Tire wear state prediction device, tire wear state prediction method, and program

Abstract

A tire wear state prediction apparatus according to the present disclosure includes: a data acquisition unit configured to acquire traveling data of a tire-mounted vehicle; a wear energy calculation section configured to calculate wear energy data based on the traveling data, the wear energy data being time-series data relating to wear energy of the tire; and a wear state calculation section configured to output a wear state of the tire based on the wear energy data.

Description

Technical Field

[0001] The present disclosure relates to a tire wear state prediction device, a tire wear state prediction method, and a program. Background Art

[0002] Conventionally, techniques for predicting the wear state of tires have been known. For example, Patent Document 1 discloses a tire wear amount estimation system using a calculation model that receives an input of at least the driving distance of a vehicle as the driving state of the vehicle and outputs the wear amount of the tire.

[0003] Prior Art Documents

[0004] Patent Documents

[0005] Patent Document 1: Japanese Unexamined Patent Application Publication No. 2022-133305 Summary of the Invention

[0006] Problems to be Solved by the Invention

[0007] In recent years, there has been a need to further improve the usefulness of techniques for predicting the wear state of tires. For example, there is a need to, instead of directly calculating the wear state of a tire based on vehicle driving data using a calculation model as in Patent Document 1, gradually calculate the wear state of the tire by generating intermediate data during the calculation process, thereby improving the accuracy and reliability of the calculated wear state.

[0008] Therefore, it may be helpful to provide a tire wear state prediction device, a tire wear state prediction method, and a program that can improve the usefulness of techniques for predicting the wear state of tires.

[0009] Solutions to the Problems

[0010] [1] A tire wear state prediction device according to an embodiment of the present disclosure includes: a data acquisition unit configured to acquire driving data of a vehicle equipped with a tire; a wear energy calculation unit configured to calculate wear energy data based on the driving data, the wear energy data being time-series data related to the wear energy of the tire; and a wear state calculation unit configured to output the wear state of the tire based on the wear energy data.

[0011] [2] The tire wear state prediction device according to an embodiment of the present disclosure is preferably the tire wear state prediction device according to [1], wherein the wear state calculation unit is configured to output the remaining groove depth of the tread of the tire as the wear state of the tire.

[0012] [3] The tire wear state prediction device according to an embodiment of the present disclosure is preferably the tire wear state prediction device described in [1] or [2], wherein the wear energy calculation unit is configured to: calculate external force data of an external force acting on the tire based on the driving data; and calculate the wear energy data of the tire based on the external force data.

[0013] [4] The tire wear state prediction device according to an embodiment of the present disclosure is preferably the tire wear state prediction device described in any one of [1] to [3], wherein the wear energy calculation unit includes a calculation model constructed by machine learning, and the calculation model is constructed by machine learning using training data generated by tire rolling simulation.

[0014] [5] The tire wear state prediction device according to an embodiment of the present disclosure is preferably the tire wear state prediction device described in [4], wherein the training data includes the results of tire rolling simulation at multiple locations on the tread of the tire.

[0015] [6] The tire wear state prediction device according to an embodiment of the present disclosure is preferably the tire wear state prediction device described in [4] or [5], wherein the training data includes the results of tire rolling simulation when the acceleration is 0.2G or greater than 0.2G.

[0016] [7] The tire wear state prediction device according to an embodiment of the present disclosure is preferably the tire wear state prediction device described in any one of [1] to [6], wherein the wear energy of the tire includes a first wear energy component in a first direction of the tire and a second wear energy component in a second direction perpendicular to the first direction.

[0017] [8] The tire wear state prediction device according to an embodiment of the present disclosure is preferably the tire wear state prediction device described in [7], wherein the wear state calculation unit is configured to calculate the wear state of the tire by applying a predetermined correction coefficient different according to the tire to the wear energy, and the predetermined correction coefficient is different between the first wear energy component and the second wear energy component.

[0018] [9] The tire wear state prediction method according to an embodiment of the present disclosure is a tire wear state prediction method executed by one or more computers, the tire wear state prediction method including: acquiring driving data of a vehicle equipped with a tire; calculating wear energy data based on the driving data, the wear energy data being time series data related to the wear energy of the tire; and outputting the wear state of the tire based on the wear energy data.

[0019]

[10] A program according to an embodiment of the present disclosure causes one or more computers to perform operations, the operations including: acquiring driving data of a vehicle equipped with tires; calculating wear energy data based on the driving data, the wear energy data being time series data related to the wear energy of the tires; and outputting a wear state of the tires based on the wear energy data.

