JP2022143401A - Wear amount estimation system, computing model generation system, and wear amount estimation method - Google Patents

Abstract

To provide a wear amount estimation system capable of improving the estimation accuracy of tire wear amount, a computing model generation system, and a wear amount estimation method.SOLUTION: A wear amount estimation system 100 is equipped with a vehicle information obtaining portion 12, a tire severity generating portion 13, and a wear amount calculating portion 14. The vehicle information obtaining portion 12 obtains information including a traveling distance and acceleration. The tire severity generating portion 13 generates a value calculated by a sum of squares of the acceleration obtained by the vehicle information obtaining portion 12 as severity information with respect to a tire wear. The wear amount calculating portion 14 has a computing model 14a calculating the wear amount of a tire 7 on the basis of inputted information, and calculates the wear amount of the tire 7 by inputting the traveling distance obtained by the vehicle information obtaining portion 12 and the severity information generated by the tire severity generating portion 13 in the computing model 14a.SELECTED DRAWING: Figure 1

Description

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

In general, tire wear progresses according to running conditions, running distance, and the like. In recent years, devices such as devices that display the measured pressure and temperature by attaching a sensor for measuring the pressure and temperature of the tire to the tire have been commercialized.

Patent Literature 1 describes a conventional tire wear estimation system. The tire wear estimation system includes at least one sensor attached to the tire to generate a first predictor variable, at least one of a lookup table and database storing data about the second predictor variable, at least one and a model that receives the predictor variables and produces an estimated wear rate for the at least one tire.

JP 2018-158722 A

The tire wear estimation system described in Patent Literature 1 estimates the amount of tire wear using, for example, wheel positions and drivetrain as vehicle influences. The inventors of the present invention have found that even under the same vehicle influence conditions described in Patent Document 1, the amount of tire wear varies depending on the degree of severity of tire wear calculated based on the acceleration of the vehicle. The inventors have noticed that there is room for improvement in the quantity estimation.

The present invention has been made in view of such circumstances, and its object is to provide a wear amount estimation system, an arithmetic model generation system, and a wear amount estimation method that can improve the accuracy of estimating the amount of tire wear. is to provide

A wear amount estimation system according to one aspect of the present invention includes a vehicle information acquisition unit that acquires information including a travel distance and acceleration of a vehicle; A tire severity generation unit that generates severity information for the vehicle, and an arithmetic model that calculates the amount of tire wear based on the input information, and the travel distance and tire severity generation acquired by the vehicle information acquisition unit a wear amount calculation unit that inputs the severity information generated by the unit to the arithmetic model to calculate the wear amount of the tire.

Another aspect of the invention is a computational model generation system. The computational model generation system includes a vehicle information acquisition unit that acquires information including travel distance and acceleration, and a tire that generates a value calculated from the sum of squares of the accelerations acquired by the vehicle information acquisition unit as severity information for tire wear. It has a severity generation unit and an arithmetic model for calculating the amount of tire wear based on the input information. A wear amount calculation unit that calculates the wear amount of a tire by inputting data into the calculation model, and learns the calculation model by comparing the wear amount measured by the tire with the wear amount calculated by the wear amount calculation unit. and a learning processing unit that causes the

Another aspect of the present invention is a wear amount estimation method. The method for estimating the amount of wear includes a vehicle information acquisition step of acquiring information including travel distance and acceleration; The mileage obtained in the vehicle information obtaining step and the severity information generated in the tire severity generating step are input to the severity generating step and an arithmetic model for calculating tire wear based on the input information. and a wear amount calculation step of calculating the wear amount of the tire.

ADVANTAGE OF THE INVENTION According to this invention, the estimation precision of the wear amount of a tire can be improved.

1 is a block diagram showing the functional configuration of a wear amount estimation system according to an embodiment; FIG. It is a block diagram which shows the functional structure of an in-vehicle measuring device. FIG. 5 is a schematic diagram for explaining calculation of tire severity. FIG. 4 is a schematic diagram for explaining wear amount estimation and learning of a computing model; 1 is a block diagram showing a functional configuration of an arithmetic model generation system; FIG. 4 is a flow chart showing a procedure of computational model generation by the computational model generation system; It is a chart which shows the result of the wear amount estimation by an Example.

