JP7578488B2 - Wear amount estimation system, computation model generation system, and wear amount estimation method - Google Patents

Description

The present invention relates to a wear amount estimation system, a calculation model generation system, and a wear amount estimation method for estimating the amount of wear of tires mounted on a vehicle.

In general, tires wear down depending on the driving conditions and distance traveled. Recently, devices have been commercialized that attach sensors to tires to measure tire pressure and temperature and display the measured pressure and temperature.

Patent document 1 describes a conventional tire wear estimation system. The tire wear estimation system has at least one sensor attached to a tire to generate a first predictive variable, at least one of a lookup table and a database that stores data related to a second predictive variable, one of the predictive variables including at least one vehicle effect, and a model that receives the predictive variables and generates an estimated wear rate for at least one tire.

JP 2018-158722 A

The tire wear estimation system described in Patent Document 1 estimates tire wear using, for example, wheel position and drive train as vehicle influences. Even when such vehicle influences are the same, the inventors realized that there is room for improvement in estimating tire wear, since factors such as the condition of the road surface with which the tire contacts can also be a factor in determining the amount of wear. In other words, they thought that when estimating tire wear, it is necessary to take into account the influence of factors such as the condition of the road surface with which the tire contacts and the wear resistance performance of various tires on tire wear.

The present invention has been made in consideration of the above circumstances, and its purpose is to provide a wear amount estimation system, a calculation model generation system, and a wear amount estimation method that can improve the accuracy of estimating the amount of tire wear.

The wear amount estimation system of one embodiment of the present invention includes a vehicle information acquisition unit that acquires information including the vehicle load and travel distance, a road surface condition estimation unit that estimates the road surface condition related to the road surface with which the tire contacts while the vehicle is traveling, and a wear amount calculation unit that has a calculation model that calculates the amount of tire wear based on input information, and inputs the information acquired by the vehicle information acquisition unit and the road surface condition estimated by the road surface condition estimation unit into the calculation model to calculate the amount of tire wear.

Another aspect of the present invention is a computational model generation system. The computational model generation system includes a vehicle information acquisition unit that acquires information including the vehicle load and travel distance, a road surface condition estimation unit that estimates the road surface condition related to the road surface with which the tire contacts while the vehicle is traveling, a wear amount calculation unit that has a computational model that calculates the amount of tire wear based on input information, inputs the information acquired by the vehicle information acquisition unit and the road surface condition estimated by the road surface condition estimation unit to the computational model to calculate the amount of tire wear, and a learning processing unit that learns the computational model by comparing the amount of wear measured on the tire with the amount of wear calculated by the wear amount calculation unit.

Another aspect of the present invention is a wear amount estimation method. The wear amount estimation method includes a vehicle information acquisition step of acquiring information including the vehicle load and travel distance, a road surface condition estimation step of estimating the road surface condition related to the road surface with which the tire contacts while the vehicle is traveling, and a wear amount calculation step of inputting the information acquired by the vehicle information acquisition step and the road surface condition estimated by the road surface condition estimation step into a computation model that calculates the tire wear amount based on the input information, and calculating the tire wear amount.

The present invention can improve the accuracy of estimating tire wear.

1 is a block diagram showing a functional configuration of a wear amount estimation system according to an embodiment; FIG. 2 is a block diagram showing a functional configuration of the vehicle-mounted measuring device. 4 is a table showing road surface conditions estimated by a road surface condition estimating unit. FIG. 4 is a schematic diagram for explaining calculation of tire severity. 1 is a chart showing an example of values in each region of tire severity. 10 is a schematic diagram for explaining wear amount estimation and learning of a computation model. FIG. FIG. 2 is a block diagram showing a functional configuration of the computation model generation system. 1 is a flowchart showing a procedure for generating a computation model by the computation model generation system.

The present invention will be described below based on a preferred embodiment with reference to Figures 1 to 8. The same or equivalent components and parts shown in each drawing are given the same reference numerals, and duplicated explanations will be omitted as appropriate. Furthermore, the dimensions of the parts in each drawing are enlarged or reduced as appropriate to facilitate understanding. Furthermore, some of the parts that are not important for explaining the embodiment will be omitted in each drawing.

(Embodiment)
1 is a block diagram showing a functional configuration of a wear amount estimation system 100 according to an embodiment. The wear amount estimation system 100 includes an on-board measurement device 70 mounted on a vehicle, a weather information server device 80, and a wear amount estimation device 10 that estimates the amount of wear of each tire 7 mounted on the vehicle.

