A friction-coefficient-computing device includes: a computing unit that calculates a slip ratio and a friction coefficient; and a maximum-friction-estimating unit that calculates an estimated maximum value of the friction. The maximum-friction-estimating unit includes: a model calculator that calculates a tire model friction, which is a friction coefficient of a tire brush model; and a parameter-estimating unit that estimates values of parameters of a tire brush model expression. The parameter-estimating unit includes a parameter-restricting unit that eliminates values of the parameters that allow the tire brush model expression to be a linear function and a quadratic function, and obtains values of the parameters that allow an inclination of an inflection point of the tire brush model expression to approach zero.

CROSS REFERENCE TO RELATED APPLICATION

This application is based on Japanese Patent Application No. 2022-121748 filed on Jul. 29, 2022, the disclosure of which is incorporated herein by reference.

TECHNICAL FIELD

The present disclosure relates to a friction-coefficient-computing device.

BACKGROUND ART

As a method for controlling a driving force of an electric car, an optimal slip ratio is estimated, at which a driving force generated in the tire is maximized, and the slip ratio is controlled based on the estimated optimal slip ratio.

SUMMARY

According to an aspect of the present disclosure, a friction-coefficient-computing device estimates an estimated maximum friction value, which is an estimated maximum value of a friction coefficient between a tire and a road surface, using a tire brush model that simulates a physical phenomenon between the tire and the road surface, and on a basis of a detection signal transmitted from a detection unit that detects information relating to the tire when a vehicle travels on the road surface. The friction-coefficient-computing device includes: a computing unit that calculates a slip ratio between the tire and the road surface, and calculates a friction coefficient between the tire and the road surface on a basis of the detection signal; and a maximum-friction-estimating unit that calculates the estimated maximum friction value using the slip ratio and the friction coefficient calculated by the computing unit, and a tire brush model expression, which is a computation expression indicating a relationship between a slip ratio and a friction coefficient in the tire brush model, and is for calculating an estimated friction coefficient between the tire and the road surface in a case where the slip ratio between the tire and the road surface is in a minute region where the slip ratio is less than a slip ratio at which wheelspin of the tire starts. Assuming that the slip ratio calculated by the computing unit is a calculated slip ratio, and the friction coefficient calculated by the computing unit is a calculated friction coefficient, the tire brush model expression is a function relating to the slip ratio of the tire brush model, and includes a plurality of parameters that varies an inclination of the tire brush model expression. The maximum-friction-estimating unit includes: a model calculator that substitutes the calculated slip ratio into the tire brush model expression to calculate a tire model friction, which is a friction coefficient of the tire brush model, and a parameter-estimating unit that estimates values of the parameters so as to make smaller a difference between the calculated friction coefficient and the tire model friction. The parameter-estimating unit includes a parameter-restricting unit that eliminates values of the parameters that allow the tire brush model expression to be a linear function and a quadratic function, and obtains values of the parameters that allow an inclination of an inflection point of the tire brush model expression to approach zero.

DETAILED DESCRIPTION

As a method for controlling the driving force of an electric car, a driving-force-controlling method is conventionally known using which an optimal slip ratio at which a driving force generated in the tire is maximized is estimated, and on the basis of the estimated optimal slip ratio, slip ratio control is performed. In this driving-force-controlling method, the optimal slip ratio is calculated using a computation expression of a cubic function relating to a slip ratio obtained from a relational expression between a driving force generated in the tire and a slip ratio in a tire brush model.

For example, the optimal slip ratio during sudden acceleration of the vehicle is a slip ratio immediately before a start of wheelspin of the tire. To obtain the optimal slip ratio, the speed of the vehicle and the speed of the tire are controlled so that the vehicle can be controlled in such a manner that a driving force generated in the tire is maximized and wheelspin of the tire is prevented.

The slip ratio has a correlation with the friction coefficient of the road surface. The friction coefficient increases as the slip ratio increases, and becomes a maximum value immediately before a start of wheelspin of the tire at which the slip ratio is optimal. The friction coefficient is information relating to the state of the road surface necessary for stable traveling of the vehicle, and in particular, information of the maximum value of the friction coefficient is important. Therefore, for example, in a navigation system, the map information is linked with the information of the maximum value of the friction coefficient of the road surface, so that the information of the maximum value of the friction coefficient can be effectively used. The friction coefficient is a value obtained by dividing the driving force generated in the tire by the normal force.

Therefore, the inventor considered calculation of an estimated maximum value of the friction coefficient using a computation expression. In a case where an estimated maximum value of the friction coefficient is calculated using the computation expression, the estimated maximum value of the friction coefficient can be accurately calculated by obtaining the optimal slip ratio. However, in order to obtain the optimal slip ratio, it is necessary to rotate the tire until immediately before a start of the wheelspin. It is not easy to rotate the tire until immediately before a start of the wheelspin.

Therefore, the inventor considered calculating an estimated maximum value of the friction coefficient by calculating a slip ratio less than the optimal slip ratio, and substituting the calculated slip ratio into the computation expression. However, the intensive consideration by the inventor has revealed that it is difficult to accurately calculate, using this method, an estimated maximum value of the friction coefficient.

The present disclosure provides a friction-coefficient-computing device that can accurately calculate an estimated maximum value of a friction coefficient.

According to an aspect of the present disclosure, a friction-coefficient-computing device estimates an estimated maximum friction value, which is an estimated maximum value of a friction coefficient between a tire and a road surface, using a tire brush model that simulates a physical phenomenon between the tire and the road surface, and on a basis of a detection signal transmitted from a detection unit that detects information relating to the tire when a vehicle travels on the road surface. The friction-coefficient-computing device includes: a computing unit that calculates a slip ratio between the tire and the road surface, and calculates a friction coefficient between the tire and the road surface on a basis of the detection signal; and a maximum-friction-estimating unit that calculates the estimated maximum friction value using the slip ratio and the friction coefficient calculated by the computing unit, and a tire brush model expression, which is a computation expression indicating a relationship between a slip ratio and a friction coefficient in the tire brush model, and is for calculating an estimated friction coefficient between the tire and the road surface in a case where the slip ratio between the tire and the road surface is in a minute region where the slip ratio is less than a slip ratio at which wheelspin of the tire starts. Assuming that the slip ratio calculated by the computing unit is a calculated slip ratio, and the friction coefficient calculated by the computing unit is a calculated friction coefficient, the tire brush model expression is a function relating to the slip ratio of the tire brush model, and includes a plurality of parameters that varies an inclination of the tire brush model expression. The maximum-friction-estimating unit includes: a model calculator that substitutes the calculated slip ratio into the tire brush model expression to calculate a tire model friction, which is a friction coefficient of the tire brush model, and a parameter-estimating unit that estimates values of the parameters so as to make smaller a difference between the calculated friction coefficient and the tire model friction. The parameter-estimating unit includes a parameter-restricting unit that eliminates values of the parameters that allow the tire brush model expression to be a linear function and a quadratic function, and obtains values of the parameters that allow an inclination of an inflection point of the tire brush model expression to approach zero.

In a case of the minute region where the slip ratio is less than the slip ratio at which wheelspin of the tire starts, the friction coefficient increases substantially in proportion to the slip ratio. Therefore, in a case where the tire brush model expression is approximated so that the friction coefficient increases substantially in proportion to the slip ratio, the parameters of the tire brush model expression include candidates that allow the tire brush model expression to be a linear function and a quadratic function.

However, according to intensive consideration by the inventor, in a case where the tire brush model expression is expressed by a linear function and a quadratic function, an estimated maximum friction value cannot be accurately calculated from the tire brush model expression.

According to intensive consideration by the inventor, the friction coefficient has a portion where when the slip ratio is a substantially maximum slip ratio, a ratio of a variation in the friction coefficient to an increase in the slip ratio is zero. Therefore, in a case where the tire brush model expression is approximated so that the friction coefficient has a portion where a ratio of a variation in the friction coefficient to an increase in the slip ratio is zero, the tire brush model expression has a portion where the inclination of the inflection point approaches zero.

As described above, according to the present disclosure, when the parameters of the tire brush model expression for calculating the friction coefficient in a case where the slip ratio is in the minute region are estimated, it is possible to exclude, from candidates for values of the parameters, candidates from which an estimated maximum friction value cannot be accurately calculated. Therefore, even in a case where the computing unit can calculate only values of the calculated slip ratio in the minute region, an estimated maximum friction value can be accurately calculated on the basis of estimated values of the parameters.

First Embodiment

The present embodiment will be described with reference toFIGS.1to11. A friction-coefficient-computing device of the present embodiment is used, for example, for a vehicle control system that controls traveling of an electric car. The vehicle control system is for controlling, for example, the rotation speed of the motor for driving the vehicle. As illustrated inFIG.1, the vehicle control system includes a detection unit S that detects various types of information relating to the behavior of the vehicle, and a control device1that controls the rotation speed of the motor on the basis of the information detected by the detection unit S. The control device1is what is called an ECU. The control device1also functions as a friction-coefficient-computing device of the present embodiment. The ECU is the abbreviation for electronic control unit.

The detection unit S is a sensor group that detects, among information relating to the behavior of the vehicle, particularly, various types of information relating to the tire during the vehicle traveling on a road surface. The detection unit S is provided for the vehicle. Specifically, the detection unit S includes a vehicle speed sensor that detects the speed of the vehicle, a wheel speed sensor that detects the rotation speed of the tire, a steering-angle sensor that detects the rotation angle of the steering wheel, a yaw rate sensor that detects the angular rotation speed of the vehicle in a yaw direction, and an acceleration sensor that detects the acceleration of the vehicle. The detection unit S also includes a torque sensor that detects the magnitude of the torque applied to the tire, and a load sensor that detects the load generated in the tire. The detection unit S transmits, to the control device1, detection signals corresponding to detected values detected by these various sensors.

The control device1includes a microcomputer including a central processing unit (CPU) and memories, such as a read-only memory (ROM) and a random-access memory (RAM), and a peripheral circuit of the microcomputer. The memories include non-transitory tangible storage media. The control device1performs various computations and processing on the basis of programs stored in the ROM. As illustrated inFIG.1, the control device1includes a computing unit10and a maximum-friction-estimating unit20.

If detection signals corresponding to detected values detected by the various sensors are input into the control device1from the detection unit S, the control device1executes programs stored in the ROM to function as the computing unit10and the maximum-friction-estimating unit20. Alternatively, the control device1may include a plurality of circuit modules corresponding to, on a one-to-one basis, the computing unit10and the maximum-friction-estimating unit20.

