JP2003237560A - Maximum road friction coefficient estimation device - Google Patents

Abstract

(57) [Summary] PROBLEM TO BE SOLVED: To accurately set a maximum road surface in consideration of various driving situations.
Estimate the coefficient of friction. [Solution] μ_mapThe estimating unit 30 calculates the maximum
Road friction coefficient μ_mapIs estimated. μ_g1The estimating unit 40
Maximum road surface friction coefficient μ during deceleration_g1Is estimated. μ _g2Push
The constant part 50 is a maximum road surface friction coefficient μ during turning._g2Estimate
I do. μ_gThe selection unit 60 determines the maximum road surface friction coefficient μ._g1, Μ_g2
The smaller one is the maximum road friction coefficient μ_gAs μ_maxPush
It is supplied to the fixed part 80. The weight coefficient determination unit 70
The weight coefficient K is determined based on the degree Gr. μ_maxEstimator
80 is the maximum road friction coefficient μ_map, Μ _g, Based on the weight coefficient K
The maximum road friction coefficient μ_maxIs estimated.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a maximum road surface friction coefficient estimating device, and more particularly to a maximum road surface friction coefficient estimating device for estimating a maximum road surface friction coefficient in consideration of various traveling situations such as straight running and turning. .

[0002]

2. Description of the Related Art Today,
We are developing a front obstacle collision prevention support system to prevent collisions with obstacles in front of our vehicles such as automobiles and humans. Such a front obstacle collision prevention support system is required to determine a timing at which the vehicle can be stopped without colliding with the front obstacle by braking of about 60% of the maximum road surface friction coefficient μ _max . As a premise, the maximum road surface friction coefficient μ _max must be obtained before reaching the friction limit region.

In Japanese Patent Laid-Open No. 2001-171504,
A driving force is generated on a certain wheel while a braking force having a magnitude corresponding to the driving force is generated on another wheel to suppress generation of acceleration / deceleration of the vehicle due to the generated driving force and braking force, and braking / driving. A road surface friction coefficient estimation device (hereinafter referred to as "prior art 1") that estimates a road surface friction coefficient based on force, wheel speed, and wheel load has been proposed.

In the above-mentioned prior art 1, there are two combinations of the wheel speed Vwi and the braking / driving force Fwi per unit wheel load.
The regression line is obtained by plotting on the dimensional coordinates, and the driving stiffness Kd is calculated from the inclination angle of the regression line. Then, the maximum road surface μ, that is, μ _max is calculated based on the relationship between the driving mast stiffness Kd and the maximum value μ _max of the road surface friction coefficient shown in FIG.

However, according to the prior art 1, when the lateral acceleration during turning is large, the road surface μ estimation control may cause the driver to feel uncomfortable because the vehicle behavior is likely to change. Therefore, the road surface μ estimation control is performed. Is not performed (paragraph number 0057). Therefore, the prior art 1 is
The maximum road surface friction coefficient μ _max could not be obtained during turning.

Further, Japanese Patent Application Laid-Open No. 11-334637 proposes a turning limit estimating device and a vehicle running stabilizing device (hereinafter referred to as "prior art 2") for stably running the vehicle during turning. ing. Prior art 2 above
Is a turning limit mu _max estimated based on the cornering force and the wheel weights is performed stabilization control of the vehicle traveling based on the turning limit mu _max. However, in Conventional Technique 2, since the cornering force cannot be obtained during steady running (straight ahead), the turning limit μ _max could not be estimated.

On the other hand, since the running condition of the vehicle continuously changes from stop condition to accelerated running, steady running, turning running, and decelerated running, even with the prior arts 1 and 2, there are various running conditions. Accordingly, there is a problem that the maximum road surface friction coefficient cannot be estimated.

The present invention has been proposed to solve the above-mentioned problems, and it is an object of the present invention to provide a maximum road surface friction coefficient estimating device which accurately estimates the maximum road surface friction coefficient in consideration of various traveling situations. To aim.

[0009]

The invention according to claim 1 is
A first maximum road surface friction coefficient estimating means for estimating a first maximum road surface friction coefficient during steady traveling, and a second maximum road surface friction coefficient estimation for estimating a second maximum road surface friction coefficient during acceleration / deceleration traveling or turning traveling. Means, a weighting factor determining means for determining a weighting factor based on the tire grip degree, and the weighting factor determination means determined by the weighting factor determining means.
And a total maximum road surface friction coefficient estimating means for estimating the total maximum road surface friction coefficient by weighting the second maximum road surface friction coefficient, respectively.

The first maximum road surface friction coefficient estimating means estimates the first maximum road surface friction coefficient during steady running, that is, when traveling straight at a constant speed.

The second maximum road surface friction coefficient estimating means estimates the second maximum road surface friction coefficient during acceleration / deceleration traveling or turning traveling. Here, the second maximum road surface friction coefficient estimating means is
The maximum road surface friction coefficient during acceleration / deceleration traveling and turning travel may be estimated respectively, and the larger one may be set as the second maximum road surface friction coefficient, or the maximum road surface friction coefficient for the smaller grip degree may be set as the second maximum road surface friction coefficient. It may be a coefficient.

The weight coefficient determining means determines the weight coefficient based on the tire grip degree. The weight coefficient is a coefficient indicating the reliability of each of the first and second maximum road surface friction coefficients, and is determined by the tire grip degree.

The total maximum road surface friction coefficient estimating means estimates the total maximum road surface friction coefficient by weighting each of the first and second maximum road surface friction coefficients based on the determination of the weighting coefficient. Accordingly, it is possible to accurately estimate the total maximum road surface friction coefficient by comprehensively considering the road surface friction coefficient during steady running, acceleration / deceleration running, or turning running.

According to a second aspect of the present invention, in the first aspect of the present invention, the weight coefficient determining means increases the weighting of the first maximum road surface friction coefficient as the tire grip degree increases, and the 2 is determined so as to reduce the weighting of the maximum road surface friction coefficient, and the weighting of the first maximum road surface friction coefficient is reduced as the tire grip degree decreases, and the weighting of the second maximum road surface friction coefficient is reduced. It is characterized in that the weighting factor is determined so as to increase the weighting.

When the tire grip is large, the frictional characteristics of the tire have a sufficient margin to the limit region, and it is considered that the vehicle is running normally. In this case, the first maximum road surface friction coefficient estimated during steady traveling is more reliable than the second maximum road surface friction coefficient estimated during acceleration / deceleration traveling or turning traveling.

On the other hand, when the tire grip is small, the friction characteristics of the tire have reached the limit region or its vicinity,
The vehicle is considered to be accelerating / decelerating or turning. In this case, the second maximum road surface friction coefficient estimated during acceleration / deceleration traveling or turning travel is more reliable than the first maximum road surface friction coefficient estimated during steady traveling.

Therefore, the weighting factor determining means determines the weighting factor as described above on the basis of the tire grip degree, so that the overall maximum road friction coefficient can be accurately estimated in consideration of the running state of the vehicle. .

The invention according to claim 3 is the invention according to claim 1 or 2.
In the invention described above, the first maximum road surface friction coefficient estimating means is a wheel speed detecting means for detecting a wheel speed, and a μ gradient estimating is for estimating a μ gradient based on the wheel speed detected by the wheel speed detecting means. Means, a vibration level calculating means for calculating a vibration level based on the wheel speed detected by the wheel speed detecting means, a μ gradient estimated by the μ gradient estimating means, and a vibration calculated by the vibration level calculating means. First estimating means for estimating the first maximum road surface friction coefficient based on the level.

The μ-gradient estimating means estimates the μ-gradient based on the wheel speed. Here, the estimation method of the μ gradient is not particularly limited, but a preferable estimation method can be selected according to the wheel speed or the vehicle body speed.

