JP7368181B2 - Wear amount estimation system and calculation model generation system - Google Patents

Description

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

In general, tires progress as wear progresses depending on driving conditions, mileage, and the like. 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 estimating device. This tire wear estimating device has a vehicle speed detection means, calculates position data of the vehicle based on a signal from a satellite received by a GPS receiver, and detects a vehicle speed V from the position data. The tire wear estimating device also measures the wheel rotation speed using a wheel speed sensor, and converts it into a wheel rotation speed Vw corrected based on the tire internal pressure detected by the pressure sensor. A speed ratio R=(Vw/V), which is the ratio of the corrected wheel rotational speed to the vehicle speed, is calculated, and the amount of tire wear is estimated based on the speed ratio R.

Japanese Patent Application Publication No. 2018-143460

In the tire wear estimating device described in Patent Document 1, the speed ratio, which is the ratio of the wheel rotation speed to the vehicle speed, has a high correlation with the amount of tire wear. For example, when a vehicle starts and stops, tire air pressure and temperature rise and fall, and tire wear progresses. We believe that there is room for improvement in the estimation accuracy.

The present invention has been made in view of the above circumstances, and an object thereof is to provide a wear amount estimation system and a calculation model generation system that can accurately estimate tire wear amount.

A wear amount estimation system according to an aspect of the present invention includes a tire information acquisition unit that acquires air pressure data and temperature data of tires mounted on a vehicle, and at least one of the air pressure data and temperature data acquired by the tire information acquisition unit. It has a tire severity calculation unit that calculates the tire severity indicating the degree of load on the tire from either data, and a calculation model that calculates the amount of tire wear based on the information on the severity of the tire. and a wear amount calculation unit that inputs the degree of severity and calculates the amount of wear of the tire using the calculation model.

Another aspect of the present invention is a computational model generation system. The calculation model generation system includes a tire information acquisition unit that acquires air pressure data and temperature data of tires mounted on a vehicle, and at least one of the air pressure data and temperature data acquired by the tire information acquisition unit. It has a tire severity calculation unit that calculates a tire severity indicating the degree of load on the tire, and a calculation model that calculates the amount of tire wear based on information on the severity of the tire, and receives the input of the tire severity. a wear amount calculation unit that calculates the wear amount of the tire using the calculation model; and a calculation model update that updates the calculation model by comparing the wear amount measured on the tire with the wear amount calculated by the wear amount calculation unit. It is equipped with a section and a section.

According to the present invention, the amount of tire wear can be estimated with high accuracy.

FIG. 1 is a block diagram showing a functional configuration of a calculation model generation system according to an embodiment. FIG. 3 is a schematic diagram for explaining learning of a calculation model. 3 is a flowchart showing a procedure for generating an arithmetic model by the arithmetic model generation system. A graph showing changes in tire pressure data over time. 2 is a graph of tire pressure data including a description of the positive gradient vector of air pressure; 2 is a graph plotting tire air pressure data and temperature data. FIG. 2 is a block diagram showing the functional configuration of the wear amount estimation system.

Hereinafter, the present invention will be explained based on a preferred embodiment with reference to FIGS. 1 to 7. Identical or equivalent components and members shown in each drawing are designated by the same reference numerals, and redundant explanations will be omitted as appropriate. Further, the dimensions of members in each drawing are shown enlarged or reduced as appropriate to facilitate understanding. Further, in each drawing, some members that are not important for explaining the embodiments are omitted.

(Embodiment)
FIG. 1 is a block diagram showing the functional configuration of a calculation model generation system 100 according to an embodiment. In the calculation model generation system 100, a tire information acquisition unit 31 acquires data on physical quantities related to the tire, such as air pressure and temperature measured by a sensor 20 disposed on the tire 10, and a tire severity calculation unit 33a calculates the tire severity. is calculated to generate the calculation model 33b. The tire severity calculated by the tire severity calculation unit 33a is an index indicating the degree of load on the tire 10 that affects the progress of wear of the tire 10. Furthermore, the calculation model 33b inputs the travel distance, speed, turning radius, etc. calculated based on the acceleration data acquired by the tire information acquisition unit 31 and the position information acquired by the position information acquisition unit 32. You can also use it as

The calculation model generation system 100 uses a learning model such as a neural network as a calculation model, uses the amount of tire wear actually measured in the tire 10 as training data, and repeats learning by performing calculations and updating the calculation model. The accuracy of the calculation model 33b is improved. The wear amount estimating device 30 functions as a device that estimates the amount of tire wear after the calculation model 33b is trained.