The wear amount estimation device 10 acquires vehicle measurement information, such as vehicle speed and position information, as well as tire measurement information measured on the tires 7, from an on-board measurement device 70 mounted on the vehicle via a communication network 9, such as the Internet. The wear amount estimation device 10 also acquires weather information from a weather information server device 80. The wear amount estimation device 10 estimates the wear amount of each tire 7 by performing calculations using a learning-type calculation model based on the acquired information.

Figure 2 is a block diagram showing the functional configuration of the on-board measurement device 70. The on-board measurement device 70 comprises a vehicle measurement unit 71, a tire measurement unit 72, an information acquisition unit 73, and a communication unit 74. Each unit in the on-board measurement device 70 can be realized in hardware terms by electronic elements and mechanical parts such as a computer CPU, and in software terms by a computer program, but here, functional blocks realized by the cooperation of these are depicted. Therefore, it will be understood by those skilled in the art that these functional blocks can be realized in various forms by combining hardware and software.

The vehicle measurement unit 71 has a speedometer 71a and a GPS receiver 71b mounted on the vehicle. The speedometer 71a measures the speed of the vehicle. The GPS receiver 71b measures the current position information (latitude, longitude, and altitude) of the vehicle. The vehicle measurement unit 71 may also have, for example, an acceleration sensor to measure the acceleration of the vehicle in three axial directions. Note that the acquisition of information regarding speed is not limited to the vehicle's speedometer 71a, and GPS position information may also be used. For example, if the acquisition interval of GPS position information is 3 seconds and the vehicle has traveled 25 m in those 3 seconds, information regarding the speed is calculated as if the vehicle had traveled 25 m at a speed of 30 km/h.

The tire measurement unit 72 has a temperature sensor 72a and a pressure sensor 72b. The temperature sensor 72a and the pressure sensor 72b are disposed on the air valve of the tire 7 mounted on the vehicle, or are firmly wrapped around the wheel by a belt or the like and fixed thereto, and measure the temperature and air pressure of the tire 7, respectively. The temperature sensor 72a may be disposed on the inner liner of the tire 7, etc.

The information acquisition unit 73 acquires vehicle measurement information (speed, position information, acceleration, etc.) measured by the vehicle measurement unit 71 and tire measurement information (tire temperature, air pressure, etc.) measured by the tire measurement unit 72. The information acquisition unit 73 associates each piece of measurement data included in the vehicle measurement information and tire measurement information with measured time information or acquired time information. The information acquisition unit 73 transmits the vehicle measurement information and tire measurement information together with the time information associated with each piece of measurement data from the communication unit 74 to the wear amount estimation device 10.

If the vehicle is equipped with a device such as a digital tachometer, the information acquisition unit 73 may acquire information such as the vehicle speed, acceleration, and position collected by the device. The communication unit 74 connects to the communication network 9 by wireless communication such as Wi-Fi (registered trademark), and transmits the vehicle measurement information, tire measurement information, and time information acquired by the information acquisition unit 73 to the wear amount estimation device 10 via the communication network 9.

Returning to FIG. 1, the weather information server device 80 provides weather information for various locations. The weather information provided by the weather information server device 80 includes information such as the amount of precipitation, snowfall, temperature, and sunshine hours for various locations. The wear amount estimation device 10 obtains weather information for the location where the vehicle is traveling from the weather information server device 80.

The wear amount estimation device 10 includes a communication unit 11, a vehicle information acquisition unit 12, a road surface condition estimation unit 13, a tire severity calculation unit 14, a wear amount calculation unit 15, and a memory unit 16. Each unit in the wear amount estimation device 10 can be realized in hardware terms by electronic elements and mechanical parts such as a computer CPU, and in software terms by a computer program, but here, functional blocks realized by the cooperation of these are depicted. Therefore, it will be understood by those skilled in the art that these functional blocks can be realized in various forms by combining hardware and software.

The communication unit 11 is connected to the communication network 9 by wireless or wired communication, and communicates with the communication unit 74 of the on-board measurement device 70. The communication unit 11 also communicates with the weather information server device 80 via the communication network 9.

The vehicle information acquisition unit 12 acquires vehicle measurement information (speed, position information, acceleration, etc.) and tire measurement information (tire temperature, air pressure, etc.) transmitted from an on-board measurement device 70 mounted on the vehicle. The vehicle information acquisition unit 12 calculates and acquires the vehicle's travel distance based on the vehicle measurement information.