BEST MODE FOR CARRYING OUT THE INVENTION The present invention will be described below based on preferred embodiments with reference to FIGS. 1 to 7. FIG. The same or equivalent constituent elements and members shown in each drawing are denoted by the same reference numerals, and duplication of description will be omitted as appropriate. In addition, the dimensions of the members in each drawing are appropriately enlarged or reduced for easy understanding. Also, in each drawing, some of the members that are not important for explaining the embodiments are omitted.

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

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

FIG. 2 is a block diagram showing the functional configuration of the in-vehicle measuring device 70. As shown in FIG. The vehicle-mounted measurement device 70 includes a vehicle measurement unit 71 , a tire measurement unit 72 , an information acquisition unit 73 and a communication unit 74 . Each part in the in-vehicle measurement device 70 can be realized by electronic elements such as a CPU of a computer, mechanical parts, etc. in terms of hardware, and realized by computer programs etc. in terms of software. It depicts the functional blocks implemented by Therefore, those skilled in the art will understand that these functional blocks can be realized in various forms by combining hardware and software.

The vehicle measurement unit 71 has a speedometer 71a, a GPS receiver 71b, and an acceleration sensor 71c 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 acceleration sensor 71c measures the acceleration of the vehicle in three axial directions.

The tire measuring section 72 has a temperature sensor 72a and a pressure sensor 72b. The temperature sensor 72a and the pressure sensor 72b are arranged in the air valve of the tire 7 mounted on the vehicle, or are firmly wrapped around the wheel with a belt or the like and fixed, and measure the temperature and air pressure of the tire 7. The temperature sensor 72a may be arranged on the inner liner of the tire 7 or the like. In addition, the acceleration sensor 71c may be arranged on the inner liner of the tire 7 .

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

The information acquisition unit 73 may acquire vehicle speed, acceleration, position information, etc. collected by the electronic control device of the vehicle or, if the vehicle is equipped with a device such as a digital tachometer. . The communication unit 74 communicates with the communication network 9 by wireless communication such as WiFi (registered trademark), and transmits the vehicle measurement information, the tire measurement information, and the time information acquired by the information acquisition unit 73 via the communication network 9. Send to the estimation device 10 .

Returning to FIG. 1, the weather information server device 80 provides weather information for various places. The weather information provided by the weather information server device 80 is information including rainfall amounts, snowfall amounts, snowfall amounts, temperatures, hours of sunshine, and the like in various locations. The wear amount estimation device 10 acquires weather information for the place where the vehicle is running from the weather information server device 80 .

The wear amount estimation device 10 includes a communication section 11 , a vehicle information acquisition section 12 , a tire severity generation section 13 , a wear amount calculation section 14 and a storage section 15 . Each part in the wear amount estimation device 10 can be realized by electronic elements such as a CPU of a computer, mechanical parts, etc. in terms of hardware, and by computer programs etc. in terms of software. It depicts the functional blocks realized by cooperation. Therefore, those skilled in the art will understand 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 in-vehicle 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 in-vehicle measurement device 70 mounted on the vehicle. The vehicle information acquisition unit 12 calculates and acquires the travel distance of the vehicle based on the vehicle measurement information.

The vehicle information acquisition unit 12 can calculate and acquire the travel distance based on the position information of the vehicle measurement information. Further, the travel distance of the vehicle may be calculated based on the speed data in the vehicle measurement information and the time data associated with the data. That is, by multiplying the speed data arranged in chronological order by the time difference up to the next point in time, the traveling distance of the vehicle can be calculated. The speed of the vehicle may be calculated from the traveling distance of the vehicle based on the position information arranged in chronological order and the interval at which the position information is obtained.

The vehicle information acquisition unit 12 does not need to calculate the mileage by itself if the information on the mileage of the vehicle is provided from the vehicle or an external device for vehicle management or the like. may be obtained.

The vehicle information acquisition unit 12 outputs the acquired travel distance to the wear amount calculation unit 14 . The vehicle information acquisition unit 12 outputs the acquired tire measurement information (tire temperature, air pressure, etc.) to the wear amount calculation unit 14 . The vehicle information acquisition unit 12 outputs the acceleration information in the vehicle measurement information and the vehicle travel distance information to the tire severity generation unit 13 .