[0020] Effects of the Invention

[0021] Therefore, a tire wear state prediction device, a tire wear state prediction method, and a program that can improve the usefulness of a technique for predicting the wear state of tires can be provided. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In the drawings:

[0023] Figure 1 (FIG. ) is a diagram illustrating a schematic configuration of a tire wear state prediction system according to an embodiment of the present disclosure;

[0024] Figure 2 is an illustration of Figure 1 a block diagram of the structure of the illustrated server;

[0025] Figure 3 is an illustration of Figure 1 an example of a flowchart of the operations of the illustrated server;

[0026] Figure 4 is a schematic diagram illustrating an external force acting on a tire mounted on a vehicle; and

[0027] Figure 5 is an illustration of Figure 1 another example of a flowchart of the operations of the illustrated server. DETAILED DESCRIPTION

[0028] Hereinafter, a tire wear state prediction device according to an embodiment of the present disclosure will be described with reference to the drawings. In the drawings, the same or corresponding parts are given the same reference numerals. In the description of the present embodiment, the description of these same or corresponding parts will be appropriately omitted or simplified.

[0029] (Structure of Tire Wear State Prediction System)

[0030] First, a summary of a tire wear state prediction system 1 according to the present embodiment will be given with reference to Figure 1 is a diagram illustrating a schematic configuration of the tire wear state prediction system 1 according to the present embodiment. As Figure 1 illustrated, the tire wear state prediction system 1 includes a server 10, a measurement device 20, and a terminal device 30. Although in Figure 1 illustrated, the tire wear state prediction system 1 includes a server 10, a measurement device 20, and a terminal device 30. Although in Figure 1A server 10, a measurement device 20, and a terminal device 30 are illustrated, but the tire wear state prediction system 1 may include any number of servers 10, any number of measurement devices 20, and any number of terminal devices 30.

[0031] The server 10 is composed of one or more computers. In the present embodiment, the server 10 is described as being composed of one computer. However, the server 10 may be composed of multiple computers such as a cloud computing system. In the present disclosure, the server 10 is also referred to as a tire wear state prediction device.

[0032] The measurement device 20 is composed of one or more computers (such as a digital tachograph, a tire pressure monitoring system (TPMS), an electronic control unit (ECU), or a car navigation device). The measurement device 20 generates at least one of the driving data (travel data) of the vehicle 2 equipped with the tire 3 and the tire state data of the tire 3, and transmits it as time series data to the server 10. For this purpose, the measurement device 20 may be mounted on the vehicle 2 or the tire 3.

[0033] The driving data of the vehicle 2 equipped with the tire 3 is, for example, time series data of the speed, acceleration, load, driving time, driving distance, or driving path of the vehicle 2 or the number of revolutions of the tire 3. For example, the driving data of the vehicle 2 may be generated by a digital tachograph. The driving data of the vehicle 2 is not limited to these examples and may include any data for indicating the driving state of the vehicle 2 equipped with the tire 3.

[0034] The tire state data of the tire 3 is, for example, time series data of the internal pressure (air pressure) or temperature of the tire 3. For example, the tire state data of the tire 3 may be generated by a TPMS. The tire state data of the tire 3 is not limited to these examples and may include any data for indicating the state of the tire 3.

[0035] The vehicle 2 is, for example, a truck. The vehicle 2 is not limited to a truck and may be any vehicle on which the tire 3 can be mounted, such as a passenger car, an engineering vehicle, a work vehicle, a motorcycle, a bicycle, or an airplane.

[0036] The terminal device 30 is, for example, a computer such as a smart phone, a tablet terminal, or a personal computer.

[0037] The network 40 is any communication network that enables the server 10, the measurement device 20, and the terminal device 30 to communicate with each other. The network 40 in the present embodiment may be, for example, the Internet, a mobile communication network, a local area network (LAN), or a combination thereof.

[0038] The tire wear state prediction system 1 is used to predict the wear state of one or more tires 3. In the tire wear state prediction system 1, for example, the server 10 obtains the driving data of the vehicle 2 equipped with the tires 3 from the measuring device 20. The server 10 calculates wear energy data (which is time-series data related to the wear energy of the tire 3) based on the driving data of the vehicle 2. Then, the server 10 calculates the wear state of the tire 3 based on the wear energy data of the tire 3. The wear state of the tire 3 can be sent from the server 10 to the terminal device 30 and displayed by the terminal device 30. Thus, by calculating the wear energy of the tire 3 based on the data obtained from the measuring device 20 and then predicting the wear state of the tire 3, the accuracy and reliability of the calculated wear state of the tire 3 can be improved. As a result, the usefulness of the technology for predicting the wear state of the tire 3 can be enhanced.

[0039] Here, the wear state of the tire 3 is an index indicating the degree of wear of the tire 3 caused by the use of the tire 3. In the present disclosure, the remaining groove depth of the tread of the tire 3 is used as the wear state of the tire 3. For example, the remaining groove depth can be evaluated by using the groove depth at a predetermined portion on the tread of the tire 3 or the average value of the groove depths at a plurality of portions on the tread. However, the wear state of the tire 3 is not limited to the remaining groove depth, and any index such as the wear amount of the tread of the tire 3 can be used. The wear energy of the tire 3 is represented as the product of the slip amount of the tread (particularly the block pattern portion) relative to the road surface and the shear force acting on the tread. The wear energy acting on a portion of the tread of the tire 3 during one rotation of the tire 3 is given by the following equation (1).