The vibration level calculation means calculates the vibration level based on the wheel speed. Here, it is preferable to extract a signal in a predetermined band from the wheel speed signal and calculate the vibration level using this signal. The vibration level does not change significantly when the vehicle runs on a low μ road to a high μ road, but changes greatly when the vehicle gets over a projection or a crack on the road surface on which the vehicle runs. Therefore, the vibration level can represent such a state change of the traveling road surface.

The first estimating means prepares in advance the relationship between the μ gradient, the vibration level, and the first maximum road surface friction coefficient,
A first maximum road surface friction coefficient corresponding to the μ gradient estimated by the μ gradient estimating means and the vibration level calculated by the vibration level calculating means is estimated. As a result, the first maximum road surface friction coefficient can be estimated without being affected by the change in the state of the traveling road surface. Since the relationship between the μ gradient, the vibration level, and the first maximum road surface friction coefficient differs depending on the type of tire, it is preferable to prepare in advance for each type of tire.

According to a fourth aspect of the present invention, in the invention according to any one of the first to third aspects, the second maximum road surface friction coefficient estimating means is an acceleration detecting means for detecting acceleration, and a tire grip degree. A coefficient determining means for determining a coefficient based on the above, and a second coefficient estimating means for estimating the second maximum road surface friction coefficient based on the acceleration detected by the acceleration detecting means and the coefficient determined by the coefficient determining means. And an estimating means.

The acceleration detecting means may detect either acceleration / deceleration or lateral acceleration. That is, the acceleration / deceleration may be detected during acceleration / deceleration traveling, and the lateral acceleration may be detected during turning traveling.

Here, the acceleration (acceleration to gravitational force) is the road surface friction coefficient μ at the frictional force F (= μM) generated in the tire.
be equivalent to. The second maximum road surface friction coefficient is a value obtained by multiplying the road surface friction coefficient μ by a coefficient (> 1). Therefore,
The coefficient determining means determines the coefficient based on the tire grip degree.

The coefficient determining means determines the above coefficient so that the second maximum road surface friction coefficient becomes large when the tire grip is large, for example. When the tire grip is small, the second maximum road surface friction coefficient is also small, so the above coefficient is determined to be a small value.

As a result, depending on the tire grip,
It is possible to accurately estimate the second maximum road surface friction coefficient.

[0027]

BEST MODE FOR CARRYING OUT THE INVENTION Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the drawings.

[First Embodiment] FIG. 1 is a view showing the arrangement of a maximum road surface friction coefficient estimating apparatus 1 according to the first embodiment. The maximum road surface friction coefficient estimating device 1 is suitable for use when the tire grip characteristics are in the vicinity of the non-limit region to the limit region.

The maximum road surface friction coefficient estimating device 1 includes wheel speed sensors 11FR, 11FL, 11R for detecting wheel speeds which are rotational angular velocities of four wheels provided on a vehicle.
R, 11RL, a vehicle body longitudinal acceleration sensor 12 that detects longitudinal acceleration (acceleration / deceleration), a vehicle body lateral acceleration sensor 13 that detects lateral acceleration, and a yaw rate sensor 14 that detects yaw rate that is a rotational angular velocity around the center of gravity of the vehicle body. When,
A steering angle sensor 15 that detects a steering angle and an electronic control unit (hereinafter referred to as “ECU”) 20 that estimates a maximum road surface friction coefficient μ _max based on a detection result of each sensor are provided.

The wheel speed sensors 11FR, 11FL,
11RR and 11RL are provided on the front right, front left, rear right, and rear left wheels of the vehicle body, respectively.

FIG. 2 is a block diagram showing the functional configuration of the ECU 20. The ECU 20 estimates a maximum road surface friction coefficient μ _map during steady running μ _map estimating unit 30 and a maximum road surface friction coefficient μ _g1 during acceleration / deceleration running μ _g1 estimating unit 4
0 and the maximum road friction coefficient μ _g2 during turning are estimated μ
_g2 estimation unit 50, μ _g selection unit 60 that selects one of the maximum road surface friction coefficients μ _g1 , μ _g2 , weight coefficient determination unit 70 that determines weight coefficient K, and overall maximum road surface friction coefficient μ and mu _max estimator 80 for estimating the _max, and a.

(Μ _map estimation unit 30) The μ _map estimation unit 30
Estimate the maximum road surface friction coefficient μ _map during steady running, that is, when traveling straight at a constant speed.

FIG. 3 is a block diagram showing the functional configuration of the μ _map estimating unit 30. The μ _map estimation unit 30 estimates the μ gradient using the first method, and the μ gradient estimation unit 32 estimates the μ gradient using the second method.
A band pass filter (BPF) 33 that extracts a signal in a predetermined band from the wheel speed, a vibration level calculation unit 34 that calculates a vibration level from a signal in the predetermined band, and a μ gradient estimation unit 3
1 and the μ gradient selecting unit 35 that selects one of the μ gradients estimated by the μ gradient estimating unit 32, the tire type determining unit 36 that determines the tire type, and the determination map for each tire type are stored. The maximum road friction coefficient μ _{map is} determined using the determination map storage unit 37 and the determination map corresponding to the tire type.
And a collating unit 38 for estimating

(Μ Gradient Estimating Unit 31) μ Gradient Estimating Unit 31
Calculates the μ gradient using the first method, specifically, the rotational vibration model of the wheel resonance system.

FIG. 4 is a block diagram showing a functional configuration of the μ gradient estimating section 31. The μ gradient estimator 31 detects a wheel speed vibration Δω of each wheel as a response output of a wheel resonance system that has received a road surface disturbance ΔT _d from the detected wheel speed ω of each wheel, and a preprocessing filter 311. The transfer function identification unit 312 that identifies the transfer function of each wheel that satisfies the wheel speed vibration Δω using the least square method, and the μ gradient calculation unit 313 that calculates the μ gradient based on the identified transfer function.
And are equipped with.

The preprocessing filter 311 is composed of a bandpass filter that passes only frequency components in a certain band, a highpass filter that passes only high frequency components including the resonance frequency component, and the like. Then, the preprocessing filter 311 removes the DC component of the wheel speed ω and outputs the wheel speed vibration Δω.

Here, the transfer function F (s) of the preprocessing filter 311 is expressed by the following equation (1). However,
c _i is the coefficient of the filter transfer function, and s is the Laplace operator.

[0038]

[Equation 1]

The transfer function identification unit 312 identifies the transfer function of each wheel that satisfies the wheel speed vibration Δω by using the least squares method using the rotational vibration model of the wheel resonance system.

Here, the derivation of the arithmetic expression on which the transfer function identification unit 312 depends will be described. Transfer function identification unit 312
The transfer function to be identified by is a quadratic model in which the road surface disturbance ΔT _d is a vibration input and the wheel speed vibration Δω detected by the preprocessing filter 311 is a response output. That is, the vibration model shown in Expression (2) is assumed.

[0041]

[Equation 2]

Here, v is an observation noise included when observing the wheel speed. By transforming equation (2), equation (3)
To get

[0043]

[Equation 3]

The equation obtained by applying the preprocessing filter of the equation (1) to the equation (3) is discretized. At this time, Δω, ΔT
_d and v are discrete data Δω (k), ΔT _d (k), and v (k) sampled for each sampling period T _s.
(K is a sampling number: k = 1, 2, 3, ...). The Laplace operator s can be discretized using a predetermined discretization method. In the present embodiment, as an example, the discretization is performed by the bilinear transformation represented by the following equation (4). Note that d is a 1-sample delay operator.

[0045]

[Equation 4]

The order m of the preprocessing filter 311 is preferably 2 or more. In the present embodiment, m =
Then, the equation (9) is obtained from the equation (5).

