JPH11295211A - Processor for change reduction and estimation apparatus for road surface state - Google Patents

Abstract

PROBLEM TO BE SOLVED: To estimate a road surface state with good accuracy. SOLUTION: Respective slip-speed computing parts 20 which are installed so as to correspond to a Dry road surface, a Snow road surface and an Ice road surface compute respective slip speeds on the basis of vehicle speeds from vehicle-speed computing parts 18 and on the basis of a braking force from a braking-force detection part 34. Respective μ-grade reference-value creation parts 22 compute, in every prescribed time, the reference value of a road-surface μgrade on the basis of the slip speeds from the slip-speed computing parts 20 and on the basis of a road surface μ computed by the braking force from the braking-force detection part 34. The reference value is stored in reference- value time-series-data storage parts 24. A time-series-data track collation part 14 computes and compares square errors on the basis of the reference value of the road-surface μ grade in every road surface state and on the basis of a very small gain which is computed by a very-small-gain computing part 36 and which is filtered and processed by a very-small-gain filtration part 26 whenever the braking force is changed by a prescribed amount. A road surface state is estimated on the basis of a compared result.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a fluctuation reduction processing device and a road condition estimation device, and more particularly, to a fluctuation reduction processing device applicable to a road condition estimation device, an ABS device, a driving force control device, and the like. The present invention relates to a road surface state estimation device to which a fluctuation reduction processing device is applied.

[0002]

2. Description of the Related Art When a vehicle is running at a certain speed, a slip occurs between a wheel and a road surface when a brake is applied, and a friction coefficient μ between the wheel and the road surface and a formula (1) are used. Slope of friction coefficient μ with respect to slip velocity ΔV,
That is, the road surface μ gradient corresponds to the slip speed ΔV expressed by the equation (1) with respect to each road surface as shown in FIGS.
It is known to change as shown in FIG.

[0003]

ΔV = (V−V _w ) (1) where V is the vehicle speed and V _w is the wheel speed.

[0004] As shown in FIG. 20, it can be seen from the road surface μ gradient-slip speed characteristic that there is a large difference between the road surfaces at the time of braking start at a low slip speed.

Therefore, in the invention described in Japanese Patent Application No. 9-326921, the O.R.
The trajectory of the road μ gradient detected by N-LINE is compared with the prepared μ gradient reference value of each road surface of Dry, Snow and Ice on the braking force P _c −μ gradient plane,
A road surface friction state estimating device for estimating the road surface friction state from the beginning of braking has been proposed.

[0006]

However, (1)
There is a relationship as shown in FIG. 20 between the slip velocity ΔV and the friction coefficient μ in the equation. That is, there is a time difference between the change in the sliding speed and the change in the friction coefficient μ due to the increase in the braking force,
A small hysteresis loop occurs. Therefore, ON
The μ gradient detected by −LINE appears as a large noise component with respect to the braking force on the horizontal axis as shown in FIG.

As a method of removing such noise at every fixed sampling time Δt, a low-pass filter of the formula (2) is widely known.

[0008]

(Equation 2) Here, X is a filtered output state quantity, u is an input state quantity to be filtered, T is a time constant for providing a desired filter effect, and i is a sampling interval updated every Δt.

FIG. 22 shows the result of the filter processing by the equation (2), but the noise is not sufficiently removed. This is because the time-dependent filtering is performed irrespective of the braking force on the horizontal axis. Therefore μ
In the comparison with the gradient reference value, there is a problem that the degree of comparison is reduced and the estimation accuracy of the road surface state is deteriorated.

SUMMARY OF THE INVENTION The present invention has been made in view of the above-described circumstances, and has as its object to provide a fluctuation reduction processing device for accurately estimating a road surface state and a road surface state estimation device to which the fluctuation reduction processing device is applied. And

[0011]

In order to achieve the above object, a first method is provided.
According to the invention, a first detecting means for detecting a first physical quantity representing a braking state or a driving state of a wheel of a vehicle, and the likelihood of slipping of the wheel and varying according to a change in the first physical quantity. Second detection means for detecting a second physical quantity;
And a reduction processing unit configured to reduce a change in the second physical quantity detected by the second detection unit in accordance with a change in the first physical quantity detected by the first detection unit.