When the wear amount calculation unit 14 estimates the amount of tire wear based on an arithmetic model that uses vehicle acceleration as an input factor, the vehicle information acquisition unit 12 outputs the acceleration data in the vehicle measurement information to the wear amount calculation unit 14. .

The vehicle information acquisition unit 12 also acquires from the storage unit 15 data used for estimating the wear amount of the tire 7 among the vehicle specification data 15a, the tire specification data 15b, and the tire position data 15c, and outputs the data to the wear amount calculation unit 14. . The storage unit 15 is a storage device configured by, for example, an SSD (Solid State Drive), hard disk, CD-ROM, DVD, etc., and stores data provided in advance regarding specifications of various vehicles and tires 7. .

The vehicle specification data 15a includes information on vehicle performance such as manufacturer, vehicle name, vehicle model, vehicle weight, drive train, overall length, vehicle width, vehicle height, and maximum payload. The tire specification data 15b includes, for example, manufacturer, product name, tire size, tire width, aspect ratio, wear resistance performance, tire strength, static rigidity, dynamic rigidity, tire outer diameter, load index, manufacturing date. etc., information about the performance of the tire 7 is included. In addition, the tire position data 15c includes the position of the tire whose wear is to be predicted on the vehicle, tire identification information, and information on the attached axle. The tire identification information is a serial number such as a manufacturing number assigned to each tire to identify each tire. The tire identification information, the tire arrangement position, and the information on the axle may be stored in the storage unit 15 by an operator's input operation when the tire is mounted on the vehicle, for example.

The tire severity generating unit 13 generates a value calculated based on the sum of squares of acceleration of the vehicle as severity information for tire wear (hereinafter referred to as tire severity information). The tire severity generator 13 uses the acceleration in the three axial directions of the vehicle, namely, the longitudinal direction, the lateral direction, and the vertical direction, as the acceleration of the vehicle, and obtains the square sum of these accelerations.

The tire severity generation unit 13 may obtain the sum of squares of the acceleration in any one or two directions among the accelerations in the three directions. When the acceleration in any one of the three axial directions is used, the tire severity generator 13 obtains the square of the acceleration in the one axial direction. Obtaining the square sum of the acceleration by using the uniaxial acceleration in the tire severity generator 13 is also included in the calculation of the sum of the squares of the acceleration.

The tire severity generator 13 may use only acceleration in the longitudinal direction of the vehicle among accelerations of the vehicle in the three axial directions. Further, the tire severity generator 13 may use acceleration in the longitudinal direction of the vehicle and one of the other two axial directions among the acceleration of the vehicle in the three axial directions.

FIG. 3 is a chart for explaining generation of tire severity information. As shown in FIG. 3, the tire severity generation unit 13 calculates and integrates the sum of squares of acceleration (V1) and the sum of squares of acceleration multiplied by the distance traveled by the vehicle with the acceleration (V2 ), and a value obtained by multiplying the sum of the squares of the acceleration by the distance traveled by the vehicle with the acceleration and dividing the accumulated value (V3) by the total travel distance is generated as the tire severity information. The tire severity generator 13 may generate any one or two of these three values as the tire severity information.

Since the wear of the tire 7 tends to progress remarkably as the acceleration of the vehicle increases, the value of the sum of the squares of the acceleration (V1) is obtained and used as one index of the tire severity information.

V2 takes into account how far the vehicle has traveled with the acceleration changing every second. If the vehicle travels a long distance with large acceleration, the amount of wear increases. On the other hand, even if a large acceleration occurs, if the vehicle travels a short distance, the amount of wear is small. it is conceivable that.

V3 is obtained by dividing the value of V2 by the total distance traveled. The wear amount estimation is performed with a period of time between the estimation at one point in time and the estimation at the next point in time. Evaluate by leveling with respect to distance.

The wear amount calculator 14 has an arithmetic model 14 a and estimates the wear amount of the tire 7 . The arithmetic model 14a is a learning model that calculates the wear amount of the tire 7 based on the input information. FIG. 4 is a schematic diagram for explaining wear amount estimation and learning of the computation model 14a. The input data to the computation model 14a is roughly classified into each system of vehicle measurement information, tire measurement information, tire severity information, and other information.

Input data related to vehicle metrology information includes vehicle acceleration and mileage. The traveled distance is obtained by the vehicle information obtaining unit 12 as described above. Input data related to tire measurement information includes tire 7 temperature and air pressure. It is assumed that the acceleration of the vehicle is used as input data to the calculation model as appropriate.