Hereinafter, the computing unit10and the maximum-friction-estimating unit20will be individually described. First, the computing unit10will be described. The computing unit10is a computing device that on the basis of various detection signals transmitted from the detection unit S, calculates a slip ratio and a friction coefficient between the tire and the road surface during occurrence of a slip between the tire and the road surface during the vehicle traveling on the road surface. The computing unit10includes a slip calculator11that calculates a slip ratio, and a friction calculator12that calculates a friction coefficient.

If detection signals corresponding to detected values detected by the various sensors are input into the slip calculator11from the detection unit S, on the basis of these detected values, the slip calculator11calculates a slip ratio between the tire and the road surface. For example, in a case where the vehicle travels straight, the slip calculator11calculates the slip ratio on the basis of the difference between the speed of the vehicle detected by the vehicle speed sensor and the rotation speed of the tire detected by the wheel speed sensor. For example, in a case where the vehicle skids laterally, the slip calculator11calculates the slip ratio on the basis of, in addition to the detected values detected by the vehicle speed sensor and the wheel speed sensor, the detected values detected by the steering-angle sensor, the yaw rate sensor, and the acceleration sensor. The slip calculator11has an output side connected to the maximum-friction-estimating unit20. Information of a slip ratio calculated by the slip calculator11is transmitted to a model calculator21, which will be described later, of the maximum-friction-estimating unit20. Hereinafter, the slip ratio calculated by the slip calculator11is also referred to as a calculated slip ratio sc.

If detection signals corresponding to detected values detected by the various sensors are input into the friction calculator12from the detection unit S, on the basis of these detected values, the friction calculator12calculates a friction coefficient of the road surface. For example, the friction calculator12calculates a friction coefficient on the basis of detected values detected by the torque sensor, the load sensor, and the acceleration sensor. The friction calculator12has an output side connected to the maximum-friction-estimating unit20. Information of a friction coefficient calculated by the friction calculator12is transmitted to an error calculator22, which will be described later, of the maximum-friction-estimating unit20. Hereinafter, the friction coefficient calculated by the friction calculator12is also referred to as a calculated friction coefficient μc.

Although not illustrated, a noise filter is provided between the computing unit10and the maximum-friction-estimating unit20. This noise filter includes, for example, a low-pass filter or the like. In a case where a calculated slip ratio sccalculated by the slip calculator11and a calculated friction coefficient μccalculated by the friction calculator12include noise caused by vibration of the vehicle or the like, the noise filter removes the noise.

The maximum-friction-estimating unit20is a computing device that calculates an estimated maximum value of a friction coefficient between the tire and the road surface on the basis of information of a calculated slip ratio sctransmitted from the slip calculator11and information of a calculated friction coefficient μctransmitted from the friction calculator12. The maximum-friction-estimating unit20uses a tire brush model, which will be described later, to calculate an estimated maximum value of the friction coefficient. The maximum-friction-estimating unit20includes the model calculator21, the error calculator22, a parameter-estimating unit23, and a maximum value calculator24.

The model calculator21calculates a tire model friction μm, which is a theoretical estimated value of a friction coefficient in a tire brush model, which will be described later, on the basis of information of a calculated slip ratio sctransmitted from the slip calculator11. The model calculator21has an input side connected to the slip calculator11. If information of a calculated slip ratio scis input into the model calculator21from the slip calculator11, the model calculator21calculates a tire model friction μmon the basis of a tire brush model expression described later. The model calculator21has an output side connected to the error calculator22. Information of a tire model friction μmcalculated by the model calculator21is transmitted to the error calculator22.

The error calculator22calculates a model error μerr, which is the difference between a tire model friction μmcalculated by the model calculator21and a calculated friction coefficient μccalculated by the friction calculator12. The error calculator22calculates the difference between a tire model friction μmand a calculated friction coefficient μccalculated at the same timing as a calculated slip ratio scused for calculating the tire model friction μm, to calculate a model error μerr.

If both information of a tire model friction μmis input from the model calculator21, and information of a calculated friction coefficient μcis input from the friction calculator12, the error calculator22calculates a model error μerr, which is a difference value between the tire model friction μmand the calculated friction coefficient μc. The model error μerris a value obtained by subtracting the calculated friction coefficient μcfrom the tire model friction μm. The model error μerris calculated as an absolute value. The error calculator22has an output side connected to the parameter-estimating unit23. Information of a model error μerrcalculated by the error calculator22is transmitted to the parameter-estimating unit23.

On the basis of information of a model error μerrtransmitted from the error calculator22, the parameter-estimating unit23estimates optimal parameters of the tire brush model expression to be described later. The parameter-estimating unit23includes an error-storing unit231and a parameter-restricting unit232.

The error-storing unit231stores information transmitted from the error calculator22. In other words, the error-storing unit231stores information relating to each of a calculated slip ratio sccalculated by the slip calculator11and a calculated friction coefficient μccalculated by the friction calculator12. The error-storing unit231is configured to be able to, every time the error-storing unit231acquires information of a model error μerrfrom the error calculator22, store the information of the model error μerr.

The error-storing unit231stores information of a predetermined number, which is preliminarily determined, of model errors μerr. The error-storing unit231of the present embodiment is configured to be able to store, for example, information of ten model errors μerr. The number of pieces of information of model errors μerrthat can be stored in the error-storing unit231is not limited to ten, and may be fewer than ten or more than ten. In the present embodiment, the error-storing unit231functions as a parameter-storing unit.

When the parameter-estimating unit23estimates optimal parameters of the tire brush model expression to be described later, the parameter-restricting unit232limits the parameters to be estimated. The parameter-restricting unit232will be described in detail later.

The parameter-estimating unit23has an output side connected to the model calculator21and to the maximum value calculator24. Information of optimal parameters estimated by the parameter-estimating unit23is output to the model calculator21and to the maximum value calculator24.

On the basis of information of parameters of the tire brush model expression estimated by the parameter-estimating unit23, the maximum value calculator24calculates an estimated maximum friction value μp, which is an estimated maximum value of the friction coefficient. If information of the parameters is input into the maximum value calculator24from the parameter-estimating unit23, the maximum value calculator24calculates an estimated maximum friction value μp.

The friction coefficient has a correlation with the slip ratio, and the value of the friction coefficient varies according to a variation in the slip ratio. For example, as indicated by a friction-slip characteristic FS indicated by a broken line ofFIG.2, in an adhesive region where wheelspin of the tire does not occur, the friction coefficient during acceleration of the vehicle increases as the slip ratio increases. In the adhesive region, the friction coefficient is maximized when the value of the slip ratio increases until immediately before a start of wheelspin of the tire. In a wheelspin region where even slight wheelspin of the tire occurs, the value of the slip ratio gradually decreases as the slip ratio increases.

The friction coefficient during deceleration of the vehicle decreases as the slip ratio decreases in the adhesive region where wheelspin of the tire does not occur. A black circle illustrated inFIG.2indicates the slip ratio immediately before a start of wheelspin of the tire, that is, the maximum slip ratio in the adhesive region where wheelspin of the tire does not occur, and indicates the magnitude of the friction coefficient at a time of a maximum slip ratio in the adhesive region where wheelspin of the tire does not occur. As described above, the friction coefficient is maximized when the value of the slip ratio is maximized in the adhesive region. Hereinafter, the slip ratio at a time when the slip ratio is maximized in the adhesive region is also referred to as a maximum slip ratio.

The friction-slip characteristic FS indicating the friction coefficient and the slip ratio having such a correlation is similar to part of a graph indicated by the tire brush model expression. The tire brush model expression is a computation expression indicating a relationship among a slip ratio, a friction coefficient, a load generated in the tire, and the like in the tire brush model. Therefore, first, the tire brush model and the tire brush model expression will be described.

The tire brush model simulates a physical phenomenon in a contact region between a tire and a road surface, and is a tire model in which a plurality of brush-like elastic objects is attached to the tire. In a case where the tire brush model is used, the driving force generated in the tire can be expressed by the tire brush model expression shown in the following Formula 1.

Fd in Formula 1 represents the driving force generated in the tire. s in Formula 1 represents the slip ratio between the tire and the road surface. H in Formula 1 is a parameter that varies the inclination of the tire brush model expression shown in Formula 1. H in Formula 1 is determined on the basis of the length of the surface where the tire of the tire brush model is installed, the width of the surface where the tire is installed, and the shear rigidity of the brush in the tire front-rear direction. H in Formula 1 can be expressed as the following Formula 2.

K in Formula 1 is a parameter that varies the inclination of the tire brush model expression shown in Formula 1. K in Formula 1 is determined on the basis of the length of the surface where the tire is installed, the width of the surface where the tire is installed, the shear rigidity of the brush in the tire front-rear direction, and the friction coefficient between the tire and the road surface. K in Formula 1 can be expressed as the following Formula 3.

a in Formulas 2 and 3 represents the length of the surface where the tire is installed in the tire brush model. b in Formulas 2 and 3 represents the width of the surface where the tire is installed in the tire brush model. Cxin Formulas 2 and 3 represents the shear rigidity of the brush in the tire front-rear direction in the tire brush model. μpin Formula 3 is an estimated maximum value of the friction coefficient, and indicates the friction coefficient in a case where the slip ratio is the maximum slip ratio.

The friction coefficient can be obtained by dividing the driving force generated in the tire by the normal force. Therefore, the tire model friction μmof the tire brush model can be calculated using the tire brush model expression shown in the following Formula 4 obtained by converting the tire brush model expression shown in Formula 1 using the normal force.

Fzin Formula 4 represents a normal force generated on the tire. The Formula 4 indicates the theoretical characteristic of the friction coefficient in the tire brush model. As shown in Formula 4, the tire model friction μmcan be obtained using a computation expression including a cubic function relating to the slip ratio. The correspondence relationship between the tire model friction μmand the slip ratio shown in Formula 4 can be expressed as a theoretical characteristic Th indicated by a solid line ofFIG.3. However, as illustrated inFIG.3, the theoretical characteristic Th may deviate from the friction-slip characteristic FS.

In such a case, as illustrated inFIG.3, the theoretical characteristic Th can be brought closer to the friction-slip characteristic FS by changing H, HK, and HK2, which are parameters of the tire brush model expression shown in Formula 4. The theoretical characteristic Th brought closer to the friction-slip characteristic FS can be used to obtain an estimated maximum friction value μp.