The vehicle's travel distance is calculated based on the speed data in the vehicle measurement information and the time data associated with that data. That is, in the speed and time data arranged in chronological order, the vehicle's travel distance can be calculated by multiplying one speed datum by the time difference to the next point in time. The travel distance referred to here does not refer to the vehicle's total travel distance, but rather represents the distance traveled by the vehicle at a given speed for one piece of acquired speed data; in other words, it is understood to be the differentiated travel distance.

For example, if speed data acquired at a certain point in time is 30 km/h and the time difference until the next measurement point is 3 seconds, these are multiplied together to calculate the distance traveled for that data as 25 m, and information regarding the distance traveled, 25 m traveled at 30 km/h, is acquired.

If information regarding the vehicle's mileage is provided in detail (e.g., every few seconds) from the vehicle or an external device for vehicle management, the vehicle information acquisition unit 12 does not need to calculate the mileage itself, and can simply acquire information regarding the mileage from the vehicle or the external device.

The vehicle information acquisition unit 12 outputs the acquired travel distance to the wear amount calculation unit 15. The vehicle information acquisition unit 12 outputs the acquired tire measurement information (tire temperature, air pressure, etc.) to the wear amount calculation unit 15. The vehicle information acquisition unit 12 outputs the position information in the acquired vehicle measurement information to the road surface condition estimation unit 13.

When the wear amount calculation unit 15 estimates the amount of tire wear based on a calculation model that uses the vehicle acceleration as an input element, the vehicle information acquisition unit 12 outputs acceleration data from vehicle measurements to the wear amount calculation unit 15.

The vehicle information acquisition unit 12 also acquires vehicle specification data 16a and tire specification data 16b from the storage unit 16. The storage unit 16 is a storage device configured, for example, by an SSD (Solid State Drive), a hard disk, a CD-ROM, a DVD, etc., and stores data provided in advance regarding the specifications of various vehicles and tires 7.

The vehicle specification data 16a includes information on the vehicle's performance, such as the manufacturer, vehicle name, vehicle model, vehicle weight, drive train, overall length, vehicle width, vehicle height, and maximum load capacity. The tire specification data 16b includes information on the performance of the tire 7, such as the manufacturer, product name, tire size, tire width, aspect ratio, wear resistance, tire strength, static stiffness, dynamic stiffness, tire outer diameter, load index, and date of manufacture.

The vehicle information acquisition unit 12 extracts data from the vehicle specification data 16a to be used in the wear amount calculation unit 15 and outputs the data to the wear amount calculation unit 15. In this embodiment, the vehicle information acquisition unit 12 reads out at least data on the maximum load of the vehicle from the storage unit 16 and outputs the data to the wear amount calculation unit 15.

The vehicle information acquisition unit 12 extracts data from the tire specification data 16b to be used in the wear amount calculation unit 15 and outputs the data to the wear amount calculation unit 15. In this embodiment, the vehicle information acquisition unit 12 reads out data on at least the wear resistance performance of the tire 7 from the storage unit 16 and outputs the data to the wear amount calculation unit 15.

The road surface condition estimation unit 13 obtains weather information corresponding to the vehicle's position from the weather information server device 80 via the communication unit 11 based on the input vehicle's position information. The weather information server device 80 provides weather information for various locations. As described above, the weather information provided by the weather information server device 80 includes information such as precipitation, snowfall, temperature, and sunshine hours.

The scale of road surface conditions for estimating the amount of wear of the tires 7 can be determined in various ways based on, for example, the amount of precipitation. In this embodiment, the road surface condition estimation unit 13 classifies the road surface conditions into five states and estimates them.

Figure 3 is a chart showing the road surface conditions estimated by the road surface condition estimation unit 13. The road surface condition estimation unit 13 estimates whether the road surface condition is dry, semi-wet, wet, icy, or snowy. There are five different conditions. A dry condition is when the amount of precipitation is less than a predetermined amount (e.g., 0.5 mm/h). A semi-wet condition is when the amount of precipitation is within a predetermined range (e.g., 0.5 mm/h or more and less than 1.5 mm/h).

