VEHICLE CONSUMABLES MANAGEMENT SYSTEM AND METHOD

A vehicle consumables management system includes a consumables remaining amount calculation unit receiving vehicle data including a brake pedal input signal, an outdoor temperature, a driving distance, a wheel velocity, and a wheel speed such as a wheel RPM, and calculating a remaining amount of a tire tread based on the driving distance and the wheel speed, and/or calculating a remaining amount of a brake pad based on at least one of the brake pedal input signal and vehicle acceleration and/or deceleration information, thereby being capable of accurately detecting the remaining amount of the tire tread of the vehicle without assistance of separate inspection equipment, and accurately predicting a wear amount and a remaining amount of the brake pad of the vehicle without additional expensive equipment.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims benefit and priority to Korean Patent Application Nos. 10-2021-0136167 filed on Oct. 13, 2021 and 10-2022-0049262 filed on Apr. 21, 2022, with the Korean Intellectual Property Office, the disclosure of which is incorporated herein in its entirety by reference.

TECHNICAL FIELD

The present disclosure generally relates to a vehicle consumables management system and method, and particularly, to a vehicle consumables management system and method which can accurately detect a remaining amount of a tire tread of a vehicle without separate inspection equipment, and can accurately predict a wear amount and a remaining amount of a brake pad of the vehicle without additional expensive equipment.

BACKGROUND

A tire of a vehicle can be worn through friction with a road surface. Excessive wear of the tire may cause occurrence of an accident due to a reduced braking capacity, cornering instability, hydroplaning, etc., and the caused accident can lead to a large accident.

A wear amount of the tire of the vehicle can be determined through a remaining amount (or wearing amount) of the tread formed on the outer circumferential surface of the tire.

In general, the wear amount of the tire tread can be checked through vision and acoustic inspections using visual inspection or separate equipment.

However, the visual inspection cannot accurately detect the wear amount of the tire tread and may be cumbersome. And the inspection using the separate equipment can accurately detect the wear amount, but extra cost and time are required.

SUMMARY

The present disclosure has been made in an effort to provide a vehicle consumables management system which can accurately detect a remaining amount of a tire tread of a vehicle without separate inspection equipment based on vehicle data, and can accurately predict a wear amount and/or a remaining amount of a brake pad of the vehicle without additional expensive equipment.

An exemplary embodiment of the present disclosure provides a vehicle consumables management system including a consumables remaining amount calculation unit receiving vehicle data including a brake pedal input signal, an outdoor temperature, a driving distance, a wheel velocity, and a wheel RPM, and calculating a tread remaining amount of a tire based on the driving distance and the wheel RPM, and/or calculating a brake pad remaining amount based on at least one of the brake pedal input signal and vehicle acceleration/deceleration information.

The consumables remaining amount calculation unit calculates a dynamic radius of the tire based on the ratio between the driving distance and the wheel RPM, and calculates the remaining tread amount based on the calculated dynamic radius.

The tire monitor unit calculates the remaining tread amount based on a ratio of the driving distance to the wheel RPM or a ratio of the wheel RPM to the driving distance.

The tire monitor unit includes a driving distance calculation unit calculating the driving distance based on positional data of the vehicle, a wheel RPM calculation unit calculating the wheel RPM based on a wheel pulse of the vehicle, and a remaining tread amount calculation unit calculating the remaining tread amount based on the driving distance from the driving distance calculation unit and the wheel RPM from the wheel RPM calculation unit.

The tire monitor unit further includes a wheel RPM correction unit correcting the wheel speed (e.g. the wheel RPM) from the wheel RPM calculation unit based on predetermined corrected data, and providing the corrected wheel speed (e.g. the corrected wheel RPM) to the remaining tread amount calculation unit.

The predetermined corrected data may include a wheel slip rate and a corrected dynamic radius of the tire.

The tire monitor unit further includes a wheel slip calculation unit calculating the wheel slip rate based on a wheel speed of the vehicle, and a dynamic radius correction unit calculating the corrected dynamic radius based on a weight of the vehicle.

The dynamic radius correction unit includes a look-up table storing a corrected dynamic radius predetermined according to the weight of the vehicle.

The driving distance calculation unit calculates the driving distance based on the positional data, and an Internet map.

The consumables remaining amount calculation unit further includes a replacement date prediction unit calculating an expected replacement date of the tire based on tire replacement history information and the remaining tread amount from the tire monitor unit.

The vehicle data further includes a rain sensor signal of the vehicle, the acceleration/deceleration information includes an acceleration and a cylinder pressure of the vehicle, and the consumables remaining amount calculation unit includes a brake pad monitoring apparatus calculating the brake pad remaining amount, and the brake pad monitoring apparatus includes a feature extraction unit extracting feature data including braking energy of the vehicle based on the vehicle data, a pad temperature prediction unit predicting the temperature of the brake pad by analyzing the feature data from the feature extraction unit in an artificial intelligence scheme, a pad wear amount calculation unit calculating a wear amount of the brake pad based on the temperature of the brake pad from the pad temperature prediction unit and the braking energy from the feature extraction unit, and a pad remaining amount calculation unit calculating the remaining amount of the brake pad based on the wear amount of the brake pad from the pad wear amount calculation unit.

The feature extraction unit includes a source storage unit storing the vehicle data, and a data extraction unit extracting the feature data from the vehicle data of the source storage unit.

The pad temperature prediction unit includes a setting value storage unit pre-storing a model setting value calculated by the machine learning of the artificial intelligence scheme to infer the temperature of the brake pad corresponding to the vehicle data, and a pad temperature calculation unit calculating the temperature of the brake pad based on the feature data from the feature extraction unit and the model setting value from the setting value storage unit.

The pad wear amount calculation unit includes a look-up table storing a value of the wear amount of the brake pad predetermined according to a value of the temperature of the brake pad and a value of the braking energy, and a pad wear amount output unit searching the wear amount of the brake pad from the look-up table based on the temperature of the brake pad from the pad temperature prediction unit and the braking energy from the feature extraction unit, and outputting the searched wear amount of the brake pad.

The pad remaining amount calculation unit outputs the remaining amount of the brake pad by subtracting the brake wear amount from the pad wear amount calculation unit from a current thickness of the brake pad.

The data extraction unit includes an interval classification unit classifying the vehicle data of the source storage unit for each of the braking interval and the non-braking interval of the vehicle, an interval length calculation unit calculating a length of the braking interval and the length of the non-braking interval based on the vehicle data from the interval classification unit, a cylinder pressure calculation unit calculating the pressure for each interval of the cylinder for providing the braking force of the vehicle based on the vehicle data from the interval classification unit, a vehicle velocity calculation unit calculating a vehicle velocity for each interval based on the vehicle data from the interval classification unit, a braking energy calculation unit calculating braking energy for each interval based on the vehicle data from the interval classification unit, an outdoor temperature calculation unit calculating an outdoor temperature for each interval based on the vehicle data from the interval classification unit, and a quantity calculation unit calculating a quantity for each interval based on the vehicle data from the interval classification unit.

The pad temperature calculation unit includes an initial temperature calculation unit calculating an initial temperature of the brake pad based on the feature data from the feature extraction unit, a data collection unit collecting and outputting the feature data from the feature extraction unit the initial temperature from the initial temperature calculation unit as one data set, a normalization unit normalizing the data set from the data collection unit based on the average and the standard deviation of the vehicle data provided from the setting value storage unit, a model generation unit generating a pad temperature prediction model based on a weight and a bias of the vehicle data loaded from the setting value storage unit, a setting value loading unit loading the weight and the bias of the vehicle data from the setting value storage unit to the model generation unit, and a prediction value output unit calculating the temperature change rate of the brake pad by inputting the data set normalized from the normalization unit into the pad temperature prediction model from the model generation unit, calculating the temperature of the brake pad by adding the initial temperature to the calculated temperature change rate, and outputting the calculated brake pad temperature.

The initial temperature is set based on a time length from a time when the start of the vehicle is turned off up to a time when the start of the vehicle starts, the outdoor temperature of the vehicle, and a value defined by a predetermined brake pad temperature characteristic curve.

The data set is classified into a data set of the braking interval of the vehicle and a data set of the non-braking interval of the vehicle, and the prediction value output unit outputs a pad temperature predicted at the end time of the interval as the brake pad temperature of the interval.

The prediction value output unit calculates the brake pad temperature in a predetermined period by summing up all brake pad temperatures of a non-braking interval and a braking interval included in the predetermined period.

According to some exemplary embodiments of the present disclosure, a vehicle consumables management system can accurately calculate a remaining amount of a tread of a tire only by analysis of vehicle data. Therefore, a wear amount of the tire can be easily determined without separate equipment for checking the wearing of the tire tread.

Further, according to certain exemplary embodiments of the present disclosure, a vehicle consumables management system can reduce an accident risk caused by delaying in replacing the tire by calculating an expected replacement date of the tire and notifying the calculated expected replacement date to a driver or user based on the calculated remaining amount of the tire tread.

Further, according to some exemplary embodiments of the present disclosure, in order to exclude a change amount of a wheel speed (e.g. a wheel RPM) according to interference of other factors in addition to a wear amount of a tire tread at the time of calculating the wheel speed (e.g. the wheel RPM), an original wheel speed (e.g. an original wheel RPM) is corrected based on predetermined correction data to accurately calculate the wear amount of the tire.

In addition, a vehicle consumables management system according to certain exemplary embodiments of the present disclosure can analyze vehicle data (e.g., CAN data of the vehicle) by an artificial intelligence scheme and accurately predict a wear amount of a brake pad through a model by machine learning.

Therefore, a vehicle consumables management system according to some exemplary embodiments of the present disclosure can estimate or determine a remaining amount of the brake pad accurately and quickly. As a result, since the vehicle consumables management device and method according to certain exemplary embodiments of the present disclosure do not require expensive equipment, the manufacturing cost can be reduced in checking the remaining amount of the brake pad.

A vehicle consumables management system according to certain exemplary embodiments of the present disclosure can be applied to a fleet vehicle system such as a rental car, a taxi and a shared vehicle.

DETAILED DESCRIPTION

Advantages and features of the present disclosure, and methods for accomplishing the same will be more clearly understood from embodiments described in detail below with reference to the accompanying drawings. However, the present disclosure is not limited to the following embodiments but may be implemented in various different forms. The embodiments are provided only to make description of the present disclosure complete and to fully provide the scope of the present disclosure to a person having ordinary skill in the art to which the present disclosure pertains, and the present disclosure will be just defined by the appended claims. Thus, in some exemplary embodiments, well-known process steps, well-known device structures and well-known technologies are not specifically described to avoid the ambiguity of the present disclosure. Throughout the whole specification, the same reference numerals denote the same elements.