An example of a method for bringing the theoretical characteristic Th closer to the friction-slip characteristic FS will be described. First, a plurality of calculated slip ratios scis calculated on the basis of detection signals transmitted from the detection unit S, and the plurality of calculated slip ratios scthat has been calculated is substituted into the tire brush model expression shown in Formula 4 to calculate a plurality of tire model frictions μm. As a result, a theoretical characteristic Th is obtained.

Then, model errors μerr, which are, respectively, differences between the plurality of tire model frictions μmand a plurality of calculated friction coefficients μccalculated at the same timing as the plurality of calculated slip ratios scused to calculate the plurality of tire model frictions μm, are obtained. Then, H, HK, and HK2, which are parameters of the tire brush model expression shown in Formula 4, are changed to make each of the obtained model errors μerrsmaller. That is, H, HK, and HK2, which are parameters of the tire brush model expression shown in Formula 4, are changed so that the model errors μerrapproach zero.

As a result, even in a case where a theoretical characteristic Th deviates from the friction-slip characteristic FS, the theoretical characteristic Th can be brought closer to the friction-slip characteristic FS. The theoretical characteristic Th brought closer to the friction-slip characteristic FS can be used to obtain an estimated maximum friction value μp.

However, in a case where a theoretical characteristic Th is brought closer to the friction-slip characteristic FS using the above method to accurately calculate an estimated maximum friction value μp, a calculated slip ratio sccalculated by the slip calculator11when the slip ratio increases to the maximum slip ratio is necessary. However, in order to increase the slip ratio to the maximum slip ratio, it is necessary to rotate the tire until immediately before a start of the wheelspin. It is not easy to rotate the tire until immediately before a start of the wheelspin.

In a case where the slip ratio does not increase to the maximum slip ratio, the slip calculator11cannot calculate a calculated slip ratio scat a time when the slip ratio increases to the maximum slip ratio. In this case, in the tire brush model expression shown in Formula 4, a theoretical characteristic Th cannot be accurately brought closer to the friction-slip characteristic FS, and it is difficult to accurately calculate an estimated maximum friction value μp.

For example, in a case where the vehicle is traveling in a state of a minute region where the slip ratio is 0.1 or less, which is sufficiently less than the slip ratio at which wheelspin of the tire starts, the slip calculator11calculates only values of the calculated slip ratio scin the minute region. In such a case, there is a possibility that an estimated maximum friction value μpcannot be obtained using the above method. Therefore, the inventor considered obtaining an estimated maximum friction value μpusing the following method even in a case where the slip calculator11calculates only values of the calculated slip ratio scin the minute region that are smaller than the maximum slip ratio.

First, in a case where a value of the calculated slip ratio scis a value in the minute region that is sufficiently less than the slip ratio at which wheelspin of the tire starts, the slip ratio in the tire brush model can be expressed as the following Formula 5.

Therefore, in a case where a value of the calculated slip ratio scis a value in the minute region, the tire brush model expression shown in Formula 4 can be replaced with the tire brush model expression shown in the following Formula 6.

Formula 6 is a computation expression indicating the relationship between the slip ratio and the friction coefficient in the tire brush model. Formula 6 is a tire brush model expression for calculating an estimated friction coefficient between the tire and the road surface in a case where the slip ratio between the tire and the road surface is in a minute region where the slip ratio is less than the slip ratio at which wheelspin of the tire starts. Formula 6 includes a plurality of parameters that varies the inclination of the tire brush model expression.

In a case where a plurality of values of the calculated slip ratio scin the minute region is obtained by the slip calculator11, the model calculator21substitutes the plurality of calculated slip ratios scinto the tire brush model expression shown in Formula 6 to calculate a plurality of tire model frictions μm. As a result, even in a case where values of the calculated slip ratio scare values in the minute region, a theoretical characteristic Th can be obtained.

The parameter-estimating unit23calculates the values of H, HK, and HK2, which are parameters of the tire brush model expression shown in Formula 6, so as to make the model errors μerrsmaller. For example, the parameter-estimating unit23obtains H in the first term, HK in the second term, and HK2in the third term of Formula 6 so that the model errors μerrapproach zero. As a result, the theoretical characteristic Th that can be obtained from the tire brush model expression shown in Formula 6 can be brought closer to the friction-slip characteristic FS.

In this manner, the inventor considered calculating an estimated maximum friction value μpusing a theoretical characteristic Th brought closer to a friction-slip characteristic FS. However, further intensive consideration by the inventor revealed that in some cases, it is difficult to bring the theoretical characteristic Th closer to the friction-slip characteristic FS.

For example, in a case where in Formula 6, HK in the second term and HK2in the third term of the parameters obtained so that the model errors μerrapproach zero are zeros, Formula 6 is a linear function relating to the slip ratio.

On the other hand, in a friction-slip characteristic FS in a case of a minute region where the slip ratio is less than the slip ratio at which wheelspin of the tire starts, the friction coefficient increases substantially linearly as the slip ratio increases. That is, in the friction-slip characteristic FS in the minute region, the friction coefficient increases substantially in proportion to the slip ratio.

Therefore, in a case where the value of H in the first term of the tire brush model expression shown in Formula 6 is calculated so as to make the model errors μerrsmaller, the theoretical characteristic Th may be linear as illustrated by a dot-dash line ofFIG.4. That is, the optimal parameters for bringing the theoretical characteristic Th closer to the friction-slip characteristic FS include values for making the theoretical characteristic Th linear. In this case, since the theoretical characteristic Th and the friction-slip characteristic FS deviate from each other, an estimated maximum friction value μpcannot be accurately calculated.

Alternatively, in a case where in Formula 6, H in the first term and HK2in the third term of the parameters obtained so that the model errors μerrapproach zero are zeros, Formula 6 is a quadratic function relating to the slip ratio. In a case where the value of HK in the second term of the tire brush model expression shown in Formula 6 is calculated so as to make the model errors μerrsmaller, the theoretical characteristic Th may have an upwardly-convex parabolic shape as illustrated by a two-dots-dash line ofFIG.4. That is, the optimal parameters for bringing the theoretical characteristic Th closer to the friction-slip characteristic FS include values for obtaining a theoretical characteristic Th having an upwardly-convex parabolic shape. In this case, since the theoretical characteristic Th and the friction-slip characteristic FS deviate from each other, an estimated maximum friction value μpcannot be accurately calculated.

As described above, in a case where the values of H, HK, and HK2of the tire brush model expression shown in Formula 6 are calculated so as to make the model errors μerrsmaller, a theoretical characteristic Th from which an estimated maximum friction value μpcannot be accurately calculated may be obtained. That is, even if the values of H, HK, and HK2of the tire brush model expression are calculated to make the model errors μerrsmaller, there is a possibility that the theoretical characteristic Th and the friction-slip characteristic FS deviate from each other. In other words, the candidates for the values of H, HK, and HK2of the tire brush model expression for bringing the theoretical characteristic Th closer to the friction-slip characteristic FS include candidates from which an estimated maximum friction value μpcannot be accurately calculated. This was found through intensive consideration by the inventor.

Therefore, the inventor considered a method for, when the values of H, HK, and HK2, which are parameters of the tire brush model expression, are estimated, excluding, from the candidates for the values of H, HK, and HK2, candidates from which an estimated maximum friction value μpcannot be accurately calculated.

As illustrated inFIG.2and the like, in the friction-slip characteristic FS, the friction coefficient increases as the slip ratio increases, but as the slip ratio approaches the maximum slip ratio, the ratio of the increase in the friction coefficient decreases as the slip ratio increases. That is, in the friction-slip characteristic FS, the increase ratio of the friction coefficient gradually decreases as the slip ratio approaches the maximum slip ratio. If the slip ratio increases to a value substantially equal to the maximum slip ratio, the friction coefficient substantially does not vary and becomes constant even if the slip ratio increases. In other words, the friction-slip characteristic FS has a shape having a staying portion where a ratio of a variation in the friction coefficient to an increase in the slip ratio is zero when the slip ratio is a substantially maximum slip ratio.

Therefore, in a case where the theoretical characteristic Th is approximated to the friction-slip characteristic FS, a graph where the tire brush model expression shown in Formula 6 is expressed by a cubic function relating to the slip ratio has a shape in which the friction coefficient increases as the slip ratio increases. The graph where the tire brush model expression shown in Formula 6 is expressed by a cubic function relating to the slip ratio also has a shape that does not have a maximum value and a minimum value, and has only one staying portion where the ratio of the variation in the friction coefficient relative to the increase in the slip ratio is zero. That is, the graph has one portion where the inclination at the inflection point is zero.

The inclination in the cubic function relating to the slip ratio shown in Formula 6 can be expressed as the following Formula 7 obtained by differentiating Formula 6 with the slip ratio.

Since the cubic function relating to the slip ratio shown in Formula 6 has one portion where the inclination is zero, the relationship among H, HK, and HK2can be expressed in Formula 8, which is the discriminant of Formula 7, as follows:

Formula 8 is used for HK2in the third term of Formula 6, so that H in the first term and HK in the second term can be used to express Formula 6 as the following Formula 9.

HK2in Formula 9 is substituted into the tire brush model expression shown in Formula 6, so that Formula 6 can be replaced with the following Formula 10.

Formula 6 is replaced with Formula 10 in this manner, so that when the parameters of the tire brush model expression are estimated, it is possible to exclude, from candidates for the values of the parameters, candidates from which an estimated maximum friction value μpcannot be accurately calculated. Therefore, as illustrated inFIG.1, the parameter-estimating unit23of the present embodiment includes the parameter-restricting unit232for excluding, from candidates for the values of the parameters, candidates from which an estimated maximum friction value μpcannot be accurately calculated. The parameter-restricting unit232is a formula-converting unit that converts Formula 6, which is a tire brush model expression, into Formula 10. When the parameter-estimating unit23estimates the optimal parameters of the tire brush model expression, the parameter-restricting unit232limits the values of the parameters.

Specifically, the parameter-restricting unit232of the present embodiment eliminates the values of the parameters that allow the tire brush model expression shown in Formula 6 to be a linear function and a quadratic function, and obtains the values of the parameters that allow the inclination of the inflection point of the tire brush model expression to be zero. The parameter-restricting unit232restricts the values of the parameters to satisfy the relationship of the above Formula 9 in terms of H, HK, and HK2, which are parameters of the tire brush model expression shown in Formula 6.