[0047]

[Equation 5]

Further, the wheel speed vibration Δ is calculated based on the least squares method.
Equation (5) is used to identify the transfer function from each data of ω.
Is transformed into the form of a linear function with respect to the parameter to be identified as shown in Expression (10). Note that " ^T " indicates the transpose of the matrix, and θ is the parameter of the transfer function to be identified.

[0049]

[Equation 6]

Here, the equation (10) satisfies the equations (11) to (13).

[0051]

[Equation 7]

Then, the transfer function identification unit 312 sequentially applies the discretized data of the wheel speed vibration Δω to the equation (10) derived as described above and applies the least squares method to obtain the unknown parameter θ. To identify the transfer function.

Specifically, the detected wheel speed vibration Δω is converted into discretized data Δω (k) (k = 1, 2, 3, ...) And the data is sampled at N points, The parameter θ of the transfer function is estimated by using the least squares arithmetic expression of Expression (14).

[0054]

[Equation 8]

Here, the amount with the symbol "^" is defined as its estimated value.

Further, instead of the above least squares method, the parameter θ is calculated by the recurrence formulas of the following formulas (15) to (17).
You may calculate using the recursive least squares method which calculates | requires.

[0057]

[Equation 9]

Here, ρ is a forgetting factor, which is usually 0.
The value is set to about 95 to 0.99. At this time, the initial values may be expressed by Expression (18) and Expression (19). In the equation (19), α is a sufficiently large positive number.

[0059]

[Equation 10]

Various modified least squares methods may be used as a method for reducing the estimation error of the least squares method.
In the present embodiment, an example will be described in which the auxiliary variable method, which is a least squares method in which an auxiliary variable is introduced, is used. According to the method,
When the relationship of Expression (10) is obtained, the parameter of the transfer function is estimated using the following Expression (20) with m (k) as an auxiliary variable.

[0061]

[Equation 11]

Further, the sequential calculation is expressed by the following expressions (21) to (23).

[0063]

[Equation 12]

The principle of the auxiliary variable method is as follows.
Substituting equation (10) into equation (20) yields equation (24).

[0065]

[Equation 13]

If the auxiliary variable is selected so that the second term on the right side of the equation (24) becomes zero, the estimated value of θ coincides with the true value of θ.
Therefore, in the present embodiment, as the auxiliary variable, equation (25)
To use.

[0067]

[Equation 14]

Then, the expression (25) delayed so that it has no correlation with the error r (k) is used. That is,
The following equation (26) is used.

[0069]

[Equation 15]

However, L is a delay time.

After the transfer function θ is identified, the μ gradient calculating section 313 calculates the μ gradient D ₀ based on the equation (27).

[0072]

[Equation 16]

Note that J ₁ is the rim-side inertia moment in the rotational vibration model, and J ₂ is the belt-side inertia moment in the rotational vibration model, which are values given in advance.

Then, the μ gradient estimating section 31 supplies the μ gradient thus obtained to the μ gradient selecting section 35 shown in FIG.

(Μ Gradient Estimating Unit 32) μ Gradient Estimating Unit 32
Uses the second method, that is, the μ gradient is estimated using the wheel speed difference between the front wheels and the rear wheels and the longitudinal acceleration.

FIG. 5 is a block diagram showing a functional configuration of the μ gradient estimating section 32. The μ gradient estimating unit 32 calculates a wheel speed difference by subtracting the wheel speed of the rear wheels from the wheel speed of the front wheels, and an estimating unit 322 that estimates the μ gradient based on the wheel speed difference and the longitudinal acceleration. Is equipped with.

The estimating unit 322 stores in advance the braking / driving force distribution ratio and the load distribution ratio for each wheel of the vehicle. Then, the μ gradient is estimated based on these parameters, the wheel speed difference from the calculator 321, and the longitudinal acceleration from the vehicle longitudinal acceleration sensor 12 shown in FIG. 1. Here, the principle for obtaining the μ gradient from the wheel speed difference and the longitudinal acceleration will be described.

FIG. 6 is a diagram showing the characteristics of the road surface friction force with respect to the slip speed. If the direction of the road friction force during driving is positive, the road friction force during braking will be negative. That is, the characteristics during driving and braking are almost symmetrical with respect to the origin. The slope of this characteristic is the μ gradient.

FIG. 7 is a diagram showing the characteristics of the wheel speed difference between the front and rear wheels with respect to the longitudinal acceleration in the case of driving the front wheels and braking the front wheels. As shown in the figure, the above characteristic is a straight line rising to the right passing through the origin.

FIG. 8 is a diagram showing the characteristic of the wheel speed difference between the front and rear wheels with respect to the longitudinal acceleration in the case of driving the rear wheels and braking the rear wheels. As shown in the figure, the above-mentioned characteristic is a straight line that descends to the right and passes through the origin.

On the other hand, a so-called FR vehicle, that is, rear wheel drive
In the case of four-wheel braking, since the distribution ratio of the driving force and the braking force to each wheel is different, the following characteristics are obtained.

FIG. 9 is a diagram showing the characteristic of the wheel speed difference between the front and rear wheels with respect to the longitudinal acceleration in the case of rear wheel driving and four-wheel braking. In the case of an FR vehicle, the right half characteristic shown in FIG. 8 is obtained during driving, and the left half characteristic in FIGS. 7 and 8 is obtained by weighting the braking / driving force distribution during braking.

Focusing on the drive of the FR vehicle, that is, the fourth quadrant in FIG. 9, the front wheels are the driven wheels and the wheel speed difference from the rear wheels is the slip speed itself. On the other hand, when the longitudinal acceleration is multiplied by a predetermined parameter (vehicle body weight), the road surface friction force acts on the vehicle. In other words, the characteristics of the fourth quadrant in FIG. 9 match the characteristics of the first quadrant shown in FIG. 6 when the vertical axis and the horizontal axis are interchanged. Therefore, the inclination of the straight line in the fourth quadrant in FIG. 9 becomes an amount proportional to the inverse of the μ gradient.

As described above, in the simple case where the braking / driving force acts on one wheel, the μ gradient can be directly obtained from the inclination of the straight line. However, if the braking / driving force distribution ratios are different, the μ gradient cannot be determined by simply obtaining the slope of the straight line. Hereinafter, the derivation of the formula for obtaining the μ gradient based on the longitudinal acceleration and the front / rear wheel speed difference will be described.

First, as shown in FIG. 6, the frictional force f _n generated between the tire (wheel) and the road surface is determined by the slip speed Δn.
Approximate v _n with a straight line passing through the origin. That is, the frictional force f _n is approximated by the following equation (28).

[0086]

[Equation 17]

Here, the subscript n is represented by each of the wheels of the vehicle, specifically, numbers 1 to 4 in the order of front right, front left, rear right, rear left. W _n is the wheel load, α _n is the slope of the road surface μ with respect to the slip speed (μ slope), and v _v is the vehicle speed (vehicle speed).

[0088] The operation force f _^'n for each wheel caused by the braking or driving, expressed by the following equation (29).

[0089]

[Equation 18]

F _t is the total value of the applied force, and k _n is the distribution ratio of braking force or driving force to each wheel (hereinafter referred to as “braking / driving force distribution ratio”).
Say. ). For example, during braking, a braking force related to the master hydraulic pressure is generated, and the force applied to each wheel is distributed due to the characteristics of each wheel cylinder and brake disc. On the contrary, at the time of driving, the driving force from the engine is applied to the driving wheels and not to the driven wheels. The distribution ratio of the total force to each wheel is the braking / driving force distribution ratio k _n .

Since the sum of the braking force component of the wheel inertia and the road frictional force balances the forces distributed to the respective wheels, the following equation (30) is established.