The first detecting means detects a first physical quantity representing a braking state or a driving state of the vehicle wheels. Here, the first physical quantity includes a braking force on the wheels and a slip speed.

[0013] The second detecting means detects a second physical quantity indicating the ease of slipping of the wheel. The second physical quantity is, specifically, a coefficient of friction between the road surface and the wheel or a gradient of the braking torque or braking force on the wheel with respect to the slip speed.
That is, when these gradients are high, the peripheral speed of the wheels is high, and slip is difficult. On the other hand, when these gradients are low, the peripheral speed of the wheels is low, and the wheels tend to slip. Thus, these slopes represent the ease with which the wheels slide. By the way, a plurality of parameters for representing the second physical quantity, for example, the relationship between the slip speed and the friction coefficient is determined by the first physical quantity, for example, as described above, as the hysteresis increases and the hysteresis decreases. Has a vortex. Therefore, the second signal detected by the second detector is
The physical quantity of fluctuates greatly according to the change of the first physical quantity.

Considering that such a change is caused by a change in the first physical quantity, the change may be reduced by performing a reduction process in accordance with the change in the first physical quantity.

Therefore, the reduction processing means according to the present invention uses the variation of the second physical quantity detected by the second detection means as:
Reduction processing is performed according to a change in the first physical quantity detected by the first detection means. The reduction processing may be performed, for example, by performing filter processing each time the first physical quantity changes by a predetermined amount, or by performing first processing while the first physical quantity changes by a predetermined amount.
Alternatively, the average of the physical quantities may be obtained.

As described above, the change of the second physical quantity representing the ease of slipping of the wheel due to the change of the first physical quantity representing the braking state or the driving state of the vehicle wheel is changed in accordance with the change of the first physical quantity. Since the reduction processing is performed, the fluctuation of the second physical quantity can be reduced.

The second physical quantity is used in a road surface condition estimation device, an ABS device, a driving force control device, and the like.
It is also applied to a BS device, a driving force control device, and the like.

A second invention in which the first invention is applied to a road surface condition estimating apparatus is as follows.

That is, the second invention is a first invention for detecting a first physical quantity representing a braking state or a driving state of a vehicle wheel.
Detecting means, a second detecting means for detecting the speed of the vehicle, and a value indicating the slipperiness of the wheel and determined according to a road surface condition on which the wheel travels, the first physical quantity, and the speed of the vehicle. Third detection means for detecting a second physical quantity;
Reduction processing means for reducing a change in the second physical quantity detected by the third detection means in accordance with a change in the first physical quantity detected by the first detection means; Calculating means for calculating a second physical quantity for each of a plurality of predetermined road surface conditions based on the first physical quantity detected by the means and the vehicle speed detected by the second detecting means; A trajectory of a second physical quantity reduced by the reduction processing means with respect to a first physical quantity representing a state or a driving state, and a second trajectory calculated for each of a plurality of road surface states by the calculation means with respect to the first physical quantity. Comparing means for comparing each of the trajectories of physical quantities with each other; and, based on a comparison result of the comparing means, a road surface state corresponding to a trajectory closest to the detected trajectory among the calculated trajectories is determined by the vehicle. It comprises an estimation means but is estimated as the actual road surface state running, the.

The first detecting means detects a first physical quantity representing a braking state or a driving state of wheels of the vehicle, the second detecting means detects a speed (vehicle speed) of the vehicle, and a third detecting means. The means detects a second physical quantity representing the ease of slip of the wheel. The second physical quantity is determined according to the road surface state on which the wheels travel, the first physical quantity, and the speed of the vehicle. In particular,
The third physical quantity is, as described above, the coefficient of friction between the road surface and the wheel or the gradient of the braking torque or braking force on the wheel with respect to the slip speed.

As described above, the reduction processing means detects the fluctuation of the second physical quantity by the first detection means which has detected the fluctuation of the second physical quantity.
Is reduced in accordance with the change in the physical quantity of.