The parameter-estimating unit23of the present embodiment calculates the values of H and HK, which are parameters of the tire brush model expression shown in Formula 10, so as to make smaller model errors μerrcalculated by the error calculator22. For example, the parameter-estimating unit23obtains H in the first term and HK in the second term of Formula 10 so that the model errors μerrapproach zero. As a result, the theoretical characteristic Th that can be obtained from the tire brush model expression shown in Formula 10 can be brought closer to the friction-slip characteristic FS. For such theoretical characteristics Th that can be obtained in this way, a theoretical characteristic Th from which an estimated maximum friction value μpcannot be accurately calculated is excluded. The parameter-estimating unit23outputs information of the calculated values of H and HK, which are parameters of the tire brush model expression, to the model calculator21and to the maximum value calculator24.

The maximum value calculator24calculates an estimated maximum friction value μpon the basis of the information of the values of H and HK, which are parameters of the tire brush model expression and have been calculated by the parameter-estimating unit23.

The estimated maximum friction value μpcan be obtained on the basis of Formulas 2 and 3 and using the following Formula 11.

H and HK in Formula 11 are parameter values estimated by the parameter-estimating unit23to be able to bring the theoretical characteristic Th closer to the friction-slip characteristic FS. Therefore, an estimated maximum friction value μpcan be calculated on the basis of Formula 11 and the values of H and HK, which are parameters of the tire brush model expression and have been calculated by the parameter-estimating unit23.

Next, an example of control processing executed by the control device1will be described with reference to flowcharts illustrated inFIGS.5to10. The control device1repeatedly executes each control processing illustrated inFIGS.5to10in every predetermined control cycle preliminarily determined.

First, processing executed by the slip calculator11illustrated inFIG.5, which is part of the control processing executed by the control device1, will be described. The slip calculator11repeatedly executes the processing illustrated inFIG.5in every predetermined control cycle in order to calculate calculated slip ratios sc.

First, in step S10, the slip calculator11detects, among detection signals transmitted from the detection unit S, information necessary to calculate a calculated slip ratio sc. For example, in a case where the vehicle travels straight, the information necessary to calculate a calculated slip ratio scis information of the speed of the vehicle detected by the vehicle speed sensor, and information of the rotation speed of the tire detected by the wheel speed sensor.

In step S12, the slip calculator11calculates a calculated slip ratio scon the basis of the information necessary to calculate the calculated slip ratio sc. In step S14, the slip calculator11transmits information of the calculated slip ratio scto the model calculator21.

Next, processing executed by the friction calculator12illustrated inFIG.6, which is part of the control processing executed by the control device1, will be described. The friction calculator12repeatedly executes the processing illustrated inFIG.6in every predetermined control cycle in order to calculate calculated friction coefficients μc.

First, in step S20, the friction calculator12detects, among detection signals transmitted from the detection unit S, information necessary to calculate a calculated friction coefficient μc. The information necessary to calculate a calculated friction coefficient μcis, for example, information of the torque applied to the tire detected by the torque sensor, information of the load generated in the tire detected by the load sensor, and information of the acceleration of the vehicle detected by the acceleration sensor.

In step S22, the friction calculator12calculates a calculated friction coefficient μcon the basis of the information necessary to calculate the calculated friction coefficient μc. The timing at which the friction calculator12executes the processing illustrated inFIG.6is the same timing as the timing at which the slip calculator11executes the processing illustrated inFIG.5. Therefore, the friction calculator12calculates calculated friction coefficients μcin the same control cycle as the control cycle in which the slip calculator11performs the processing for calculating calculated slip ratios sc.

In step S24, the friction calculator12transmits information of the calculated friction coefficient μcto the error calculator22.

Next, processing executed by the model calculator21illustrated inFIG.7, which is part of the control processing executed by the control device1, will be described. The model calculator21repeatedly executes the processing illustrated inFIG.7every time information of a calculated slip ratio scis input from the slip calculator11.

If information of a calculated slip ratio scis input from the slip calculator11, in step S30, the model calculator21calculates a tire model friction μmon the basis of Formula 6 and the information of the calculated slip ratio sctransmitted from the slip calculator11. Specifically, the model calculator21substitutes the calculated slip ratio scinto Formula 6 of the tire brush model expression to perform the computation to calculate a tire model friction μm. In step S32, the model calculator21transmits information of the calculated tire model friction μmto the error calculator22.

Next, processing executed by the error calculator22illustrated inFIG.8, which is part of the control processing executed by the control device1, will be described. The error calculator22repeatedly executes the processing illustrated inFIG.8every time both information of a tire model friction μmis input from the model calculator21and information of a calculated friction coefficient μcis input from the friction calculator12.

In step S40, the error calculator22calculates, as a model error μerr, an absolute value of a value obtained by subtracting the calculated friction coefficient μcfrom the tire model friction μm.

As described above, the processing in which the slip calculator11calculates a calculated slip ratio scand the processing in which the friction calculator12calculates a calculated friction coefficient μcare repeatedly executed in the same control cycle. Therefore, a model error μerrcalculated by the error calculator22is an error between a tire model friction μmand a calculated friction coefficient μccalculated on the basis of a calculated slip ratio sccalculated in the same control cycle.

In step S42, the error calculator22transmits information of the calculated model error μerrto the parameter-estimating unit23.

Next, processing executed by the parameter-estimating unit23illustrated inFIG.9, which is part of the control processing executed by the control device1, will be described. The parameter-estimating unit23repeatedly executes the processing illustrated inFIG.9every time information of a model error μerris input from the error calculator22.

In a case where the processing of step S50is executed in a state where the error-storing unit231stores ten pieces of information of model errors μerr, the parameter-estimating unit23erases, among the ten pieces of information of the old model errors μerr, the information of the oldest model error μerr. Then, the parameter-estimating unit23stores newly input information of a model error μerrin the error-storing unit231. That is, the error-storing unit231updates one piece of stored information of a model error μerreach time the error-storing unit231acquires one piece of information of a model error μerrfrom the error calculator22.

Then, in step S54, the parameter-estimating unit23obtains H of the first term and HK of the second term, which are parameters of Formula 10, so that model errors μerrstored in the error-storing unit231approach zero. For example, in a case where the error-storing unit231stores ten pieces of information of model errors μerr, the parameter-estimating unit23obtains H of the first term and HK of the second term, which are parameters of Formula 10, so that each of the ten model errors μerrapproaches zero.

As a method for obtaining H and HK so that the model errors μerrapproach zero, for example, a method using an adaptive filter can be employed. Specifically, the adaptive filter may use recursive least squares or a Kalman filter.

As a result, the theoretical characteristic Th that can be obtained from the tire brush model expression shown in Formula 10 can be brought closer to the friction-slip characteristic FS. For such theoretical characteristics Th that can be obtained in this way, a theoretical characteristic Th from which an estimated maximum friction value μpcannot be accurately calculated is excluded.

In step S56, the parameter-estimating unit23transmits information of the calculated values of H and HK to the model calculator21and to the maximum value calculator24.

The parameter-estimating unit23transmits information of the calculated values of H and HK to the model calculator21to update the tire brush model expression shown in Formula 6 used when in step S30, the model calculator21calculates a tire model friction μm. Therefore, in the processing of step S30executed in a control cycle executed after the processing of step S56is executed, the model calculator21calculates a tire model friction μmon the basis of the information of the parameter values transmitted from the parameter-estimating unit23. Specifically, the model calculator21calculates a tire model friction μm, in a state where each of the values of H in the first term, HK in the second term, and HK2in the third term of Formula 6 is updated with the value of H and the value of HK transmitted from the parameter-estimating unit23.

Next, processing executed by the maximum value calculator24illustrated inFIG.10, which is part of the control processing executed by the control device1, will be described. The maximum value calculator24repeatedly executes the processing illustrated inFIG.10each time information of the values of H and HK is input from the parameter-estimating unit23.

If information of the values of H and HK is input from the parameter-estimating unit23, in step S60, the maximum value calculator24calculates an estimated maximum friction value μpon the basis of Formula 11 and the information of the values of H and HK input from the parameter-estimating unit23. Specifically, the maximum value calculator24substitutes the values of H and HK into Formula 11 to perform the computation to calculate an estimated maximum friction value μp.

The maximum value calculator24calculates an estimated maximum friction value μpeach time information of the values of H and HK is input from the parameter-estimating unit23. Each time both the slip calculator11calculates a calculated slip ratio scand the friction calculator12calculates a calculated friction coefficient μcin every predetermined control cycle, the parameter-estimating unit23estimates the values of H and HK, and transmits the estimated information to the maximum value calculator24.

Therefore, as illustrated inFIG.11, each time the slip calculator11detects, from the detection unit S, information necessary to calculate a calculated slip ratio sc, and the friction calculator12detects, from the detection unit S, information necessary to calculate a calculated friction coefficient μc, the maximum value calculator24calculates an estimated maximum friction value μp. In other words, each time information of a model error μerrstored in the error-storing unit231and calculated on the basis of a calculated slip ratio scand a calculated friction coefficient μcis updated, the maximum value calculator24calculates an estimated maximum friction value μp.

In step S62, the maximum value calculator24outputs information of the calculated estimated maximum friction value μpto, for example, a motor-driving circuit that controls the rotation speed of the motor for driving the vehicle. As a result, when the control device1controls the rotation speed of the motor for driving the vehicle, the information of the estimated maximum friction value μpcalculated by the friction-coefficient-computing device can be used.

As described above, the control device1of the present embodiment includes the maximum-friction-estimating unit20that calculates an estimated maximum friction value μpusing a calculated slip ratio scand a calculated friction coefficient μc, and a tire brush model expression for calculating a friction coefficient in a case where the slip ratio is in a minute region. The tire brush model expression is a function relating to the slip ratio of the tire brush model, and includes a plurality of parameters that varies the inclination of the inflection point of the tire brush model expression. The maximum-friction-estimating unit20includes the model calculator21that substitutes a calculated slip ratio scinto the tire brush model expression to calculate a tire model friction μm, and the parameter-estimating unit23that estimates the values of the parameters so as to make smaller the difference between the calculated friction coefficient μcand the tire model friction μm. The parameter-estimating unit23includes the parameter-restricting unit232that obtains the values of the parameters so as to eliminate the value of the parameter that allows the tire brush model expression to be a linear function and a quadratic function and so as to allow the inclination of the inflection point of the tire brush model expression to be zero.

Consequently, when the parameters of a tire brush model expression for calculating a friction coefficient in a case where the slip ratio is in a minute region are estimated, it is possible to exclude, from candidates for the values of the parameters, candidates from which an estimated maximum friction value μpcannot be accurately calculated. Therefore, even in a case where the slip calculator11can calculate only values of the calculated slip ratio scin the minute region, the theoretical characteristic Th that can be obtained from the tire brush model expression can be brought closer to the friction-slip characteristic FS. An estimated maximum friction value μpcan be accurately calculated on the basis of the values of the parameters estimated to be able to bring the theoretical characteristic Th closer to the friction-slip characteristic FS.