The tire severity information-related input data is the tire severity information calculated by the tire severity generation unit 13 . The tire severity information is calculated based on the acceleration and travel distance of the vehicle as described above.

The input data of other information includes the road surface condition, temperature, amount of precipitation, etc. estimated based on weather information, the maximum load of the vehicle included in the vehicle specification data 15a, and the wear resistance of the tire 7 included in the tire specification data 15b. performance, etc. As the wear resistance performance of the tire 7, for example, a tire wear index value obtained by indexing the wear resistance performance of various tread formulations based on the Lambourn wear test, with the standard formulation being 100, is used. The input data of other information is the position of the tire 7 included in the tire position data 15c, tire identification information, and information on the axle. It should be noted that the maximum payload of the vehicle included in the vehicle specification data 15a is used for simplification of calculation. The load applied to each tire 7 varies depending on how luggage is loaded. Based on this, the load applied to each tire 7 is calculated by distributing the weight. In addition, for example, by attaching a dedicated sensor to the suspension of the vehicle body and constantly measuring the load applied to each axle, the total weight, and the load, the tire 7 changes in real time regardless of the maximum load. Calculate the load applied to each

The arithmetic model 14a uses a learning model such as a neural network, for example. The computational model 14a is constructed using a technique such as a DNN (Deep Neural Network) or a decision tree. Further, the calculation model 14a may be a multiple linear regression model for input information, for example, and may be model-generated by learning.

FIG. 5 is a block diagram showing the functional configuration of the arithmetic model generation system 110. As shown in FIG. The arithmetic model generation system 110 includes a tire wear amount measurement device 60 and an arithmetic model generation device 20 having a learning processing unit 21 in addition to the configuration of the wear amount estimation system 100 .

The tire wear amount measuring device 60 directly measures the depth of grooves provided in the tread of the tire 7 to acquire the wear amount of the tire 7 . A worker may measure the depth of each groove using a measuring instrument, a camera, or visually, and the tire wear amount measuring device 60 may store measurement data input by the worker. Further, the tire wear amount measuring device 60 may be a dedicated device that measures the depth of the groove by a mechanical or optical method and stores the wear amount.

Specifically, for example, when the tire has four grooves, the tire wear amount measuring device 60 measures at four points in the width direction, and furthermore, measures the same groove in the circumferential direction, for example, at intervals of 120°, at three points. Measure in place. Thereby, the uneven wear data in the width direction or the circumferential direction of the tire is also stored in the tire wear amount measuring device 60 . Note that the tire wear amount measuring device 60 may indirectly measure the depth of the groove by calculation from information on the running distance and the number of rotations and speed of the tire, since the diameter of the tire changes due to wear of the tire. In addition, the direct measurement of the groove depth may be combined with the prediction by calculation from the running distance and the number of revolutions/speed of the tire.

The arithmetic model generating device 20 has a learning processing section 21 in addition to each component of the wear amount estimating device 10 . Parts corresponding to each component of the wear amount estimation device 10 in the computation model generating device 20 have functions equivalent to those of the wear amount estimation device 10, but the computation model 14a is before or during learning.

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. 4, in the learning process of the computing model 14a, the wear amount of the tire 7 as output data is estimated by the computing model 14a based on the input information and compared with the teacher data. The amount of wear of the tire 7 measured by the tire wear amount measuring device 60 is used as the teaching data.

The learning processing unit 21 compares the amount of wear of the tire 7 estimated by the arithmetic model 14a with the teacher data, newly sets various coefficients in the arithmetic process such as weighting in the arithmetic model 14a, and repeats updating of the model. to perform learning. The wear amount estimation system 100 estimates the wear amount of the tire 7 using the arithmetic model 14 a that has been learned by the arithmetic model generation system 110 . Note that the learning processing unit 21 can use a known learning method such as gradient boosting. Also, for verification of the computational model 14a, a known verification method such as random data sampling or cross-validation can be used.