A wet state is when the amount of precipitation is greater than or equal to a predetermined amount (e.g., 1.5 mm/h). A frozen state is when the amount of precipitation is greater than or equal to a predetermined amount (e.g., 0.5 mm/h) and the temperature is less than or equal to 0°C. A snowy state is when the amount of snowfall is greater than 0 or when the amount of snowfall is greater than 0.

The road surface condition estimation unit 13 outputs the estimated road surface condition to the wear amount calculation unit 15. In addition, the road surface condition estimation unit 13 outputs the data on temperature and sunshine hours among the weather information acquired from the weather information server device 80 to the tire severity calculation unit 14.

The tire severity calculation unit 14 calculates the severity of weather conditions for tire 7 wear based on data on temperature and sunshine hours. Generally, the wear progression of the tire 7 is affected by factors such as tissue damage caused by temperature changes and exposure to sunlight for the resin material that constitutes the tire 7. The tire severity calculation unit 14 determines the severity of tire 7 wear based on the temperature and sunshine hours, and outputs the tire severity to the wear amount calculation unit 15.

Figure 4 is a schematic diagram for explaining the calculation of tire severity. In Figure 4, the horizontal axis represents temperature and the vertical axis represents sunshine hours, and the temperature and sunshine hours measured over a predetermined measurement period (e.g., January) are plotted. Sunshine hours are the hours during a day when the sun is shining as observed by a sunshine meter, and the hours measured at the location where the vehicle is traveling are obtained from the weather information server device 80 and used. The tire severity calculation unit 14 uses, for example, the average temperature during the vehicle's traveling time in a day as the temperature.

The tire severity calculation unit 14 divides the temperature on the horizontal axis in FIG. 4 into three, and the sunshine hours on the vertical axis into three, dividing the data into a total of nine sections A1 to A9. The tire severity calculation unit 14 calculates the frequency data in each section as tire severity. For example, if the total number of plots is 10,000, and there are 50 pieces of data that fall into the A1 region, the tire severity calculation unit 14 calculates the frequency in A1 as 0.005 (= 50/10,000). Note that the number of divisions on each axis is not limited to 3, and may be 2 or more.

Figure 5 is a chart showing an example of values for each range of tire severity. The sum of the frequencies for each section is 1. The air temperature is high in sections A3, A6, and A9, and tire 7 is expected to be hot, so if the frequency in these sections is high, it is thought that the amount of wear will be large. Also, if the frequency in sections A7, A8, and A9 is high, the hours of sunlight are long, and it is thought that the effect of sunlight on tire 7 will result in high wear.

The wear amount calculation unit 15 has a calculation model 15a and estimates the wear amount of the tire 7. The calculation model 15a is a learning model that calculates the wear amount of the tire 7 based on input information. FIG. 6 is a schematic diagram for explaining the wear amount estimation and learning of the calculation model 15a. The input data to the calculation model 15a is roughly classified into the following categories: vehicle measurement information, tire measurement information, weather information, vehicle specification data, and tire specification data.

The input data related to the vehicle measurement information includes the vehicle acceleration and the distance traveled. The distance traveled is calculated by the vehicle information acquisition unit 12 as described above. The input data related to the tire measurement information includes the tire temperature and air pressure. The input data related to the weather information includes the road surface condition estimated by the road surface condition estimation unit 13 and the tire severity calculated by the tire severity calculation unit 14. The tire severity is calculated based on the air temperature and the hours of sunlight as described above.

The input data related to the vehicle specification data includes the maximum load capacity of the vehicle. Furthermore, the input data related to the tire specification data includes the wear resistance performance of the tire 7. As the wear resistance performance of the tire 7, a tire wear index value that indexes the wear resistance performance of various tread blends, with the standard blend being set at 100, according to the Lambourn abrasion test specifically specified in JIS-K6264, can be used. This tire wear index value is to be included in advance in the tire specification data 16b.

The computation model 15a uses a learning model such as a neural network. The computation model 15a is constructed using a method such as a deep neural network (DNN) or a decision tree. The computation model 15a may also be a multiple linear regression model for input information, for example, which is generated by learning.

Figure 7 is a block diagram showing the functional configuration of the computational model generation system 110. In addition to the configuration of the wear amount estimation system 100, the computational model generation system 110 includes a tire wear amount measurement device 60 and a computational model generation device 20 having a learning processing unit 21.

The tire wear measuring device 60 directly measures the depth of the grooves in the tread of the tire 7 to obtain the wear of the tire 7. An operator may measure the depth of each groove using a measuring tool, a camera, or visually, and the tire wear measuring device 60 may store the measurement data input by the operator. The tire wear measuring device 60 may also be a dedicated device that measures the groove depth using a mechanical or optical method and stores the wear amount.

