How to predict tire ride comfort

Abstract

This invention provides a method for predicting the ride comfort of tires that can easily and accurately evaluate the ride comfort performance of tires mounted on a vehicle. [Solution] The present invention provides a method for predicting the ride comfort of a tire, which predicts the ride comfort performance of a tire to be evaluated mounted on a vehicle. The method includes: a model calculation step of calculating a vehicle motion model based on a tire model of the tire to be evaluated, a vehicle model of the vehicle, and a road surface model; a measurement step of inputting the vehicle motion model into a driving simulator 10 and operating the driving simulator 10 to measure vibrations generated by the driving simulator 10; and a prediction step of predicting the ride comfort performance of the tire to be evaluated based on the frequency characteristics of the vibrations measured in the measurement step.

Description

[Technical Field]

[0001] The present invention relates to a method for predicting tire riding comfort. [Background Art]

[0002] Conventionally, the riding comfort performance of a tire has been evaluated sensorially by a test driver having evaluation skills, after mounting the evaluation target tire on an actual vehicle and running the vehicle on a test course such as a tire test site.

[0003] However, in such sensory evaluation, it is necessary to manufacture the tire to be evaluated, and a test driver needs to drive the vehicle equipped with the evaluation target tire, which results in high cost and time required for the evaluation.

[0004] Therefore, for example, the following Patent Document 1 proposes a method for evaluating the riding comfort of a tire, in which an evaluation target tire is mounted on a non-driving wheel, and the evaluation is performed based on vibration generated when the tire rides over a protrusion provided on a drum.

[0005] However, in the method of Patent Document 1, since the riding comfort performance of the tire is evaluated based on the vibration intensity of the tire, the correlation with the sensory evaluation performed by an evaluation driver may be low in some cases. [Prior Art Document] [Patent Document]

[0006] [Patent Document 1] Japanese Unexamined Patent Application Publication No. 2012-137419 [Summary of the Invention] [Problem to be Solved by the Invention]

[0007] Therefore, an object of the present invention is to provide a method capable of simply and accurately evaluating the riding comfort performance of an evaluation target tire mounted on a vehicle. [Means for Solving the Problem]

[0008] The present invention includes embodiments shown below.

[0009] [1] A method for predicting the ride comfort performance of a tire to be evaluated mounted on a vehicle, comprising: a model calculation step of calculating a vehicle motion model based on a tire model of the tire to be evaluated, a vehicle model of the vehicle, and a road surface model; a measurement step of inputting the vehicle motion model into a driving simulator and operating the driving simulator, and measuring vibrations generated by the driving simulator; and a prediction step of predicting the ride comfort performance of the tire based on the frequency characteristics of the vibrations measured in the measurement step.

[0010] [2] The tire ride comfort prediction method according to [1] above, wherein the prediction step predicts different evaluation items of ride comfort performance based on the intensity of different frequencies in the frequency characteristics of vibration measured in the measurement step.

[0011] [3] The tire ride comfort prediction method according to [1] above, wherein the measurement step measures vibrations at multiple different locations of the driving simulator, and the prediction step predicts different evaluation items of ride comfort performance based on the frequency characteristics of vibrations measured at different locations in the measurement step.

[0012] [4] The tire ride comfort prediction method according to [1] above, wherein the model calculation step calculates a first vehicle motion model and a second vehicle motion model for which the tire model is different, the measurement step operates the driving simulator with the first vehicle motion model and the second vehicle motion model as inputs to measure the vibration of the driving simulator, and the prediction step predicts the ride comfort performance of the tire to be evaluated by comparing the frequency characteristics of the vibration measured with the first vehicle motion model as input and the frequency characteristics of the vibration measured with the second vehicle motion model as input. [Effects of the Invention]

[0013] According to this embodiment, the ride comfort performance of a tire being evaluated mounted on a vehicle can be evaluated simply and accurately. [Brief explanation of the drawing]