Next, operations of the wear amount estimation system 100 and the arithmetic model generation system 110 will be described. FIG. 6 is a flow chart showing the procedure of computational model generation by the computational model generation system 110 . The vehicle information acquisition unit 12 starts acquiring vehicle measurement information and tire measurement information (S1). In step S1, the vehicle information acquisition unit 12 of the computational model generation device 20 stores necessary information such as vehicle specifications, tire specifications, tire positions, vehicle maximum load, and tire wear resistance performance as other information. 15. The vehicle information acquisition unit 12 starts calculating the travel distance (S2).

The tire severity generator 13 generates tire severity information based on the acceleration and travel distance of the vehicle (S3). Tire severity information is generated by calculating the values of V1, V2 and V3 as described above.

The wear amount calculation unit 14 acquires input data from the vehicle information acquisition unit 12 and the tire severity generation unit 13, and calculates and estimates the wear amount of the tire 7 using the arithmetic model 14a (S4). When the road surface condition or the like is used as input data for the computation model 14a, a processing unit (not shown) for estimating the road surface condition is provided, and the estimated road surface condition is input to the wear amount calculation unit 14 from the processing unit.

The learning processing unit 21 compares the wear amount of the tire 7 calculated by the arithmetic model 14a and the wear amount of the tire 7 as teacher data measured by the tire wear amount measuring device 60 (S5). The learning processing unit 21 updates the arithmetic model 14a based on the comparison result of step S7 (S7), and terminates the process. By repeating these processes, the computational model generation device 20 updates the computational model 14a and improves the accuracy of tire wear amount estimation.

The wear amount estimation system 100 estimates the wear amount of the tire 7 using the learned computational model 14 a generated by the computational model generation 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 S4 in the flowchart shown in FIG.

The wear amount estimation system 100 can improve the accuracy of the wear amount estimation of the tire 7 by using the tire severity information based on the traveled distance and the sum of squares of the acceleration of the vehicle as input data for the computation model 14a. Similarly, the computational model generation system 110 uses tire severity information based on the traveled distance and the sum of squares of vehicle acceleration as input data for the computational model 14a, thereby estimating the amount of wear of the tire 7 with high accuracy. 14a can be generated.

When the mileage of the vehicle is measured every moment and provided from an in-vehicle device or the like, the wear amount calculation unit 14 may use the provided mileage instead of the calculation by the vehicle information acquisition unit 12. . The wear amount estimation system 100 can also estimate the uneven wear amount by constructing the arithmetic model 14a that outputs the uneven wear amount of the tire 7 . In this case, the wear amount estimation system 100 estimates the uneven wear of the tire 7 based on the travel distance and the tire severity information based on the sum of the squares of the acceleration of the vehicle. The wear amount estimation system 100 can estimate uneven wear, which is partial wear of the tire 7 caused by changes in acceleration such as sudden start or deceleration of the vehicle. Further, the computational model generation system 110 can generate the computational model 14a for estimating the uneven wear amount by having the computational model 14a learn the amount of wear measured in each groove of the tire as teaching data of uneven wear. .

The tire severity generation unit 13 of the wear amount estimation system 100 calculates each value of V1, V2, and V3 to generate tire severity information, thereby taking into account the distance traveled by the vehicle with ever-changing acceleration. can obtain severity information that contributes to tire wear.

In the wear amount estimation system 100 and the computational model generation system 110, the wear amount calculation unit 14 may use all of the values of V1, V2 and V3, or use any one or any combination of two tire severity values. Information may be used.

Further, the tire severity generator 13 may use all accelerations in the three axial directions of the vehicle, any one axial acceleration, or a combination of arbitrary two axial accelerations. It is preferable that the tire severity generation unit 13 generates tire severity information including acceleration in the longitudinal direction of the vehicle among accelerations in the three axial directions of the vehicle.

(Example)
In the example, the calculation model 14a was generated based on the data measured in the actual vehicle, the wear amount of the tire 7 was estimated, and the estimation accuracy was verified. The vehicles used in the example were 17 truck vehicles, and the amount of tire wear was measured for each month over four months and used as teaching data. The computational model 14a was constructed by regression analysis and decision tree analysis. In addition, the tire severity generating unit 13 generates tire severity information using accelerations in three axial directions of the vehicle.

FIG. 7 is a chart showing the results of wear amount estimation according to the example. As shown in FIG. 7, the wear amount is estimated in five cases. In case C1, the wear amount calculator 14 estimates the wear amount of the tire 7 based on the traveled distance calculated based on the position information obtained by the GPS receiver. In case C2, the wear amount calculator 14 uses the V1 value generated by the tire severity generator 13 as tire severity information in addition to the travel distance to estimate the tire wear amount.