[0040] [Mathematical formula 1]

[0041]

[0042] where: Ew is the frictional energy, τ is the shear force, s is the slip amount, and θ is the rotational amount of the tire 3. It is known that the wear energy and the wear amount of the tread are related to each other. Therefore, the wear state of the tire 3, such as the remaining groove depth of the tread of the tire 3, can be calculated based on the time-series data of the wear energy acting on the tread of the tire 3.

[0043] Next, reference will be made to Figure 2 to describe in detail the server 10 as a tire wear state prediction device. Figure 2 is a block diagram illustrating the structure of the server 10. As Figure 2 illustrated, the server 10 includes a communication unit 11, an output unit 12, an input unit 13, a storage unit 14, and a controller 15. In the server 10, the communication unit 11, the output unit 12, the input unit 13, the storage unit 14, and the controller 15 are connected in such a manner that they can communicate with each other either wired or wirelessly.

[0044] The communication unit 11 includes a communication module for connecting to the network 40. The communication module is a communication module compatible with mobile communication standards such as 4G (4th generation) or 5G (5th generation). The communication module may be a communication module compatible with standards such as wired LAN or wireless LAN. The communication module may be a communication module compatible with short-range wireless communication standards (such as (Wi-Fi is a registered trademark in Japan, other countries, or both), (Bluetooth is a registered trademark in Japan, other countries, or both) or infrared communication, etc.). In this embodiment, the server 10 is connected to the network 40 via the communication unit 11. This enables the server 10 to communicate with the measurement device 20, the terminal device 30, other computers, etc.

[0045] The output unit 12 includes one or more output devices. Examples of the output devices included in the output unit 12 include a display, a speaker, and a light. This enables the output unit 12 to output images, sounds, light, etc.

[0046] The input unit 13 includes one or more input devices. Examples of the input devices included in the input unit 13 include a touch panel, a camera, and a microphone. The input unit 13 receives, for example, an input operation from a user of the server 10.

[0047] The storage unit 14 is, for example, a semiconductor memory, a magnetic memory, or an optical memory. The storage unit 14 is used, for example, as a main storage unit, an auxiliary storage unit, or a cache memory. The storage unit 14 stores any information used in the operation of the server 10. For example, the storage unit 14 stores system programs, application programs, embedded software, databases, etc. For example, the information stored in the storage unit 14 can be updated using the information obtained from the network 40 via the communication unit 11.

[0048] For example, the storage unit 14 may store the tire identification information of each of one or more tires 3 to be measured by the measurement device 20. The tire identification information of the tire 3 is information that can uniquely identify the tire 3. The tire identification information is, for example, an identifier (ID) of the tire 3 uniquely assigned by the server 10. The tire identification information is not limited to this, and may be, for example, the manufacturing number of the tire 3. The storage unit 14 may store information related to the tire 3 in association with the tire identification information of the tire 3.

[0049] The controller 15 includes one or more processors. Examples of processors include general-purpose processors such as a central processing unit (CPU) and dedicated processors dedicated to specific processing. The controller 15 is not limited to one or more processors and may include one or more dedicated circuits. Examples of dedicated circuits include a field-programmable gate array (FPGA) and an application-specific integrated circuit (ASIC). The controller 15 controls each of the foregoing components such as the communication unit 11, the output unit 12, the input unit 13, and the storage unit 14 to implement the functions of the server 10 including the functions of these components. In the present disclosure, the controller 15 controls the foregoing components in the server 10 and thus can operate as a data acquisition unit 151, a wear energy calculation unit 152, a wear state calculation unit 153, and a model construction unit 154 (described in detail later).

[0050] (Operation of Tire Wear State Prediction Device)

[0051] Reference will be made to Figure 3 、 Figure 4 and Figure 5 to describe the operation of the server 10 as a tire wear state prediction device. Figure 3 is a flowchart illustrating an example of the operation of the server 10. Figure 4 is a schematic diagram illustrating an external force acting on the tire 3 mounted on the vehicle 2. Figure 5 is a flowchart illustrating another example of the operation of the server 10. In Figure 3 and Figure 5 the flowchart of illustrates the operation of the server 10. The described operations correspond to the tire wear state prediction method executed by the server 10.

[0052] First, reference will be made to Figure 3 to describe the operation of predicting the wear state of the tire 3 using the server 10.

[0053] In the description of this operation, it is assumed that the storage unit 14 in the server 10 stores the tire identification information of the tire 3 and information related to the tire 3 associated with the tire identification information of the tire 3. The information related to the tire 3 includes, for example, at least one of the structural information of the tire 3, the structural information of the vehicle 2 on which the tire 3 is mounted, and the position information of the position of the tire 3 mounted on the vehicle 2. The structural information of the tire 3 is, for example, the type, model, material properties, belt angle, size, weight, etc. of the tire 3. The structural information of the tire 3 may also include manufacturing data unique to the tire 3, such as the length of the folded portion of the carcass (ply) or the belt width measured by an X-ray inspection machine when the tire 3 is shipped from the factory. Even for tires 3 of the same model, due to manufacturing deviations between the tires 3, the manufacturing data may be different. Taking this into account can further improve the prediction accuracy of the wear state of the tire 3. The structural information of the vehicle 2 on which the tire 3 is mounted includes the type, model, displacement, number of tires and axles installed, the type of road surface on which the vehicle 2 mainly travels, etc.