According to the above embodiment, the following effects can be obtained.

(1) In the above embodiment, in the tire brush model expression shown in the above Formula 6, the parameters of the tire brush model expression are indicated by H, HK, and HK2in Formula 6. The parameter-restricting unit232restricts the values of the parameters to satisfy the relationship of the above Formula 9 in terms of the parameters.

As shown in Formula 11, an estimated maximum friction value μpcan be calculated on the basis of H and HK, which are parameters in Formula 6. Therefore, an estimated maximum friction value μpcan be easily calculated as compared with a case where H, HK, and HK2, which are parameters in the above Formula 6, are defined by a relational expression different from Formula 9.

(2) In the above embodiment, the parameter-estimating unit23includes the error-storing unit231that acquires information of a model error μerrrelating to each of a calculated slip ratio scand a calculated friction coefficient μc. The error-storing unit231stores only ten pieces of acquired information of model errors μerr. The parameter-estimating unit23estimates the values of the parameters of the tire brush model expression using a plurality of pieces of information of model errors μerrstored in the error-storing unit231.

Each time the error-storing unit231acquires one piece of information of a model error μerrrelating to each of a calculated slip ratio scand a calculated friction coefficient μc, the error-storing unit231updates one piece of stored information of a model error μerr.

Consequently, when the parameter-estimating unit23estimates the values of the parameters of the tire brush model expression, the parameter-estimating unit23can estimate the values of the parameters using information of model errors μerrin addition to the updated information of the model error μerr. Therefore, it is possible to suppress the power consumption of the parameter-estimating unit23and to increase the processing speed at the time of the estimation of the values of the parameters as compared with a case where all pieces of information of model errors μerrused at the time of the estimation of the value of the parameter are updated for each estimation.

Modification of First Embodiment

In the first embodiment described above, an example has been described in which the parameter-estimating unit23estimates the values of the parameters of the tire brush model expression each time the error-storing unit231acquires, from the error calculator22, one piece of information of a model error μerr, but the example is not limitative. For example, the parameter-estimating unit23may be configured to estimate the values of the parameters of the tire brush model expression each time the error-storing unit231acquires, from the error calculator22, a plurality of (for example, two) pieces of information of model errors μerr.

Second Embodiment

Next, a second embodiment will be described with reference toFIGS.12to17. The present embodiment is different from the first embodiment in that a computing unit10includes a calculation memory13and a calculation determiner14. Further, the present embodiment is different from the first embodiment in part of control processing executed by the computing unit10. Except the differences, the present embodiment is similar to the first embodiment. Therefore, in the present embodiment, the portions different from those of the first embodiment will be mainly described, and description of the portions similar to those of the first embodiment may be omitted.

As illustrated inFIG.12, the computing unit10includes the calculation memory13and the calculation determiner14in addition to a slip calculator11and a friction calculator12.

The calculation memory13stores information of calculated slip ratios sccalculated by the slip calculator11, and information of calculated friction coefficients μccalculated by the friction calculator12. The calculation memory13stores information of a calculated slip ratio scand information of a calculated friction coefficient μcthat have been calculated in the same control cycle, and are associated with each other in the calculation memory13.

The calculation memory13stores the predetermined number, which has been preliminarily determined, of pieces of information of calculated slip ratios scand pieces of information of calculated friction coefficients μc. The calculation memory13of the present embodiment is configured to be able to store, for example, ten pieces of information of calculated slip ratios scand ten pieces of information of calculated friction coefficients μcthat are associated with each other in the calculation memory13. The calculation memory13may be configured to be able to store fewer than ten pieces of information of calculated slip ratios scand fewer than ten pieces of information of calculated friction coefficients μc, or may be configured to be able to store more than ten pieces of information of calculated slip ratios scand more than ten pieces of information of calculated friction coefficients μc.

The calculation determiner14determines whether a calculated slip ratio sccalculated by the slip calculator11and a calculated friction coefficient μccalculated by the friction calculator12are normal. On the basis of the information of calculated slip ratios scand the information of calculated friction coefficients μcstored in the calculation memory13, the calculation determiner14determines whether the calculated slip ratios sccalculated by the slip calculator11and the calculated friction coefficients μccalculated by the friction calculator12are normal.

Next, control processing executed by the calculation memory13will be described with reference toFIG.13. The calculation memory13repeatedly executes the processing illustrated inFIG.13each time both information of a calculated slip ratio scis input from the slip calculator11and information of a calculated friction coefficient μcis input from the friction calculator12.

First, in step S70, the calculation memory13acquires information of a calculated slip ratio scfrom the slip calculator11, and acquires information of a calculated friction coefficient μcfrom the friction calculator12. If the calculation memory13acquires information of a calculated slip ratio scfrom the slip calculator11and information of a calculated friction coefficient μcfrom the friction calculator12, in step S72, the calculation memory13stores the input information of the calculated slip ratio scand the input information of the calculated friction coefficient μc. When storing information of a calculated slip ratio scand information of a calculated friction coefficient μc, the calculation memory13stores the information of the calculated slip ratio scand the information of the calculated friction coefficient μcthat have been calculated in the same control cycle and are associated with each other in the calculation memory13.

Then, in step S74, the calculation memory13determines whether the number of pieces of information of calculated slip ratios scand the number of pieces of information of calculated friction coefficients μcstored in the calculation memory13are equal to or more than a predetermined number preliminarily determined. The calculation memory13of the present embodiment is configured to be able to store ten pieces of information of calculated slip ratios scand ten pieces of information of calculated friction coefficients μc. Therefore, in step S74, the calculation memory13determines whether the number of pieces of information of calculated slip ratios scand the number of pieces of information of calculated friction coefficients μcstored in the calculation memory13are ten or more. The calculation memory13repeatedly executes the processing of steps S70and S72until the number of pieces of information of calculated slip ratios scand the number of pieces of information of calculated friction coefficients μcstored in the calculation memory13amount to ten or more.

As illustrated inFIG.14, the calculation memory13of the present embodiment includes a first address M1to a 10th address M10in which ten pieces of information of calculated slip ratios scand ten pieces of information of calculated friction coefficients μcare stored and associated with each other. If pieces of information of calculated slip ratios scand pieces of information of calculated friction coefficients μcare input, the calculation memory13stores the pieces of information of the calculated slip ratios scand the pieces of information of the calculated friction coefficients μcin the first address M1to the 10th address M10in the order of the input. That is, the calculation memory13stores the pieces of information of the calculated slip ratios scand the pieces of information of the calculated friction coefficients μcin the first address M1to the 10th address M10in chronological order.

In the calculation memory13of the present embodiment, information of the oldest calculated slip ratio scand information of the oldest calculated friction coefficient μcare input in the first address M1. The calculation memory13of the present embodiment is configured such that pieces of information of newer calculated slip ratios scand pieces of information of newer calculated friction coefficients μcare input in the first address M1to the 10th address M10in this order.

If it is determined that the number of stored pieces of information of calculated slip ratios scand the number of stored pieces of information of calculated friction coefficients μcare ten or more, in step S76, the calculation memory13collectively transmits the ten pieces of information of the calculated slip ratios scand the ten pieces of information of the calculated friction coefficients μcto the calculation determiner14. Then, in step S78, the calculation memory13collectively erases the ten pieces of transmitted information of the calculated slip ratios scand the ten pieces of transmitted information of the calculated friction coefficients μc.

Next, control processing executed by the calculation determiner14will be described with reference toFIG.15. The calculation determiner14repeatedly executes the processing illustrated inFIG.15each time each of the ten pieces of information of calculated slip ratios scand the ten pieces of information of calculated friction coefficients μcare input from the calculation memory13.

If the ten pieces of information of calculated slip ratios scare input from the calculation memory13, in step S80, the calculation determiner14calculates an averaged slip ratio save, which is the averaged value of the ten calculated slip ratios sc.

In step S81, the calculation determiner14calculates ten averaged-slip-ratio errors savearr, which are difference values between each of the ten calculated slip ratios scand the averaged slip ratio save. The averaged-slip-ratio error savearris a value obtained by subtracting each of the ten calculated slip ratios scfrom the averaged slip ratio save, and is calculated as an absolute value.

In step S82, on the basis of each of the ten calculated averaged-slip-ratio errors savearr, the calculation determiner14determines whether each of the ten calculated slip ratios scis normal. Specifically, the calculation determiner14determines whether each of the ten calculated averaged-slip-ratio errors savearris equal to or less than a slip ratio threshold sth.

In a case where a calculated averaged-slip-ratio error savearris equal to or less than the slip ratio threshold sth, the calculation determiner14determines that the calculated slip ratio sccorresponding to the averaged-slip-ratio error savearrdetermined to be equal to or less than the slip ratio threshold sthis normal. On the other hand, in a case where a calculated averaged-slip-ratio error savearris not equal to or less than the slip ratio threshold sth, the calculation determiner14determines that the calculated slip ratio sccorresponding to the averaged-slip-ratio error savearrdetermined to be not equal to or less than the slip ratio threshold sthis abnormal.

The slip ratio threshold sthis an evaluated maximum value of an allowable variation amount of a calculated slip ratio scin a case where a plurality of calculated slip ratios scis calculated on the basis of the information detected from the detection unit S in a predetermined control cycle. The slip ratio threshold sthis preliminarily set in the calculation determiner14, and can be obtained by, for example, a preliminarily performed experiment.

In step S83, the calculation determiner14erases, among the ten calculated slip ratios sc, information of a calculated slip ratio scwhose averaged-slip-ratio error savearrhas been determined to be not equal to or less than the slip ratio threshold sth, and erases information of the calculated friction coefficient μcassociated with the calculated slip ratio scin question.

In a case where it is determined that all the ten calculated slip ratios scare abnormal, the calculation determiner14erases all the ten pieces of information of the calculated slip ratios scand the calculated friction coefficients μcstored in the calculation memory13. On the other hand, in a case where it is determined that at least one of the ten calculated slip ratios scis normal, in step S84, the calculation determiner14calculates an averaged friction coefficient μave, which is the averaged value of the ten calculated friction coefficients μc.

In step S85, the calculation determiner14calculates an averaged-friction-coefficient error μavearr, which is a difference value between the averaged friction coefficient μaveand each calculated friction coefficient μcassociated with the calculated slip ratio scthat has not been determined to be abnormal. The averaged-friction-coefficient error μavearris a value obtained by subtracting each calculated friction coefficient μcfrom the averaged friction coefficient μave, and is calculated as an absolute value.