[0014] [Figure 1] Block diagram showing an example of an apparatus for implementing a tire ride comfort prediction method according to one embodiment of the present invention. [Figure 2] This diagram shows the relationship between a sensor that measures vibrations that have a significant contribution to the sensory evaluation score, the section in which the vibration is measured, and the frequency band that has a significant contribution to the sensory evaluation score. [Figure 3] Flowchart showing a method for predicting the ride comfort of a tire according to one embodiment of the present invention. [Figure 4] A graph showing an example of the frequency characteristics of a reference tire and an evaluation tire. [Figure 5] A graph showing an example of the frequency characteristics of a reference tire and an evaluation tire. [Figure 6] A graph showing an example of the frequency characteristics of a reference tire and an evaluation tire. [Modes for carrying out the invention]

[0015] Embodiments will be described based on the drawings. Note that the embodiments described below are merely examples, and any modifications made as appropriate without departing from the spirit of the present invention are included within the scope of the present invention.

[0016] (1) Configuration of prediction device 1 The configuration of the prediction device 1 that performs the tire ride comfort prediction method of this embodiment will be described with reference to Figure 1. The prediction device 1 comprises a driving simulator 10 and a processing device 20.

[0017] In the prediction apparatus 1, the processing device 20 causes the driving simulator 10 to operate based on a tire model of a tire to be evaluated for which ride comfort is to be predicted. From vibrations generated during such operation, the processing device 20 predicts a sensory evaluation result obtained when a driver drives an actual vehicle equipped with the evaluation target tire for evaluation, that is, tire ride comfort performance when the evaluation target tire is mounted on an actual vehicle.

[0018] (2) Driving Simulator 10 The driving simulator 10 is connected to the processing device 20. In the present embodiment, the driving simulator 10 includes a seat 11, an operation input unit 12, a display device 13, a swing unit 14, a simulator control unit 15, and vibration sensors 16a, 16b, 16c.

[0019] In the present embodiment, the seat 11 is used as the driver's seat of the driving simulator 10. In the present embodiment, the operation input unit 12 receives operation inputs from the driver of the driving simulator 10. The display device 13 outputs an image in accordance with the execution result of the simulation. The swing unit 14 rocks the machine base on which the seat 11, the operation input unit 12, and the display device 23 are mounted. This reproduces vehicle vibration during traveling.

[0020] The simulator control unit 15 controls the operation of the driving simulator 10. Based on a simulation model (vehicle motion model) input from the processing device 20, the simulator control unit 15 controls the operations of the operation input unit 12, the display device 13, and the swing unit 14 that constitute the driving simulator 10.

[0021] The vibration sensor is, for example, an accelerometer, and measures vibrations generated during operation of the driving simulator 10 in a time series. The vibration sensors 16a, 16b, 16c can be provided at one or a plurality of locations on the driving simulator 10, the body surface of the driver, or the like.

[0022] In this embodiment, a first vibration sensor 16a is provided on the floor of the machine stand where the driver's feet are placed, a second vibration sensor 16b is provided on the driver's arm, and a third vibration sensor 16c is provided on the seat surface of the seat 21. The vibration data measured by vibration sensors 16a, 16b, and 16c is sent to the processing unit 20.

[0023] In addition to the above, vibration sensors may also be attached to other parts of the driver's body, such as the backrest of the seat 21, the steering wheel, and other areas that the driver comes into contact with, as well as to the driver's body surface, such as the head, neck, shoulder blades, back, chest, abdomen, thighs, back of the knees, calves, shins, and soles of the feet.

[0024] (3) Processing apparatus 20 The processing unit 20 is a computer-based device comprising a control unit 22 and a storage unit 24. The processing unit 20 is connected to a display 26 and an input device 28 including a keyboard and mouse. The storage unit 24 stores a program for controlling the processing unit 20, along with score correction data and evaluation vibration data, which will be described later.

[0025] The processing unit 20 functions as a device having a model creation unit 31, a data acquisition unit 32, a frequency analysis unit 33, and a prediction unit 34 by reading and executing a program stored in the memory unit 24.

[0026] The model creation unit 31 calculates a vehicle motion model used in the simulation in the driving simulator 10. For example, when the input device 28 receives a tire model of the tire to be evaluated for which ride comfort performance is predicted in the simulation, a vehicle model of the vehicle on which the tire to be evaluated is mounted, and a road surface model of the road on which the vehicle drives, the model creation unit 31 calculates a vehicle motion model based on the tire model, vehicle model, and road surface model.