In the drawings, the thickness of various layers and regions are exaggerated for clarity. Throughout the specification, like reference numerals refer to like elements.

In this specification, terms including as first, second, third, and the like are used for describing various constituent elements, but the constituent elements are not limited by the terms. The terms are used only for distinguishing one component from the other component. For example, a first component may be named as a second component or a third component and similarly, the second component or the third component may also be interchangeably named as the first component without departing from the scope of the present disclosure.

In the present specification, the singular form also includes the plural form, unless the context indicates otherwise.

In the present specification, the term ‘and/or’ indicates respective listed components or various combinations thereof

Unless otherwise defined, all terms (including technical and scientific terms) used in the present specification may be used as the meaning which may be commonly understood by the person with ordinary skill in the art, to which the present disclosure pertains. Terms defined in commonly used dictionaries should not be interpreted in an idealized or excessive sense unless expressly and specifically defined.

Hereinafter, a vehicle consumables management system according to the present disclosure will be described in detail as follows with reference toFIGS.1to26.

FIG.1is a block diagram of a vehicle consumables management system according to an exemplary embodiment of the present disclosure.

A vehicle consumables management system10000according to an exemplary embodiment of the present disclosure may include a consumables remaining amount calculation unit1111, a server2000, and a vehicle3000. The consumables remaining amount calculation unit1111is configured to calculate at least one of a tread remaining amount of a tire and a remaining amount of a brake pad. The consumables remaining amount calculation unit1111of the vehicle consumables management system10000may include, for example, a tire life management apparatus1000illustrated inFIG.1and a brake pad monitoring apparatus4000illustrated inFIG.9.

Here, exemplary embodiments of the tire life management apparatus1000of the consumables remaining amount calculation unit1111will be described in detail as follows with reference toFIGS.1to8.

Hereinafter, a brake pad monitoring device of a vehicle and a method thereof according to the present disclosure will be described in detail as follows with reference toFIGS.1to8.

FIG.1is a block diagram of a tire life management device1000and related peripheral components according to an exemplary embodiment of the present disclosure.

As illustrated inFIG.1, the vehicle3000may transmit vehicle data to the server2000through a communication device included in or connected to the vehicle3000. Then, the server2000may store the vehicle data transmitted from the vehicle3000.

The tire life management device1000according to an exemplary embodiment of the present disclosure may be configured to calculate (or estimate, predict or infer) a life of a tire of the vehicle3000by analyzing the vehicle data provided from the server2000. Here, the life of the tire may be calculated based on, for example, but not limited to, a remaining amount (or a wear amount) of a tread of the tire. Here, the vehicle data may include, for example, but not limited to, a wheel speed, a wheel pulse, a tire pressure, a drivetrain signal (e.g., an engine displacement of the vehicle3000and a transmission type of the vehicle3000) and positional data (or location data).

When the vehicle3000includes a plurality of tires, the tire life management device1000may calculate the life for each of the plurality of tires individually. As one example, when the vehicle3000includes four tires (e.g., a front left tire, a front right tire, a rear left tire, and a rear right tire) mounted on four wheels, respectively, the tire life management device1000may calculate the life of the front left tire, the life of the front right tire, the life of the rear left tire, and the life of the rear right tire individually. Alternatively, the tire life management device1000may also selectively calculate the life of some tires, for example, the life only for m (m is a natural number smaller than1) tires among n (n is a natural number larger than m) tires.

Further, the tire life management device1000may calculate (or estimate or predict or infer) an expected replacement date of the tire based on the remaining amount of the tread of the tire, for example. As another exemplary embodiment, the tire life management device1000may further receive replacement history information of the tire from the server2000, and more accurately calculate (or estimate or predict or infer) the expected replacement date of the tire based on the replacement history information and the remaining tread amount for the tire.

The tire life management device1000may transmit or provide the calculated life and expected replacement date of the tire to the server2000. In this case, the tire life management device1000may periodically calculate the life and the expected replacement date of the tire, and periodically transmit or provide the calculated life and the expected replacement date of the tire to the server2000.

Meanwhile, when the calculated life of the tire is lower than a predetermined threshold, the tire life management device1000may further transmit alarm or warning data to the server2000together with the life (e.g., the remaining life) and expected replacement date of the tire.

The server2000may transmit or provide data (e.g., at least one of the remaining tread amount of the tire, the expected replacement date of the tire, and the alarm data) transmitted from the tire life management device1000to a corresponding vehicle.

FIG.2is a block diagram of a tire life management device according to an exemplary embodiment of the present disclosure, andFIGS.3A and3Bare graphs for describing a change of a wheel Revolution Per Minute (RPM) per driving distance according to a remaining tread amount of the vehicle3000. Here, RPM may be an example of a wheel speed. The wheel speed may be a speed of a wheel, for example, but not limited to, a rotational speed of a wheel, a frequency of rotation of a wheel, a number of turns of a wheel in a certain time period (such as RPM), and the like.

The tire life management device1000according to the present disclosure may include a tire monitor unit1100and a replacement date prediction unit1200as in the exemplary embodiment illustrated inFIG.2.

The tire monitor unit1100may be configured to calculate the remaining tread amount of the tire of the vehicle3000based on a ratio between a driving distance of the vehicle3000(or a movement distance of the vehicle3000) and a wheel speed (e.g. a wheel RPM) of the vehicle3000. For example, the tire monitor unit1100may also calculate the remaining tread amount based on a ratio of the driving distance to the wheel speed (e.g. the wheel RPM). As another example, the tire monitor unit1100may also calculate the remaining tread amount based on a ratio of the wheel speed (e.g. the wheel RPM) to the driving distance. The driving distance may be a distance in which the vehicle300has been driven.

The tire monitor unit1100may calculate a dynamic radius of the tire based on the ratio between the driving distance and the wheel speed or RPM (e.g., the driving distance/the wheel RPM or the wheel RPM/the driving distance), and calculate the remaining tread amount of based on the calculated dynamic radius. Here, the dynamic radius may be calculated using Equation 1 below.

In Equation 1 above, DR represents the dynamic radius of the tire and π means a circumference ratio.

Meanwhile, a factor affecting not only the tread wear amount of the tire but also a size of the dynamic radius of the tire may further include a wheel slip rate of the vehicle3000, a weight of the vehicle3000, the weight of the tire, a pressure of the tire, a temperature of the tire, etc. Here, when the values or sizes of other factors other than the tread wear amount are equal to each other, it may be regarded that a change of the dynamic radius of the vehicle3000is affected only by the tread wear amount.

As time elapsed, the wear amount of the tread of the tire is accumulated, and as a result, the remaining tread amount of the tire is continuously reduced. As a result, the radius (e.g., dynamic radius) of the tire decreases, and the driving distance per wheel speed (e.g. RPM), i.e., the size of the dynamic radius decreases. In other words, as the remaining tread amount of the tire decreases, the wheel RPM per driving distance increases, so this is described as below with reference toFIG.3.

FIG.3Aillustrates an example of a daily driving distance (or a daily average driving distance) of the vehicle3000.

FIG.3Billustrates examples of a first line L1and a second line L2having different slopes. The first line L1having a relatively lower slope indicates a change amount of the wheel RPM according to the elapse of the time for a tire (e.g., a new tire which is not worn at all) with a remaining tread amount of first 100% and the second line L2having a relatively higher slope indicates the change amount of the wheel RPM according to the elapse of the time for a tire having a remaining tread amount (e.g., a remaining tread amount of 20% based on the remaining tread amount of 100%) of first 20%. Here, the first straight line L1and the second straight line L2illustrated inFIG.3Bindicate change rates of the wheel RPM per driving distance ofFIG.3A. Further, the tire having the remaining tread amount of 100% and the tire having the remaining tread amount of 20% are tested in a state in which all remaining conditions (e.g., a tire specification, a tire temperature, a tire pressure, etc.,) other than the remaining tread amount are the same.

As illustrated inFIG.3, as the remaining tread amount decreases, the wheel RPM increases. In other words, as the remaining tread amount decreases, the radius of the tire decreases, and as a result, the RPM (e.g., wheel RPM) of the tire for moving a predetermined driving distance also increases.

The tire monitor unit1100may periodically predict the remaining tread amount of the tire by detecting a change (e.g., a change rate of the dynamic radius) in ratio between the driving distance of the vehicle3000and the wheel RPM through Equation 1. Consequently, the size of the dynamic radius may correspond to the remaining tread amount.

Further, the tire monitor unit1100compares the calculated remaining tread amount of the tire with a predetermined threshold, and as a comparison result, when the calculated remaining tread amount of the tire is smaller than the threshold, the tire monitor unit1100may further generate the alarm data. The alarm data may include at least one of visual contents and auditory contents for informing that a tread wear level of the tire reaches a risk level.

As illustrated in the exemplary embodiment ofFIG.2, the tire monitor unit1100according to an exemplary embodiment of the present disclosure may include a driving distance calculation unit1110, a wheel RPM calculation unit1120, and a remaining tread amount calculation unit1130.

The driving distance calculation unit1110may calculate the driving distance of the vehicle3000based on the positional data of the vehicle3000(e.g. information on a position or location of the vehicle3000). For example, the positional data of the vehicle3000may be acquired from a terminal installed in or associated with the vehicle3000.

As an example, the terminal may include an inertial measurement device (for instance, an inertial sensor) and a satellite positioning system (for example, a global navigation satellite system (GNSS)), so the terminal may provide, to the server2000, the positional data of the vehicle3000generated by at least one of the inertial measurement device and the satellite positioning system or a combination thereof, and the server2000may provide the positional data to the driving distance calculation unit1110. As such, the driving distance calculation unit1110may determine the driving distance of the vehicle3000based on the positional data (e.g., GNSS data) generated by at least one of the inertial measurement device of the vehicle3000and the satellite positioning system of the vehicle3000or the combination thereof. Here, for example, the GNSS data may include global positioning system (GPS) data.