In step S86, on the basis of the one calculated averaged-friction-coefficient error μavearror on the basis of each of the plurality of calculated averaged-friction-coefficient errors μavearr, the calculation determiner14determines whether each calculated friction coefficient μcassociated with the calculated slip ratio scthat has not been determined to be abnormal is normal. Specifically, the calculation determiner14determines whether each of the calculated averaged-friction-coefficient errors μavearris equal to or less than a friction coefficient threshold μth.

In a case where the calculated averaged-friction-coefficient error μavearris equal to or less than the friction coefficient threshold μth, the calculation determiner14determines that the calculated friction coefficient μccorresponding to the averaged-friction-coefficient error μavearrdetermined to be equal to or less than the friction coefficient threshold μthis normal. On the other hand, in a case where the calculated averaged-friction-coefficient error μavearris not equal to or less than the friction coefficient threshold μth, the calculation determiner14determines that the calculated friction coefficient μccorresponding to the averaged-friction-coefficient error μavearrdetermined to be not equal to or less than the friction coefficient threshold μthis abnormal.

The friction coefficient threshold μthis an evaluated maximum value of an allowable variation amount of a calculated friction coefficient μcin a case where a plurality of calculated friction coefficients μcis calculated on the basis of the information detected from the detection unit S in a predetermined control cycle. The friction coefficient threshold μthis preliminarily set in the calculation determiner14, and can be obtained by, for example, a preliminarily performed experiment.

In step S83, the calculation determiner14erases, among the calculated friction coefficients μcassociated with the calculated slip ratios scnot determined to be abnormal, information of a calculated friction coefficient μcwhose averaged-friction-coefficient error μavearrhas been determined to be not equal to or less than the friction coefficient threshold μth, and erases information of the calculated slip ratio scassociated with the calculated friction coefficient μcin question.

As a result, for example, in a case where the temporal variations in the calculated slip ratio scand the calculated friction coefficient μcare illustrated as inFIG.16, the calculated friction coefficient μcthat deviates very much from the moving averaged AL of the calculated friction coefficients μccan be determined as an outlier. Then, the information of the calculated friction coefficient μcthat is the outlier can be erased, and the information of the calculated slip ratio scassociated with the calculated friction coefficient μcin question can be erased.

In a not illustrated case where a calculated slip ratio scthat deviates very much from the moving averaged of the calculated slip ratios scexists, the calculated slip ratio sccan be determined as an outlier. Then, the information of the calculated slip ratio scthat is the outlier can be erased, and the information of the calculated friction coefficient μcassociated with the calculated slip ratio sccan be erased.

In step S87, the calculation determiner14transmits, to the maximum-friction-estimating unit20, the information of the calculated slip ratios scand the information of the calculated friction coefficients μcthat have not been erased in step S83. Specifically, the calculation determiner14transmits, to the model calculator21, pieces of information of the calculated slip ratios sc, among the ten calculated slip ratios scacquired from the calculation memory13, except a piece of information of the calculated slip ratio scerased in step S83. In addition, the calculation determiner14transmits, to the error calculator22, pieces of information of the calculated friction coefficients μc, among the ten calculated friction coefficients μcacquired from the calculation memory13, except a piece of information of the calculated friction coefficient μcerased in step S83. Then, on the basis of the input information of the calculated slip ratios scand the input information of the calculated friction coefficients μc, the maximum-friction-estimating unit20calculates an estimated maximum friction value μpby executing the processing illustrated inFIGS.7to10.

As described above, the computing unit10of the present embodiment includes the calculation memory13that stores ten pieces of information of calculated slip ratios scand ten pieces of information of calculated friction coefficients μc. The computing unit10also includes the calculation determiner14that determines whether each of the calculated slip ratios scand each of the calculated friction coefficients μcstored in the calculation memory13is normal.

A parameter-estimating unit23estimates the values of the parameters of the tire brush model expression on the basis of the calculated slip ratios scand the calculated friction coefficients μcdetermined to be normal by the calculation determiner14.

Consequently, in a case where a calculated slip ratio scand a calculated friction coefficient μcare abnormal due to noise caused by vibration of the vehicle or the like, the calculation determiner14can determine that the calculated slip ratio scand the calculated friction coefficient μcare abnormal. Then, the calculation determiner14transmits, to the maximum-friction-estimating unit20, only information of normal calculated slip ratios scand information of normal calculated friction coefficients μc. Therefore, as illustrated inFIG.17, when a theoretical characteristic Th is obtained, the theoretical characteristic Th that does not include an outlier can be obtained. Therefore, when an estimated maximum friction value μpis calculated on the basis of the theoretical characteristic Th, an estimated maximum friction value μpcan be accurately calculated.

According to the above embodiment, the following effects can be obtained.

(1) In the above embodiment, on the basis of the difference between an averaged-slip-ratio error savearrand the slip ratio threshold sth, the calculation determiner14determines whether the calculated slip ratio scstored in the calculation memory13is normal. In addition, on the basis of the difference between an averaged-friction-coefficient error μavearrand the friction coefficient threshold μth, the calculation determiner14determines whether the calculated friction coefficient μcstored in the calculation memory13is normal.

Consequently, it is possible to easily determine whether a calculated slip ratio scand a calculated friction coefficient μcare normal.

Modification of Second Embodiment

In the second embodiment described above, on the basis of the difference between an averaged-slip-ratio error savearrand the slip ratio threshold sth, the calculation determiner14determines whether the calculated slip ratio scis normal. In addition, on the basis of the difference between an averaged-friction-coefficient error μavearrand the friction coefficient threshold μth, the calculation determiner14determines whether the calculated friction coefficient μcis normal. However, a method for determining whether a calculated slip ratio scis normal and a method for determining whether a calculated friction coefficient μcis normal are not limited to this method.

For example, the calculation determiner14may use a moving average to determine whether a calculated slip ratio scand a calculated friction coefficient μcare normal.

Third Embodiment

Next, a third embodiment will be described with reference toFIGS.18to23. The present embodiment is different from the second embodiment in that a computing unit10does not include the calculation determiner14. Further, the present embodiment is different from the second embodiment in part of control processing executed by the computing unit10. Except the differences, the present embodiment is similar to the second embodiment. Therefore, in the present embodiment, the portions different from those of the second embodiment will be mainly described, and description of the portions similar to those of the second embodiment may be omitted.

As illustrated inFIG.18, the computing unit10does not include the calculation determiner14. Similarly to the second embodiment, a calculation memory13stores, in a first address M1to a 10th address M10, ten pieces of information of calculated slip ratio scand ten pieces of information of calculated friction coefficients μcthat have been calculated in the same control cycles and are associated with each other in the calculation memory13, respectively.

However, for the calculation memory13of the present embodiment, as illustrated inFIG.19, the information of a calculated slip ratio scstored in each of the first address M1to the 10th address M10is determined on the basis of the value of the calculated slip ratio sc. That is, the information of a calculated slip ratio scstored in each of the first address M1to the 10th address M10of the calculation memory13is determined depending on the value of the stored calculated slip ratio sc.

The value of a calculated slip ratio scstored in each of the first address M1to the 10th address M10has a predetermined range. In the present embodiment, the first address M1to the 10th address M10are set to store pieces of information of calculated slip ratios scwhose values are in the range of 0.0 to 0.1. Each of the first address M1to the 10th address M10is set to store Information of a calculated slip ratio scin every region obtained by equally dividing, by ten, a range of the value of a calculated slip ratio scfrom 0.0 to 0.1. In other words, the information of a calculated slip ratio scstored in the first address M1to the 10th address M10is set such that among values of a calculated slip ratio scin the range from 0.0 to 0.1, any of the ten regions which have the same range is stored.

For example, in a case where the value of a calculated slip ratio scinput from a slip calculator11is zero or more and less than 0.01, the information of the calculated slip ratio scis stored in the first address M1. In a case where the value of a calculated slip ratio scinput from the slip calculator11is 0.01 or more and less than 0.02, the information of the calculated slip ratio scis stored in the second address M2. In a case where the value of a calculated slip ratio scinput from the slip calculator11is 0.08 or more and less than 0.09, the information of the calculated slip ratio scis stored in the ninth address M9. In a case where the value of a calculated slip ratio scinput from the slip calculator11is 0.09 or more and 0.1 or less, the information of the calculated slip ratio scis stored in the 10th address M10. Although details of the values of calculated slip ratios scinput into the third address M3to the eighth address M8are not described, pieces of information of calculated slip ratios schaving a range of 0.01 each are stored similarly as in the first address M1and the like.

As described above, each of the first address M1to the 10th address M10is preliminarily determined such that the values of the stored calculated slip ratios scare different from each other. The information of calculated slip ratios scstored in the first address M1to the 10th address M10is not limited to calculated slip ratios scin the range from 0.0 to 0.1. For example, the information of calculated slip ratios scstored in the first address M1to the 10th address M10may be in a range narrower than the range from 0.0 to 0.1 (for example, a range from 0.0 to 0.08). Alternatively, the information of calculated slip ratios scstored in the first address M1to the 10th address M10may be in a range wider than the range from 0.0 to 0.1 (for example, a range from 0.0 to 0.15).

The ranges of the values of calculated slip ratios scstored in the first address M1to the 10th address M10may be set to be different from each other. For example, the range of the value of a calculated slip ratio scstored in each of the first address M1to the 10th address M10may be set to be wider in the order from the first address M1to the 10th address M10. Alternatively, the range of the value of a calculated slip ratio scstored in each of the first address M1to the 10th address M10may be set to be narrower in the order from the first address M1to the 10th address M10.

Next, control processing executed by the calculation memory13will be described with reference toFIG.20. The calculation memory13repeatedly executes the processing illustrated inFIG.20each time both information of a calculated slip ratio scis input from the slip calculator11and information of a calculated friction coefficient μcis input from a friction calculator12.

First, in step S70, the calculation memory13acquires information of a calculated slip ratio scfrom the slip calculator11, and acquires information of a calculated friction coefficient μcfrom the friction calculator12. Then, in step S71, on the basis of the value of the calculated slip ratio scacquired from the slip calculator11, the calculation memory13detects, among the first address M1to the 10th address M10, an address corresponding to the acquired calculated slip ratio sc.