The calculating means calculates a second physical quantity for each of a plurality of predetermined road surface conditions based on the first physical quantity detected by the first detecting means and the vehicle speed detected by the second detecting means. Calculate. That is, the calculating means includes a first physical quantity, a third physical quantity for calculating the second physical quantity,
Is determined in advance for each of the plurality of road surface states, and a third physical quantity is calculated for each of the plurality of road surface states based on the detected first physical quantity and the detected vehicle speed. From the third physical quantity obtained, the second
May be calculated. Further, the calculating means obtains in advance a relationship determined according to the vehicle speed between the first physical quantity and the second physical quantity for each of a plurality of road surface conditions, and based on the detected first physical quantity and the detected vehicle speed, obtains a plurality of relations. The second physical quantity may be calculated for each road surface condition.

The comparison means includes a trajectory of the second physical quantity, which has been reduced by the reduction processing means for the first physical quantity representing the braking state or the driving state of the wheel, and a plurality of road surface conditions by the arithmetic means for the first physical quantity. The second calculated by
Is compared with each of the physical quantity trajectories. The estimating means estimates a road surface state corresponding to a trajectory closest to the detected trajectory in the calculated trajectories as an actual road surface state in which wheels are traveling, based on a comparison result of the comparing means. .

As described above, the second physical quantity whose fluctuation has been reduced according to the change of the first physical quantity is detected, and the trajectory of the detected second physical quantity and each of a plurality of road surface conditions are corresponded. Since each of the calculated trajectories of the second physical quantity is compared, the trajectory closest to the detected trajectory among the trajectories calculated corresponding to each of the plurality of road surface conditions is selected based on the comparison result. Then, the road surface state corresponding to the selected trajectory corresponds to the actual road surface state where the wheels are traveling, and the actual road surface state can be accurately estimated.

Incidentally, the second physical quantity includes an error due to various causes.

Therefore, the first detecting means determines the first physical quantity, the second detecting means determines the vehicle speed, the third detecting means determines the second physical quantity, and the predetermined value from when the braking of the wheels is started. The detection means is detected every time, and the calculation means calculates the second physical quantity for each of the plurality of road surface conditions each time the first physical quantity changes by a predetermined amount after the braking of the wheel is started.

Each time the first physical quantity changes by a predetermined amount from the time when braking is started on the wheel, the comparing means detects the second physical quantity and the plurality of road surface conditions detected at the predetermined time intervals. Each time and each time the first physical quantity changes by a predetermined amount, the second physical quantity calculated may be compared.

As described above, each time the first physical quantity changes by a predetermined amount from the time when braking is started on the wheels, the second physical quantity detected at a predetermined time interval and the plurality of road surface conditions are determined. Since each physical quantity calculated every time the first physical quantity changes by a predetermined amount is compared, the influence of an error can be reduced, and the road surface state can be estimated more accurately.

[0029]

Embodiments of the present invention will be described below in detail with reference to the drawings.

As shown in FIG. 1, a road surface state estimating apparatus (to which a fluctuation reduction processing apparatus is applied) according to this embodiment is
A wheel speed sensor 28 for detecting a wheel speed, a braking force detecting unit 34 for detecting a braking force acting on a wheel, and a small gain calculating unit 3 for calculating a small gain to be described later as a road surface μ gradient.
6, and an arithmetic circuit 1 for calculating the road surface condition and the slip speed
0 is provided.

The arithmetic circuit 10 determines a predetermined road surface condition, that is, a road surface μ corresponding to Dry, Snow, Ice.
A reference value creation storage circuit 12D, 12S, 12I for creating and storing a reference value of the gradient each time the braking force detected by the braking force detection unit 34 changes by a predetermined amount, and each time the braking force changes by a predetermined amount. Small gain storage circuit 12 for storing small gain
J. Reference value creation storage circuits 12D, 12
The S, 12I and the minute gain storage circuit 12J are provided with the time series data of the reference value of the road surface μ gradient stored in the reference value creation storage circuits 12D, 12S, 12I, and are actually detected and provided with a filter effect described later. Micro gain storage circuit 12
Time-series data of the reference value of the road μ gradient stored in J;
And a time-series data trajectory matching unit 14 that calculates the road surface state by comparing the two. Time-series data trajectory matching unit 14
Is connected to a slip speed selector 16.