The calculation model generation system 100 can perform learning of a calculation model for a tire 10 having a certain specification by driving a vehicle equipped with the tire 10 (including wheels). Tire specifications include information about tire performance, such as manufacturer, product name, tire size, tire width, aspect ratio, tire strength, static stiffness, dynamic stiffness, tire outer diameter, load index, and manufacturing date. is included.

A pressure sensor 21, a temperature sensor 22, and an acceleration sensor 23 are arranged on the tire 10 or wheel 15. The pressure sensor 21 and the temperature sensor 22 are disposed, for example, by being attached to an air valve of the tire 10 or fixed to the wheel 15, and measure the air pressure and temperature of the tire 10, respectively. Moreover, the pressure sensor 21 and the temperature sensor 22 may be arranged on the inner liner of the tire 10 or the like.

The acceleration sensor 23 is disposed, for example, on the tire 10 or the wheel 15, and measures the acceleration occurring in the tire 10. Note that, in order to identify each tire, the tires 10 may be attached with, for example, an RFID or the like provided with unique identification information. In addition to the tire severity level, the calculation model generation system 100 may also use tire data measured by the tires such as acceleration, travel distance, speed, turning radius, etc. as factors to be input into the calculation model 33b.

The wear amount estimating device 30 includes a tire information acquisition section 31, a position information acquisition section 32, a wear amount calculation section 33, and a calculation model updating section 34. The wear amount estimating device 30 is, for example, an information processing device such as a PC (personal computer). Each part of the wear amount estimating device 30 can be realized in terms of hardware by electronic elements such as the CPU of a computer, mechanical parts, etc., and can be realized by computer programs in terms of software, but here, those parts will be explained. It depicts functional blocks realized through collaboration. Therefore, those skilled in the art will understand that these functional blocks can be realized in various forms by combining hardware and software.

The tire information acquisition unit 31 acquires tire data including the air pressure, temperature, acceleration, and time of measurement measured in the tire 10 from the pressure sensor 21, temperature sensor 22, and acceleration sensor 23 by wireless communication or the like, and calculates the amount of wear by calculating the amount of tire data. Output to 33. The position information acquisition unit 32 acquires position data including the position of the vehicle equipped with the tire 10 calculated by a GPS receiver or the like and the calculated time, and outputs it to the wear amount calculation unit 33.

The wear amount calculation section 33 includes a tire severity calculation section 33a and a calculation model 33b. The tire severity calculation section 33a calculates the tire severity indicating the degree of load on the tire 10 from at least one of the air pressure data and temperature data input from the tire information acquisition section 31. The air pressure and temperature of the tire 10 rise and fall depending on the number of brakes, changes in outside temperature, high-speed driving, starting and stopping of the vehicle, tire burst and burst premonitory conditions, etc., which affects the wear of the tire 10. The factor of the severity of the tire 10 exerted by the temperature is taken as an input to the calculation model 33b.

Furthermore, the tire severity calculation unit 33a calculates the number of positive gradient and negative gradient change points in the air pressure data as the tire severity, and inputs the number to the calculation model 33b. The tire severity calculation unit 33a reflects in the calculation model 33b that the amount of wear of the tire 10 is accelerated when changes in air pressure and temperature occur frequently.

Further, the tire severity calculation unit 33a calculates the magnitude of the positive slope vector in the air pressure data as the tire severity, and inputs the calculated tire severity to the calculation model 33b. The tire severity calculation unit 33a reflects in the calculation model 33b that in a state where the air pressure data is rising with a positive slope, the load on the tire 10 increases and the amount of wear is accelerated.