For example, in a case where the value of a calculated slip ratio scinput from the slip calculator11is 0.005, the calculation memory13detects a corresponding address as the first address M1. In a case where the value of a calculated slip ratio scinput from the slip calculator11is 0.085, the calculation memory13detects a corresponding address as the ninth address M9.

Then, in step S73, the calculation memory13stores, in the corresponding address, the information of the calculated slip ratio scacquired from the slip calculator11. In addition, the calculation memory13stores the information of a calculated friction coefficient μccalculated in the same control cycle as the calculated slip ratio scin the same address as the address where the information of the calculated slip ratio scis stored. As a result, the information of the calculated slip ratio scand the information of the calculated friction coefficient μcthat have been calculated in the same control cycle are stored and associated with each other in the same address.

When the calculation memory13stores the information of the calculated slip ratio scin the corresponding address in step S73, in a case where information of a calculated slip ratio schas been already stored in the address to be stored, the calculation memory13erases the stored old information of the calculated slip ratio scto store the information of the calculated slip ratio sc. That is, in a case where the information of a calculated slip ratio scacquired in a past control cycle has been already stored in an address to be stored, the calculation memory13updates the information of the calculated slip ratio scto a newly acquired information of a calculated slip ratio sc.

Each time information of a calculated slip ratio scis input from the slip calculator11, the calculation memory13repeats the processing illustrated inFIG.20to store the information of the calculated slip ratio scand information of the calculated friction coefficient μcin a corresponding address among the first address M1to the 10th address M10. As a result, the information of the calculated slip ratio scand the information of the calculated friction coefficient μcare stored in every preliminarily determined region of calculated slip ratios sc, in the calculation memory13.

In step S76, the calculation memory13transmits, to a maximum-friction-estimating unit20, the stored information of the calculated slip ratios scand the stored information of the calculated friction coefficients μc. Specifically, the calculation memory13transmits, to a model calculator21, the information of the calculated slip ratio scstored in each of the first address M1to the 10th address M10. In addition, the calculation memory13transmits, to an error calculator22, the information of the calculated friction coefficient μcstored in each of the first address M1to the 10th address M10. Then, on the basis of the input information of the calculated slip ratios scand the input information of the calculated friction coefficients μc, the maximum-friction-estimating unit20calculates an estimated maximum friction value μpby executing the processing illustrated inFIGS.7to10.

In some cases, the control processing illustrated inFIG.20are not executed sufficient times, such as a case immediately after the start of driving of the vehicle, and thus information of a calculated slip ratio scand information of a calculated friction coefficient μcare not stored in all the first address M1to the 10th address M10. Even in such a case, on the basis of the information of the addresses in which information of calculated slip ratios scand information of calculated friction coefficients μcare stored, the maximum-friction-estimating unit20may calculate an estimated maximum friction value μpby executing the processing illustrated inFIGS.7to10.

In a case where information of a calculated slip ratio scand information of a calculated friction coefficient μcstored in each of the first address M1to the 10th address M10are not updated for a predetermined period, the calculation memory13may erase the information of the calculated slip ratios scand the information of the calculated friction coefficients μc. For example, in a case where even after a predetermined time elapses after information of a calculated slip ratio scand information of a calculated friction coefficient μcare stored in the first address M1, the information has not been updated, the information of the calculated slip ratio scand the information of the calculated friction coefficient μcstored in the first address M1may be erased after the predetermined time elapses. Alternatively, in a case where information of a calculated slip ratio scand information of a calculated friction coefficient μchave not been updated in the first address M1, the information of the calculated slip ratio scand the information of the calculated friction coefficient μcstored in the first address M1may be erased after the control processing is performed for a predetermined number of control cycles.

As described above, the information of the calculated slip ratios scand the information of the calculated friction coefficient μcare erased, so that in a case where the state of an actual road surface and the state of a past road surface are different, it is possible to avoid an estimated maximum friction value μpfrom being calculated on the basis of the past information.

As described above, the computing unit10of the present embodiment includes the calculation memory13that stores ten pieces of information of calculated slip ratios scand ten pieces of information of calculated friction coefficients μc.

The calculation memory13includes the first address M1to the 10th address M10corresponding to the magnitude of a calculated slip ratio sc. The calculation memory13stores information of a calculated slip ratio scand information of a calculated friction coefficient μcin an address preliminarily determined on the basis of the magnitude of the calculated slip ratio sc, among the first address M1to the 10th address M10.

Consequently, the calculation memory13can store information of a calculated slip ratio scand information of a calculated friction coefficient μcin every preliminarily determined region of calculated slip ratios sc. Therefore, when a theoretical characteristic Th is obtained, the theoretical characteristic Th can be obtained on the basis of the information of the calculated slip ratio scand the information of the calculated friction coefficient μcof each region. Therefore, the theoretical characteristic Th can be easily brought closer to the friction-slip characteristic FS.

A method for obtaining a theoretical characteristic Th in a case where pieces of information of calculated slip ratios schaving relatively close values and pieces of information of calculated friction coefficients μchaving relatively close values are repeatedly input into the calculation memory13, as illustrated inFIG.21, will be considered. In such a case, if pieces of information of calculated slip ratios scand pieces of information of calculated friction coefficients μcare stored in the first address M1to the 10th address M10in the order of the input, there is a possibility that the theoretical characteristic Th deviates from the friction-slip characteristic FS, such as the theoretical characteristic Th indicated by a dot-dash line ofFIG.22.

The reason is that the theoretical characteristic Th is obtained in a state where the values of the plurality of calculated slip ratios scare locally detected in one region, and the values of calculated slip ratios scin the other regions are not detected. In such a case, when the maximum-friction-estimating unit20estimates the parameters of the tire brush model expression, the cubic function passing through the approximate values of the values of the local calculated slip ratios scincludes candidates from which an estimated maximum friction value μpcannot be accurately calculated. Then, the theoretical characteristic Th may deviate from the friction-slip characteristic FS, such as the theoretical characteristic Th indicated by the dot-dash line ofFIG.21.

On the other hand, according to the present embodiment, the calculation memory13stores information of a calculated slip ratio scand information of a calculated friction coefficient μcin every preliminarily determined region of calculated slip ratios sc. In addition, in a case where in a state where a piece of information of a calculated slip ratio scand a piece of information of a calculated friction coefficient μcare stored in a predetermined region, a piece of information of a new calculated slip ratio scand a piece of information of a new calculated friction coefficient μcare input, these pieces of information are updated. In addition, in regions where the piece of information of the calculated slip ratio scand the piece of information of the calculated friction coefficient μcare not input, previously input pieces of information of calculated slip ratios scand previously input pieces of information of calculated friction coefficients μcare maintained.

Therefore, even in a case where pieces of information of calculated slip ratios schaving relatively close values and pieces of information of calculated friction coefficients μchaving relatively close values are repeatedly input into the calculation memory13, it is possible to avoid the values of the plurality of calculated slip ratios scfrom being locally detected in one region, as illustrated inFIG.23. In addition, a state where the values of calculated slip ratios scof the other regions have not been input is avoided.

Therefore, when the maximum-friction-estimating unit20estimates the parameters of the tire brush model expression, it is possible to suppress the inclusion of candidates from which an estimated maximum friction value μpcannot be accurately calculated. Then, the theoretical characteristic Th can be easily brought closer to the friction-slip characteristic FS.

Fourth Embodiment

Next, a fourth embodiment will be described with reference toFIGS.24to30. The present embodiment is different from the third embodiment in that a computing unit10includes a data-complementing unit15. Further, the present embodiment is different from the third embodiment in part of control processing executed by the computing unit10. Except the differences, the present embodiment is similar to the third embodiment. Therefore, in the present embodiment, the portions different from those of the third embodiment will be mainly described, and description of the portions similar to those of the third embodiment may be omitted.

As illustrated inFIG.24, the computing unit10of the present embodiment includes the data-complementing unit15. The data-complementing unit15calculates estimated values of a slip ratio and a friction coefficient to be stored in an address where information of a calculated slip ratio scand information of a calculated friction coefficient μcare not stored, among a first address M1to a 10th address M10of a calculation memory13.

For example, as illustrated inFIG.25, it is assumed that information of a calculated slip ratio scand information of a calculated friction coefficient μcare not stored in the second address M2among the first address M1to the 10th address M10of the calculation memory13. Further, it is assumed that information of a calculated slip ratio scand information of a calculated friction coefficient μcare stored in the addresses except the second address M2, among the first address M1to the 10th address M10of the calculation memory13. In such a case, the data-complementing unit15stores, in the second address M2, information of a calculated slip ratio scand information of a calculated friction coefficient μcthat have been estimated on the basis of the information of the calculated slip ratios scand the information of the calculated friction coefficients μcstored in the addresses except the second address M2.

InFIG.25, pieces of information of calculated friction coefficients μcstored in the calculation memory13are indicated by black circles, and a piece of information of a calculated friction coefficient μcnot stored in the calculation memory13is indicated by a white circle.

Hereinafter, an address where information of a calculated slip ratio scand information of a calculated friction coefficient μcare stored is referred to as an information-registered address, and an address where information of a calculated slip ratio scand information of a calculated friction coefficient μcare not stored is referred to as an information-unregistered address. An estimated value of a slip ratio to be stored in an information-unregistered address is also referred to as an estimated slip ratio se, and an estimated value of a friction coefficient to be stored in an information-unregistered address is also referred to as an estimated friction coefficient μe.

The data-complementing unit15of the present embodiment uses an approximate curve to obtain an estimated slip ratio seand an estimated friction coefficient μeof an information-unregistered address. Specifically, the data-complementing unit15may use linear approximation to obtain an estimated slip ratio seand an estimated friction coefficient μeof an information-unregistered address. For example, a method for obtaining an estimated slip ratio seand an estimated friction coefficient μein a case where the first address M1and the third address M3are information-registered addresses and the second address M2is an information-unregistered address will be described.

As illustrated inFIG.25, a virtual line passing through the value of a calculated slip ratio scstored in each of the first address M1and the third address M3adjacent to the second address M2, which is an information-unregistered address, is a virtual line CL. The data-complementing unit15calculates, as an estimated slip ratio se, a value positioned on the virtual line CL, among the values of slip ratios of 0.01 or more and less than 0.02 included in the second address M2. In addition, the data-complementing unit15calculates, as an estimated friction coefficient μe, the value of a friction coefficient corresponding to the estimated slip ratio sethus obtained. Consequently, the data-complementing unit15can calculate an estimated slip ratio seand an estimated friction coefficient μeto be stored in an information-unregistered address on the basis of calculated slip ratios scand calculated friction coefficients μcstored in information-registered addresses adjacent to the information-unregistered address.