Reference value creation storage circuits 12D, 12S, 12
Since I has the same configuration, the reference value creation storage circuit 1
Only 2D will be described, and other description will be omitted. The reference value creation storage circuit 12D includes a vehicle speed calculation unit 18 connected to the wheel speed sensor 28. A slip speed calculator 20 for calculating the slip speed is connected to the vehicle speed calculator 18.
A braking force detector 34 is connected to the slip speed calculator 20. The slip speed calculation unit 20 is connected to a μ gradient reference value creation unit 22 that creates a reference value of a road surface μ gradient. The vehicle speed calculator 18 and the braking force detector 34 are connected to the μ gradient reference value generator 22. The μ slope reference value creation unit 22 is connected to a reference value time series data storage unit 24 that stores time series data of the road surface μ slope reference value created by the μ slope reference value creation unit 22. The reference value time-series data storage unit 24 is connected to the time-series data track collation unit 14.

The minute gain storage circuit 12J includes a minute gain filter section 26 for performing a filtering process on the calculated minute gain each time the braking force changes by a predetermined amount, and a minute gain filtered by the minute gain filter section 26. And a small-gain time-series data storage unit 25 for storing the time-series data. The braking force detection unit 34 and the small gain calculation unit 36 are connected to the small gain filter unit 26. The minute gain filter unit 26 is connected to the minute gain time-series data storage unit 26. The minute gain time series data storage unit 25 stores the time series data trajectory comparison unit 1
4 is connected.

The slip speed selection unit 16 includes the respective slip speed calculation units 2 of the reference value creation storage circuits 12D, 12S, and 12I.
0 is connected.

The braking force detector 34 estimates a braking force acting as a frictional force from the road surface to the wheels as follows according to a dynamic model of the wheels.

[0036] That is, the wheel, and the brake torque T _B acting in the direction opposite to the rotational direction of the wheel to the wheel, and tire torque T _f by the braking force F acting in the rotational direction of the wheel as a frictional force to the wheel , Works. Brake torque T _B is derived from the braking force which acts to prevent rotation of the wheel relative to the wheel of the brake disc, the braking force F and the tire torque T _f is the friction coefficient between the wheel and the road surface When μ _B , the wheel radius is r, and the wheel load is W, it is expressed by the equations (3) and (4).

[0037]

## EQU3 ## F = μ _B W (3)

[0038]

T _f = F × r = μ _B Wr (4) Therefore, the equation of motion of the wheel is

[0039]

(Equation 5) Becomes Here, I is the moment of inertia of the wheel, and ω is the rotational speed (wheel speed) of the wheel.

[0040] detecting the wheel acceleration (d [omega / dt), by obtaining a brake torque T _B based on the wheel cylinder pressure applied to the brake disc, it is possible to estimate the braking force F based on the equation (5). Specifically, a dynamic model equivalent to the equation (5) in which the driving torque of the wheel determined from the accelerator opening and the like and the braking force F as a disturbance acts on the wheel is configured as an observer. In this observer,
The disturbance and rotation speed of the equivalent model are corrected and corrected for each control cycle so that the deviation between the rotation position obtained by performing the second-order integration of Equation (5) and the rotation position actually detected is equal to zero. The estimated disturbance is estimated as a braking force.

Next, the small gain calculating section 36 will be described.
First, as shown in FIG. 16, a description will be given of a small gain as a road surface μ gradient, which is a gradient of a friction coefficient between a road surface and a wheel with respect to a slip speed (see equation (1)).

A vehicle having a body weight W has a vehicle speed ω _v
The vibration phenomenon at the wheels when traveling in, that is, the vibration phenomenon of the vibration system composed of the vehicle body, wheels and the road surface,
This will be described with reference to a model shown in FIG. 3 which is equivalently modeled by a wheel rotation axis.

In the model of FIG. 3, the braking force acts on the road surface via the surface of the tread 115 of the tire in contact with the road surface. However, this braking force actually acts on the vehicle body as a reaction (braking force) from the road surface. For this reason, the equivalent model 117 in terms of the rotation axis in terms of the vehicle body weight is a friction element 116 (road surface μ) between the tread of the tire and the road surface.
And connected to the opposite side of the wheel 113 via the. This is the same as the large inertia under the wheels, that is, the weight of the vehicle body can be simulated by the mass on the side opposite to the wheels, as in the chassis dynamo device.