Furthermore, the tire severity calculation unit 33a calculates the number of times the air pressure data and temperature data exceed the set values of the tire air pressure and temperature as the tire severity, and inputs the calculated number to the calculation model 33b. The tire severity calculation unit 33a calculates using a calculation model 33b that in a state where the air pressure data and temperature data exceed the set values of the tire air pressure and temperature, the load on the tire 10 increases and the amount of wear is accelerated. reflected in

Further, the wear amount calculation section 33 may calculate the travel distance, speed, and turning radius of the vehicle based on the position data input from the position information acquisition section 32, and input them to the calculation model 33b. The wear amount calculation unit 33 acquires position data from the position information acquisition unit 32 every moment, and calculates the traveling distance based on the position data from the start of travel to the present, and calculates the speed and turning radius based on the time change of the position data. You can ask for it. Further, the wear amount calculation unit 33 may calculate the travel distance, speed, and turning radius of the vehicle based on information acquired from the vehicle side. The wear amount calculation unit 33 calculates the travel distance, speed, and turning radius of the vehicle based on information such as engine rotation speed, vehicle speed, wheel speed, steering angle, throttle opening, etc. provided by the CAN bus of the vehicle. Can be done.

FIG. 2 is a schematic diagram for explaining learning of the calculation model 33b. The input data to the calculation model 33b are tire data including air pressure, temperature, and acceleration, and the tire severity calculated by the tire severity calculation unit 33a. Further, the travel distance, speed, and turning radius calculated based on the position data may be input to the calculation model 33b. Furthermore, as input data to the calculation model 33b, in addition to these, weather data, data from a digital tachograph mounted on the vehicle, and information on road surface conditions may be used. The weather data includes, for example, the temperature and precipitation of the region where the vehicle is traveling, and is used as input data to the calculation model 33b. Further, digital tachograph data such as vehicle weight and speed data is used as input data to the calculation model 33b. Further, as the information on the road surface condition, for example, the unevenness of the road surface on which the vehicle is traveling, the temperature, the dryness and humidity, etc. are used as input data to the calculation model 33b.

The weather data includes, for example, the temperature and precipitation of the region where the vehicle is traveling, and is used as input data to the calculation model 33b. Further, digital tachograph data such as vehicle weight and speed data is used as input data to the calculation model 33b. Further, as the information on the road surface condition, for example, the unevenness of the road surface on which the vehicle is traveling, the temperature, the dryness and humidity, etc. are used as input data to the calculation model 33b.

The calculation model 33b uses a learning model such as a neural network, for example. The calculation model 33b inputs tire data, mileage, speed, turning radius, etc. to the nodes of the input layer, and executes calculations using a path from the input layer that is weighted to the intermediate layer. The calculation model 33b further performs calculations using a weighted path from the intermediate layer to the output layer, and outputs the amount of tire wear from the nodes of the output layer. In a learning model such as a neural network, in addition to linear calculations, nonlinear calculations may be performed using an activation function or the like.

The calculation model updating unit 34 updates the calculation model 33b by comparing the amount of tire wear as a calculation result with the teacher data. The calculation model update section 34 includes a wear amount comparison section 34a and an update processing section 34b. The wear comparison section 34a compares the tire wear amount calculated by the calculation model 33b with the tire wear amount as teacher data measured by the tire measuring device 40, and outputs the error to the update processing section 34b.

The update processing unit 34b updates the weighting of the path on the calculation model based on the error in the amount of wear calculated by the calculation model 33b. The accuracy of the calculation model is improved by repeating calculation of the amount of tire wear by the calculation model 33b, comparison with teacher data by the wear amount comparison section 34a, and updating of the calculation model by the update processing section 34b.

The tire measuring device 40 directly measures the depth of grooves provided in the tread of the tire 10. An operator may measure the depth of each groove using a measuring instrument, a camera, visual inspection, etc., and the tire measuring device 40 may store measurement data input by the operator. Further, the tire measuring device 40 may be a dedicated device that measures and stores the groove depth using a mechanical or optical method.