[0092]

[Formula 19]

Here, J _n is the moment of inertia of the nth wheel,
r _n is the tire radius of the nth wheel. Then, the formula (28)
From the equation (30), the following equation (31) is obtained.

[0094]

[Equation 20]

On the other hand, the vehicle is accelerated or decelerated by the sum of the frictional forces of the wheels. When the longitudinal acceleration is a and the vehicle body weight is M, the following expression (32) is established.

[0096]

[Equation 21]

Considering that the sum of the loads on the wheels is equal to the vehicle body weight M, the following equation (33) is established.

[0098]

[Equation 22]

Here, G is the gravitational acceleration. From the above, the slip speed (v _n -v _v) has the following formula (3
4) is established.

[0100]

[Equation 23]

However, the variables of the equation (34) have the relationships of the following equations (35) to (38).

[0102]

[Equation 24]

Note that w _n is a load distribution ratio to each wheel, and j _n is a ratio of the wheel inertia to the vehicle body inertia. Furthermore, g _v
And g _n are the ratios of the acceleration of the vehicle body and the wheels to the gravitational acceleration G.

In a general vehicle, j _n is (j _n ˜0.
Since the relationship of 01 << 1) is satisfied, the term regarding the wheel inertia in Expression (34) is ignored. As a result, the following equation (39) is obtained.

[0105]

[Equation 25]

When the vehicle body speed v _v is deleted while paying attention to the difference between the wheel speeds of the respective wheels, the following equation (40) is obtained.

[0107]

[Equation 26]

In the equation (40), the subscript n indicates the front wheel and the subscript m indicates the rear wheel. When determining the wheel speed difference, the wheel speed of the right rear wheel is used when the wheel speed of the right front wheel is used, and the wheel speed of the left rear wheel is used when the wheel speed of the left front wheel is used.

Therefore, if the longitudinal acceleration, the wheel speeds of the front and rear wheels, the braking / driving force distribution ratio, and the load distribution ratio are known, the estimating unit 322 can obtain the μ gradients α _n and α _m of the front wheels and the rear wheels. it can.

Here, since the equation (40) has two unknown variables, μ gradients α _n and α _m , it is possible to use the time series data of the wheel speed and the longitudinal acceleration as follows, Estimate the μ gradient by applying the least squares method of.

Looking inside the parentheses on the right side of the equation (40), there are braking / driving force distribution ratio k _n and load distribution ratio w _n . The braking / driving force distribution ratio k _n has a unique value due to the structure of the proportion valve and the brake disc during braking, for example. When driving, in the case of a two-wheel drive vehicle, the driving wheels are 0.5,
The driven wheel is 0. That is, the braking / driving force distribution ratio k _n is
It is generally known. Further, the load distribution ratio w _n is known when the vehicle is stopped (or during steady running), and can be corrected using the acceleration / deceleration g _v even during acceleration / deceleration.

When the unknown variables α _n and α _m in the equation (40) are x _n and the other known variables are h _n , the following equation (41) is established.

[0113]

[Equation 27]

Note that h _n and x _n are as in the following equations (42) and (43).

[0115]

[Equation 28]

Considering the time series data of each variable, the equation (41) can be expressed by the following equations (44) to (47).

[0117]

[Equation 29]

Applying the method of least squares, the following equation (4
As shown in 8), the unknown variable X can be obtained.

[0119]

[Equation 30]

Further, the online least squares method is applied to successively calculate the following equations (49) and (50).

[0121]

[Equation 31]

Ρ is a forgetting constant. Note that h ^j is expressed by the following expression (51) and D ^j is expressed by the following expression (52).

[0123]

[Equation 32]

Then, the following equation (53) can be obtained.

[0125]

[Expression 33]

The μ-gradient estimating unit 32 shows the μ-gradient thus obtained by using the second method as shown in FIG.
And the tire type determination unit 36.

Since the μ-gradient estimating unit 32 can directly estimate the μ-gradient based on the equation (40), if the wheel speed difference and the longitudinal acceleration are known, the μ-gradient estimating unit 32 is not limited to high-speed running, but is also low-speed running. The μ gradient can be estimated with high accuracy.

Further, the μ-gradient estimating unit 32 estimates the μ-gradient by using the braking / driving force distribution ratio k _n and the load distribution ratio w _n which differ depending on the system configuration of the vehicle. It can be calculated.

(BPF33) On the other hand, BPF3 shown in FIG.
3 is a shake resonance point from the wheel speed ω (for example, 35 to 40H).
A signal in a predetermined band including z) is extracted and supplied to the vibration level calculation unit 34. The band of the signal extracted by the BPF 33 is not limited to the above band, and may be another band.

(Vibration Level Calculator 34) The vibration level calculator 34 calculates the vibration level G (N) based on the signal extracted by the BPF 33. Where vibration level G
(N) is defined as the following equation (54).

[0131]

[Equation 34]

Ω (k) is the output of the BPF 33. Further, ρ is a forgetting factor, which is normally 0.95 to 0.
Set the value to about 99. The vibration level calculation unit 33 specifically calculates the following expression (55) sequentially in order to obtain the vibration level G (N) defined by the above expression (54).

[0133]

[Equation 35]

Then, the vibration level calculation unit 34 uses the equation (5
5) The vibration level G (N) obtained by
Supply to 8.

(Μ Gradient Selection Section 35) μ Gradient Selection Section 35
Selects one of the μ-gradients estimated by the μ-gradient estimation unit 31 and the μ-gradient estimation unit 32 according to the vehicle speed (wheel speed). Specifically, when the vehicle speed is 40 km / h or more, the μ gradient estimated by the μ gradient estimation unit 31 is selected,
When the vehicle speed is 10 to 40 km / h, the μ gradient estimated by the μ gradient estimating unit 32 is selected, and the selected μ gradient is compared with the matching unit 3
Supply to 8.

(Tire Type Determining Section 36) The tire type determining section 36 is a summer tire (including a worn studless tire) or a studless tire, using the μ gradient estimated by the μ gradient estimating section 32. The type of tire is determined.

First, the μ gradient is used to determine whether the vehicle is traveling on a high μ road. Specifically, the μ gradient estimation unit 3
The μ gradient supplied from 2 is sequentially stored, and it is determined whether the μ gradient has become maximum. Then, when it is determined that the μ gradient becomes maximum, the high μ road flag (for example, a signal of logic H) indicating the high μ road is raised. When the μ gradient is not the maximum, the high μ road flag is kept low. Thus, the reason why the road surface is a high μ road is determined because the μ gradient becomes large when the road surface is a high μ road such as asphalt, and the tire type can be easily determined. .

Next, when the high μ road flag is raised, the tire type determining portion 36 determines whether the μ gradient exceeds the first threshold value. When it is determined that the μ gradient exceeds the first threshold value, it is determined to be a summer tire, and when it is determined that it does not exceed the first threshold value, it is determined to be a studless tire, and the determination result is supplied to the matching unit 38.

Note that the tire type determination is not limited to such a method, and another method may be used. Further, instead of the μ gradient estimated by the μ gradient estimation unit 32, the μ gradient estimated by the μ gradient estimation unit 31 may be used.

(Determination Map Storage Unit 37) The determination map storage unit 37 stores the vibration level, μ gradient and maximum road surface friction coefficient μ.
stores the first and second determination map showing the relationship _map. The first determination map is a map used when a summer tire is attached, and the second determination map is a map used when a studless tire is attached.