In FIG. 3, the inertia of the wheel 113 including the tire rim is represented by J _w , and the spring element 11 between the rim and the tread 115 is represented by J _w .
4, the spring constant is K, the wheel radius is R, the inertia of the tread 115 is J _t , and the friction element 11 between the tread 115 and the road surface.
The friction coefficient of the 6 mu, the inertia of the equivalent model 117 of the body weight of the rotary shaft conversion and J _V, the transfer characteristic from the braking torque T _b 'caused by the wheel cylinder pressure to the wheel speed omega _w is the wheel motion From the equation,

[0045]

(Equation 6) Becomes Note that s is an operator of Laplace transform. Here, when the tire is gripping the road, tread 1
15 and the vehicle equivalent model 117 are considered to be directly connected. In this case, the vehicle equivalent model 117 and the tread 115
And the inertia of the wheel 113 resonates. That is, this vibration system can be considered as a wheel resonance system including the wheels, the vehicle body, and the road surface. The resonance frequency ω∞ of the wheel resonance system at this time is given by

[0046]

(Equation 7) Becomes

Here, the friction state in which the expression (7) holds in FIG. 16 corresponds to the area A1 before reaching the peak μ.

Conversely, when the friction coefficient μ of the tire approaches the peak μ, the friction coefficient μ of the tire surface hardly changes with respect to the slip ratio. That is, the component accompanying the inertial vibration of the tread 115 does not affect the vehicle equivalent model 117. That is, the tread 115 is equivalent to the vehicle equivalent model 1
17 is separated, and the tread 115 and the wheel 113 resonate. At this time, the wheel resonance system can be regarded as being composed of wheels and a road surface. The resonance frequency ω ′ ′ is equal to the value obtained by setting the vehicle equivalent inertia J _v to 0 in the equation (7). That is,

[0049]

(Equation 8) Becomes This state corresponds to the region A2 near the peak μ in FIG. When the brake is applied beyond the peak μ, the operation instantaneously shifts to the area A3, and the tire is locked.

[0050] Suppose the body equivalent inertia J _v is the wheel inertia J _w, greater than the tread inertia J _t. In this case, the resonance frequency ω∞ ′ of the wheel resonance system in the case of Expression (8) is ω in Expression (7).
The frequency is shifted to a higher frequency side than ∞.

[0051] Here, when the micro exciting the braking force at the wheel and the vehicle body and the road surface and the vibration system of the resonance frequency ω∞ consisting ((8)) (in this case, a finely excite the brake pressure P _b), the wheels The speed ω _w also minutely vibrates around the average wheel speed at the resonance frequency ω∞. Here, small amplitude P _v of the resonance frequency ω∞ brake pressure P _b in this case, if the small amplitude of the resonance frequency ω∞ wheel speed to omega _wv, the fine gain G _d

[0052]

G _d = ω _wv / P _v (9) It should be noted that this small gain G _d is used as the brake pressure P _b
The ratio of the wheel speed ω _w with respect to (ω _w / P _b) of the resonance frequency ω
Considering the vibration component of ∞,

[0053]

G _d = ((ω _w / P _b ) | s = jω∞) (10)

Since the minute gain G _d is a vibration component of the resonance frequency ω∞ of (ω _w / P _b ) as shown in the equation (10), when the frictional state reaches a region near the peak μ,
Since the resonance frequency shifts to ω∞ ′, it sharply decreases.
That is, it can be said that the minute gain _Gd is a physical quantity that defines the road surface μ characteristic.

[0055] Then, fine gain calculation unit 36, as shown in FIG. 2, when the micro-exciting the brake pressure in the vibration system of the resonance frequency ω∞ ((7) expression), the resonance frequency of the wheel speed V _w omega The wheel speed minute amplitude detecting unit 40 for detecting the minute amplitude of ∞ (wheel speed minute amplitude ω _wv ), and the brake pressure minute amplitude detecting unit 42 for detecting the minute amplitude P _v of the brake pressure at the resonance frequency ω∞ are detected. was composed of wheel speed small amplitude omega _wv a divider 44 which outputs the micro-gain G _d is divided by the braking pressure differential small amplitude P _v, from.