Specifically, for example, when a tire has four grooves, the tire measuring device 40 measures at four locations in the width direction, and further measures at three locations in the circumferential direction of the same groove, for example, at 120° intervals. measure. Thereby, uneven wear data in the width direction or circumferential direction of the tire is also stored in the tire measuring device 40. Note that the tire measuring device 40 may indirectly measure the depth of the groove by calculating from information on the mileage and the rotation speed/speed of the tire, since the diameter changes due to wear of the tire. In addition, a method that directly measures the groove depth may be used in combination with a method that predicts the depth by calculation based on the travel distance and the number of rotations and speed of the tire.

Next, the operation of the calculation model generation system 100 will be explained. FIG. 3 is a flowchart showing a procedure for generating a calculation model by the calculation model generation system 100. In the wear amount estimating device 30, the tire information acquisition unit 31 starts acquiring tire data including the air pressure, temperature, and acceleration measured in the tire 10 from the pressure sensor 21, temperature sensor 22, and acceleration sensor 23 (S1). Furthermore, the position information acquisition unit 32 starts acquiring position data (S2). The tire severity calculation unit 33a calculates the tire severity based on the air pressure data and temperature data (S3). The wear amount estimating device 30 calculates the travel distance, speed, and turning radius based on the position data (S4).

The tire severity calculation unit 33a accumulates data over a predetermined period of time (S5). The predetermined period may be, for example, one driving period of the vehicle, several days, or one week, but is not limited to these. After a predetermined period has elapsed, the tire severity calculation unit 33a inputs each data to the calculation model 33b and estimates the amount of tire wear (S6). The wear comparison unit 34a compares the tire wear amount calculated by the calculation model 33b with the tire wear amount as teacher data measured by the tire measuring device 40 (S7). The wear comparison unit 34a outputs the difference between the tire wear amount calculated by the calculation model 33b and the tire wear amount measured by the tire measurement device 40 as a result of the comparison to the update processing unit 34b.

The update processing unit 34b updates the calculation model based on the tire wear amount error input from the wear amount comparison unit 34a (S8), and ends the process. By repeating these processes, the wear amount estimating device 30 updates the calculation model and improves the accuracy of tire wear amount estimation.

FIG. 4 is a graph showing changes in the air pressure data of the tire 10 over time, and FIG. 5 is a graph of the air pressure data of the tire 10 including an explanation of the positive gradient vector of the air pressure. At the locations circled in FIG. 4, the air pressure of the tire 10 changes from a positive slope to a negative slope, and from a negative slope to a positive slope. The tire severity calculation unit 33a calculates the number of change points at which the air pressure data of the tire 10 changes from a positive slope to a negative slope, or from a negative slope to a positive slope, as a tire severity level. Further, the tire severity calculation unit 33a determines positive gradient vectors V1 to V4, etc. in the air pressure data, as shown in FIG. 5, and calculates the magnitude of the vector as the tire severity.

The wear amount estimating device 30 constructs a calculation model 33b that takes into account the tire severity by inputting the tire severity calculated from the air pressure data and temperature data of the tires 10 when the vehicle is running as input to the calculation model 33b, The amount of tire wear can be estimated with high accuracy using the calculation model 33b. The wear amount estimating device 30 calculates the number of change points at which the air pressure data of the tire 10 changes from a positive slope to a negative slope, and from a negative slope to a positive slope, as the tire severity degree, and uses it as an input to the calculation model 33b. The accuracy of estimating the amount of wear can be improved. In addition, the wear amount estimating device 30 calculates the positive gradient vectors V1 to V4, etc. in the air pressure data of the tire 10, calculates the magnitude of the vector as the tire severity degree, and uses it as an input to the calculation model 33b, thereby estimating the tire wear amount. Estimation accuracy can be improved.