[0027] The tire model is configured considering information such as tire size and tread pattern shape, rubber properties such as tread rubber, and reinforcing materials such as carcass and belts. The road surface model is configured considering a road surface profile with a height z set for each position represented by xy coordinates on the simulator map. The vehicle model is a model of a vehicle equipped with the tire under evaluation.

[0028] The road surface model includes sections that model different types of road surfaces. For example, the road surface model includes a good road section that models a dry asphalt road surface, an uneven road section that models an uneven road with numerous continuous bumps, and a harsh road section that models a harsh road with pavement joints and steps.

[0029] The data acquisition unit 32 acquires vibration data (hereinafter, this vibration data may also be called simulation vibration data) sent from the vibration sensors 16a, 16b, and 16c of the driving simulator 10. The data acquisition unit 32 stores the acquired simulation vibration data in the storage unit 24, associating it with information that identifies the measured vibration sensor.

[0030] The frequency analysis unit 33 performs frequency analysis based on the simulation vibration data acquired by the data acquisition unit 32. In other words, the frequency analysis unit 33 performs a Fourier transform on the vibration data measured by vibration sensors 26a, 26b, and 26c installed in the driving simulator 10 and the driver to obtain the relationship between vibration frequency and intensity (amplitude).

[0031] The frequency analysis unit 33 stores the frequency and intensity of vibrations measured by vibration sensors 26a, 26b, and 26c in the storage unit 24, along with information that identifies the measured vibration sensors 26a, 26b, and 26c.

[0032] The prediction unit 34 predicts the results of the sensory evaluation in an actual vehicle for one or more evaluation items based on the results of the frequency analysis of the simulation vibration data in the frequency analysis unit 33 and the score correction data and evaluation vibration data stored in the storage unit 24.

[0033] In this embodiment, the evaluation results are predicted for the following four evaluation items: the first evaluation item is the sensory evaluation perceived by the driver on the soles of their feet and feet; the second evaluation item is the sensory evaluation perceived by the driver on their arms; the third evaluation item is the sensory evaluation perceived on their thighs; and the fourth evaluation item is the sensory evaluation perceived on their buttocks.

[0034] (4) Score correction data Next, we will explain the evaluation correction data stored in the memory unit 24.

[0035] In Driving Simulator 10, the vibrations of a real car cannot be accurately reproduced, and the feel of the controls experienced by the driver may differ from that of a real car in a certain way. Such differences in feel may not be dependent on the tire model that forms the basis of the vehicle motion model in Driving Simulator 10.

[0036] Therefore, score correction data is obtained through experiments to adjust the subjective score from the driving simulator 10 to be closer to the subjective score from an actual vehicle, and this data is input in advance from the input device 28 and stored in the storage unit 24.

[0037] Specifically, drivers will drive a real car equipped with specified tires on a test course and evaluate subjective scores for the first to fourth evaluation items.

[0038] Furthermore, a vehicle motion model is created that mimics the conditions under which subjective evaluations were conducted using a real vehicle, based on a tire model of the tires fitted to the actual vehicle, the road surface profile of the test course driven by the actual vehicle, and a vehicle model of the vehicle used. A driver operates a driving simulator 10 that operates based on this vehicle driving model and evaluates subjective scores for the first to fourth evaluation items.

[0039] Then, based on the difference between the subjective rating from the actual vehicle and the subjective rating from the driving simulator 10, score correction data is determined for each evaluation item to bring the subjective rating from the driving simulator 10 closer to the subjective rating from the actual vehicle, and the score correction data is stored in the storage unit 24.

[0040] Furthermore, if the correlation coefficient between the sensory evaluation score from the actual vehicle and the sensory evaluation score from the driving simulator 10, or the correlation coefficient between the vibration measured during the sensory evaluation from the actual vehicle and the vibration measured during the sensory evaluation from the driving simulator 10, is greater than or equal to a predetermined value, then score correction data will be generated. If the correlation coefficient between both sensory evaluation scores or the correlation coefficient of the vibration measured during the sensory evaluation is lower than the predetermined value, then score correction data does not need to be generated. If the correlation coefficient is lower than the predetermined value, the vehicle motion model may be modified until the correlation coefficient between both sensory evaluation scores or the correlation coefficient of the vibration measured during the sensory evaluation is greater than or equal to the predetermined value before generating score correction data.