Meanwhile, the driving distance calculation unit1110may more precisely calculate the driving distance by utilizing map data including, for example, but not limited to, an Internet map (e.g., an Internet map such as Google map, Naver map, any map provided by an internet service provider, etc.) produced based on a distance matrix application programming interface (API) and map stored in memory. For example, the driving distance calculation unit1110may correct the positional data (e.g., GNSS data) generated based on the satellite positioning system by utilizing the Internet map, and calculate a more accurate driving distance of the vehicle3000based on the corrected positional data. Unlike this, the terminal of the vehicle3000may also precisely correct the positional data (e.g., GNSS data) through the Internet map, and then provide the corrected positional data (e.g., corrected GNSS data) to the server2000. In such a case, the driving distance calculation unit1110receives the corrected positional data from the server2000to calculate the driving distance of the vehicle3000. As another exemplary embodiment, a correction task of the positional data utilizing the Internet map may also be performed by the server2000instead of the terminal or the driving distance calculation unit1110, for example.

The wheel speed calculation unit or wheel RPM calculation unit1120may calculate the wheel speed of the vehicle3000, such as the wheel RPM of the vehicle3000, based on a wheel pulse of the vehicle3000. Meanwhile, the wheel RPM calculation unit1120may calculate the wheel speed of each wheel, for example, but not limited to, the RPM of each wheel. As an example, when the vehicle3000includes a front left wheel mounted with a front left tire, a front right wheel mounted with a front right tire, a rear left wheel mounted with a rear left tire, and a rear right wheel mounted with a rear right tire, the wheel RPM calculation unit1120may individually calculate each of the wheel speed (e.g. the RPM) of the front left wheel, the wheel speed (e.g. the RPM) of the front right wheel, the wheel speed (e.g. the RPM) of the rear left wheel, and the wheel speed (e.g. the RPM) of the rear right wheel. Alternatively, the wheel RPM calculation unit1120may also selectively calculate one or more wheel speeds (wheel RPMs) only for some wheels (e.g. m wheels among n wheels).

Meanwhile, the driving distance from the driving distance calculation unit1110and the wheel RPM from the wheel RPM calculation unit1120may be calculated based on data (e.g., the positional data and the wheel pulse) extracted in the same time interval (an interval between the same time stamps).

The remaining tread amount calculation unit1130may calculate the remaining tread amount of the tire based on the driving distance from the driving distance calculation unit1110and the wheel speed (e.g. the wheel RPM) from the wheel RPM calculation unit1120. For example, the remaining tread amount calculation unit1130may calculate the dynamic radius of the tire by substituting the calculated driving distance and the wheel speed (e.g. the wheel RPM) into the Equation 1 described above, and calculate the remaining tread amount of the tire based on the calculated dynamic radius. For example, when the vehicle3000includes the front left tire, the front right tire, the rear left tire, and the rear right tire described above, the remaining tread amount calculation unit1130may individually calculate the remaining tread amount of the front left tire, the remaining tread amount of the front right tire, the remaining tread amount of the rear left tire, and the remaining tread amount of the rear right tire. Alternatively, the remaining tread amount calculation unit1130may also selectively calculate the remaining tread amount only for some tires (e.g. m tires among n tires).

The remaining tread amount calculation unit1130may transmit the calculated remaining tread amount of the tire to the server2000and a replacement date prediction unit1200. In this exemplary embodiment, the remaining tread amount calculation unit1130may provide, to the server2000, a value (e.g., a dynamic radius of the tire) calculated through Equation 1 as the remaining tread amount as it is, and unlike this, the remaining tread amount calculation unit1130may also find or retrieve the remaining tread amount corresponding to the size of the dynamic radius calculated through Equation 1 from a predetermined look-up table stored in memory, and provide the remaining tread amount to the server2000. The look-up table may store predetermined remaining tread amounts having various sizes according to dynamic radius having various sizes.

Further, the remaining tread amount calculation unit1130compares the calculated remaining tread amount with a threshold, and, when the calculated remaining tread amount is smaller than the threshold as the comparison result, the remaining tread amount calculation unit1130may further generate the alarm data. The threshold may include a plurality of thresholds having different levels or sizes. For example, the threshold may include a first threshold which is set as a lowest value, a third threshold which is set as a highest value, and a second threshold set between the first threshold and the third threshold. In this case, the remaining tread amount calculation unit1130may provide different types of alarm data depending on a threshold interval in which the calculated remaining tread amount is located. For example, when the calculated remaining tread amount is smaller than the third threshold and equal to or larger than the second threshold, the remaining tread amount calculation unit1130may transmit, to the server2000, first alarm data (e.g., alarm data which allows an alarm message to be displayed with a blue color on a display of the vehicle3000) of a first step together with the calculated remaining tread amount of the tire. When the calculated remaining tread amount is smaller than the second threshold and equal to or larger than the first threshold, the remaining tread amount calculation unit1130may transmit, to the server2000, second alarm data (e.g., alarm data which allows an alarm message to be displayed with a yellow color on a display of the vehicle3000) of a second step together with the calculated remaining tread amount of the tire. When the calculated remaining tread amount is smaller than the first threshold, the remaining tread amount calculation unit1130may transmit, to the server2000, third alarm data (e.g., alarm data which allows an alarm message to be displayed with a red color on a display of the vehicle3000) of a third step together with the calculated remaining tread amount of the tire.

The replacement date prediction unit1200may calculate (or estimate or predict or infer) the expected replacement date of the tire based on the remaining amount of the tread of the tire calculated from the remaining tread amount calculation unit1130. As another exemplary embodiment, the replacement date prediction unit1200may further receive replacement history information of the tire from the server2000, and more accurately calculate the expected replacement date of the tire based on the replacement history information and the remaining tread amount for the tire. Meanwhile, the replacement date prediction unit1200may further receive information on a specification of the tire from the server2000. The information on the tire specification may include, for example, but not limited to, the size of the tire, the type of the tire (e.g., a snow tire), a manufacturer of the tire, etc.

Meanwhile, the vehicle3000may include a rental car, a taxi, and a sharing vehicle, and the server2000may be a server of a fleet vehicle company which services such a rental car, the taxi, and the sharing vehicle.

FIG.4is a block diagram of the tire life management device1000ofFIG.1according to another exemplary embodiment, andFIG.5is an example of a look-up table storing a corrected dynamic radius according to an embodiment of the present disclosure.

The tire life management device1000according to another exemplary embodiment of the present disclosure may include the tire monitor unit1100and the replacement date prediction unit1200as illustrated inFIG.4.

The tire monitor unit1100may include the driving distance calculation unit1110, the wheel speed calculation unit or wheel RPM calculation unit1120, a wheel slip calculation unit1140, a corrected dynamics radius calculation unit1150, a wheel speed correction unit or wheel RPM correction unit1160, and the remaining tread amount calculation unit1130. In other words, the tire life management device ofFIG.4may further include the wheel slip calculation unit1140, the corrected dynamic radius calculation unit1150, and the wheel RPM correction unit1160as compared with the tire life management device1000ofFIG.2.

Since the driving distance calculation unit1110, the wheel RPM calculation unit1120, and the remaining tread amount calculation unit1130ofFIG.4are the same as, or similar with, the driving distance calculation unit1110, the wheel RPM calculation unit1120, and the remaining tread amount calculation unit1130ofFIG.2, respectively, the driving distance calculation unit1110, the wheel RPM calculation unit1120and the remaining tread amount calculation unit1130ofFIG.4are described with reference toFIG.2and related contents.

The wheel slip calculation unit1140may calculate the slip rate of the wheel. For example, when the wheel slip occurs, the wheel speed (e.g. the RPM of the wheel) after the wheel slip occurs is smaller than the wheel speed (e.g. the wheel RPM) before the wheel slip occurs, so the wheel slip rate may be calculated based on the wheel speed. Meanwhile, when the vehicle3000includes the front left wheel, the front right wheel, the rear left wheel, and the rear right wheel described above, the wheel slip calculation unit1140selects a wheel which rotates at the highest (or the fastest) speed among four wheels described above, sets the speed of the selected wheel as a reference speed (e.g., a vehicle speed), and compares the reference speed with speeds of respective other wheels to calculate the slip rate of each wheel.

The corrected dynamic radius calculation unit1150may include a look-up table pre-storing a dynamic radius size change amount according to the weight of the vehicle3000. For example, after a tolerance weight of the vehicle3000(e.g., a weight of only the vehicle not including a passenger) is set to a reference value, a size change rate of the dynamic radius according to the increase in weight of the vehicle3000from the reference value (e.g., a size reduction rate of the dynamic radius according to an increase in weight of the vehicle3000as compared with a reference weight) may be stored in the look-up table. As an example, the reduction rate of the dynamic radius according to a difference between a current measured weight of the vehicle3000and the reference value may be stored in the look-up table as a corrected dynamic radius.

Meanwhile, the corrected dynamic radius calculation unit1150may provide a predetermined size reduction rate of the dynamic radius of the tire according to, for example, the tire specification, the tire pressure, the tire temperature, and the drivetrain signal in addition to the weight of the vehicle3000. As one example for this, the dynamic radius change rate according to the vehicle weight, the tire specification, the tire pressure, the tire temperature, and a power signal may be stored in the look-up table. An example of the look-up table is illustrated inFIG.5. For example, a look-up table310ofFIG.5may include a value of a corrected dynamic radius predetermined according to a value of the tire pressure and the weight of the vehicle. For example, as illustrated inFIG.5, the look-up table310may include a plurality of corrected dynamic radius values (CR11, CR12, CR13, . . . , CR54, CR55) defined by a matrix combination of a plurality of tire pressure values (T1, T2, T3, T4, T5) and a plurality of vehicle weight values (M1, M2, M3, M4, M5).

The corrected dynamic radius calculation unit1150may retrieve a corrected dynamic radius value from the look-up table310based on the tire pressure and the vehicle weight (e.g., a current measured vehicle weight), and output the retrieved value as the corrected dynamic radius. For example, as illustrated inFIG.5, when the tire pressure value is T3 and the vehicle weight value is M4, the corrected dynamic radius calculation unit1150may select and output CR34 as the corrected dynamic radius value. In other words, as a corrected dynamic radius value corresponding to the current measured tire pressure T3 and the current measured vehicle weight M4, CR34 may be retrieved and output.

Meanwhile, the driving distance from the driving distance calculation unit1110, the wheel speed (e.g. the wheel RPM) from the wheel RPM calculation unit1120, the wheel slip rate from the wheel slip calculation unit1140, and the corrected dynamic radius from the corrected dynamic radius calculation unit1150may be all calculated based on data (e.g., the positional data, the wheel pulse, the wheel speed, and the vehicle weight) extracted in the same time interval (e.g., the interval between the same time stamps).