FIG. 6 is a graph in which air pressure data and temperature data of the tire 10 are plotted. The tire severity calculation unit 33a calculates the number of times the air pressure data and the temperature data are equal to or greater than the air pressure set value Ps and the temperature set value Ts as the tire severity level. The wear amount estimating device 30 can improve the accuracy of estimating the tire wear amount by inputting the calculated tire severity degree to the calculation model 33b. Furthermore, the calculation model generation system 100 may calculate the degree of tire severity by focusing on temperature data among the air pressure data and temperature data of the tire 10.

FIG. 7 is a block diagram showing the functional configuration of the wear amount estimation system 110. After the calculation model 33b for estimating tire wear amount is generated by the calculation model generation system 100 as described above, the wear amount estimating device 30 including the calculation model 33b can be configured. The wear estimation system 110 includes a sensor 20 and a wear estimation device 30, and uses the calculation model 33b generated by the calculation model generation system 100 to accurately estimate the wear amount of the tire 10 mounted on a vehicle.

The wear amount estimating device 30 includes a tire information acquisition section 31, a position information acquisition section 32, a wear amount calculation section 33, and a notification section 35, and can be used by being mounted on a vehicle. The tire information acquisition unit 31 and the position information acquisition unit 32 have the same configuration and operation as described based on FIG. 1, and their description will be omitted for the sake of brevity.

The wear amount calculation unit 33 uses the calculation model generated by the calculation model generation system 100 as the calculation model 33b. The wear amount calculation unit 33 may calculate the tire wear amount after accumulating each data acquired by the tire information acquisition unit 31 and the position information acquisition unit 32 for a predetermined period, or may calculate the tire wear amount moment by moment at the acquired timing. may be calculated.

As described above, the tire severity calculation unit 33a calculates the tire severity based on the air pressure data and temperature data of the tire 10, and inputs the calculated tire severity to the calculation model 33b. The wear amount estimation system 110 includes a wear amount calculation part including the calculation model 33b provided in a server device or the like external to the vehicle connected via a communication network, and transmits information such as air pressure data and temperature data of the tires 10 from the vehicle to the server device. The wear amount may be estimated by transmitting the information to, etc.

The notification unit 35 notifies passengers, such as the driver of the vehicle, of the amount of tire wear through display on the display device 51, audio output from the speaker 52, etc. in order to make the occupants of the vehicle, such as the driver, aware of the current amount of tire wear. Further, the notification unit 35 may notify a vehicle control device 53 mounted on the vehicle of the current amount of tire wear. The vehicle control device 53 can perform vehicle control such as automatic driving and collision avoidance based on the current amount of tire wear.

Next, features of the wear amount estimation system 110 and the calculation model generation system 100 according to each embodiment will be described.
The wear amount estimation system 110 includes a tire information acquisition section 31, a tire severity calculation section 33a, and a wear amount calculation section 33. The tire information acquisition unit 31 acquires air pressure data and temperature data of the tires 10 mounted on the vehicle. The tire severity calculation unit 33a calculates a tire severity level indicating the degree of load on the tire 10 from at least one of the air pressure data and temperature data acquired by the tire information acquisition unit 31. The wear amount calculation unit 33 has a calculation model 33b that calculates the amount of tire wear based on information on the degree of severity of the tire 10, and calculates the amount of wear of the tire 10 by inputting the degree of tire severity using the calculation model 33b. Thereby, the wear amount estimation system 110 can accurately estimate the tire wear amount using the calculation model 33b that takes into account the degree of tire severity.

Furthermore, the tire severity calculation unit 33a calculates the number of positive slope and negative slope change points in the air pressure data as the tire severity. Thereby, the wear amount estimation system 110 can improve the estimation accuracy of the tire wear amount.

Furthermore, the tire severity calculation unit 33a calculates the magnitude of the positive gradient vector in the air pressure data as the tire severity. Thereby, the wear amount estimation system 110 can improve the estimation accuracy of the tire wear amount.

Furthermore, the tire severity calculation unit 33a calculates the number of times the air pressure data and temperature data are equal to or greater than the set values of the air pressure and temperature of the tire 10 as the tire severity. Thereby, the wear amount estimation system 110 can improve the estimation accuracy of the tire wear amount.