[0041] (5) Evaluation vibration data Next, we will explain the evaluation vibration data stored in the memory unit 24.

[0042] As illustrated in Figure 2, the evaluation vibration data is a table that shows, for each evaluation item of the sensory evaluation, the sensor used to measure vibrations that have a significant contribution to the sensory evaluation score, the section in the road surface model where vibrations are measured, and the frequency band.

[0043] For example, in the first evaluation item, the first vibration sensor 16a installed on the floor is associated with measuring vibrations that have a large contribution to the sensory evaluation score, the harshness path section is associated with the section in which vibrations are measured, and the first frequency band is associated with the frequency band that has a large contribution to the sensory evaluation score.

[0044] In the second evaluation item, the second vibration sensor 16b, installed on the driver's arm, is associated with the measurement of vibrations that have a significant contribution to the subjective rating, the section of poorly maintained roads is associated with the measurement of vibrations, and the second frequency band is associated with the frequency band that has a significant contribution to the subjective rating.

[0045] In the third evaluation item, the third vibration sensor 16c, installed on the seat surface of seat 21, is associated with measuring vibrations that have a significant contribution to the sensory evaluation score, the good road section is associated with the section in which vibrations are measured, and the third frequency band is associated with the frequency band that has a significant contribution to the sensory evaluation score.

[0046] In the fourth evaluation item, the third vibration sensor 16c, installed on the seat surface of seat 21, is associated with measuring vibrations that have a large contribution to the sensory evaluation score, the good road section is associated with the section in which vibrations are measured, and the fourth frequency band is associated with the frequency band that has a large contribution to the sensory evaluation score.

[0047] The evaluation vibration data described above is stored in the storage unit 24 after pre-determining, through experiments, sensors that measure vibrations highly correlated with the sensory evaluation score for each evaluation item, sections in the road surface model where vibrations are measured, and frequency bands. The specified sensors, sections, and frequency bands are input from the input device 28.

[0048] (6) Method for predicting tire ride comfort Next, we will explain how to predict the ride comfort performance of the tires to be evaluated, mounted on a vehicle, using the prediction device 1 described above, mainly with reference to Figures 3 to 6.

[0049] First, in the model calculation step, the model creation unit 31 creates a vehicle motion model based on a tire model of the tire to be evaluated for predicting ride comfort performance, a vehicle model of the vehicle on which the tire to be evaluated is mounted, and a road surface model of the road surface on which the vehicle drives.

[0050] Furthermore, the model creation unit 31 replaces the tire model from the tire under evaluation to the reference tire, and creates a vehicle motion model for the reference tire using the same vehicle model and road surface model as the vehicle motion model created for the tire under evaluation. The model creation unit 31 stores both motion models for the tire under evaluation and the vehicle motion model for the reference tire in the storage unit 24 (step S1).

[0051] The standard tires are those that have been fitted to actual vehicles and subjected to subjective evaluation, and whose subjective scores for the first to fourth evaluation items are known.

[0052] Next, the vehicle motion model of the tire to be evaluated, created by the model creation unit 31, is input into the driving simulator 10 to operate the driving simulator 10. Then, vibrations generated in the driving simulator 10 and the driver when the driver operates the driving simulator 10, which operates based on the vehicle motion model of the tire to be evaluated, are measured by vibration sensors 16a, 16b, and 16c. The measurement results measured by vibration sensors 16a, 16b, and 16c are then acquired by the data acquisition unit 32 and stored in the storage unit 24 as vibration data for the tire to be evaluated.

[0053] Furthermore, the vehicle motion model of the reference tire created by the model creation unit 31 is input to the driving simulator 10 to operate the driving simulator 10. Then, vibrations generated in the driving simulator 10 and the driver when the driver operates the driving simulator 10, which operates based on the vehicle motion model of the reference tire, are measured by vibration sensors 16a, 16b, and 16c. The measurement results measured by vibration sensors 16a, 16b, and 16c are then acquired by the data acquisition unit 32 and stored in the storage unit 24 as vibration data of the reference tire (step S2).