[0054] In step S101, the controller 15 in the server 10 acquires the driving data of the vehicle 2 on which the tire 3 is mounted as time-series data as the data acquisition unit 151.

[0055] For example, the controller 15 in the server 10 acquires the driving data of the vehicle 2 on which the tire 3 is mounted from the measuring device 20 via the communication unit 11. The control unit 15 may store the acquired driving data in the storage unit 14 as information related to the tire 3.

[0056] In step S101, the controller 15 in the server 10 as the data acquisition unit 151 may acquire data other than the driving data of the vehicle 2.

[0057] As an example, the controller 15 in the server 10 may acquire the tire state data of the tire 3 from the measuring device 20 via the communication unit 11 as time-series data. As another example, the controller 15 may acquire the outdoor temperature data from the measuring device 20 or the weather information service via the communication unit 11. The outdoor temperature data is, for example, the data of the air temperature, humidity or precipitation at the location where the vehicle 2 on which the tire 3 is mounted is located.

[0058] As another example, the controller 15 can obtain the initial value of the wear state of the tire 3 from the terminal device 30 via the communication unit 11. The initial value of the wear state of the tire 3 can be, for example, the wear state of the tire 3 in the unused state when shipped from the factory, or the wear state of the tire 3 at a certain point in time during the use of the tire 3. In this operation example, the initial value of the wear state of the tire 3 is the initial value of the remaining groove depth of the tread of the tire 3. The initial value of the remaining groove depth of the tread can be a measured value, or the remaining groove depth of the tread calculated as a result of this operation at a certain point in the past. The control unit 15 can store the acquired data in the storage unit 14 in association with the tire identification information of the tire 3.

[0059] In step S102, the controller 15 in the server 10, as the wear energy calculation unit 152, calculates time series data related to the wear energy of the tire 3 based on the driving data of the vehicle 2 equipped with the tire 3. Hereinafter, the time series data related to the wear energy of the tire 3 is also referred to as the wear energy data of the tire 3.

[0060] The wear energy data of the tire 3 can include time series data of the wear energy acting on a predetermined part of the tread of the tire 3. In addition to or instead of the time series data of the wear energy, the wear energy data of the tire 3 can also include time series data of the slip amount and the shear force acting on a predetermined part of the tread of the tire 3. In such a case, as described above using equation (1), the wear energy acting on the tire 3 at a certain point in time can be calculated by the product of the slip amount and the shear force at that point in time.

[0061] Any method can be used to calculate the wear energy data of the tire 3. The controller 15 in the server 10 can pre-store the association algorithm between the driving data of the vehicle 2 equipped with the tire 3 and the wear energy data of the tire 3 in the storage unit 14. The controller 15 can use this association algorithm to calculate the wear energy data of the tire 3 based on the driving data of the vehicle 2 equipped with the tire 3.

[0062] In the present embodiment, an association algorithm for calculating the wear energy data of the tire 3 is constructed by statistical methods such as machine learning or Bayesian estimation. In this operation example, the association algorithm includes one or more calculation models (described in detail later) constructed by machine learning. The calculation model acquires the driving data of the vehicle 2 equipped with the tire 3 and outputs the wear energy data of the tire 3. Therefore, the server 10 can further improve the calculation accuracy of the wear energy data of the tire 3 by machine learning. The association algorithm may include a predetermined relational expression not based on statistical methods. The controller 15 can store the calculated wear energy data of the tire 3 in the storage unit 14 in association with the tire identification information of the tire 3.

[0063] As an example, the association algorithm may include two calculation models 102A and 102B that are sequentially executed.

[0064] The first calculation model 102A is a calculation model for calculating the external force data on the tire 3 based on the driving data of the vehicle 2 equipped with the tire 3. That is, the first calculation model 102A acquires the driving data of the vehicle 2 equipped with the tire 3 and outputs the external force data of the external force acting on the tire 3 (also referred to as the external force data on (acting on) the tire 3). The external force data on the tire 3 is time-series data representing the force applied to the tire 3. The external force data may be composed of one or more external force components. In this operation example, as Figure 4 illustrated, in the state where the tire 3 is mounted on the vehicle 2, the external force data is composed of an external force component Fx in the traveling direction of the vehicle 2, an external force component Fy in the rotation axis direction of the tire 3, and an external force component Fz in the vertical direction. Therefore, the external force data on the tire 3 can represent the magnitude and direction of each force applied to the tire 3. The number of components constituting the external force data on the tire 3 is not limited to three.