Specifically, for example, if a tire has four grooves, the tire wear measuring device 60 measures at four locations in the width direction, and then measures three locations in the circumferential direction of the same groove, for example at 120° intervals. As a result, uneven wear data in the width direction or circumferential direction of the tire is also stored in the tire wear measuring device 60. Note that the tire wear measuring device 60 may indirectly measure groove depth by calculation from information on the mileage and the tire rotation speed and speed, since tire diameter changes due to wear. In addition, a device that directly measures groove depth may be used in combination with a device that predicts groove depth by calculation from the mileage and the tire rotation speed and speed.

The computational model generating device 20 has a learning processing unit 21 in addition to each component of the wear amount estimating device 10. The parts of the computational model generating device 20 that correspond to each component of the wear amount estimating device 10 have the same functions as those of the wear amount estimating device 10, but the computational model 15a is either before learning or is currently being learned.

The learning processing unit 21 acquires the wear amount of the tire 7 from the tire wear amount measuring device 60 via the communication unit 11. Referring to FIG. 6, in the learning process of the calculation model 15a, the wear amount of the tire 7 is estimated as output data by the calculation model 15a based on the input information, and compared with the teacher data. The teacher data uses the wear amount of the tire 7 measured by the tire wear amount measuring device 60.

The learning processing unit 21 performs learning by comparing the wear amount of the tire 7 estimated by the computation model 15a with the teacher data, setting various coefficients in the computation process such as weighting anew in the computation model 15a, and repeatedly updating the model. The wear amount estimation system 100 estimates the wear amount of the tire 7 using the computation model 15a that has been learned by the computation model generation system 110. The learning processing unit 21 can use a known learning method such as gradient boosting. Also, known verification methods such as random data sampling and cross verification can be used to verify the computation model 15a.

Next, the operation of the wear amount estimation system 100 and the computational model generation system 110 will be described. FIG. 8 is a flowchart showing the procedure for generating a computational model by the computational model generation system 110. The vehicle information acquisition unit 12 starts acquiring vehicle measurement information and tire measurement information (S1). In addition, in step S1, the vehicle information acquisition unit 12 of the computational model generation device 20 reads out the maximum load capacity of the vehicle and the wear resistance performance of the tires from the memory unit 16. The vehicle information acquisition unit 12 starts calculating the traveled distance as described above (S2).

The road surface condition estimation unit 13 acquires weather information from the weather information server device 80 based on the vehicle's position information (S3), and estimates the road surface condition based on the acquired weather information (S4). The tire severity calculation unit 14 plots the temperature and sunshine hours as shown in FIG. 4, determines the frequency for each section, and calculates the tire severity (S5).

The wear amount calculation unit 15 acquires input data from the vehicle information acquisition unit 12, the road surface condition estimation unit 13, and the tire severity calculation unit 14, and calculates and estimates the wear amount of the tire 7 using the calculation model 15a (S6). The learning processing unit 21 compares the wear amount of the tire 7 calculated using the calculation model 15a with the wear amount of the tire 7 measured as teacher data by the tire wear amount measurement device 60 (S7). The learning processing unit 21 updates the calculation model based on the comparison result in step S7 (S8), and ends the process. The calculation model generation device 20 repeats these processes to update the calculation model 15a, thereby improving the accuracy of the tire wear amount estimation.

The wear amount estimation system 100 estimates the wear amount of the tire 7 by using the trained computation model 15a generated by the computation model generating device 20. The wear amount estimation system 100 estimates the wear amount of the tire 7 by executing the processes from step S1 to step S6 in the flowchart shown in FIG. 8.

The wear amount estimation system 100 estimates the wear amount of the tire 7 based on a calculation model 15a that includes the vehicle load (maximum load), travel distance, and road surface conditions as input data, thereby simulating the friction conditions with the road surface and improving the accuracy of the wear amount estimation. The same is true for the calculation model generation system 110, which can generate a calculation model 15a that accurately estimates the wear amount of the tire 7.