In case C3, the wear amount calculator 14 uses the V2 value generated by the tire severity generator 13 as tire severity information in addition to the travel distance to estimate the tire wear amount. In case C4, the wear amount calculator 14 uses the V3 value generated by the tire severity generator 13 as tire severity information in addition to the travel distance to estimate the tire wear amount. In case C5, the wear amount calculation unit 14 uses the V1, V2, and V3 values generated by the tire severity generation unit 13 as tire severity information in addition to the travel distance to estimate the tire wear amount.

In each case, the RMSE (root mean square error) obtained by plotting the actually measured wear amount and the wear amount predicted by the computation model 14a was better in the decision tree analysis than in the regression analysis. Also, in contrast to case C1, in the case of case C2, in addition to the travel distance, the value of V1 is used as tire severity information, resulting in improved estimation accuracy at least in regression analysis. In addition, cases C3, C4, and C5 also resulted in improved estimation accuracy.

Next, features of the wear amount estimation system 100 and the computational model generation system 110 according to each embodiment will be described.
The wear amount estimation system 100 includes a vehicle information acquisition section 12 , a tire severity generation section 13 and a wear amount calculation section 14 . The vehicle information acquisition unit 12 acquires information including travel distance and acceleration. The tire severity generation unit 13 generates a value calculated from the sum of squares of acceleration (V1) acquired by the vehicle information acquisition unit 12 as severity information for tire wear. The wear amount calculation unit 14 has an arithmetic model 14a that calculates the wear amount of the tire 7 based on the input information, and includes the travel distance acquired by the vehicle information acquisition unit 12 and the tire severity generation unit 13. The wear amount of the tire 7 is calculated by inputting the severity information into the computation model 14a. As a result, the wear amount estimation system 100 can improve the accuracy of the wear amount estimation by using the value calculated by the sum of the squares of the acceleration of the vehicle as severity information for tire wear.

In addition, the tire severity generator 13 constantly calculates a value obtained by multiplying the sum of squares of the acceleration by the distance traveled by the vehicle with the acceleration, and generates an integrated value (V2) as severity information. As a result, the wear amount estimation system 100 can generate tire severity information in consideration of the distance traveled by the vehicle with acceleration that changes from moment to moment, and improve the accuracy of wear amount estimation.

In addition, the tire severity generator 13 constantly calculates a value obtained by multiplying the sum of the squares of the acceleration by the distance traveled by the vehicle with the acceleration, and calculates a value (V3) obtained by dividing the integrated value (V2) by the total travel distance. Generate as severity information. As a result, the wear amount estimation system 100 can use the value of V2 in each period of wear amount estimation by leveling the travel distance.

In addition, the tire severity generating unit 13 calculates the sum of squares of acceleration (V1), the sum of squares of acceleration multiplied by the distance traveled by the vehicle with the acceleration (V2), and the sum of the sum of squares of acceleration (V2). A value obtained by multiplying the sum by the distance traveled by the vehicle by the acceleration is calculated every moment, and severity information including a value (V3) obtained by dividing the integrated value by the total travel distance is generated. As a result, the wear amount estimation system 100 can improve the accuracy of wear amount estimation using V1, V2, and V3 as tire severity information.

The wear amount calculator 14 calculates the wear amount of the tire 7 by inputting the position of the tire on the vehicle into the computation model 14a. Thereby, the wear amount estimation system 100 can estimate the wear amount of the tire 7 according to the position of the tire 7 on the vehicle.

The computational model generation system 110 includes a vehicle information acquisition unit 12 , a tire severity generation unit 13 , a wear amount calculation unit 14 and a learning processing unit 21 . The vehicle information acquisition unit 12 acquires information including travel distance and acceleration. The tire severity generator 13 generates a value calculated from the sum of the squares of the accelerations acquired by the vehicle information acquisition unit 12 as severity information for tire wear. The wear amount calculation unit 14 has an arithmetic model 14a that calculates the wear amount of the tire 7 based on the input information, and includes the travel distance acquired by the vehicle information acquisition unit 12 and the tire severity generation unit 13. The severity information is input to the computation model 14a to calculate the wear amount of the tire. The learning processing unit 21 compares the amount of wear measured by the tire 7 and the amount of wear calculated by the wear amount calculating unit 14 to learn the computation model 14a. Accordingly, the computational model generation system 110 can generate the computational model 14a for estimating the wear amount of the tire 7 with high accuracy.