FIG. 10A is a diagram showing a first determination map for summer tires, and FIG. 10B is a diagram showing a second determination map for studless tires. According to the first determination map, the maximum road surface friction coefficient μ _map is low μ (for example, μ = 0.2), medium μ (for example, μ = 0.5), and high μ (depending on the vibration level and the μ gradient. For example, it is determined to be one of the three levels of μ = 0.8). Further, according to the second determination map, the maximum road surface friction coefficient μ _map is the vibration level and μ
Depending on the slope, it is determined as either low μ or medium μ.

(Verifying Unit 38) When the tire type determining unit 36 determines that the tire type is a summer tire, the comparing unit 38 selects the first determination map, and the tire type determining unit 36 determines that it is a studless tire. At this time, the second judgment map is selected. Then, for the selected determination map, the vibration level G (N) calculated by the vibration level calculation unit 33 and μ
Maximum road surface friction coefficient μ _map (any one of low μ, medium μ, and high μ) by collating with the μ gradient selected by the gradient selection unit 34
Ask for. The matching unit 38 determines the maximum road surface friction coefficient μ
_{The map} is supplied to the μ _max estimation unit 80 shown in FIG.

(Μ _g1 estimating unit 40) The μ _g1 estimating unit 40 estimates the μ gradient based on the wheel speed and the longitudinal acceleration, and based on the μ gradient, the grip degree Gr1 during acceleration / deceleration traveling and the maximum road friction coefficient μ Estimate _g1 .

FIG. 11 is a block diagram showing the functional structure of the μ _g1 estimation unit 40. The μ _g1 estimation unit 40 includes a μ gradient estimation unit 41 that estimates the μ gradient based on the wheel speed and the longitudinal acceleration, an LPF 42 that smooths the μ gradient, and a μ gradient
An LPF 43 for lowering the response characteristic of the gradient, and a grip degree calculator 44 for calculating the tire grip degree Gr1.
When provided with a coefficient determination section 45 for determining a coefficient K _g1, a multiplier 46 which multiplies the coefficient K _g1 and the longitudinal acceleration, the.

The μ gradient estimator 41 is constructed in the same manner as the μ gradient estimator 32 described above.
Supply to F42 and LPF43.

The time constant of the LPF 42 is set smaller than that of the LPF 43. Therefore, LPF4
2 smooths and outputs the μ gradient obtained by the μ gradient estimating unit 41. On the other hand, the LPF 43 delays and outputs the response speed of the μ gradient.

The grip degree calculator 44 calculates the grip degree Gr1 by dividing the μ gradient supplied from the LPF 42 by the μ gradient supplied from the LPF 43. In other words, the grip calculation unit 44 uses the μ
Divide the slope by the μ slope through the slow filter.

When the grip degree Gr1 is small,
The μ gradient is decreasing, and the friction characteristics of the tire are in a state of being close to the limit. On the other hand, when the grip degree Gr1 is large,
The μ gradient is increasing, and the friction characteristics of the tire are in a state where there is a sufficient margin to the limit. Then, the grip degree calculation unit 4
4 supplies the grip degree Gr1 thus obtained to the coefficient determination unit 45.

The coefficient determining section 45 compares the grip degree Gr1.
Corresponding coefficient K_g1Function indicating the relationship between _g1= F1 (Gr1)
I remember. Note that the function F1 has continuity.
For example, it may be linear or non-linear.

FIG. 12 is a diagram showing the function K _g1 = F1 (Gr1) stored in the coefficient determining section 45. According to this function F1, the grip degree Gr1 is from the minimum value to the predetermined value X
In the cases up to, the coefficient K _g1 is 1.0. Then, as the grip degree Gr1 becomes larger than the predetermined value X, the coefficient K
_g1 also increases and reaches 2.0. When the coefficient K _g1 reaches 2.0, the coefficient K is increased even if the grip degree Gr1 is further increased.
_g1 remains at 2.0.

Then, the coefficient determining section 45 uses the function F1.
_{Is used} to determine the coefficient K _g1 corresponding to the grip degree Gr1 calculated by the grip calculation unit 44, and this coefficient K _g1 is supplied to the multiplier 46.

The multiplier 46 is a vehicle longitudinal acceleration sensor 12
The longitudinal acceleration detected in 1 is multiplied by a coefficient K _g1 , and the obtained value is supplied to the μ _g selection unit 60 shown in FIG. 2 as the maximum road surface friction coefficient μ _g1 during acceleration / deceleration traveling. The multiplier 4
When the maximum road surface friction coefficient μ _g1 becomes larger than 1, 6 supplies the maximum road surface friction coefficient μ _g1 = 1 to the μ _g selection unit 60.

(Μ _g2 estimation unit 50) The μ _g2 estimation unit 50 estimates the μ gradient from the wheel speed, and estimates the grip degree Gr2 and the maximum road surface friction coefficient μ _g2 during turning based on the μ gradient.

FIG. 13 is a block diagram showing the functional configuration of the μ _g2 estimation unit 50. The μ _g2 estimating unit 50 includes a μ gradient estimating unit 51 that estimates a μ gradient based on a wheel speed, an LPF 52 that smoothes the μ gradient, an LPF 53 that reduces the response characteristic of the μ gradient, and a grip degree of a tire. a grip factor calculation unit 54 for calculating a Gr2, and coefficient determination section 55 for determining a coefficient K _g2, and includes a multiplier 56 for multiplying the coefficient K _g2 and lateral acceleration, a.

The μ-gradient estimating unit 51 is configured similarly to the μ-gradient estimating unit 31, estimates the μ-gradient using the rotational vibration model, and estimates the estimated μ-gradient to the LPF 52 and the LPF 53.
Supply to.

The grip degree calculating section 54 calculates the grip degree Gr2 by dividing the μ gradient supplied from the LPF 52 by the μ gradient supplied from the LPF 53, and supplies the grip degree Gr2 to the coefficient determining section 55.

The coefficient determining section 45 compares the grip degree Gr2.
Corresponding coefficient K_g2Function indicating the relationship between _g2= F2 (Gr2)
I remember.

FIG. 14 is a diagram showing the function K _g2 = F2 (Gr2) stored in the coefficient determining section 55. According to this function F2, the coefficient K _g2 is 1.0 when the grip degree Gr2 is from the minimum value to 0.4. Then, as the grip degree Gr2 becomes larger than 0.4, the coefficient K _g2 also becomes large and reaches 2.0. When the coefficient K _g2 reaches 2.0, the coefficient K _g2 remains 2.0 even if the grip degree Gr2 is further increased.

Then, the coefficient determining section 55 determines the function F2
It was used to determine the coefficients K _g2 corresponding to the grip degree Gr2 calculated by the gripping operation unit 54 supplies the coefficient K _g2 to the multiplier 56.

[0160] The multiplier 56 multiplies the coefficient K _g2 lateral acceleration detected by the vehicle body lateral acceleration sensor 13, maximum road mu _g selection shown in FIG. 2 as a friction coefficient mu _g2 during turning speed running and the obtained value Supply to the part 60. The multiplier 56 is
When the maximum road surface friction coefficient μ _g2 becomes larger than 1, the maximum road surface friction coefficient μ _g2 = 1 is supplied to the μ _g selection unit 60.

[0161] (μ _g selecting section 60) μ _g selection unit 60, μ _g1
The maximum road friction coefficient μ _g1 estimated by the estimation unit 40 and μ _{g 2}
A larger one of the maximum road surface friction coefficients μ _g2 estimated by the estimation unit 50 is selected, and the selected one is supplied to the μ _max estimation unit 80 as the maximum road surface friction coefficient μ _g .

It should be noted that the μ _g selecting section 60 has a grip degree Gr
The maximum road surface friction coefficient corresponding to the smaller one of 1 and Gr2 may be selected. In addition, the μ _g selection unit 60 determines whether or not the vehicle is turning based on the steering angle detected by the steering angle sensor 15, and when the _vehicle is turning, selects the maximum road friction coefficient μ _g2 to turn the vehicle. The maximum road friction coefficient μ _g1 may be selected when the _vehicle is not running.