Here, in consideration of the case where there is no means for measuring the actual load of the vehicle, it is assumed that the vehicle is loaded with the maximum load. In this case, the wear amount estimation system 100 may calculate the axle load that each tire 7 mounted on each axle of the vehicle receives by distributing it to each axle based on the center of gravity of the vehicle and the axle distance, and input it to the calculation model 15a. In addition, when the wear amount calculation unit 15 of the wear amount estimation system 100 can measure the load currently loaded on the vehicle, the load measured from time to time may be used as input data for the calculation model 15a instead of the maximum load as the vehicle specification data. For example, when a dedicated sensor for measuring the load is attached to the suspension part of the vehicle body and configured to constantly measure each axle load, total weight, maximum load, etc., the wear amount of the tire 7 can be estimated using the real-time axle load as input data for the calculation model 15a.

When the vehicle's mileage is measured from moment to moment and provided by an on-board device, the wear amount calculation unit 15 may use the provided mileage instead of the calculation by the vehicle information acquisition unit 12.

The road surface condition estimation unit 13 of the wear amount estimation system 100 acquires weather information based on the vehicle's position, thereby acquiring the external environment that the road surface that the tire 7 is in contact with is subjected to, and can estimate the road surface condition according to the location where the vehicle is traveling and use it to estimate the amount of wear of the tire 7.

In addition, the road surface condition estimation unit 13 can estimate various conditions such as dry, semi-humid, wet, icy, and snowy by using the temperature and precipitation (including snowfall and snowfall) contained in the weather information, thereby improving the accuracy of estimating the amount of wear on the tires 7.

The wear amount estimation system 100 can improve the accuracy of estimating the wear amount of the tire 7 by calculating the severity of wear of the tire 7 based on the temperature and the hours of sunlight using the tire severity calculation unit 14 and using this as input data for the calculation model 15a. In addition, the wear amount estimation system 100 can estimate the wear amount that reflects the wear resistance performance of the tread compound of each tire 7 by using a tire wear index value that indexes the wear resistance performance of the tire 7 as input data related to tire specification data.

Next, features of the wear amount estimation system 100 and the computation model generation system 110 according to each embodiment will be described.
The wear amount estimation system 100 includes a vehicle information acquisition unit 12, a road surface condition estimation unit 13, and a wear amount calculation unit 15. The vehicle information acquisition unit 12 acquires information including the load and travel distance of the vehicle. The road surface condition estimation unit 13 estimates the road surface condition related to the road surface with which the tire 7 comes into contact while the vehicle is traveling. The wear amount calculation unit 15 has a calculation model 15a that calculates the wear amount of the tire 7 based on input information, and inputs the information acquired by the vehicle information acquisition unit 12 and the road surface condition estimated by the road surface condition estimation unit 13 to the calculation model 15a to calculate the wear amount of the tire 7. In this way, the wear amount estimation system 100 can improve the accuracy of wear amount estimation by simulating the friction state with the road surface.

The road surface condition estimation unit 13 also estimates the road surface condition based on meteorological information. This allows the wear amount estimation system 100 to obtain the external environment that the road surface that the tire 7 is in contact with is subjected to, estimate the road surface condition depending on the location where the vehicle is traveling, and use this to estimate the amount of wear of the tire 7.

The road surface condition estimation unit 13 also uses the precipitation level and air temperature as meteorological information. This allows the wear amount estimation system 100 to estimate various conditions such as dry, semi-humid, wet, icy, and snowy, thereby improving the accuracy of estimating the wear amount of the tire 7.

The wear amount estimation system 100 further includes a tire severity calculation unit 14 that calculates the severity of weather conditions for the tires based on the air temperature and the hours of sunshine. The wear amount calculation unit 15 inputs the severity calculated by the tire severity calculation unit 14 into a computation model 15a to calculate the wear amount of the tire 7. This enables the wear amount estimation system 100 to improve the accuracy of estimating the wear amount of the tire 7.

The wear amount calculation unit 15 also inputs a wear index value that indexes the wear resistance performance of the tire 7 into the calculation model 15a to calculate the wear amount of the tire. This allows the wear amount estimation system 100 to estimate the wear amount that reflects the wear resistance performance of the tread compound of each tire 7.