The wheel RPM correction unit1160may correct the wheel speed (e.g. the wheel RPM) from the wheel RPM calculation unit1120based on predetermined corrected data, and provide the corrected wheel speed (e.g. the corrected wheel RPM) to the remaining tread amount calculation unit1130. Here, the predetermined corrected data may include, for example, the wheel slip rate and the corrected dynamic radius of the tire. When the corrected data includes the wheel slip rate and the corrected dynamic radius, the wheel RPM correction unit1160may correct an original wheel speed such as an original wheel RPM (e.g., the wheel RPM from the wheel RPM calculation unit1120) based on the wheel slip rate from the wheel slip calculation unit1140and the corrected dynamic radius from the corrected dynamic radius calculation unit1150. That is, factors which may affect the wheel speed (e.g. the wheel RPM) may include the tread wear amount of the tire, the wheel slip rate, the dynamic radius change by the vehicle weight, etc., as described above, so the wheel RPM correction unit1160may correct the original wheel speed such as the original wheel RPM (e.g., the wheel RPM measured based on the wheel pulse) based on the dynamic radius change rate according to the wheel slip rate and the vehicle weight change in order to exclude the change amount of the wheel speed (e.g. the wheel RPM) according to interference of other factors in addition to the tread wear amount of the tire at the time of calculating the wheel speed such as the wheel RPM. For example, as the slip rate of any one wheel is higher, the one wheel rotates less than a reference wheel, and as a result, in order to compensate the change amount of the wheel speed (e.g. the wheel RPM) according to the wheel slip rate, the wheel RPM correction unit1160may correct the wheel speed (e.g. the RPM) of the one wheel to be higher than the original wheel speed (e.g. the original wheel RPM) as the slip rate of the one wheel is higher. Further, as the weight of the vehicle increases to higher than a tolerance weight, the dynamic radius of the tire further decreases, and as the dynamic radius of the tire decreases, the wheel RPM per the same driving distance increases, and as a result, in order to compensate the change amount of the wheel speed (e.g. the wheel RPM) according to the weight of the vehicle3000, the wheel RPM correction unit1160may correct the wheel speed (e.g. the RPM) of the one wheel to be higher as the reduction rate of the dynamic radius (e.g., the dynamic radius of the tire mounted on the one wheel) of the one wheel increases.

When the vehicle3000includes the front left wheel, the front right wheel, the rear left wheel, and the rear right wheel described above, the wheel RPM correction unit1160may correct each of the wheel speed (e.g. the wheel RPM) of the front left wheel, the wheel speed (e.g. the wheel RPM) of the front right wheel, the wheel speed (e.g. the wheel RPM) of the rear left wheel, and the wheel speed (e.g. the wheel RPM) of the rear right wheel provided from the wheel RPM calculation unit1120. As another exemplary embodiment, the wheel RPM calculation unit1120may also selectively correct the wheel speed (e.g. the wheel RPM) only for some wheels (e.g. m wheels among n wheels).

The wheel speed (e.g. the wheel RPM) corrected by the wheel RPM correction unit1160may be provided to the remaining tread amount calculation unit1130. Then, the remaining tread amount calculation unit1130may calculate the remaining tread amount based on the corrected wheel speed (e.g. the corrected wheel RPM) and the driving distance. Since the remaining tread amount calculation unit1130ofFIG.4is substantially the same as or similar with the remaining tread amount calculation unit1130ofFIG.2described above, a detailed description of the remaining tread amount calculation unit1130ofFIG.4is made with reference toFIG.2and related contents. For example, the remaining tread amount calculation unit1130ofFIG.4just receives the wheel speed (e.g. the wheel RPM) corrected unlike the remaining tread amount calculation unit1130ofFIG.2, and performs the substantially same task (or the substantially similar process) as the remaining tread amount calculation unit1130ofFIG.2.

FIG.6is a flowchart for describing a tire life management method according to an exemplary embodiment of the present disclosure.

The tire life management method according to an exemplary embodiment of the present disclosure may include a tire monitoring step and/or an expected tire replacement date predicting step.

The tire monitoring step may include S10, S20, and S30. Detailed description thereof is as follows.

First, a driving distance of the vehicle3000may be calculated (step S10). The driving distance of the vehicle3000may be calculated based on positional data of the vehicle3000, for example. Here, the positional data of the vehicle3000may include GNSS data provided from a terminal of the vehicle3000. As another example, the driving distance of the vehicle3000may be calculated based on the positional data and an Internet map. For example, the positional data may be more precisely corrected through the Internet map.

Thereafter, a wheel speed (e.g. a wheel RPM) of the vehicle3000may be calculated (step S20). For example, the wheel speed (e.g. the wheel RPM) of the vehicle3000may be calculated based on a wheel pulse of the vehicle3000.

Next, a remaining tread amount of the vehicle3000may be calculated (step S30). For example, the step S30of calculating the remaining tread amount of the vehicle3000may be performed based on the calculated driving distance calculated at step S10and the wheel speed (e.g. the wheel RPM) calculated at step S20. In this case, the remaining tread amount of the tire of the vehicle3000may be calculated based on a ratio between the driving distance of the vehicle3000and the wheel speed (e.g. the wheel RPM) of the vehicle3000. For example, the remaining tread amount of the tire may be calculated based on the ratio of the driving distance to the wheel speed (e.g. the wheel RPM). As another example, the remaining tread amount of the tire may also be calculated based on the ratio of the wheel speed (e.g. the wheel RPM) to the driving distance. As an example, in the tire monitoring step, a dynamic radius of the tire may be calculated based on the ratio between the driving distance and the wheel speed (e.g. the wheel RPM) and the remaining tread amount (e.g., the driving distance/the wheel RPM or the wheel RPM/the driving distance) may be calculated based on the calculated dynamic radius. Here, the dynamic radius may be defined as Equation 1 described above.

Thereafter, the expected replacement date of the tire may be calculated (step S40). For example, the expected replacement date of the tire may be calculated based on tire replacement history information and the calculated remaining tread amount.

Meanwhile, the tire monitoring step may further include a step of comparing the calculated remaining tread amount with a predetermined threshold, and when it is confirmed that the calculated remaining tread amount is smaller than the threshold as a comparison result, generating alarm data.

FIG.7is a flowchart for describing a tire life management method according to another exemplary embodiment of the present disclosure, andFIG.8is a detailed flowchart for a step of correcting an wheel speed inFIG.7according to another exemplary embodiment of the present disclosure.

The tire life management method according to another exemplary embodiment of the present disclosure may further include a step S20-1of correcting an wheel speed (e.g. an wheel RPM) in addition to the exemplary embodiment ofFIG.7. Here, the wheel speed (e.g. the wheel RPM) may be corrected by using predetermined corrected data. The corrected data may include, for example, a wheel slip rate and a corrected dynamic radius.

As illustrated inFIG.8, the step S20-1of correcting the wheel speed (e.g. the wheel RPM) ofFIG.7may include, for example, a step S21-1of calculating the wheel slip rate and a step S22-1of calculating the corrected dynamic radius.

The wheel slip rate may be calculated based on a wheel speed of the vehicle3000, for example.

The corrected dynamic radius may be calculated based on a weight of the vehicle3000, for example. In this case, the corrected dynamic radius may be calculated by using the look-up table5000ofFIG.5storing a corrected dynamic radius predetermined according to the weight of the vehicle3000for example.

Meanwhile, the weight of the vehicle3000may be changed by the number of passengers of the vehicle, so the corrected dynamic radius calculation unit1150may predict the number of passengers by using vehicle data, calculate weights of a total number of passengers by multiplying the predicted number of passengers by a predetermined average weight, and calculate a final vehicle weight by adding the calculated weights to the weight of the vehicle. In addition, the corrected dynamic radius calculation unit1150may find or retrieve and output a value of the corrected dynamic radius corresponding to the calculated final vehicle weight from the look-up table.

Next, a brake pad monitoring apparatus4000of the consumables remaining amount calculation unit1111according to an exemplary embodiment the present disclosure will be described in detail as follows with reference toFIGS.9to26.

FIG.9is a block diagram of a brake pad monitoring apparatus4000according to an exemplary embodiment of the present disclosure andFIG.10is a diagram illustrating a look-up table310ofFIG.9.

According to an exemplary embodiment of the present disclosure, the brake pad monitoring apparatus4000may analyze vehicle data provided from the outside of the vehicle3000or the consumables remaining amount calculation unit1111(e.g., the server2000) in an artificial intelligence scheme to calculate the remaining amount of the brake pad of the vehicle. As in the exemplary embodiment illustrated inFIG.9, the brake pad monitoring apparatus4000according to an exemplary embodiment of the present disclosure may include a feature extraction unit100, a pad temperature prediction unit200, a pad wear amount calculation unit300, and a pad remaining amount calculation unit400.

Meanwhile, the brake pad monitoring apparatus4000may further receive acceleration/deceleration information of the vehicle in addition to the vehicle data, for example. Here, the acceleration/deceleration information of the vehicle may be an output signal output according to a brake pedal input signal to be described below. For example, the acceleration/deceleration information of the vehicle may include an acceleration or deceleration of the vehicle and a pressure of a cylinder (e.g., a master cylinder) to be described below. Meanwhile, the vehicle data may further include the acceleration/deceleration information.

The vehicle data as control area network (CAN) data for communication between various electronic parts (and/or electronic control units (ECUs)) of the vehicle, and the vehicle data may include, for example, the brake pedal input signal, a pressure (hereinafter, referred to as a cylinder pressure) of a cylinder (e.g., the master cylinder) of the vehicle, a wheel velocity of the vehicle, an outdoor temperature of the vehicle, and a rain sensor signal of the vehicle.

For example, the brake pedal input signal may include a change amount of the brake pedal input signal over time, the cylinder pressure may include a change amount of the cylinder pressure over time, the wheel velocity may include a change amount of an wheel velocity of any one wheel (i.e., a velocity of a rear right wheel of the vehicle) over time, the outdoor temperature may include a change amount of the outdoor temperature over time, and the rain sensor signal may include a change amount of the rain sensor signal over time. Here, the time may include, for example, a non-braking interval and a braking interval defined by the brake pedal input signal. For example, the time may include four non-braking intervals and three braking intervals. In this case, seven intervals may be categorized into non-braking and braking, and alternatively arranged along a time axis. For example, seven intervals described above may be arranged along the time axis in the order of a first non-braking interval, a first braking interval, a second non-braking interval, a second braking interval, a third non-braking interval, a third braking interval, and a fourth non-braking interval.