[0054] Furthermore, when acquiring vibration data for the tires being evaluated and the reference tires, the driver may also perform a subjective evaluation.

[0055] Next, the frequency analysis unit 33 performs frequency analysis on the three vibration data of the tire under evaluation measured by the vibration sensors 16a, 16b, and 16c to obtain a waveform showing the relationship between the frequency and intensity of the tire under evaluation. The frequency analysis unit 33 also performs frequency analysis on the vibration data of the reference tire measured by the vibration sensors 16a, 16b, and 16c to obtain a waveform showing the relationship between the frequency and intensity of the reference tire (step S3).

[0056] The frequencies and intensities of the evaluated tire and the reference tire are stored in the storage unit 24. In this embodiment, if the road surface model includes sections that model different road surfaces, frequency analysis is performed on the vibration data divided into sections, and the obtained vibration frequencies and intensities are stored in the storage unit 24 in association with the sections.

[0057] Next, the prediction unit 34 predicts the sensory evaluation for the first to fourth evaluation items based on the frequency analysis results of the tire under evaluation stored in the memory unit 24, the frequency analysis results of the reference tire, and the evaluation vibration data (step S4).

[0058] Specifically, in order for the prediction unit 34 to predict the sensory evaluation for the first evaluation item, it first identifies the sensor and section that measure vibrations that have a large contribution to the evaluation of the first evaluation item as the first vibration sensor 16a and the harshness road section, as shown in Figure 2. The prediction unit 34 retrieves the results of the frequency analysis of the vibration data of the evaluation target tire and the reference tire measured by the identified first vibration sensor 16a in the harshness road section from the storage unit 24.

[0059] The prediction unit 34 then compares the intensity in the first band based on the relationship between the frequency and intensity of vibrations of the tire under evaluation and the reference tire, which is retrieved from the memory unit 24. In the example shown in Figure 4, since the vibration in the first band of the tire under evaluation is greater than that of the reference tire, the prediction unit 34 deducts points from the sensory evaluation score for the first evaluation item for the tire under evaluation compared to the reference tire.

[0060] To predict the sensory evaluation for the second evaluation item, the prediction unit 34 identifies the sensor and section that measure vibrations that have a large contribution to the evaluation of the second evaluation item as the second vibration sensor 16b and the unmaintained road section, and retrieves the results of the frequency analysis of the vibration data of the evaluation target tire and the reference tire measured by the identified second vibration sensor 16b in the unmaintained road section from the storage unit 24.

[0061] The prediction unit 34 then compares the intensity in the second band based on the relationship between the frequency and intensity of vibrations of the tire under evaluation and the reference tire, which is retrieved from the memory unit 24. In the example shown in Figure 5, since the vibration in the second band of the tire under evaluation is slightly smaller than that of the reference tire, the prediction unit 34 adds a higher score to the subjective evaluation score for the second evaluation item for the tire under evaluation than for the reference tire.

[0062] In order to predict the sensory evaluation for the third and fourth evaluation items, the prediction unit 34 identifies the sensor and section that measure vibrations that have a large contribution to the evaluation of the third and fourth evaluation items as the third vibration sensor 16c and the good road section, and retrieves the results of the frequency analysis of the vibration data of the evaluation target tire and the reference tire measured by the identified third vibration sensor 16c in the good road section from the storage unit 24.

[0063] The prediction unit 34 then compares the intensity in the third and fourth bands based on the relationship between the frequency and intensity of vibrations of the tire under evaluation and the reference tire, which is retrieved from the memory unit 24. In the example shown in Figure 6, since the vibration in the third band of the tire under evaluation is smaller than that of the reference tire, the prediction unit 34 adds points to the subjective evaluation score for the third evaluation item for the tire under evaluation compared to the reference tire. Also, since the vibration in the fourth band of the tire under evaluation is larger than that of the reference tire, the prediction unit 34 subtracts points from the subjective evaluation score for the fourth evaluation item for the tire under evaluation compared to the reference tire.