[0065] The second calculation model 102B is a calculation model for calculating the wear energy data of the tire 3 based on the external force data on the tire 3 calculated by the first calculation model 102A. That is, the second calculation model 102B acquires the external force data on the tire 3 and outputs the wear energy data of the tire 3. As described above, the wear energy data of the tire 3 includes time-series data of at least one of the wear energy, slip amount, and shear force acting on a predetermined portion of the tread of the tire 3.

[0066] The second calculation model 102B may be composed of a plurality of calculation models each for outputting wear energy, slip amount, or shear force. In this case, different machine learning methods may be used to construct a plurality of calculation models according to the wear energy data to be output. Alternatively, regardless of the wear energy data to be output, the same machine learning method may be used to construct a plurality of calculation models.

[0067] The two calculation models 102A and 102B can further obtain data other than the aforementioned data as inputs. For example, in addition to obtaining the aforementioned data, the two calculation models 102A and 102B can further obtain the tire state data of the tire 3 as an input. Specifically, in Figure 3 step S102, the controller 15 in the server 10, as the wear energy calculation unit 152, can calculate the wear energy data of the tire 3 based on the driving data of the vehicle 2 equipped with the tire 3 and the tire state data of the tire 3. As described above, the tire state data of the tire 3 is, for example, time series data of the internal pressure (air pressure) or temperature of the tire 3. Therefore, the internal pressure or temperature of the tire 3 can be taken into account, thereby improving the accuracy of the output of the calculation model.

[0068] For example, in addition to obtaining the aforementioned data, the two calculation models 102A and 102B can further obtain information related to the tire 3 as an input. Specifically, in Figure 3 step S102, the controller 15 in the server 10, as the wear energy calculation unit 152, can calculate the wear energy data of the tire 3 based on the driving data of the vehicle 2 equipped with the tire 3 and the information related to the tire 3. As described above, the information related to the tire 3 includes at least one of the structural information of the tire 3, the structural information of the vehicle 2 equipped with the tire 3, and the position information of the position where the tire 3 is installed on the vehicle 2. Even in the case of the same driving data, the external force acting on the tire 3 may be different depending on the structure of the tire 3, the structure of the vehicle 2 equipped with the tire 3, the position where the tire 3 is installed on the vehicle 2, etc. In addition, even for the same model of tire 3, the external force acting on each tire 3 may be different due to manufacturing deviations between the tires 3. Therefore, using the information related to the tire 3 can improve the accuracy of the output of the calculation model.

[0069] Thus, in step S102, the controller 15 in the server 10, as the wear energy calculation unit 152, preferably calculates the external force data acting on the tire 3 based on the driving data of the vehicle 2 equipped with the tire 3, and calculates the wear energy data of the tire 3 based on the external force data acting on the tire 3. By using multiple calculation models in this way to calculate intermediate data such as the external force data acting on the tire 3 and then gradually calculating the wear energy data of the tire 3, compared with the case of using a single calculation model to directly calculate the time series data related to the wear energy of the tire 3 based on the driving data of the vehicle 2, the accuracy and reliability of the calculated values can be improved. However, the correlation algorithm can include any number of calculation models and can be configured to directly calculate the time series data related to the wear energy of the tire 3 based on the driving data of the vehicle 2 using a single calculation model.

[0070] The wear energy, slip amount, and / or shear force included in the wear energy data of the tire 3 can each be composed of a plurality of components. In this operation example, in the wear energy data of the tire 3, the wear energy of the tire 3 includes a first wear energy component in a first direction of the tire 3 and a second wear energy component in a second direction perpendicular to the first direction. For example, the first direction is the circumferential direction of the tire 3, and the second direction is the width direction of the tire 3. Thus, the wear energy data of the tire 3 can represent the magnitude and direction of the wear energy acting on the tire 3. Therefore, in the wear energy data of the tire 3, each of the slip amount and the shear force can also include a component in the first direction and a component in the second direction. The number of components constituting the wear energy data of the tire 3 is not limited to two and can be any number. The first direction is not limited to the circumferential direction of the tire 3 and can be any direction.

[0071] In step S103, the controller 15 in the server 10 outputs the wear state of the tire 3 based on the wear energy data of the tire 3 as the wear state calculation unit 153.

[0072] Any method can be used to calculate the wear state of the tire 3. For example, the controller 15 in the server 10 can pre-store the correlation algorithm between the wear energy data of the tire 3 and the wear amount of the tire 3 in the storage unit 14. The controller 15 can use this correlation algorithm to calculate the wear amount of the tire 3 during a predetermined time period based on the total wear energy data of the tire 3 during that time period. In this operation example, the controller 15 outputs the remaining groove depth of the tread of the tire 3 as the wear state of the tire 3. The controller 15 can calculate the current remaining groove depth of the tire 3 as the current wear state of the tire 3 by subtracting the calculated wear amount of the tire 3 from the initial value of the remaining groove depth of the tread of the tire 3. Representing the wear state of the tire 3 as the remaining groove depth of the tread in this way enables the user of the tire wear state prediction system 1 to easily grasp the wear state of the tire 3. However, the wear state of the tire 3 can be represented by any index (such as the wear amount of the tread of the tire 3, etc.).