(Weight coefficient determining section 70) Weight coefficient determining section 7
0 is the maximum road surface friction coefficient μ _g selected by the μ _g selection unit 60
The weighting coefficient K is determined based on the grip degree Gr corresponding to. Specifically, the weighting factor K is brought closer to 1 as the grip degree Gr approaches 1, and the weighting factor K is made closer to zero as the grip degree Gr approaches zero.

That is, since the grip degree Gr has a large value (a value close to 1) when the vehicle is traveling steadily, the weighting coefficient K is set to make the reliability of μ _max higher than μ _g. Set the value close to 1. Further, when the vehicle is traveling in acceleration / deceleration or in turning, the grip degree Gr has a small value, so in order to make the reliability of μ _g higher than μ _max ,
The weighting factor K is set to a value close to zero.

The weighting factor determining section 70 uses a traction control system (TRC: Tactio).
n Control system), antilock
Brake system (ABS: Antilock Br)
vehicle stability control system (VSC: Vehicle Stability Co) such as an ake system
When the control of the control system is being performed, the weighting factor K may be brought close to zero.

(Μ _max estimation unit 80) The μ _max estimation unit 80
The maximum road surface friction coefficient μ _max is estimated in consideration of steady running and acceleration / deceleration running or turning running. Specifically, the maximum road friction coefficient μ _map estimated by the μ _map estimation unit 30 and the maximum road friction coefficient μ selected by the μ _g selection unit 60
_The following equation (56) is calculated using _g and the weighting factor K determined by the weighting factor determining unit 70.

[0167]

[Equation 36]

As described above, the maximum road surface friction coefficient estimating apparatus 1 according to the first embodiment has the maximum road surface friction coefficient μ _map during steady traveling and the maximum road surface friction coefficient during acceleration / deceleration traveling or turning traveling. Based on μ _g , the overall maximum road friction coefficient μ _max can be estimated. At this time, since the weighting coefficient K is obtained based on the grip degree Gr during acceleration / deceleration traveling or turning traveling, the states during steady traveling, acceleration / deceleration traveling or turning traveling can be considered respectively, and the maximum value can be obtained with high accuracy. The road friction coefficient μ _max can be estimated.

The present invention is not limited to such an embodiment, but may be as follows.

The μ _map estimating unit 30 causes the μ gradient estimating unit 31 and the μ gradient estimating unit 32 to estimate the μ gradient, respectively.
For example, when the vehicle speed is 40 km / h or more, the μ gradient estimating unit 31 may estimate the μ gradient, and when the vehicle speed is less than 40 km / h, the μ gradient estimating unit 32 may estimate the μ gradient.

Further, in the above-described embodiment, G (N) represented by the equations (54) and (55) as the vibration level.
However, the present invention is not limited to this, and may be the magnitude of the amplitude of the signal output from the BPF 33, for example.

Further, the weighting factor determining section 70 determines the weighting factor K based on the grip degree Gr.
The weighting factor K may be determined based on a parameter equivalent to r, that is, a parameter indicating the grip state of the tire.

Further, the maximum road friction coefficient μ _map was obtained in three stages of low μ, medium μ and high μ, but it may be obtained in four or more stages.

[Second Embodiment] Next, a second embodiment will be described. The same parts as those in the first embodiment are designated by the same reference numerals, and detailed description thereof will be omitted.

[0175] maximum road friction coefficient estimating apparatus according to the second embodiment, in place of the mu _g2 estimating unit 50, and a mu _g2 estimating section 50A of the configuration shown in FIG. 15.

FIG. 15 is a block diagram showing the functional configuration of the μ _g2 estimation unit 50A. The μ _g2 estimation unit 50A uses μ _g2
In addition to the configuration of the estimation unit 50, a flag generation unit 57 is further provided. The flag generator 57 determines that the value of the coefficient K _g2 is 1.4.
A determination flag indicating the timing at which is becomes. Here, the principle of _{obtaining the} maximum road surface friction coefficient μ _g2 using the determination flag will be described.

(Principle) FIG. 16 (A) shows a high μ road with 20
It is the figure which showed typically the relationship of the amount of drift with respect to lateral acceleration at the time of slalom running at [km / h]. FIG. 2B is a diagram schematically showing the relationship between the lateral acceleration and the drift amount when slalom running at 20 [km / h] on a low μ road.

According to FIG. 16 (A), when the vehicle runs on a high μ road, the relationship between the lateral acceleration and the drift amount is a slender elliptic graph which is inclined downward to the right around the origin.
In this case, the relationship between the lateral acceleration and the drift amount is almost linear.

On the other hand, according to FIG. 9B, when traveling on a low μ road, the relationship between the drift amount and the lateral acceleration is an S-shaped graph symmetrical with respect to the origin. In the region where the lateral acceleration is approximately −0.18 to 0.18 [G] (linear region), the relationship between the drift amount and the lateral acceleration is almost linear, and the tire grip has not reached the limit yet. At this time, the slope of the regression line L becomes substantially constant (negative value). Lateral acceleration is less than -0.18 [G] or 0.18
In the region [G] and above (non-linear region), the tire grip reaches its limit, and the slope of the regression line L greatly changes from a negative value to a positive value as the absolute value of the lateral acceleration increases.

FIG. 17 is a graph showing the characteristics of the road surface friction coefficient μ with respect to the slip angle. The road surface friction coefficient μ is almost proportional to the slip angle from zero to (0.7 × μ _max ) (linear region). However, the road friction coefficient μ is
When (0.7 × μ _max ) is exceeded, the slip angle does not become so large even if it becomes large (non-linear region).

The linear and non-linear regions shown in FIG. 17 substantially correspond to the linear and non-linear regions shown in FIG. 16B. Therefore, the road surface friction coefficient μ when the slope of the regression line L shown in FIG.
Is about 0.7 times the maximum road surface friction coefficient μ _g2 .

If the wheel load is M and the lateral acceleration (against gravitational acceleration) is Gy [G], the lateral force M.Gy.g and the frictional force .mu.Mg are in equilibrium. It holds.

[0183]

[Equation 37]

Therefore, the value of the lateral acceleration Gy [G] becomes the same as the value of the road surface friction coefficient μ. From this, the maximum road surface friction coefficient μ _g2 is _calculated by the following equation (58) using the lateral acceleration Gy (= road surface friction coefficient μ) when the slope of the regression line L shown in FIG. 16 (B) becomes zero. Required by.

[0185]

[Equation 38]

According to the equation (58), the judgment flag is generated.
The lateral acceleration, the coefficient K _g2Multiply by = 1.4
Therefore, the road surface friction coefficient μ_g2Can be asked. Na
The coefficient by which the lateral acceleration Gy is multiplied depends on the tire model.
However, according to the experimental results, 1.3 to 1.5 is
preferable.

As described above, the coefficient K _g2 is 1.4 when the inclination α of the drift amount with respect to the lateral acceleration becomes zero.
It is the timing to become. Therefore, when the determination flag is generated, the maximum road surface friction coefficient μ _g2 can be obtained by multiplying the lateral acceleration by the coefficient K _g2 = 1.4.

(Flag Generating Unit 57) FIG. 18 is a block diagram showing the functional structure of the flag generating unit 57.

The flag generator 57 includes a drift amount calculator 91 for calculating the drift amount, a regression line calculator 92 for calculating the slope α of the regression line between the lateral acceleration and the drift amount, a slope α of the regression line and a threshold value. And a threshold value determination unit 93 that generates a flag when the inclination α becomes positive.

