based on the average acceleration.

[0029] [2] The tire wear prediction method described in [1] above, wherein the multiple operating modes may include an operating mode in which the acceleration interval in either the longitudinal or lateral direction contains 0. Because usage modes frequently include zero in the acceleration interval, it becomes possible to improve the accuracy of predicting tire wear.

[0030] [3] The tire wear prediction method described in [1] or [2] above, wherein the plurality of usage modes include a first usage mode in which the acceleration interval in the longitudinal direction includes 0, and a second usage mode in which the acceleration interval in the lateral direction includes 0. Because usage modes frequently include zero in the acceleration interval, it becomes possible to improve the accuracy of predicting tire wear.

[0031] [4] The tire wear prediction method described in any of [1] to [3] above may also be configured such that all measured and classified driving modes are set to the usage mode. The increased number of operating modes allows for improved accuracy in predicting tire wear.

[0032] [5] As in the first embodiment, the tire wear prediction system 2 includes a measurement data acquisition unit 20 that acquires multiple measurement data having acceleration in at least two directions, longitudinal and lateral, of the vehicle measured by the acceleration sensor 10 while the vehicle is in motion; a driving mode setting unit 21 that sets multiple subdivided sections for each of the at least two directions of acceleration and sets multiple driving modes composed of combinations of sections in at least two directions; a classification unit 22 that classifies each of the acquired multiple measurement data into one of the multiple driving modes; and a usage mode setting unit 23 that sets at least some of the multiple driving modes into usage modes; and each usage The system may also include: a frequency calculation unit 24 for calculating the frequency of each mode; a tire load acquisition unit 25 for acquiring tire loads that satisfy the longitudinal and lateral accelerations for each usage mode; a parameter acquisition unit 26 for acquiring cornering stiffness, aligning stiffness, driving stiffness, contact width, and contact length that satisfy the tire load for each usage mode from the results of tire tests or simulations; a first wear amount calculation unit 27 for calculating the tire wear amount for each usage mode using equations (1) to (6); and a second wear amount calculation unit 28 for calculating the tire wear amount for all usage modes based on the tire wear amount for each usage mode.

[0033] Although embodiments of this disclosure have been described above with reference to the drawings, it should be understood that the specific configurations are not limited to these embodiments. The scope of this disclosure is indicated not only by the description of the embodiments above but also by the claims, and further includes all modifications within the meaning and scope equivalent to the claims.

[0034] The structures adopted in each of the above embodiments can be adopted in any other embodiment. The specific configuration of each part is not limited to the embodiments described above, and various modifications are possible without departing from the spirit of this disclosure.

[0035] (A) In the above embodiment, the measurement data is measured for acceleration in two directions, the longitudinal direction and the lateral direction, but is not limited to this. The measurement data may also include acceleration in the vertical direction. In this case, one driving mode may have acceleration not only in the longitudinal direction and the lateral direction, but also acceleration in the vertical direction.

[0036] (B) In the above embodiment, the acceleration frequency distribution data D2 shown in Figure 4 is generated by the measurement data acquisition unit 20, the driving mode setting unit 21, and the classification unit 22, but the system is not limited to this. For example, all measurement data D1 may be classified into one of several driving modes, and acceleration frequency distribution data D2 in which the frequency of each driving mode is calculated may be acquired externally.

[0037] (C) In the above embodiment, for each usage mode, the cornering stiffness, aligning stiffness, driving stiffness, contact width, and contact length that satisfy the tire load are obtained from the results of tire tests, but are not limited to this. For example, each parameter may be obtained from the results of simulations.

[0038] (D) In ​​the above embodiment, as shown in Figure 5, the multiple usage modes include, but are not limited to, a first usage mode and a second usage mode. For example, the multiple usage modes may include a usage mode in which the acceleration interval in either the longitudinal direction or the lateral direction contains 0. In this case, for example, as shown by the shaded lines in Figure 6, the first usage mode (M 0,5 M 0,4 M 0,3 M 0,2 M 0,1 M 0,0 M 0,-1 M 0,-2 M 0,-3 M 0,-4 M 0,-5 Only the second operating mode (M) may be set to the operating mode. Also, as shown by the shaded lines in Figure 7 for the driving modes set to the operating mode, the second operating mode (M) may include 0 in the interval of acceleration in the left and right directions. 5,0 M 4,0 M 3,0 M 2,0 M 1,0 M 0,0 M -1,0 M -2,0 M -3,0 M -4,0 M -5,0 Only the first mode may be set to the usage mode. Alternatively, as shown by the shaded lines in Figure 8 for the driving modes set to the usage mode, all measured and classified driving modes (including the first and second usage modes) may be set to the usage mode.

[0039] (E) In the above embodiment, one or more processors execute each step constituting the tire wear prediction method. A program is used to cause one or more processors to execute each step constituting the tire wear prediction method, but is not limited to this. Some or all of the steps constituting the tire wear prediction method may be executed by a human.

[0040] For example, the execution order of operations, procedures, steps, and stages in the devices, systems, programs, and methods shown in the claims, specifications, and drawings can be implemented in any order, as long as the output of a previous process is not used in a later process. Even if the flow in the claims, specifications, and drawings is described using terms such as "first," "next," etc., for convenience, it does not mean that the execution must be in that order.

[0041] Each component shown in Figure 2 is implemented by executing a predetermined program on one or more processors, but each component may also be configured with dedicated memory or dedicated circuitry. In the above embodiment, each component is implemented on the processor of a single computer, but each component may be distributed and implemented on multiple computers or in the cloud. In other words, the above method may be executed on one or more processors.