[0073] Similar to the other correlation algorithms described above, the correlation algorithm for calculating the wear state of the tire 3 can be constructed by statistical methods such as machine learning or deep learning. Thus, the server 10 can further improve the calculation accuracy of the wear amount of the tire 3 through machine learning. The correlation algorithm can include a predetermined relational expression that is not based on a statistical method. The controller 15 can store the calculated wear amount and remaining groove depth of the tire 3 in association with the tire identification information of the tire 3 in the storage unit 14.

[0074] In step S103, the controller 15 in the server 10, acting as the wear state calculation unit 153, can calculate the wear state of the tire 3 by applying a predetermined correction coefficient that varies according to the tire 3 to the wear energy. The predetermined correction coefficient can vary according to, for example, information related to the tire 3. As described above, the information related to the tire 3 includes at least one of the structural information of the tire 3, the structural information of the vehicle 2 on which the tire 3 is mounted, and the position information of the tire 3 mounted on the vehicle 2. Even when the wear energy acting on the tire 3 is the same, the amount of wear of the tire 3 may vary depending on the structure of the tire 3 such as the material properties of the rubber, or the manufacturing data unique to the tire 3. Additionally, even with the same driving data, the wear energy acting on the tire 3 may vary depending on the type (roughness) of the road surface on which the vehicle 2 mainly travels, the structure of the vehicle 2 on which the tire 3 is mounted, or the position of the tire 3 mounted on the vehicle 2. By taking these factors into account and applying a predetermined correction coefficient that varies according to the tire 3, the accuracy of the wear state of the tire 3 calculated by this process can be improved. In the case where, as described above, the wear energy includes a first wear energy component and a second wear energy component, the predetermined correction coefficient may vary between the first wear energy component and the second wear energy component. This can further improve the accuracy of the wear state of the tire 3 calculated by this process.

[0075] Referring again to Figure 3 , the controller 15 in the server 10 can output the wear state of the tire 3 calculated in step S103 by any method. For example, the controller 15 can display the wear state of the tire 3 via an output unit 12 such as a display. Alternatively, the controller 15 can send a request for displaying the wear state of the tire 3 to the terminal device 30 via the communication unit 11. In such a case, the terminal device 30 can display the remaining groove depth of the tire 3 as the wear state of the tire 3 based on the request received from the server 10 via a display or the like. As a result, the user of the tire wear state prediction system 1 can view the visualized wear state of the tire 3. Thus, the tire wear state prediction system 1 can improve the usefulness of the technology for predicting the wear state of the tire 3. In addition to outputting the wear state, the controller 15 can also output intermediate data in the calculation of the wear state of the tire 3, such as the wear energy acting on the tire 3. Presenting to the user not only the wear state of the tire 3 but also the intermediate data in the calculation of the wear state can improve the reliability of the wear state calculated using machine learning or the like.

[0076] In step S104, if the wear state of the tire 3 is outside a predetermined threshold range, the controller 15 in the server 10 can output an alarm. The predetermined threshold range can be associated with at least one of replacement, retreading, and rotation of the tire 3. For example, when the predetermined threshold range is associated with the replacement of the tire 3, if the wear state of the tire 3 is outside the predetermined threshold range, an alarm can be output to prompt the replacement of the tire 3.

[0077] Any method can be used to output the alarm. The controller 15 in the server 10 can display information or output sound or light via the output unit 12. Alternatively, the controller 15 can send a request for outputting an alarm to the terminal device 30 via the communication unit 11. In such a case, the terminal device 30 can output an alarm via a display or the like based on the request received from the server 10. As a result, the user of the tire wear state prediction system 1 can be prompted to perform actions such as replacement, retreading, or rotation of the tire 3.

[0078] Next, the operation of constructing a calculation model using the server 10 will be described with reference to Figure 5 As described above, the wear energy calculation unit 152 uses two calculation models 102A and 102B. These calculation models can be constructed by the model construction unit 156.

[0079] In step S201, the controller 15 in the server 10 generates training (teaching) data for constructing each calculation model as the model construction unit 154.

[0080] Any method can be used to generate the training data. For example, the controller 15 in the server 10 can generate training data in which past measured values corresponding to the input and output in the calculation model are explanatory variables and target variables. Thus, the accuracy of the output of the calculation model can be improved by accumulating measurement data. The controller 15 in the server 10 can generate training data in which, in addition to or instead of past measured values, virtual time series data generated by simulation are explanatory variables and target variables. For example, it is preferable to generate virtual time series data that maintains summary statistics (mean, variance, etc.) based on actual vehicle driving data or the like by using a known stochastic process method such as Karhunen - Loeve expansion (KL expansion). In this way, even in the initial stage where the accumulated measurement data is small, a highly accurate calculation model can be efficiently constructed. A vehicle driving simulator, a three - dimensional tire rolling simulator, etc. can be used to generate virtual time series data by simulation. The three - dimensional tire rolling simulator uses isogeometric analysis (IGA), finite element method (FEM), etc.