The drift amount calculation unit 91 uses the wheel speed sensor 1
1FR, 11FL, 11RR, 11RL, the wheel speed detected, the lateral acceleration Gy detected by the vehicle body lateral acceleration sensor 13, the actual yaw rate γ1 detected by the yaw rate sensor 14, the steering detected by the steering angle sensor 15. Angle θ
Based on, the drift amount D, which is a parameter for estimating the turning state indicating the spin tendency / drift tendency, is calculated.

FIG. 19 is a block diagram showing the functional structure of the drift amount calculation unit 91. Drift amount calculation unit 91
Is a reference yaw rate calculation unit 101 that calculates a reference yaw rate γ0 determined by the vehicle model and the vehicle state quantity,
A calculator 102 that calculates a residual yaw rate γ _err from the reference yaw rate γ0 and the actual yaw rate γ1 and a steering angle conversion unit 103 that converts the residual yaw rate γ _err into a steering angle equivalent amount to obtain a drift amount D are provided. ing.

The reference yaw rate calculating unit 101 calculates the reference yaw rate γ0 according to the following equation (59) using the steering angle θ, the lateral acceleration Gy, and the vehicle speed V obtained from the wheel speed.

[0193]

[Formula 39]

Here, the gear ratio N, the wheel base L, and the stability factor Kh may be stored in advance. The expression (59) can also be applied to the case of bank traveling.

The calculator 102 is a reference yaw rate calculator 1.
Obtains a residual yaw rate gamma _err from the reference yaw rate γ0 calculated by 01 by subtracting the actual yaw rate .gamma.1, and supplies the residual yaw rate gamma _err on the steering angle conversion unit 103.

The steering angle conversion unit 103 converts the residual yaw rate γ _err supplied from the computing unit 102 into a drift amount D which is a steering angle equivalent amount, and the converted drift amount D is shown in FIG. It is supplied to the straight line calculation unit 92.

The regression line calculation unit 92 calculates the slope α of the regression line represented by the drift amount D with respect to the lateral acceleration Gy. Here, the time series data of the lateral acceleration Gy is Gy.
(I), where D (i) is the time series data of the drift amount D, the average value Gyave of the lateral acceleration Gy and the average value D of the drift amount D for N time series data per unit time.
ave is expressed by the following equations (60) and (61), respectively.

[0198]

[Formula 40]

Next, the average value Gyav of the lateral acceleration Gy.
e, using the average value Dave of the drift amount D,
2), the equation (63) and the equation (64), σx, σy
And σxy are respectively calculated.

[0200]

[Formula 41]

Finally, the slope α of the regression line is calculated according to the equation (65).

[0202]

[Equation 42]

The threshold determination unit 93 sets zero as the threshold, and the slope α of the regression line calculated by the regression line calculation unit 92.
Is above the threshold. Then, when the inclination α exceeds the threshold, that is, when the regression line rises to the right, a determination flag is generated. In the present embodiment, the slope α
However, the present invention is not limited to this, and may be arbitrarily set within a predetermined range including zero.

Then, in the multiplier 56 shown in FIG. 15, when the determination flag is generated by the flag generation unit 57, K _g2 =
The maximum road surface friction coefficient μ _g2 is obtained by setting 1.4 and multiplying the lateral acceleration Gy [G] detected by the vehicle body lateral acceleration sensor 13 by 1.4.

In the case where the judgment flag is not generated, the multiplier 56 is similar to the first embodiment,
The maximum road surface friction coefficient μ _g2 is obtained by multiplying K _g2 determined by the coefficient determination unit 55 by the lateral acceleration.

As described above, according to the second embodiment,
The large road friction coefficient estimating device 1 is
When the lateral acceleration Gy and the drift amount D change suddenly
Even if the drift amount D of the lateral acceleration Gy is changed,
The amount of change, that is, the slope α of the regression line is calculated, and the slope α and threshold
The maximum road friction coefficient during turning μ _g2
Can be accurately estimated. Especially slalom running
The maximum road friction coefficient μ_g2
As a result, the maximum road friction can be estimated.
Coefficient μ_maxThe estimation accuracy of can also be improved.

[Third Embodiment] Next, a third embodiment will be described. The same parts as those in the above-mentioned embodiment are designated by the same reference numerals, and detailed description thereof will be omitted.

The maximum road surface friction coefficient estimating apparatus 1 according to the third embodiment is suitable for use when the tire grip characteristics are in the limit region during acceleration / deceleration traveling or turning traveling.

(1) During Acceleration / Deceleration Travel When the tire grip characteristics are in the limit region during acceleration / deceleration travel, the μ gradient estimation unit 41 of the μ _g1 estimation unit 40 is based on the third method (deceleration motion model). To estimate the torque gradient. The torque gradient is a physical quantity equivalent to the μ gradient,
It is requested as follows. Hereinafter, the braking torque gradient will be described as an example, but the driving torque gradient can be similarly obtained.

A motion equation based on the deceleration motion model is represented by the following formula (66).

[0211]

[Equation 43]

I: the i-th wheel of the vehicle (in the case of a four-wheel vehicle, 1 to
4) K _i : Braking torque gradient τ with respect to the slip speed of the i-th wheel: Sample time J: Wheel inertia ω _i [k]: Wheel speed (angular speed) of the i-th wheel k is a sampling time with the sampling time τ as a unit Yes,
k = 1, 2, 3 ... Further, f _i is expressed by the following equation (6
7) is satisfied.

[0213]

[Equation 44]

[0214] R _c: the effective radius of the wheels M: vehicle mass T _i: y intercept of the case (the ^{`F</sup> <sub>i</sub> the braking force of the i-wheel) brake torque R <sub>c</sub> F <sup><sub>i`</sub>} expressed by a linear function of the slip speed (Note that 1
The slope of the next function is K _i ) T _bi : Brake torque applied to the i-th wheel corresponding to the pedal effort

Then, by applying the online parameter identification method to the equation (66), the braking torque gradient K
_i can be estimated. Specifically, the μ _g1 estimation unit 4
0 executes the processing of the following steps ST1 and ST2.

At step ST1, equations (68) and (69) are calculated.

[0217]

[Equation 45]

At step ST2, the following equation (70) is calculated.

[0219]

[Equation 46]

Here, L _i [k] is given by equation (71), P
_i [k] satisfies Expression (72).

[0221]

[Equation 47]

Λ is a forgetting factor (for example, 0.98),
" ^T " indicates the transpose of the matrix.

The μ gradient estimator 41 can obtain the first element of the matrix θ _i as the braking torque gradient by repeatedly calculating the recurrence formulas of step ST1 and step ST2.

Then, the μ _g1 estimation unit 40 uses the braking torque gradient thus obtained as the μ gradient to estimate the maximum road surface friction coefficient μ _g1 in the same manner as in the above-described embodiment.

The method of calculating the braking torque gradient is not limited to the above equation, and is disclosed in, for example, Japanese Patent Laid-Open No. 10-11.
The method described in Japanese Patent No. 4263 may be used.

(At the time of ABS control) μ _g1 estimation unit 40
During the S control, when the μ gradient becomes equal to or less than a predetermined threshold value,
That is, the maximum road surface friction coefficient μ _g1 may be estimated based on the wheel pressure or the vehicle body deceleration when the braking state near the limit is reached. For example, any of the following expressions (73) to (76) may be used.

[0227]

[Equation 48]

If a wheel cylinder oil pressure P detected by a wheel pressure sensor (not shown), a constant k corresponding to a pad μ for converting the wheel cylinder oil pressure P into a braking force, and a wheel weight W during ABS control are used, The maximum road surface friction coefficient _g1 may be obtained from the following equation (77).