For example, the brake pedal input signal as a signal for judging whether the brake pedal is pressed may have a value of 0 (i.e., the brake pedal is not pressed) or 1 (the brake pedal is pressed). A brake pedal sensor of the vehicle may measure whether the brake pedal input signal is pressed or not. The brake pedal input signal may be provided from the brake pedal sensor.

The master cylinder may be a cylinder that provides braking force to the vehicle by supplying hydraulic pressure to the brake pad in response to the pressing of the brake pedal, and the pressure (hereinafter, referred to as cylinder pressure) of the master cylinder may mean pressure provided by the master cylinder or the hydraulic pressure. The cylinder pressure may be measured by a cylinder pressure sensor of the vehicle.

The wheel velocity may mean a rotational velocity of each wheel of the vehicle, and the velocity of each wheel may be individually measured by each wheel velocity sensor provided in each wheel. For example, the vehicle may include a front left wheel, a front right wheel, a rear left wheel, and a rear right wheel, and the wheel velocity may include a rotational velocity of the front left wheel, a rotational velocity of the front right wheel, a rotational velocity of the rear left wheel, and a rotational velocity of the rear right wheel.

The outdoor temperature may mean a temperature of an outside of the vehicle. For example, the outdoor temperature may be measured by a temperature sensor of the vehicle or received through a network.

The rain sensing signal may be a signal acquired from a rain sensor of the vehicle, and the rain sensing signal may include information indicating to which quantity of the rain the vehicle is exposed. The rain sensor may sense the amount of general water applied to the outside of the vehicle in addition to the rain.

The feature extraction unit100may extract feature data of the vehicle based on vehicle data input from the outside of the feature extraction unit100. To this end, the feature extraction unit100may include, for example, a source storage unit (or one or more memories)110and a data extraction unit120.

The source storage unit110may store the vehicle data input from the outside of the feature extraction unit100. For example, the source storage unit110may store the brake pedal input signal, the cylinder pressure, the wheel velocity, the outdoor temperature, and the rain sensor signal provided from various electronic parts (e.g. sensors or controllers) of the vehicle.

The data extraction unit120may extract the feature data from the vehicle data stored in the source storage unit110. The feature data may include braking energy of the vehicle. As an example, the feature data may include a length of the non-braking interval (e.g., a time duration of the non-braking interval), a length of the braking interval (e.g., a time duration of the braking interval), the cylinder pressure, a velocity of the vehicle (hereinafter, referred to as vehicle velocity), the braking energy, the outdoor temperature of the vehicle, and a quantity of rain. For example, the cylinder pressure may include a pressure of a cylinder for each interval, the vehicle velocity may include a velocity of a vehicle for each interval, the braking energy may include braking energy for each interval, the outdoor temperature may include an outdoor temperature of a vehicle for each interval, and the quantity may include a quantity of rain for each interval. Here, the interval may include the non-braking interval and the braking interval, and for example, the cylinder pressure for each interval may include a cylinder pressure in the non-braking interval and a cylinder pressure in the braking interval. There may be a plurality of non-braking intervals and braking intervals, and the plurality of braking intervals and the plurality of non-braking intervals may be alternatively arranged along the time axis. As one example, the plurality of braking intervals and the plurality of non-braking intervals may be arranged along the time axis in the order of a first non-braking interval, a first braking interval, a second non-braking interval, a second braking interval, a third non-braking interval, a third braking interval, and a fourth non-braking interval, . . . , an (n-1)th non-braking interval, an (n-1)th braking interval, an nthnon-braking interval, and an nth braking interval. Here, n may be a natural number equal to or larger than 6, but not limited thereto. In such a case, the cylinder pressure for each interval may include a cylinder pressure in the first non-braking interval, a cylinder pressure in the first braking interval, a cylinder pressure in the second non-braking interval, a cylinder pressure in the second braking interval, a cylinder pressure in the (n-1)th non-braking interval, a cylinder pressure in the (n-1)th braking interval, a cylinder pressure in the nthnon-braking interval, and a cylinder pressure in the nthbraking interval. The vehicle velocity for each interval, the braking energy for each interval, the outdoor temperature for each interval, and the quantity for each interval may also include corresponding physical quantities in each non-braking interval and each braking interval as described above. A numerical value in each interval may mean, for example, but not limited to, an average value of the corresponding physical quantities in the interval. For example, the cylinder pressure in the first braking interval may mean an average pressure of the cylinder in the first braking interval, the vehicle velocity in the first braking interval may mean an average vehicle velocity in the first braking interval, the braking energy in the first braking interval may mean average braking energy in the first braking interval, the outdoor temperature in the first braking interval may mean an average outdoor temperature in the first braking interval, and the quantity in the first braking interval may mean an average quantity in the first braking interval.

The pad temperature prediction unit200may predict the temperature of the brake pad by analyzing the feature data from the feature extraction unit100by the artificial intelligence scheme, but not limited thereto. To this end, the pad temperature prediction unit200may include, for example, a setting value storage unit (or one or more memories)210and a pad temperature calculation unit220.

The setting value storage unit210may store a predetermined model setting value. The model setting value is data prestored in the setting value storage unit210.

The model setting value may be calculated through machine learning of the artificial intelligence scheme to calculate or infer the temperature of the brake pad of the vehicle corresponding to the vehicle data, for example. As a specific example, the model setting value may be calculated through machine learning for predetermined learning data, so the model setting value may include, for example, a statistical value for the vehicle data, a weight for the vehicle data, and a bias for the vehicle data. Here, the learning data may be data (or a data set) corresponding to the vehicle data. Through the machine learning through the learning data, the model learning unit may generate a model setting value to calculate or infer a brake pad temperature corresponding to the vehicle data. For example, the model setting value may include a weight and a bias for minimizing a value of a cost function. Meanwhile, the statistical value of the model setting value may include, for example, an average of the vehicle data and a standard deviation of the vehicle data.

To this end, the model learning unit may include, for example, a learning feature extraction unit and a setting value generation unit.

The learning feature extraction unit may extract the learning feature data from the learning data.

The setting value generation unit may generate a learning model based on the learning feature data of the learning feature extraction unit, and generate the model setting value by training the generated learning model. Meanwhile, the learning data may further include information on the brake pad temperature unlike the vehicle data, so the brake pad temperature includes a label. That is, the learning data may include a label corresponding to a class (e.g., a predicted temperature level or size of the brake pad) of input data.

A machine learning model may provide an algorithm that may be used for calculating or inferring and learning the data by learning a model for a data set (e.g., input data) as a file learned to recognize a specific type of pattern. After the model is learned, the input data (i.e., data not including the label) which is not previously displayed may be inferred by using the model and prediction (e.g., class prediction) for the input data may be made.

Meanwhile, the machine learning model may include, for example, an artificial neural network such as deep learning, neural network, convolution neural network, and recurrent neural network.

The machine learning may target, when it is assumed that each input data (e.g., vehicle data not including the label) given based on pre-known feature data belongs to any one class among a predetermined plurality of classes (e.g., a predicable brake pad temperature), determining to which class among the plurality of classes new input data belongs.

The pad temperature calculation unit220may calculate the temperature of the brake pad based on the feature data from the feature extraction unit100(e.g., the data extraction unit120of the feature extraction unit100) and the model setting value from the setting value storage unit210.

The pad wear amount calculation unit300may calculate the wear amount of the brake pad based on the temperature of the brake pad from the pad temperature prediction unit200and the braking energy from the feature extraction unit100. To this end, according to an exemplary embodiment of the present disclosure, the pad wear amount calculation unit300may include, for example, a look-up table310and a pad wear amount output unit320.

The look-up table310may be stored in a memory storing a value of a brake pad wear amount predetermined according to a value of the temperature of the brake pad and a value of the braking energy. For example, as illustrated inFIG.10, the look-up table310may include wear amount values W11, W12, W13, . . . , W54, W55 of a plurality of brake pads defined by a matrix combination of values T1, T2, T3, T4, and T5 of temperatures of a plurality of brake pads and values E1, E2, E3, E4, and E5 of a plurality of braking energy.

The pad wear amount output unit320may search for a value of a brake wear amount from the look-up table310based on the temperature of the brake pad from the pad temperature prediction unit200and the braking energy from the feature extraction unit100, and output the searched value of the brake wear amount. For example, as illustrated inFIG.10, when the value T3 of the predicted brake pad temperature and the value E4of the braking energy are input, the pad wear amount calculation unit300may select and output W34 as the value of the brake pad wear amount.

The pad remaining amount calculation unit400calculates the remaining amount of the brake pad based on the wear amount of the brake pad from the pad wear amount calculation unit300. For example, the pad remaining amount calculation unit400calculates the remaining amount of the brake pad by subtracting the brake wear amount from the pad wear amount output unit320from a current thickness of the brake pad. Meanwhile, the remaining amount of the brake pad calculated from the pad remaining amount calculation unit400may be transmitted to a device associated with a customer through a network or a cloud system.

FIG.11is a detailed block diagram of the data extraction unit120ofFIG.9.

As illustrated inFIG.11, the data extraction unit120may include an interval classification unit121, an interval length calculation unit122, a cylinder pressure calculation unit123, a vehicle velocity calculation unit124, a braking energy calculation unit125, an outdoor temperature calculation unit126, and a quantity calculation unit127.

The interval classification unit121may classify the vehicle data stored in the source storage unit110into the non-braking interval and the braking interval of the vehicle. For example, the interval classification unit121may define non-braking intervals and braking intervals based on the brake pedal input signal. As a more specific example, an interval in which the value of the brake pedal input signal is 0 may be defined as non-braking intervals and an interval in which the value of the brake pedal input signal is 1 may be defined as the braking intervals.

The interval length calculation unit122may calculate lengths of the non-braking intervals and length of the braking intervals based on the vehicle data from the interval classification unit121. For example, as illustrated inFIG.11, the interval length calculation unit122may calculate the length (i.e., duration) of each of the non-braking intervals and the length (i.e., duration) of each of the braking intervals based on the brake pedal input signal.