The calculation model generation system 100 includes a tire information acquisition section 31, a tire severity calculation section 33a, a wear amount calculation section 33, and a calculation model update section 34. The tire information acquisition unit 31 acquires air pressure data and temperature data of tires mounted on a vehicle. The tire severity calculation unit 33a calculates a tire severity level indicating the degree of load on the tire 10 from at least one of the air pressure data and temperature data acquired by the tire information acquisition unit 31. The wear amount calculation unit 33 has a calculation model 33b that calculates the amount of tire wear based on information on the degree of severity of the tire 10, and calculates the amount of wear of the tire 10 by inputting the degree of tire severity using the calculation model 33b. The calculation model update unit 34 compares the amount of wear measured by the tire 10 with the amount of wear calculated by the wear amount calculation unit 33, and updates the calculation model 33b. Thereby, the arithmetic model generation system 100 can generate the arithmetic model 33b that can accurately estimate the amount of tire wear, taking into account the degree of tire severity.

The above description has been based on the embodiments of the present invention. Those skilled in the art will understand that these embodiments are illustrative and that various modifications and changes are possible and 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.

10 tires, 31 tire information acquisition section, 33 wear amount calculation section,
33a tire severity calculation unit, 33b calculation model, 34 calculation model update unit,
100 Arithmetic model generation system, 110 Wear amount estimation system.

Claims (4)

a tire information acquisition unit that acquires air pressure data and temperature data of tires mounted on the vehicle;
a tire severity calculation unit that calculates a tire severity level indicating the degree of load on the tire from at least one of the air pressure data and temperature data acquired by the tire information acquisition unit;
a wear amount calculation unit that has a calculation model that calculates the amount of tire wear based on information on the degree of severity of the tire, and that calculates the amount of wear of the tire by inputting the degree of tire severity and uses the calculation model ;
The wear amount estimation system is characterized in that the tire severity calculation unit calculates the number of change points of positive slope and negative slope in the air pressure data as the tire severity. a tire information acquisition unit that acquires air pressure data and temperature data of tires mounted on the vehicle;
a tire severity calculation unit that calculates a tire severity level indicating the degree of load on the tire from at least one of the air pressure data and temperature data acquired by the tire information acquisition unit;
a wear amount calculation unit that has a calculation model that calculates the amount of tire wear based on information on the degree of severity of the tire, and that calculates the amount of wear of the tire by inputting the degree of tire severity and uses the calculation model;
The wear amount estimation system is characterized in that the tire severity calculation unit calculates the magnitude of a positive gradient vector in the air pressure data as the tire severity. a tire information acquisition unit that acquires air pressure data and temperature data of tires mounted on the vehicle;
a tire severity calculation unit that calculates a tire severity level indicating the degree of load on the tire from at least one of the air pressure data and temperature data acquired by the tire information acquisition unit;
a wear amount calculation unit that has a calculation model that calculates the amount of tire wear based on information on the degree of severity of the tire, and that calculates the amount of wear of the tire by inputting the degree of tire severity and uses the calculation model;
The wear amount estimation system is characterized in that the tire severity calculation unit calculates, as the tire severity, the number of times that the air pressure data and the temperature data exceed set values for tire air pressure and temperature. a tire information acquisition unit that acquires air pressure data and temperature data of tires mounted on the vehicle;
a tire severity calculation unit that calculates a tire severity level indicating the degree of load on the tire from at least one of the air pressure data and temperature data acquired by the tire information acquisition unit;
a wear amount calculation unit having a calculation model that calculates the amount of tire wear based on information on the degree of severity of the tire, and inputting the degree of tire severity and calculating the amount of wear of the tire using the calculation model;
a calculation model updating unit that compares the amount of wear measured by the tire with the amount of wear calculated by the wear amount calculation unit and updates the calculation model ;
The calculation model generation system is characterized in that the tire severity calculation unit calculates the number of change points of positive slope and negative slope in the air pressure data as the tire severity.

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