[0081] In step S202, the controller 15 in the server 10, as a model building unit 154, performs machine learning based on the training data generated in step S201 to build a computational model.

[0082] As an example, a second computational model 102B can be built by machine learning using the following training data, which is generated by a tire rolling simulation using IGA or FEM. As described above, the second computational model 102B is a computational model for calculating the wear energy data of the tire 3 based on the external force data acting on the tire 3.

[0083] Here, in order to collect the measured values of the external force data and the wear energy data (which are used as the training data for building the second computational model 102B), a tread observation device can be used to perform a tread observation test on the tire 3. The tread observation device is a device that can simulate the running of a vehicle with a tire mounted on a platform, and includes, for example, a high-resolution sampling camera capable of observing the tread of the tire. By measuring the behavior of the tire tread using the tread observation device, the correspondence between the wear energy calculated based on the slip amount and the shear force occurring on the tire surface and the wear amount can be obtained. However, typical tread observation devices have limitations on the magnitude and direction of the external force that can be reproduced acting on the tire 3. For example, typically, the tread observation device can only stably reproduce the acceleration applied to the tire 3 during rolling to about 0.1G to 0.2G. In the present disclosure, the magnitude of the acceleration is expressed in "G" (which is the unit of acceleration based on the standard acceleration of gravity). 1.0G is 9.80665m / s 2 . In addition, when the direction of the external force acting on the tire 3 is the circumferential direction of the tire 3 (the traveling direction of the vehicle 2) or the width direction of the tire 3 (the rotation axis direction of the tire 3), the accuracy of the test using the tread observation device tends to be higher. Therefore, in the tread observation test, situations such as the acceleration being 0.2G or greater than 0.2G, and the external force acting on the tire 3 in a direction inclined with respect to the circumferential direction of the tire 3 cannot be reproduced, and sufficient variation of the training data cannot be ensured. By building the second computational model 102B (a computational model for calculating wear energy data) using the training data generated by tire rolling simulation in addition to or instead of using the measured values of the tread observation test, sufficient variation of the training data can be ensured, and the accuracy of the output of the computational model can be improved. Therefore, the training data for building the computational model for calculating the wear energy data preferably includes the results of tire rolling simulation in the case where the acceleration is 0.2G or greater than 0.2G. The training data for building the computational model more preferably includes the results of tire rolling simulation in the case where the direction of the external force acting on the tire 3 is at an angle greater than 0 degrees and less than 90 degrees with respect to the circumferential direction of the tire 3.

[0084] In addition, the training data for constructing the calculation model for calculating the wear energy data preferably includes the results of tire rolling simulations at a plurality of locations on the tread surface of the tire 3. For example, these plurality of locations may be arranged in the tire width direction or in the tire circumferential direction on the tread surface of the tire 3. This reduces the possibility that the wear energy data calculated using the calculation model is biased towards a specific location on the tread surface.

[0085] In addition to time series data such as the driving data of the vehicle 2 equipped with the tire 3, in steps S201 and S202, any information can also be used to construct the calculation model. For example, in addition to the driving data of the vehicle 2 equipped with the tire 3, information related to the tire 3 can also be used, such as the tire state data of the tire 3, the structural information of the tire 3, the structural information of the vehicle 2 equipped with the tire 3, or the position information of the tire 3 mounted on the vehicle 2. In addition, for example, outdoor temperature data or manufacturing data unique to the tire 3 can be used. The outdoor temperature data is, for example, data on the air temperature, humidity, or precipitation at the location where the vehicle 2 equipped with the tire 3 is located. This can improve the accuracy of the output of the calculation model constructed by machine learning.

[0086] As described above, in the present embodiment, the server 10 as the tire wear state prediction device includes: a data acquisition unit 151 configured to acquire the driving data of the vehicle 2 equipped with the tire 3; a wear energy calculation unit 152 configured to calculate wear energy data as time series data related to the wear energy of the tire 3 based on the driving data of the vehicle 2 equipped with the tire 3; and a wear state calculation unit 153 configured to output the wear state of the tire 3 based on the wear energy data of the tire 3.

[0087] With this structure, instead of directly calculating the wear state of the tire based on the driving data of the vehicle 2 equipped with the tire 3, the server 10 gradually calculates the wear state of the tire by generating intermediate data such as the wear energy acting on the tire 3. This can improve the accuracy and reliability of the calculated wear state. Therefore, the server 10 according to the present disclosure can improve the usefulness of the technology for predicting the wear state of the tire 3.