The cylinder pressure calculation unit123may calculate the pressure for each interval of the cylinder (e.g., an average cylinder pressure for each interval) for providing the braking force of the vehicle based on the vehicle data from the interval classification unit121. For example, the cylinder pressure calculation unit123may calculate the average cylinder pressure in each of the non-braking intervals and each of the braking intervals based on the cylinder pressure as illustrated inFIG.11. A unit of the pressure may be bar.

The vehicle velocity calculation unit124may calculate a velocity of the vehicle for each interval of the vehicle based on the vehicle data from the interval classification unit121. For example, the vehicle velocity calculation unit124may calculate the vehicle velocity (e.g., average vehicle velocity) in each of the non-braking intervals and each of the braking intervals based on the wheel velocity as illustrated inFIG.11. Meanwhile, when the vehicle includes a plurality of wheels, the vehicle velocity calculation unit124may calculate a vehicle velocity (e.g., average vehicle velocity for each interval) in each of the non-braking intervals and each of the braking intervals based on a rotational velocity of the fastest wheel among the plurality of wheels. The unit of the vehicle velocity may be km/h.

The braking energy calculation unit125may calculate a braking energy for each interval (e.g., average braking energy for each interval) of the vehicle based on the vehicle data from the interval classification unit121. For example, the braking energy for each interval may be calculated based on the vehicle velocity as illustrated inFIG.11. In this case, as described above, the vehicle velocity may be calculated based on the wheel velocity. Therefore, the braking energy for each interval may be calculated based on the wheel velocity. As such, the braking energy calculation unit125may calculate the braking energy (e.g., average braking energy for each interval) in each of the non-braking intervals and each of the braking intervals based on the vehicle velocity caused by the wheel velocity. The unit of the braking energy as J (joule) may be calculated based on the mass and the vehicle velocity of the vehicle.

Meanwhile, when braking energy by a front-side brake of the vehicle and braking energy by a rear-side brake of the vehicle are intended to be separately calculated, the braking energy may include first braking energy and second braking energy. For example, the first braking energy means braking energy related to any one brake pad (hereinafter, referred to as a “first brake pad”) of a left wheel and a right wheel of a front of the vehicle, and the second braking energy means braking energy related to any one brake pad (hereinafter, referred to as a second brake pad) of the left wheel and the right wheel of a rear of the vehicle. In other words, the first braking energy means braking energy related to front braking force of the vehicle and the second braking energy means braking energy related to rear braking force of the vehicle. In such a case, the braking energy calculation unit125may calculate the first braking energy (e.g., average first braking energy for each interval) in each of the non-braking intervals and each of the braking intervals and the second braking energy (e.g., average second braking energy for each interval) in each of the non-braking intervals and each of the braking intervals based on the vehicle velocity caused by the wheel velocity.

Meanwhile, since the brake pad of the front left wheel and the brake pad of the front right wheel are braked with the substantially same pressure, the first braking energy may be regarded as the front-side braking energy of the vehicle, and because the brake pad of the rear left wheel and the brake pad of the rear right wheel are braked with the substantially same pressure, the second braking energy may be regarded as the rear-side braking energy of the vehicle. The braking energy by the front-side brake of the vehicle may be larger than the braking energy by the rear-side brake of the vehicle.

The outdoor temperature calculation unit126may calculate an outdoor temperature of the vehicle for each interval (e.g., an average outdoor temperature for each interval) of the vehicle based on the vehicle data from the interval classification unit121. In other words, the outdoor temperature calculation unit126may calculate an outdoor temperature of the vehicle in each of the non-braking intervals and each of the braking intervals (e.g., an average outdoor temperature for each interval) based on the vehicle data. The unit of the outdoor temperature may be ° C. or ° F.

The quantity calculation unit127may calculate a quantity of rain for each interval (e.g., an average quantity for each interval) based on the vehicle data from the interval classification unit121. For example, the quantity calculation unit127may calculate the quantity of rain based on the rain sensor signal as illustrated inFIG.11. In other words, the quantity calculation unit127may calculate the quantity of rain in each of the non-braking intervals and each of the braking intervals (e.g., an average quantity for each interval) based on the quantity.

FIG.12is a block diagram of another exemplary embodiment of the pad temperature prediction unit200ofFIG.9.

When the braking energy includes the first braking energy and the second braking energy as described above (or when the first braking energy and the second braking energy are separately calculated), the pad temperature prediction unit200may include two independent pad temperature prediction units, e.g., a first pad temperature prediction unit200aand a second pad temperature prediction unit200b.

The first pad temperature prediction unit200amay predict a temperature of the first brake pad (e.g., a brake pad of a front-side wheel of the vehicle) by analyzing first feature data from the data extraction unit120by the artificial intelligence scheme. The first pad temperature prediction unit200amay include, for example, a first setting value storage unit210aand a first pad temperature calculation unit220a. Here, the first feature data may include a length of the non-braking interval (e.g., a time duration of the non-braking interval), a length of the braking interval (e.g., a time duration of the braking interval), a cylinder pressure, a vehicle velocity, a first braking energy, an outdoor temperature, and a quantity of rain.

The first setting value storage unit (or one or more memories)210amay store a predetermined first model setting value. The first model setting value is data prestored in the setting value storage unit210.

The first pad temperature calculation unit220amay calculate the temperature of the first brake pad based on the first feature data from the data extraction unit120and the first model setting value from the first setting value storage unit210a.

The second pad temperature prediction unit200bmay predict a temperature of the second brake pad (e.g., a brake pad of a rear-side wheel of the vehicle) by analyzing second feature data from the data extraction unit120by the artificial intelligence scheme. The second pad temperature prediction unit200bmay include, for example, a second setting value storage unit210band a second pad temperature calculation unit220b. Here, the second feature data may include a length of the non-braking interval (e.g., a time duration of the non-braking interval), a length of the braking interval (e.g., a time duration of the braking interval), a cylinder pressure, a vehicle velocity, a second braking energy, a outdoor temperature, and a quantity of rain. Remaining information of the first feature data and remaining information of the second feature data other than the braking energy are the substantially same as each other.

The second setting value storage unit (or one or more memories)210bmay store a predetermined second model setting value. The second model setting value is data prestored in the second setting value storage unit210b.

The second pad temperature calculation unit220bmay calculate the temperature of the second brake pad based on the second feature data from the data extraction unit120and the second model setting value from the second setting value storage unit210b.

Since the first setting value storage unit210aand the second setting value storage unit210bofFIG.12are the same as the setting value storage unit210ofFIG.9, the first setting value storage unit210aand the second setting value storage unit210bare described with reference to the setting value storage unit210ofFIG.9and a related disclosure. However, the first model setting value and the second model setting value have different values. The reason is that the first model setting value and the second model setting value are generated based on different learning data.

Since the first pad temperature calculation unit220aand the second pad temperature calculation unit220bofFIG.12are the same as the pad temperature calculation unit220ofFIG.9, the first pad temperature calculation unit220aand the second pad temperature calculation unit220bare described with reference to the pad temperature calculation unit220ofFIG.9and a related disclosure thereto.

FIG.13is a detailed block diagram of the first pad temperature calculation unit220aofFIG.9.

As illustrated inFIG.13, the first pad temperature calculation unit220amay include a first initial temperature calculation unit221a, a first data collection unit222a, a first normalization unit223a, a first model generation unit224a, a first prediction value output unit225a, and a first setting value loading unit226a.

The first initial temperature calculation unit221amay calculate an initial temperature of the first brake pad based on the first feature data from the data extraction unit120. A first initial temperature may be set based on a time length from a time when the vehicle is turned off up to a time when the vehicle starts or is turned on, the outdoor temperature of the vehicle, and a value defined by a predetermined first brake pad temperature characteristic curve. The time length from the time when the vehicle is turned off up to the time when the vehicle starts or is turned on may be calculated based on a time stamp included in index data of the first feature data.

The first data collection unit222amay collect and output the first feature data from the data extraction unit120and the first initial temperature from the first initial temperature calculation unit221aas one first data set. The first data set includes the first feature data including the first braking energy, and the first initial temperature.

The first normalization unit223amay normalize the first data set from the first data collection unit222abased on a first average and a first standard deviation of the vehicle data provided from the first setting value storage unit210a.

The first model generation unit224amay generate a first brake pad temperature prediction model based on a first weight and a first bias of the vehicle data retrieved or loaded from the first setting value storage unit210a.

The first setting value loading unit226amay load the first weight and the first bias of the vehicle data from the first setting value storage unit210ato the first model generation unit224a.

The first prediction value output unit225ainputs the first data set normalized from the first normalization unit223ainto the first brake pad temperature prediction model from the first model generation unit224ato calculate a first temperature change rate of the first brake pad and adds the first initial temperature to the calculated first temperature change rate to calculate the temperature of the first brake pad, and output the calculated brake pad temperature. For example, when the first data set of the first non-braking interval is input into the first brake pad temperature prediction model, the first brake pad temperature prediction model predicts and calculates a change amount (hereinafter, referred to as a temperature change amount of the first non-braking interval) of the first brake pad temperature at an end time of the first non-braking interval. Thereafter, the calculated temperature change amount of the first non-braking interval is added to the first initial temperature to calculate the prediction temperature of the first non-braking interval for the first brake pad. That is, a sum of the first initial temperature and the temperature change amount of the first non-braking interval may be defined as a first brake pad prediction temperature (hereinafter, referred to as a first non-braking interval prediction temperature) in the first non-braking interval. Thereafter, the first non-braking interval prediction temperature is set as a first initial temperature of an immediately contiguous next interval (e.g., the first braking interval). Thereafter, for example, when the first data set of the first braking interval is input into the first brake pad temperature prediction model, the first brake pad temperature prediction model predicts and calculates a change amount (hereinafter, referred to as a temperature change amount of the first braking interval) of the first brake pad temperature at the end of the first braking interval. Thereafter, the calculated temperature change amount of the first braking interval is added to the first non-braking interval prediction temperature set as the first initial temperature to calculate the prediction temperature of the first braking interval for the first brake pad. That is, a sum of the first non-braking interval prediction temperature set as the first initial temperature and the temperature change amount of the first braking interval may be defined as a first brake pad prediction temperature (hereinafter, referred to as a first braking interval prediction temperature) in the first braking interval. By such a scheme, a first non-braking interval prediction temperature, a first braking interval prediction temperature, a second non-braking interval prediction temperature, a second braking interval prediction temperature, a third non-braking interval prediction temperature, a third braking interval prediction temperature, and a fourth non-braking interval prediction temperature for the first brake pad may be calculated. That is, the first prediction value output unit225amay calculate an interval-specific prediction temperature. In general, a prediction temperature change amount in the non-braking interval tends to decrease and the prediction temperature change amount in the braking interval tends to increase. Subsequently, the first prediction value output unit225asums up all of the first non-braking interval prediction temperature, the first braking interval prediction temperature, the second non-braking interval prediction temperature, the second braking interval prediction temperature, the third non-braking interval prediction temperature, the third braking interval prediction temperature, and the fourth non-braking interval prediction temperature for the first brake pad to finally calculate the prediction temperature of the first brake pad for a predetermined period (or time).