[0042] The system includes a processor. For example, the processor may be a central processing unit (CPU), a microprocessor, or any other processing unit capable of executing computer executable instructions. The system also includes memory for storing system data. For example, memory includes computer storage media, such as RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, DVD or other optical disc storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other media that can be used to store desired data and that the system can access. [Explanation of Symbols]

[0043] 2: Tire wear prediction system 10: Accelerometer 20: Measurement data acquisition unit 21: Driving mode setting section 22: Classification section 23: Usage Mode Setting Section 24: Frequency calculation unit 25: Tire load acquisition unit 26: Parameter acquisition section 27: First wear amount calculation unit 28: Second wear amount calculation unit D1: Measurement data D4: Parameters h i : Tire wear k s : Axial elastic modulus L: Mileage l i :Ground length r: Tire radius w i :Ground width

Claims

1. The system acquires multiple measurement data points that have acceleration in at least two directions, the longitudinal direction and the lateral direction of the vehicle, measured by an acceleration sensor while the vehicle is in motion. This involves setting multiple subdivided intervals for each of at least two directions of acceleration, and setting multiple driving modes that consist of combinations of at least two such intervals. The acquired multiple measurement data are each classified into one of the aforementioned multiple driving modes, Setting at least some of the aforementioned multiple driving modes to use mode, Calculate the frequency of each of the aforementioned usage modes, For each usage mode, the tire load is obtained that satisfies the acceleration in the longitudinal and lateral directions, For each usage mode, the cornering stiffness, aligning stiffness, driving stiffness, contact width, and contact length that satisfy the aforementioned tire load are obtained from the results of tire tests or simulations. For each usage mode, calculate the amount of tire wear using equations (1) to (6), Based on the tire wear amount for each usage mode, the tire wear amount for all usage modes will be calculated, A method for predicting tire wear, including the following. [Math 1] Here, h ｉ represents the tire wear amount of the running distance L in the i-th usage mode, H ０ represents the initial groove depth, Elz ｉ represents the belt lateral bending stiffness in the i-th usage mode, S ｘｉ represents the value obtained by multiplying the frequency by the square of the ratio of the longitudinal acceleration to the gravitational acceleration in the i-th usage mode, S ｙｉ represents the value obtained by multiplying the frequency by the square of the ratio of the lateral acceleration to the gravitational acceleration in the i-th usage mode, F ｚｉ represents the tire load in the i-th usage mode, w ｉ represents the contact width in the i-th usage mode, l ｉ represents the contact length in the i-th usage mode, CS ｉ represents the cornering stiffness in the i-th usage mode, AS ｉ represents the aligning stiffness in the i-th usage mode, DS ｉ represents the driving stiffness in the i-th usage mode, ks represents the axial elastic coefficient, and r represents the tire radius.

2. The tire wear prediction method according to claim 1, wherein the plurality of usage modes include a usage mode in which the acceleration interval in either the longitudinal or lateral direction includes 0.

3. The tire wear prediction method according to claim 2, wherein the plurality of usage modes include a first usage mode in which the acceleration interval in the longitudinal direction includes 0, and a second usage mode in which the acceleration interval in the lateral direction includes 0.

4. The tire wear prediction method according to claim 3, wherein all the driving modes measured and identified are set to the usage mode.

5. A measurement data acquisition unit that acquires multiple measurement data sets having acceleration in at least two directions, the longitudinal direction and the lateral direction of the vehicle, measured by an acceleration sensor while the vehicle is in motion, A driving mode setting unit sets multiple driving modes, which are composed of combinations of at least two driving modes, by setting multiple subdivided sections for each of at least two directions of acceleration. A classification unit that classifies each of the acquired multiple measurement data into one of the multiple driving modes, A usage mode setting unit that sets at least some of the aforementioned multiple driving modes as usage modes, A frequency calculation unit that calculates the frequency of each of the aforementioned usage modes, For each usage mode, a tire load acquisition unit acquires tire loads that satisfy acceleration in the longitudinal and lateral directions, For each usage mode, a parameter acquisition unit obtains the cornering stiffness, aligning stiffness, driving stiffness, contact width, and contact length that satisfy the aforementioned tire load from the results of tire tests or simulations. For each usage mode, a first wear amount calculation unit calculates the tire wear amount using equations (1) to (6), A second wear amount calculation unit calculates the tire wear amount for all usage modes based on the tire wear amount for each usage mode, A tire wear prediction system equipped with the following features. [Math 2] Here, h ｉ H represents the amount of tire wear over the distance L traveled in the i-th usage mode, and H ０ This represents the initial groove depth, Elz ｉ This represents the lateral bending stiffness of the belt in the i-th usage mode, S ｘｉ This represents the value obtained by multiplying the frequency by the square of the ratio of the longitudinal acceleration to the gravitational acceleration in the i-th usage mode, S ｙｉ This represents the value obtained by multiplying the frequency by the square of the ratio of the lateral acceleration to the gravitational acceleration in the i-th usage mode, F ｚｉ represents the tire load in the i-th usage mode, and w ｉ This represents the ground contact width in the i-th usage mode, l ｉ This represents the grounding length in the i-th usage mode, CS ｉ This represents the cornering stiffness in the i-th usage mode, and AS ｉ This represents the aligning stiffness in the i-th usage mode, and DS ｉ represents the driving stiffness in the i-th usage mode, ks represents the axial modulus of elasticity, and r represents the tire radius.

Images