The computation model generation system 110 includes a vehicle information acquisition unit 12, a road surface condition estimation unit 13, a wear amount calculation unit 15, and a learning processing unit 21. The vehicle information acquisition unit 12 acquires information including the load and travel distance of the vehicle. The road surface condition estimation unit 13 estimates the road surface condition related to the road surface that the tire 7 contacts while the vehicle is traveling. The wear amount calculation unit 15 has a computation model 15a that calculates the wear amount of the tire 7 based on the input information, and inputs the information acquired by the vehicle information acquisition unit 12 and the road surface condition estimated by the road surface condition estimation unit 13 to the computation model 15a to calculate the wear amount of the tire 7. The learning processing unit 21 compares the wear amount measured on the tire 7 with the wear amount calculated by the wear amount calculation unit 15 to train the computation model 15a. As a result, the computation model generation system 110 can generate a computation model 15a that accurately estimates the wear amount of the tire 7.

The wear amount estimation method includes a vehicle information acquisition step, a road surface condition estimation step, and a wear amount calculation step. The vehicle information acquisition step acquires information including the vehicle's load and travel distance. The road surface condition estimation step estimates the road surface condition related to the road surface with which the tire 7 comes into contact while the vehicle is traveling. The wear amount calculation step calculates the wear amount of the tire 7 by inputting the information acquired in the vehicle information acquisition step and the road surface condition estimated in the road surface condition estimation step into a calculation model 15a that calculates the tire wear amount based on the input information. This wear amount estimation method can improve the accuracy of the wear amount estimation by simulating the friction condition with the road surface.

The above describes the embodiments of the present invention. These embodiments are illustrative, and it will be understood by those skilled in the art that various modifications and changes are possible within the scope of the claims of the present invention, and that such modifications and changes are also within the scope of the claims of the present invention. Therefore, the descriptions and drawings in this specification should be treated as illustrative rather than restrictive.

7 Tire, 12 Vehicle information acquisition unit, 13 Road surface condition estimation unit,
14 Tire severity calculation unit, 15 Wear amount calculation unit, 15a Calculation model,
21 learning processing unit, 100 wear amount estimation system,
110 Computational model generation system.

Claims (6)

A vehicle information acquisition unit that acquires information including a load and a mileage of a vehicle;
a road surface condition estimating unit that estimates a road surface condition related to a road surface that a tire contacts while the vehicle is traveling;
a tire severity calculation unit that sets a plurality of sections based on the magnitude of values of air temperature and sunshine hours, and calculates a frequency in each of the sections as a severity of weather conditions for the tires;
a wear amount calculation unit having a calculation model for calculating a tire wear amount based on input information, the wear amount calculation unit inputting the information acquired by the vehicle information acquisition unit , the road surface condition estimated by the road surface condition estimation unit , and the severity calculated by the tire severity calculation unit into the calculation model to calculate a tire wear amount;
A wear amount estimation system comprising: The wear amount estimation system according to claim 1, characterized in that the road surface condition estimation unit estimates the road surface condition based on meteorological information. The wear amount estimation system according to claim 2, characterized in that the weather information is precipitation level and air temperature. 4. The wear amount estimation system according to claim 1, wherein the wear amount calculation unit calculates the amount of wear of the tire by inputting a wear index value that indexes the wear resistance performance of the tire into the calculation model. A vehicle information acquisition unit that acquires information including a load and a mileage of a vehicle;
a road surface condition estimating unit that estimates a road surface condition related to a road surface that a tire contacts while the vehicle is traveling;
a tire severity calculation unit that sets a plurality of sections based on the magnitude of values of air temperature and sunshine hours, and calculates a frequency in each of the sections as a severity of weather conditions for the tires;
a wear amount calculation unit having a calculation model for calculating a tire wear amount based on input information, the wear amount calculation unit inputting the information acquired by the vehicle information acquisition unit , the road surface condition estimated by the road surface condition estimation unit , and the severity calculated by the tire severity calculation unit into the calculation model to calculate a tire wear amount;
a learning processing unit that learns the calculation model by comparing the amount of wear measured on the tire with the amount of wear calculated by the wear amount calculation unit;
A computational model generation system comprising: A vehicle information acquisition step of acquiring information including a load and a mileage of the vehicle;
a road surface condition estimating step of estimating a road surface condition related to a road surface that the tires contact while the vehicle is traveling;
a tire severity calculation step of setting a plurality of sections based on the magnitude of values of air temperature and sunshine hours, and calculating a frequency in each of the sections as a severity of weather conditions for the tires;
a wear amount calculation step of calculating a tire wear amount by inputting the information acquired by the vehicle information acquisition step, the road surface condition estimated by the road surface condition estimation step , and the severity calculated by the tire severity calculation step into a calculation model that calculates a tire wear amount based on input information;
A wear amount estimating method comprising:

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