FIG.14is a detailed block diagram of the second pad temperature calculation unit220bofFIG.12.

As illustrated inFIG.14, the second pad temperature calculation unit220bmay include a second initial temperature calculation unit221b, a second data collection unit222b, a second normalization unit223b, a second model generation unit224b, a second prediction value output unit225b, and a second setting value loading unit226b.

Here, the second initial temperature calculation unit221b, the second data collection unit222b, the second normalization unit223b, the second model generation unit224b, the second prediction value output unit225b, and the second setting value loading unit226bare the substantially same as the first initial temperature calculation unit221a, the first data collection unit222a, the first normalization unit223a, the first model generation unit224a, the first prediction value output unit225a, and the first setting value loading unit226a, respectively.

The second initial temperature calculation unit221bmay calculate an initial temperature of the second brake pad based on the second feature data from the data extraction unit120. A second initial temperature may be set based on a time length from a time when the vehicle is turned off up to a time when the vehicle starts or is turned on, the outdoor temperature of the vehicle, and a value defined by a predetermined second brake pad temperature characteristic curve. Meanwhile, the time length from the time when the vehicle is turned off up to the time when the vehicle starts is turned on may be calculated based on a time stamp included in index data of the second feature data. In this case, a second brake pad temperature characteristic curve has a different characteristic from the first brake pad temperature characteristic curve.

The second data collection unit222bmay collect and output the second feature data from the data extraction unit120and the second initial temperature from the second initial temperature calculation unit221bas one second data set. The second data set includes the second feature data including the second braking energy, and the second initial temperature.

The second normalization unit223bmay normalize the second data set from the second data collection unit222bbased on a second average and a second standard deviation of the vehicle data provided from the second setting value storage unit210b.

The second model generation unit224bmay generate a second brake pad temperature prediction model based on a second weight and a second bias of the vehicle data retrieved or loaded from the second setting value storage unit210b.

The second setting value loading unit226bmay load the second weight and the second bias of the vehicle data from the second setting value storage unit210bto the second model generation unit224b.

The second prediction value output unit225binputs the second data set normalized from the second normalization unit223binto the second brake pad temperature prediction model from the second model generation unit224bto calculate a second temperature change rate of the second brake pad and adds the second initial temperature to the calculated second temperature change rate to calculate the temperature of the second brake pad, and output the calculated brake pad temperature. For example, when the second data set of the first non-braking interval is input into the second brake pad temperature prediction model, the second brake pad temperature prediction model predicts and calculates a change amount (hereinafter, referred to as a temperature change amount of the first non-braking interval) of the second brake pad temperature at an end of the first non-braking interval. Thereafter, the calculated temperature change amount of the first non-braking interval is added to the second initial temperature to calculate the prediction temperature of the first non-braking interval for the second brake pad. That is, a sum of the second initial temperature and the temperature change amount of the first non-braking interval may be defined as a second brake pad prediction temperature (hereinafter, referred to as a first non-braking interval prediction temperature) in the first non-braking interval. Thereafter, the first non-braking interval prediction temperature is set as a second initial temperature of an immediately contiguous next interval (e.g., the first braking interval). Thereafter, for example, when the second data set of the first braking interval is input into the second brake pad temperature prediction model, the second brake pad temperature prediction model predicts and calculates a change amount (hereinafter, referred to as a temperature change amount of the first braking interval) of the second brake pad temperature at the end time of the first braking interval. Thereafter, the calculated temperature change amount of the first braking interval is added to the first non-braking interval prediction temperature set as the second initial temperature to calculate the prediction temperature of the first braking interval for the second brake pad. That is, a sum of the first non-braking interval prediction temperature set as the second initial temperature and the temperature change amount of the first braking interval may be defined as a second brake pad prediction temperature (hereinafter, referred to as a first braking interval prediction temperature) in the first braking interval. By such a scheme, the first non-braking interval prediction temperature, the first braking interval prediction temperature, the second non-braking interval prediction temperature, the second braking interval prediction temperature, the third non-braking interval prediction temperature, the third braking interval prediction temperature, and the fourth non-braking interval prediction temperature for the second brake pad may be calculated. That is, the first prediction value output unit225amay calculate the interval-specific prediction temperature for the second brake pad. In general, a prediction temperature change amount in the non-braking interval tends to decrease and the prediction temperature change amount in the braking interval tends to increase. Subsequently, the first prediction value output unit225asums up all of the first non-braking interval prediction temperature, the first braking interval prediction temperature, the second non-braking interval prediction temperature, the second braking interval prediction temperature, the third non-braking interval prediction temperature, the third braking interval prediction temperature, and the fourth non-braking interval prediction temperature for the second brake pad to finally calculate the prediction temperature of the second brake pad for a predetermined period (or time).

Meanwhile, when there is only one braking energy, the pad temperature calculation unit220ofFIG.9may include an initial temperature calculation unit, a data collection unit, a normalization unit, a model generation unit, a setting value loading unit, and a prediction value output unit. In this case, the initial temperature calculation unit, the data collection unit, the normalization unit, the model generation unit, the setting value loading unit, and the prediction value output unit may be the substantially the same as the first initial temperature calculation unit221a, the first data collection unit222a, the first normalization unit223a, the first model generation unit224a, the first prediction value output unit225a, and the first setting value loading unit226aofFIG.13, respectively. Therefore, a detailed configuration of the pad temperature calculation unit220ofFIG.9is described with reference toFIG.13(orFIG.14) and a related disclosure.

FIG.15is a block diagram according to another exemplary embodiment of the pad wear amount calculation unit300ofFIG.9.

When the braking energy includes the first braking energy and the second braking energy as described above (or when the first braking energy and the second braking energy are separately calculated), the pad wear amount calculation unit300may include two independent pad wear amount calculation units, e.g., a first pad wear amount calculation unit300aand a second pad wear amount calculation unit300bas illustrated inFIG.15.

The first pad wear amount calculation unit300amay include a first look-up table310aand a first pad wear amount output unit320a.

The first look-up table310amay store a value of a wear amount of the first brake pad predetermined according to a value of the temperature of the first brake pad and a value of the braking energy.

The first pad wear amount output unit320amay search for a wear amount of the first brake pad from the first look-up table310abased on the temperature of the first brake pad from the first pad temperature prediction unit200aand the first braking energy from the feature extraction unit100, and output the searched wear amount of the first brake pad.

The second pad wear amount calculation unit300bmay include a second look-up table310band a second pad wear amount output unit320b.

The second look-up table310bmay be stored in one or more memories and may store a value of a wear amount of the second brake pad predetermined according to a value of the temperature of the second brake pad and a value of the braking energy.

The second pad wear amount output unit320bmay search for a wear amount of the second brake pad from the second look-up table310bbased on the temperature of the second brake pad from the second pad temperature prediction unit200band the second braking energy from the feature extraction unit100, and output the searched wear amount of the second brake pad.

Here, since the first look-up table310aand the second look-up table310bare the substantially same as the look-up table310ofFIG.10, the first look-up table310aand the second look-up table310bare described with reference toFIG.10and a related description.

Further, since the first pad wear amount output unit320aand the second pad wear amount output unit320bare the same as or substantially similar to the pad wear amount output unit320ofFIG.9, the first pad wear amount output unit320aand the second pad wear amount output unit320bare described with reference toFIG.9and a related description.

FIG.16is a block diagram according to another exemplary embodiment of the pad remaining amount calculation unit400ofFIG.9.

When the braking energy includes the first braking energy and the second braking energy as described above, the pad remaining amount calculation unit400may include two independent pad remaining amount calculation units, e.g., a first pad remaining amount calculation unit400aand a second pad remaining amount calculation unit400b.

The first pad remaining amount calculation unit400amay calculate the remaining amount of the first brake pad based on the wear amount of the first brake pad from the first pad wear amount calculation unit300a. For example, the first pad remaining amount calculation unit400amay calculate the remaining amount of the first brake pad by subtracting the first brake wear amount from the first pad wear amount output unit320afrom a current thickness of the first brake pad. Meanwhile, the remaining amount of the first brake pad calculated from the first pad remaining amount calculation unit400amay be transmitted to a device associated with the customer through a network or a cloud system. Meanwhile, the first pad remaining amount calculation unit400amay generate an alarm or warning when the calculated remaining amount of the first brake pad is smaller than a predetermined first threshold.

The second pad remaining amount calculation unit400bmay calculate the remaining amount of the second brake pad based on the wear amount of the second brake pad from the second pad wear amount calculation unit300b. For example, the second pad remaining amount calculation unit400bmay calculate the remaining amount of the second brake pad by subtracting the second brake wear amount from the second pad wear amount output unit320bfrom a current thickness of the second brake pad. Meanwhile, the remaining amount of the second brake pad calculated from the second pad remaining amount calculation unit400bmay be transmitted to a device associated with the customer through a network or a cloud system. Meanwhile, the second pad remaining amount calculation unit400bmay generate the alarm or warning when the calculated remaining mount of the second brake pad is smaller than a predetermined second threshold. Here, the second threshold may be different from the first threshold. As a specific example, the second threshold may be smaller or larger than the first threshold.

FIG.17is a diagram illustrating an artificial neural network structure applied to a model generation unit and a setting value loading unit ofFIGS.13and14.

The model generation unit (e.g., the first model generation unit224aor the second model generation unit224b) and the setting value loading unit (e.g., the first setting value loading unit226aor the second setting value loading unit226b) may generate a prediction model for predicting a brake pad temperature through an artificial neural network structure illustrated inFIG.17.

For example, the model generation unit may generate a brake pad temperature prediction model of the artificial neural network structure illustrated inFIG.17based on the weight of the vehicle data and the bias of the vehicle data loaded through the setting value loading unit.

The artificial neural network may be a network of a structure in which multiple neurons are connected to each other, and may receive data (e.g., vehicle data) to be predicted through an input layer901. As the input data is processed through hidden layers902of various steps, a final result (e.g., a brake pad temperature) may be output through an output layer903.