[0064] The change in the sensory rating relative to the reference tire can be determined by various methods. For example, the integral value of vibration intensity may be calculated in the frequency band where the vibration intensity of the tire being evaluated and the reference tire are compared, and the change in the sensory rating may be determined based on the difference in the calculated integral values. Alternatively, the change in the sensory rating may be determined based on the peak values ​​of vibration intensity of the tire being evaluated and the reference tire in the frequency band where the vibration intensity is compared.

[0065] Furthermore, the prediction unit 34 may correct the amount of change in the sensory score determined for each evaluation item based on the score correction data stored in the storage unit 24.

[0066] (7) Effects In the tire ride comfort prediction method of this embodiment described above, the driving simulator 10 is operated using a vehicle motion model created based on the tire model of the tire to be evaluated. By measuring the resulting vibrations of the driving simulator 10 and the driver, the subjective evaluation results of the tire to be evaluated can be predicted, thereby predicting the ride comfort performance. Therefore, it is not necessary to actually prototype the tire to be evaluated or to mount the prototype tire on a real vehicle and run it on a test course, making it possible to easily predict the ride comfort performance of the tire.

[0067] Furthermore, by creating evaluation vibration data that identifies sensors, sections, and frequency bands with high correlation to subjective ratings through prior experiments, and then predicting the ride comfort performance of the tires from vibrations generated by the operation of the driving simulator based on this evaluation vibration data, it is possible to predict the ride comfort performance of the tires with high accuracy.

[0068] Furthermore, in this embodiment, in addition to the tire under evaluation, a vehicle motion model is also created for a reference tire that has been mounted on an actual vehicle and subjected to subjective evaluation by driving on a test course. Vibrations are then measured for a driving simulator 10 that operates based on the vehicle motion model of the reference tire. Therefore, the tire under evaluation can be evaluated by comparing it with the reference tire, for which subjective evaluation scores from actual vehicles are known, and the ride comfort performance of the tire under evaluation can be predicted with high accuracy.

[0069] Furthermore, the prediction unit 34 corrects the amount of change in the sensory score determined for each evaluation item based on the score correction data stored in the memory unit 24. This makes it possible to make predictions that are closer to those of a real car, even when there are differences in the feel of operation between the driving simulator 10 and a real car with a certain tendency. [Explanation of Symbols]

[0070] 1...Prediction device, 10...Driving simulator, 11...Seat, 12...Operation input unit, 13...Display device, 14...Oscillation unit, 15...Simulator control unit, 16a...Vibration sensor, 16b...Vibration sensor, 16c...Vibration sensor, 20...Processing device, 22...Control unit, 24...Storage unit, 26...Display, 28...Input device, 31...Model creation unit, 32...Data acquisition unit, 33...Frequency analysis unit, 34...Prediction unit

Claims

1. In a method for predicting the ride comfort performance of a tire being evaluated and mounted on a vehicle, A model calculation step in which a vehicle motion model is calculated based on the tire model of the tire to be evaluated, the vehicle model of the vehicle, and the road surface model, A measurement step involves inputting a vehicle motion model into a driving simulator, operating the driving simulator, and measuring the vibrations generated by the driving simulator. A method for predicting the ride comfort of a tire, comprising: a prediction step of predicting the ride comfort performance of the tire to be evaluated based on the frequency characteristics of vibrations measured in the measurement step.

2. The method for predicting the ride comfort of a tire according to claim 1, wherein the prediction step predicts different evaluation items of ride comfort performance based on the intensity of different frequencies in the frequency characteristics of vibration measured in the measurement step.

3. The measurement step involves measuring vibrations at multiple different locations in the driving simulator. The method for predicting the ride comfort of a tire according to claim 1, wherein the prediction step predicts different evaluation items of ride comfort performance based on the frequency characteristics of vibrations measured at different locations in the measurement step.

4. The aforementioned model calculation step calculates a first vehicle motion model and a second vehicle motion model with different tire models, The measurement step involves inputting the first vehicle motion model and the second vehicle motion model to operate the driving simulator and measuring the vibration of the driving simulator. The method for predicting the ride comfort of a tire according to claim 1, wherein the prediction step involves comparing the frequency characteristics of vibrations measured by inputting the first vehicle motion model with the frequency characteristics of vibrations measured by inputting the second vehicle motion model to predict the ride comfort performance of the tire to be evaluated.

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