A method using which the data-complementing unit15calculates an estimated slip ratio seand an estimated friction coefficient μemay be a method except linear approximation. For example, the data-complementing unit15may use logarithmic approximation to obtain an estimated slip ratio seand an estimated friction coefficient μe. Alternatively, the data-complementing unit15may use a moving average to obtain an estimated slip ratio seand an estimated friction coefficient μe.

Next, control processing executed by each of the calculation memory13and the data-complementing unit15will be described with reference toFIGS.26and27. The calculation memory13repeatedly executes the processing illustrated inFIG.26each time both information of a calculated slip ratio scis input from a slip calculator11and information of a calculated friction coefficient μcis input from a friction calculator12. The processing of steps S70, S71, and S73illustrated inFIG.26is similar to the processing of each step of the third embodiment described with reference toFIG.20, and thus the description is omitted.

After in step S73, the calculation memory13stores information of a calculated slip ratio scand information of a calculated friction coefficient μcassociated with each other in a corresponding address, in step S731, the calculation memory13determines whether the first address M1to the 10th address M10include an information-unregistered address. In a case where it is not determined that the first address M1to the 10th address M10include an information-unregistered address, the processing of steps S732and S733is skipped.

On the other hand, in a case where it is determined that the first address M1to the 10th address M10include an information-unregistered address, in step S732, the calculation memory13transmits, to the data-complementing unit15, a signal for requesting information of an estimated slip ratio seand information of an estimated friction coefficient μe. Further, the calculation memory13transmits, to the data-complementing unit15, information of calculated slip ratios scand information of calculated friction coefficients μcstored in information-registered addresses, and information of whether each of the first address M1to the 10th address M10is an information-registered address or an information-unregistered address.

In addition, as illustrated inFIG.27, in step S90, the data-complementing unit15determines whether a signal transmitted from the calculation memory13for requesting information of an estimated slip ratio seand information of an estimated friction coefficient μehas been received. In step S90, the data-complementing unit15waits until a signal transmitted from the calculation memory13for requesting information of an estimated slip ratio seand information of an estimated friction coefficient μeis received.

If the request signal is received, in step S92, the data-complementing unit15uses an approximate curve to obtain an estimated slip ratio seand an estimated friction coefficient μeto be stored in the information-unregistered address. Specifically, the data-complementing unit15uses linear approximation to obtain an estimated slip ratio seand an estimated friction coefficient μeon the basis of the information of the calculated slip ratios scand the information of the calculated friction coefficients μcstored in the information-registered addresses. In a case where among the first address M1to the 10th address M10, a plurality of addresses are information-unregistered addresses, the data-complementing unit15obtains an estimated slip ratio seand an estimated friction coefficient μeto be stored in each of the plurality of information-unregistered addresses.

Then, in step S94, the data-complementing unit15transmits, to the calculation memory13, information of the estimated slip ratio seand information of the estimated friction coefficient μethat have been calculated.

Returning toFIG.26, in step S733, the calculation memory13determines whether information of the estimated slip ratio seand information of the estimated friction coefficient μetransmitted from the data-complementing unit15have been received. In step S733, the calculation memory13waits until information of an estimated slip ratio seand information of an estimated friction coefficient μetransmitted from the data-complementing unit15have been received.

In a case where in step S733, it is determined that information of the estimated slip ratio seand information of the estimated friction coefficient μetransmitted from the data-complementing unit15have been received, in step S77, the calculation memory13stores the information of the estimated slip ratio sein a corresponding information-unregistered address. The calculation memory13also stores the information of the estimated friction coefficient μein the corresponding information-unregistered address. As a result, in each of the first address M1to the 10th address M10, either of information of a calculated slip ratio scand information of an estimated slip ratio seis stored, and either of information of a calculated friction coefficient μcand information of an estimated friction coefficient μeis stored. Then, the calculation memory13proceeds to the processing of step S79.

In step S79, the calculation memory13transmits, to a maximum-friction-estimating unit20, the slip ratio information and the friction coefficient information stored in each of the first address M1to the 10th address M10. In a case where in step S731, it is determined that the first address M1to the 10th address M10include an information-unregistered address, the slip ratio information includes information of an estimated slip ratio seestimated by the data-complementing unit15in addition to information of calculated slip ratios sccalculated by the slip calculator11. Further, in a case where in step S731, it is determined that the first address M1to the 10th address M10include an information-unregistered address, the friction coefficient information includes information of an estimated friction coefficient μeestimated by the data-complementing unit15in addition to information of calculated friction coefficients μccalculated by the friction calculator12.

Then, the maximum-friction-estimating unit20calculates an estimated maximum friction value μpby executing the processing illustrated inFIGS.7to10on the basis of input information of each of the calculated slip ratios sc, the estimated slip ratio se, the calculated friction coefficients μc, and the estimated friction coefficient μe.

As described above, in the control device1of the present embodiment, the computing unit10includes the data-complementing unit15that allows storage of information of an estimated slip ratio seand information of an estimated friction coefficient μein an information-unregistered address among the first address M1to the 10th address M10. The data-complementing unit15estimates an estimated slip ratio seand an estimated friction coefficient μeon the basis of information of calculated slip ratios scand information of calculated friction coefficients μcstored in information-registered addresses.

Consequently, even in a case where an information-unregistered address exists among any of the first address M1to the 10th address M10, the computing unit10can obtain an estimated slip ratio seand an estimated friction coefficient μecorresponding to the information-unregistered address. Therefore, when a theoretical characteristic Th is obtained, the theoretical characteristic Th can be obtained on the basis of information of each of calculated slip ratios sc, an estimated slip ratio se, calculated friction coefficients μc, and an estimated friction coefficient μe. Therefore, the theoretical characteristic Th can be easily brought closer to the friction-slip characteristic FS.

A method for obtaining a theoretical characteristic Th in a case where a calculated slip ratio scand a calculated friction coefficient μcrise stepwise, as illustrated inFIG.28, will be considered. In such a case, in the calculation memory13, information-registered addresses and information-unregistered addresses exist among the first address M1to the 10th address M10. Then, in a case where a theoretical characteristic Th is obtained on the basis of only information of calculated slip ratios scand information of calculated friction coefficients μcstored in the information-registered addresses, there is a possibility that the theoretical characteristic Th deviates from the friction-slip characteristic FS, such as a theoretical characteristic Th indicated by a solid line ofFIG.29.

The reason is that the theoretical characteristic Th is obtained in a state where among the first address M1to the 10th address M10, slip ratio information and friction coefficient information are stored in a plurality of addresses, and slip ratio information and friction coefficient information are not stored in the other addresses. In such a case, when the maximum-friction-estimating unit20estimates the parameters of the tire brush model expression, the graph of the cubic function passing through the approximate values of the values of the local calculated slip ratios scincludes candidates from which an estimated maximum friction value μpcannot be accurately calculated. Then, the theoretical characteristic Th may deviate from the friction-slip characteristic FS, such as the theoretical characteristic Th indicated by the solid line ofFIG.29.

On the other hand, according to the present embodiment, estimated slip ratios seand estimated friction coefficient μecorresponding to information-unregistered addresses can be obtained on the basis of information of calculated slip ratios scand information of calculated friction coefficients μcstored in information-registered addresses. Therefore, in each of the first address M1to the 10th address M10, either of information of a calculated slip ratio scand information of an estimated slip ratio secan be stored, and either of information of a calculated friction coefficient μcand information of an estimated friction coefficient μecan be stored.

Therefore, even in a case where the values of a calculated slip ratio scand a calculated friction coefficient μcrising stepwise are detected, it is possible to avoid a state where pieces of slip ratio information and pieces of friction coefficient information are not stored, as illustrated inFIG.30. Then, when the maximum-friction-estimating unit20estimates the parameters of the tire brush model expression, it is possible to avoid the inclusion of candidates for the parameters to be a graph of a cubic function passing only the approximate values of the values of the calculated slip ratios scwhere the theoretical characteristic Th concentrates.

Therefore, when the maximum-friction-estimating unit20estimates the parameters of the tire brush model expression, it is possible to suppress the inclusion of candidates from which an estimated maximum friction value μpcannot be accurately calculated. Then, the theoretical characteristic Th can be easily brought closer to the friction-slip characteristic FS.

Other Embodiments

Although the representative embodiments of the present disclosure have been described above, the present disclosure is not limited to the above-described embodiments, and can be variously modified as follows, for example:

In the embodiments, the friction-coefficient-computing device is used for the vehicle control system that controls the traveling of the electric car and is included in the ECU that controls the rotation speed of the motor for driving the vehicle, and the like, but the example is not limitative.

For example, the friction-coefficient-computing device may be used in a brake system that controls braking of the vehicle, and may be included in an ECU that controls the brake. Alternatively, the friction-coefficient-computing device may be used alone, and provided in the vehicle. In this case, the friction-coefficient-computing device includes a microcomputer including a CPU and memories, such as a ROM and a RAM, and a peripheral circuit of the microcomputer.

In the above-described embodiment, the parameter-restricting unit232obtains the values of the parameters so as to allow the inclination of the inflection point of the tire brush model expression to be zero, but the example is not limitative. While the inclination of the inflection point of the tire brush model expression can be brought closer to zero, the values of the parameters restricted by the parameter-restricting unit232may be values that do not allow the inclination of the inflection point of the tire brush model expression to be zero.

In the above-described embodiment, the region where the slip ratio is 0.1 or less is set as a minute region, and the tire brush model expression in the minute region is shown by Formula 6 and the like, but the example is not limitative. As long as the value of the slip ratio is sufficiently less than the slip ratio at which wheelspin of the tire starts, the tire brush model expression shown by Formula 6 and the like can be adopted even in a region where the slip ratio includes a value more than 0.1.

Needless to say, the elements constituting the above embodiments are not necessarily essential, except for cases, such as a case where it is clearly indicated that the elements are particularly essential, and a case where it is considered that the elements are obviously essential in principle.

In the above-described embodiment, in a case where numerical values, such as the numbers, numerical values, amounts, and ranges, of constituent elements of the embodiments are mentioned, the specific numbers are not limitative, except for cases, such as a case where it is clearly indicated that the numerical values are particularly essential, and a case where the numerical values are obviously limited to the specific numbers in principle.

In the above-described embodiments, when the shapes, positional relationships, and the like of the constituent elements and the like are mentioned, the shapes, positional relationships, and the like are not limitative, except for cases, such as a case where it is clearly indicated, and a case where the specific shapes, positional relationships, and the like are limitative in principle.