[0088] Although the present disclosure has been illustrated above by way of examples and drawings, various changes and modifications can be made by those of ordinary skill in the art based on the present disclosure. Therefore, such changes and modifications are included within the scope of the present disclosure. For example, the structures, functions, etc. included in each embodiment can be rearranged without logical inconsistency. The structures, functions, etc. included in each embodiment can be used in combination with other embodiments, and multiple structures, functions, etc. can be combined into one structure, function, etc., one structure, function, etc. can be divided into multiple structures, functions, etc., or a part of these structures, functions, etc. can be omitted.

[0089] For example, although the above embodiments describe the case where the controller 15 in a server 10 operates as the data acquisition unit 151, the wear energy calculation unit 152, the wear state calculation unit 153, and the model construction unit 154, the present disclosure is not limited thereto. Multiple controllers 15 and servers 10 dedicated to each operation can be provided.

[0090] For example, although the above embodiments describe the case where the wear energy data of the tire 3 is calculated using two calculation models 102A and 102B that are sequentially executed, the present disclosure is not limited thereto. Any number of calculation models (i.e., one or more than one calculation model) can be used to calculate the wear energy data of the tire 3. For example, the wear energy data of the tire 3 can be calculated using one calculation model for obtaining the driving data of the vehicle 2 equipped with the tire 3 and outputting the wear energy data of the tire 3.

[0091] For example, the following embodiment is also possible: a general-purpose computer is used as the server 10 according to the above embodiments. Specifically, a program for processing to implement the functions of the server 10 according to the above embodiments is stored in the memory of the general-purpose computer, and is read and executed by the processor in the general-purpose computer. Thus, the present disclosure can also be implemented as a program executable by a processor or a non-transitory computer-readable medium storing the program. Examples of the non-transitory computer-readable medium include magnetic recording devices, optical discs, magneto-optical recording media, and semiconductor memories.

[0092] Industrial Applicability

[0093] Therefore, a tire wear state prediction device, a tire wear state prediction method, and a program that can improve the usefulness of the technology for predicting the wear state of a tire can be provided.

[0094] [Contribution to the United Nations-led Sustainable Development Goals (SDGs)]

[0095] SDGs have been proposed to create a sustainable society. Embodiments of the present disclosure are considered to be technologies that can contribute to, for example, "Goal 12: Responsible Consumption and Production" and "Goal 13: Climate Action".

[0096] Description of Reference Numerals

[0097] 1: Tire wear state prediction system; 2: Vehicle; 3: Tire; 10: Server (tire wear state prediction device); 11: Communication unit; 12: Output unit; 13: Input unit; 14: Storage unit; 15: Controller; 151: Data acquisition unit; 152: Wear energy calculation unit; 153: Wear state calculation unit; 154: Model construction unit; 20: Measuring device; 30: Terminal device; 40: Network; 102A, 102B: Calculation models; Fx, Fy, Fz: External force components

Claims

1. A tire wear state prediction device, comprising: A data acquisition unit configured to acquire driving data of a vehicle equipped with a tire; A wear energy calculation unit configured to calculate wear energy data based on the driving data, the wear energy data being time series data related to the wear energy of the tire; And A wear state calculation unit configured to output the wear state of the tire based on the wear energy data.

2. The tire wear state prediction device according to claim 1, wherein, The wear state calculation unit is configured to output the remaining groove depth of the tread of the tire as the wear state of the tire.

3. The tire wear state prediction device according to claim 1 or 2, wherein, The wear energy calculation unit is configured to: Calculate external force data of an external force acting on the tire based on the driving data; And Calculate the wear energy data of the tire based on the external force data.

4. The tire wear state prediction device according to claim 1, wherein, The wear energy calculation unit includes a calculation model constructed by machine learning, and The calculation model is constructed by machine learning using training data generated by tire rolling simulation.

5. The tire wear state prediction device according to claim 4, wherein, The training data includes results of the tire rolling simulation at multiple locations on the tread of the tire.

6. The tire wear state prediction device according to claim 4, wherein, The training data includes results of the tire rolling simulation when the acceleration is 0.2G or greater than 0.2G.

7. The tire wear state prediction device according to claim 1, wherein, The wear energy of the tire includes a first wear energy component in a first direction of the tire and a second wear energy component in a direction perpendicular to the first direction.

8. The tire wear state prediction device according to claim 7, wherein, The wear state calculation unit is configured to calculate the wear state of the tire by applying a predetermined correction coefficient different according to the tire to the wear energy, and The predetermined correction coefficient is different between the first wear energy component and the second wear energy component.

9. A tire wear state prediction method, executed by one or more computers, the tire wear state prediction method comprising: Acquiring driving data of a vehicle equipped with a tire; Calculating wear energy data based on the driving data, the wear energy data being time series data related to the wear energy of the tire; And Outputting the wear state of the tire based on the wear energy data.

10. A program for causing one or more computers to perform operations, the operations including: Acquiring driving data of a vehicle equipped with a tire; Calculating wear energy data based on the driving data, the wear energy data being time series data related to the wear energy of the tire; And Outputting the wear state of the tire based on the wear energy data.

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