The wear amount estimation method includes a vehicle information acquisition step, a tire severity generation step, and a wear amount calculation step. The vehicle information acquisition step acquires information including travel distance and acceleration. The tire severity generation step generates a value calculated from the sum of squares of acceleration (V1) acquired in the vehicle information acquisition step as severity information for tire wear. In the wear amount calculation step, the travel distance acquired in the vehicle information acquisition step and the severity information generated in the tire severity generation step are input to the arithmetic model 14a that calculates the tire wear amount based on the input information. to calculate the amount of tire wear. According to this method, the value calculated from the sum of the squares of the acceleration of the vehicle is used as the information on the degree of severity of tire wear, thereby improving the accuracy of estimating the amount of wear.

The above has been described based on the embodiments of the present invention. Those skilled in the art will appreciate that these embodiments are illustrative and that various variations and modifications are possible within the scope of the claims of the present invention, and that such variations and modifications also fall within the scope of the claims of the present invention. It is about to be done. Accordingly, the description and drawings herein are to be regarded in an illustrative rather than a restrictive sense.

7 tires 12 vehicle information acquisition unit 13 tire severity generation unit
14 wear amount calculation unit 14a computation model 21 learning processing unit
100 wear amount estimation system, 110 arithmetic model generation system.

Claims (7)

a vehicle information acquisition unit that acquires information including travel distance and acceleration;
a tire severity generation unit that generates a value calculated from the sum of squares of accelerations acquired by the vehicle information acquisition unit as severity information for tire wear;
It has an arithmetic model for calculating the amount of tire wear based on the input information, and inputs the mileage acquired by the vehicle information acquisition unit and the severity information generated by the tire severity generation unit into the arithmetic model. a wear amount calculation unit that calculates the wear amount of the tire by
A wear amount estimation system comprising: 2. The tire severity generating unit calculates a value obtained by multiplying the sum of squares of acceleration by a distance traveled by the vehicle with the acceleration, and generates the integrated value as the severity information. The wear amount estimation system described in . The tire severity generation unit constantly calculates a value obtained by multiplying the sum of squares of acceleration by the distance traveled by the vehicle with the acceleration, and generates a value obtained by dividing the integrated value by the total traveled distance as the severity information. The wear amount estimation system according to claim 1, characterized in that: The tire severity generation unit calculates and integrates the sum of squares of acceleration, the sum of squares of acceleration multiplied by the distance traveled by the vehicle with the acceleration, and the sum of the squares of acceleration. 2. The wear amount estimation system according to claim 1, wherein the wear amount estimation system generates the severity information including a value obtained by dividing the value obtained by multiplying the traveled distance by the total traveled distance. 5. The wear amount estimation system according to any one of claims 1 to 4, wherein the wear amount calculation unit calculates the tire wear amount by inputting the position of the tire on the vehicle into the arithmetic model. a vehicle information acquisition unit that acquires information including travel distance and acceleration;
a tire severity generation unit that generates a value calculated from the sum of squares of accelerations acquired by the vehicle information acquisition unit as severity information for tire wear;
It has an arithmetic model for calculating the amount of tire wear based on the input information, and inputs the mileage acquired by the vehicle information acquisition unit and the severity information generated by the tire severity generation unit into the arithmetic model. a wear amount calculation unit that calculates the wear amount of the tire by
a learning processing unit that compares the amount of wear measured by the tire with the amount of wear calculated by the wear amount calculation unit and learns the arithmetic model;
A calculation model generation system characterized by comprising: a vehicle information acquisition step of acquiring information including travel distance and acceleration;
a tire severity generation step for generating a value calculated by the sum of squares of accelerations acquired in the vehicle information acquisition step as severity information for tire wear;
The mileage obtained in the vehicle information obtaining step and the severity information generated in the tire severity generating step are input to an arithmetic model for calculating the tire wear quantity based on the input information, and the tire wear quantity is calculated. A wear amount calculation step to calculate;
A wear amount estimation method, comprising:

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