FIG.18is a block diagram of a pad remaining amount calculation unit and an alarm unit ofFIG.9.

The brake pad monitoring apparatus4000according to an exemplary embodiment of the present disclosure may further include an alarm unit500as illustrated inFIG.18.

The alarm unit500compares the remaining amount of the brake pad calculated by the pad remaining amount calculation unit400with a predetermined threshold, and determines whether to output the alarm according to a comparison result. For example, when the calculated remaining amount of the brake pad is smaller than the predetermined threshold, the alarm unit500outputs the alarm. The alarm unit500may be disposed inside the vehicle.

Meanwhile, when the pad remaining amount calculation unit400includes the first pad remaining amount calculation unit400aand the second pad remaining amount calculation unit400bas illustrated inFIG.16, the alarm unit500may include a first alarm unit and a second alarm unit.

In this case, the first alarm unit compares the remaining amount of the first brake pad calculated by the first pad remaining amount calculation unit400awith a predetermined first threshold, and determines whether to output the alarm according to a comparison result. For example, when the calculated remaining amount of the first brake pad is smaller than the first threshold, the first alarm unit outputs the alarm. The first alarm unit may be disposed inside the vehicle.

Meanwhile, the second alarm unit compares the remaining amount of the second brake pad calculated by the second pad remaining amount calculation unit400bwith a predetermined second threshold, and determines whether to output the alarm according to a comparison result. For example, when the calculated remaining amount of the second brake pad is smaller than the second threshold, the second alarm unit outputs the alarm. The second alarm unit may be disposed inside the vehicle.

Here, the second threshold may be different from the first threshold. As a specific example, the second threshold may be smaller or larger than the first threshold.

FIG.19is a flowchart for describing a brake pad monitoring method according to an exemplary embodiment of the present disclosure.

The brake pad monitoring method according to an exemplary embodiment of the present disclosure includes a step of calculating a remaining amount of a brake pad of a vehicle by analyzing vehicle data input from the outside of the vehicle by an artificial intelligence scheme.

For example, as illustrated inFIG.19, according to the brake pad monitoring method according to an exemplary embodiment of the present disclosure, first, feature data including braking energy of a vehicle is extracted based on the vehicle data input from the outside of the vehicle (S100).

Thereafter, a temperature of a brake pad is predicted by analyzing the extracted feature data by the artificial intelligence scheme (S200).

Next, a wear amount of the brake pad is calculated based on the predicted temperature of the brake pad and the extracted braking energy (S300).

Subsequently, the remaining amount of the brake pad is calculated based on the calculated wear amount of the braked pad (S400). For example, the remaining amount of the brake pad may be calculated by subtracting the calculated brake wear amount from a current thickness of the brake pad.

FIG.20is a flowchart for describing an exemplary embodiment of a step of extracting feature data ofFIG.19.

The step S100of extracting the feature data inFIG.19may include one or more of steps illustrated inFIG.20.

First, the vehicle data input from the outside of the vehicle is stored (S110).

Thereafter, the feature data is extracted from the stored vehicle data (S120).

FIG.21is a flowchart for describing an exemplary embodiment of a step of predicting a temperature of a brake pad ofFIG.19.

The step S200of predicting the temperature of the brake pad inFIG.19may include one or more of steps illustrated inFIG.21.

First, a model setting value calculated by the machine learning of the artificial intelligence scheme to calculate or infer the temperature of the brake pad corresponding to the vehicle data is stored (S210).

Thereafter, the temperature of the brake pad is calculated based on the extracted feature data and the stored model setting value (S220).

FIG.22is a flowchart for describing an exemplary embodiment of a step of calculating a wear amount of the brake pad ofFIG.19.

The step S300of calculating the wear amount of the brake pad inFIG.19may include one or more of steps illustrated inFIG.22.

First, the look-up table310storing a value of the wear amount of the brake pad predetermined according to a value of the temperature of the brake pad and a value of the braking energy is generated (S310).

Thereafter, the wear amount of the brake pad from the look-up table310is searched based on the predicted temperature of the brake pad and the extracted braking energy, and the wear amount of the searched brake pad is outputted (S320).

FIG.23is a flowchart for describing an exemplary embodiment of a step of extracting feature data ofFIG.20.

The step S120of extracting the feature data inFIG.20may include one or more of steps illustrated inFIG.23.

First, the stored vehicle data for each of the braking interval and the non-braking interval of the vehicle is classified (S121).

Subsequently, the length of the braking interval and the length of the non-braking interval is calculated based on the classified vehicle data (S122).

Next, the pressure for each interval of the cylinder for providing the braking force of the vehicle is calculated based on the classified vehicle data (S123).

Thereafter, the vehicle velocity for each interval is calculated based on the classified vehicle data (S124).

Next, the braking energy for each interval based on the classified vehicle data is calculated (S125).

Subsequently, the outdoor temperature of the vehicle for each interval is calculated based on the classified vehicle data (S126).

Thereafter, the quantity of rain for each interval is calculated based on the classified vehicle data (S127).

FIG.24is a flowchart for describing a step of calculating a temperature of a brake pad ofFIG.21.

The step S220of calculating the temperature of the brake pad inFIG.21may include one or more of steps illustrated inFIG.24.

First, the initial temperature of the brake pad is calculated based on the extracted feature data (S221).

Thereafter, the extracted feature data and the calculated initial temperature are collected and outputted as one data set (S222).

Next, the data set based on the average and the standard deviation of the stored vehicle data is normalized (S223).

Thereafter, the brake pad temperature prediction model generated based on the weight and the bias of the stored vehicle data (S224).

Next, the average and the standard deviation of the stored vehicle data is loaded to the brake pad temperature prediction model (S225).

Subsequently, the temperature change rate of the brake pad is calculated by inputting the normalized data set into the brake pad temperature prediction model, calculating the temperature of the brake pad by adding the initial temperature to the calculated temperature change rate, and the calculated brake pad temperature is outputted (S226).

FIG.25is a flowchart for describing an embodiment of determining whether to output an alarm depending on a temperature of a brake pad ofFIG.19.

First, the remaining amount of the brake pad is calculated (S400), and the calculated remaining amount of the brake pad and a predetermined threshold are compared to each other (S510).

Thereafter, when the comparison result of the step S510is that the remaining amount of the brake pad is smaller than the threshold at the step S510, the alarm is outputted (S520).

However, when the comparison result of the step S510is that the remaining amount of the brake pad is equal to or larger than the threshold, the step S510is repeated.

FIG.26is a graph for illustrating a pad wear prediction curve calculated by the brake pad monitoring apparatus4000and a method for monitoring a brake pad according to an exemplary embodiment of the present disclosure.

As illustrated inFIG.26, when a feature for the vehicle data is extracted, the wear amount of the brake pad may be calculated based on braking energy of the feature (i.e., feature data) and the brake pad temperature predicted by the temperature prediction model.

The remaining amount of the brake pad may be calculated based on the calculated wear amount of the brake pad.

Meanwhile, the vehicle consumables management system10000according to an exemplary embodiment of the present disclosure may transmit information on a remaining amount of the consumables (e.g., at least one of the tire and the brake pad) to a display device of the vehicle3000. As a result, the display device may display the remaining amount of the tire tread and the remaining amount of the brake pad on a screen. In this case, the vehicle consumables management system10000may transmit information on consumables remaining amount through the server2000to the vehicle3000, and unlike this, may also transmit the information on the consumables remaining amount to the display device of the vehicle3000and a control unit or controller controlling the display device without passing through the server2000.

Meanwhile, it will be able to be appreciated that a block of each of the drawings of a processing flowchart and combinations of the drawings can be performed by computer program instructions. Since the computer program instructions may be mounted on a universal computer, a special computer or a processor of other programmable data processing equipment, the instructions performed by the computer or a processor of other programmable data processing equipment generate a means of performing functions described in a block(s) of the flowchart. Since the computer program instructions may also be stored in a computer usable or computer readable memory which may direct a computer or other programmable data processing equipment in order to implement a function in a specific scheme, the instructions stored in the computer usable or computer readable memory can also produce manufacturing items including an instruction means performing a function described in the block(s) of the flowchart. Since the computer program instructions can also be mounted on the computer or other programmable data processing equipment, instructions that perform the computer or other programmable data processing equipment by generating a processor executed by the computer as a series of operational steps are performed on the computer or other programmable data processing equipment can provide steps for executing the functions described in the block(s) of the flowchart.

Further, each block may represent a part of a module, a segment, or a code that includes one or more executable instructions for executing a specified logical function(s). It should also be noted that in some alternative embodiments, the functions mentioned in the blocks may occur out of order. For example, two successive illustrated blocks may in fact be performed substantially concurrently and the blocks may sometimes be performed in reverse order according to the corresponding function.

In this case, the term “unit” used in the exemplary embodiment means software and hardware components such as one or more processors or controller, FPGA or ASIC and the “unit” performs predetermined roles. However, the “unit” is not a meaning limited to software or hardware. The “unit” may be configured to reside on an addressable storage medium and may be configured to reproduce one or more processors. Accordingly, as one example, the “unit” includes components such as software components, object oriented software components, class components, and task components, processes, functions, attributes, procedures, subroutines, segments of a program code, drivers, firmware, microcodes, circuitry, data, databases, data structures, tables, arrays, and variables. Functions provided in the components and the “units” may be combined into a smaller number of components and “units” or further separated into additional components and “units”. Moreover, the components and the ‘units’ may be implemented to reproduce one or more CPUs in a device or a secure multimedia card.

It will be appreciated that those skilled in the art that the present specification belongs to the technical field of the technical field may be practiced in other specific forms without changing the technical spirit or essential features. Therefore, it should be appreciated that the aforementioned embodiments are illustrative in all aspects and are not restricted. The scope of the present disclosure is represented by claims to be described below rather than the detailed description, and it is to be interpreted that the meaning and scope of the claims and all the changes or modified forms derived from the equivalents thereof come within the scope of the present disclosure.

Meanwhile, preferred embodiments of the present disclosure have been disclosed in the present disclosure and the drawing and although specific terminologies are used, but they are used in a general meaning for easily describe the technical content of the present disclosure and help understanding the present disclosure and are not limited to the scope of the present disclosure. In addition to the embodiments disclosed herein, it is apparent to those skilled in the art that other modified examples based on the technical spirit of the present disclosure can be executed.