[0229]

[Equation 49]

As a result, the maximum road surface friction coefficient μ _g1 during ABS control can be accurately estimated.
It is possible to obtain _max accurately.

(At the time of TRC control) μ _g1 estimating unit 40
During C control, the longitudinal acceleration (against gravitational acceleration) detected by the vehicle body longitudinal acceleration sensor 12 may be averaged by the LPF, and the averaged lateral acceleration may be set as the maximum road surface friction coefficient _μg1 . As a result, the maximum road friction coefficient μ _g1 during TRC control can be accurately estimated.
It is possible to obtain _max accurately.

(2) During turning, the μ _g2 estimation unit 50 determines the lateral acceleration (gravity acceleration) detected by the vehicle body lateral acceleration sensor 13 during VSC control or when the buzzer for notifying the VSC control sounds. The averaged lateral acceleration may be averaged by the LPF and used as the maximum road friction coefficient μ _g2 . As a result, the maximum road surface friction coefficient μ _g2 during VSC control can be accurately estimated, so that the maximum road surface friction coefficient μ _max can be accurately obtained.

As described above, various embodiments of the present invention have been described. However, the present invention is not limited to these, and various design modifications can be made within the scope of the claims. You may go. In addition, it goes without saying that the respective embodiments may be implemented in any combination.

[0234]

According to the maximum road surface friction coefficient estimating device of the present invention, the first maximum road surface friction coefficient during steady running and the second maximum road surface surface during accelerating or decelerating running or turning running are determined based on the weighting factor determination. By weighting each friction coefficient and estimating the total maximum road surface friction coefficient, it is possible to accurately estimate the maximum road surface friction coefficient by comprehensively considering various running situations.

[Brief description of drawings]

FIG. 1 is a diagram showing a configuration of a maximum road surface friction coefficient estimating device 1 according to a first embodiment.

FIG. 2 is a block diagram showing a functional configuration of an ECU 20.

FIG. 3 is a block diagram showing a functional configuration of a μ _map estimation unit 30.

FIG. 4 is a block diagram showing a functional configuration of a μ gradient estimation unit 31.

FIG. 5 is a block diagram showing a functional configuration of a μ gradient estimating section 32.

FIG. 6 is a diagram showing characteristics of road surface friction force with respect to slip speed.

FIG. 7 is a diagram showing a characteristic of a wheel speed difference between front and rear wheels with respect to a longitudinal acceleration in the case of driving front wheels and braking front wheels.

FIG. 8 is a diagram showing a characteristic of a wheel speed difference between front and rear wheels with respect to longitudinal acceleration in the case of rear wheel driving / rear wheel braking.

FIG. 9 is a diagram showing a characteristic of a wheel speed difference between front and rear wheels with respect to a longitudinal acceleration in the case of rear wheel driving and four-wheel braking.

10A is a diagram showing a first determination map for a summer tire, and FIG. 10B is a diagram showing a second determination map for a studless tire.

FIG. 11 is a block diagram showing a functional configuration of a μ _g1 estimating unit 40.

FIG. 12 is a function K _g1 = stored in a coefficient determination unit 45
It is a figure which shows F1 (Gr1).

FIG. 13 is a block diagram showing a functional configuration of a μ _g2 estimation unit 50.

FIG. 14 is a function K _g2 = stored in a coefficient determination unit 55
It is a figure which shows F2 (Gr2).

FIG. 15 is a block diagram showing a functional configuration of a μ _g2 estimating unit 50A provided in a maximum road surface friction coefficient estimating device according to a second embodiment of the present invention.

FIG. 16 (A) is a diagram schematically showing the relationship between the lateral acceleration and the drift amount when slalom running at 20 [km / h] on a high μ road, and FIG. 16 (B) is a low μ road. 20
It is the figure which showed typically the relationship of the amount of drift with respect to lateral acceleration at the time of slalom running at [km / h].

FIG. 17 is a diagram showing a characteristic of a road surface friction coefficient μ with respect to a slip angle.

FIG. 18 is a block diagram showing a functional configuration of a flag generator 57.

FIG. 19 is a block diagram showing a functional configuration of a drift amount calculation unit 91.

[Explanation of symbols]

1 Maximum road surface friction coefficient estimation device 11FR, 11FL, 11RR, 11RL Wheel speed sensor 12 Vehicle body longitudinal acceleration sensor 13 Vehicle body lateral acceleration sensor 14 Yaw rate sensor 15 Steering angle sensor 20 ECU 30 μ _map estimation unit 40 μ _g1 estimation unit 50 μ _g2 estimation part 60 mu _g selector 70 weight coefficient determining unit 80 mu _max estimator

   ─────────────────────────────────────────────────── ─── Continued front page    (72) Inventor Katsuhiro Asano             Aichi Prefecture Nagachite Town Aichi District             Ground 1 Toyota Central Research Institute Co., Ltd. (72) Inventor Eiichi Ono             Aichi Prefecture Nagachite Town Aichi District             Ground 1 Toyota Central Research Institute Co., Ltd. (72) Inventor Ken Sugai             Aichi Prefecture Nagachite Town Aichi District             Ground 1 Toyota Central Research Institute Co., Ltd. (72) Inventor Shu Asami             Aichi Prefecture Nagachite Town Aichi District             Ground 1 Toyota Central Research Institute Co., Ltd. (72) Inventor Sogura Igaki             1 Toyota Town, Toyota City, Aichi Prefecture Toyota Auto             Car Co., Ltd. (72) Inventor Masakatsu Nonaka             1 Toyota Town, Toyota City, Aichi Prefecture Toyota Auto             Car Co., Ltd. F term (reference) 3D046 BB23 BB28 BB29 HH08 HH21                       HH25 HH26 HH36 HH46 JJ05

Claims (4)

[Claims] 1. A first maximum road surface friction coefficient estimating means for estimating a first maximum road surface friction coefficient during steady traveling, and a second maximum road surface friction coefficient for estimating acceleration / deceleration traveling or turning traveling. Maximum road friction coefficient estimating means, weight coefficient determining means for determining a weight coefficient based on a tire grip degree, and the first and second maximum road surfaces based on the weight coefficient determining means determined by the weight coefficient determining means A maximum road surface friction coefficient estimating device comprising: total maximum road surface friction coefficient estimating means for estimating a total maximum road surface friction coefficient by weighting each friction coefficient. 2. The weighting factor determining means increases the weighting of the first maximum road surface friction coefficient and reduces the weighting of the second maximum road surface frictional coefficient as the tire grip degree increases. A coefficient is determined, and the weighting coefficient is determined such that the weighting of the first maximum road surface friction coefficient is reduced and the weighting of the second maximum road surface friction coefficient is increased as the tire grip degree decreases. The maximum road surface friction coefficient estimating device according to claim 1. 3. The first maximum road surface friction coefficient estimating means is a wheel speed detecting means for detecting a wheel speed, and a μ gradient estimating is for estimating a μ gradient based on the wheel speed detected by the wheel speed detecting means. Means, a vibration level calculation means for calculating a vibration level based on the wheel speed detected by the wheel speed detection means, a μ gradient estimated by the μ gradient estimation means, and a vibration calculated by the vibration level calculation means First estimating means for estimating the first maximum road surface friction coefficient based on the level,
The maximum road surface friction coefficient estimating device according to claim 1, further comprising: 4. The second maximum road surface friction coefficient estimating means includes: acceleration detecting means for detecting acceleration; coefficient determining means for determining a coefficient based on a tire grip degree; and acceleration detected by the acceleration detecting means. 4. A second estimating means for estimating the second maximum road surface friction coefficient based on the coefficient determined by the coefficient determining means and the coefficient determined by the coefficient determining means. The maximum road surface friction coefficient estimating device described in the paragraph.