Here, the wheel speed minute amplitude detecting section 40 can be realized as an arithmetic section as shown in FIG. 4 for performing a filtering process for extracting a vibration component of the resonance frequency ω∞. For example, since the resonance frequency ω∞ of this vibration system is about 40 [Hz],
Considering the controllability, one cycle is 24 [ms], about 41.7.
[Hz], and a band-pass filter 75 having this frequency as a center frequency is provided. This filter only the frequency component of about 41.7 [Hz] from near the wheel speed signal omega _i is extracted. Further, the filter output is full-wave rectified by a full-wave rectifier 76 and DC-smoothed, and only a low-frequency vibration component is passed from the DC-smoothed signal by a low-pass filter 77 to output a wheel speed minute amplitude _ωwv . I do.

Incidentally, an integral multiple of the cycle, for example, 24 of one cycle
[Ms], the wheel speed minute amplitude detection unit 40 can also be obtained by continuously taking in time series data of 48 [ms] in two cycles and obtaining a correlation with a unit sine wave and a unit cosine wave of 41.7 [Hz]. Can be realized.

[0058] Here, a description will be given small excitation means for applying a brake pressure differential small amplitude P _v of the resonance frequency around the mean braking pressure P _m. First, the part (valve control system) that converts the average brake pressure command and the minute excitation command into actual braking torque for the wheels is, as shown in FIG. 5, a master cylinder 48, a control valve 52, a wheel cylinder 56, and a reservoir 58. And an oil pump 60.

The brake pedal 46 is a brake pedal 4
6 is connected to a pressure increasing valve 50 of a control valve 52 via a master cylinder 48 which increases the pressure in accordance with the pedaling force. The control valve 52 is connected to a reservoir 58 as a low pressure source via a pressure reducing valve 54. Further, a wheel cylinder 56 for applying the brake pressure supplied by the control valve to the brake disc is connected to the control valve 52. This control valve 52 is
The opening and closing of the pressure increasing valve 50 and the pressure reducing valve 54 are controlled based on the input valve operation command.

The control valve 52 is connected to the pressure increasing valve 5
0 is controlled to open only the wheel cylinder 5
The hydraulic pressure (wheel cylinder pressure) of the master cylinder 48 (master cylinder pressure) is proportional to the pressure obtained when the driver depresses the brake pedal 46.
To rise. Conversely, when the pressure is controlled so as to open only the pressure reducing valve 54, the wheel cylinder pressure decreases to the pressure of the reservoir 58 (reservoir pressure), which is approximately atmospheric pressure. Further, when both valves are controlled to be closed, the wheel cylinder pressure is maintained.

The braking force (corresponding to the wheel cylinder pressure) applied to the brake disk by the wheel cylinder 56 includes a pressure increasing time during which the high hydraulic pressure of the master cylinder 48 is supplied, a pressure reducing time during which the low hydraulic pressure of the reservoir 58 is supplied,
And the ratio of the holding time during which the supply oil pressure is held, and the master cylinder pressure and the reservoir pressure detected by a pressure sensor or the like.

Accordingly, a desired brake torque can be realized by controlling the increasing / decreasing time of the control valve 52 in accordance with the master cylinder pressure. Further, the minute excitation of the brake pressure can be performed by performing the pressure increase / decrease control at a cycle corresponding to the resonance frequency simultaneously with the pressure increase / decrease control of the control valve 52 for realizing the average braking force.

FIG. 6 shows specific control contents.
As described above, a half period of the period of the minute excitation (for example, 24 [ms])
Each mode of pressure increase and pressure reduction is switched every T / 2,
The pressure increase / decrease command to the valve increases from the moment the mode is switched.
Pressure time t_i, Decompression time t_rPressure for each time
・ Output the pressure reduction command and output the hold command for the remaining time.
You. The average braking force is increased according to the master cylinder pressure.
Time t_iAnd decompression time t _rIs determined by the ratio of
Pressure increase / decrease mode every half cycle T / 2 corresponding to resonance frequency
Switch, the micro-vibration around the average braking force
Motion is applied.

[0064] Incidentally, the brake pressure differential small amplitude P _v is the master cylinder pressure, the length of the pressure increasing time t _i of the valve shown in FIG. 6, and so determined in a predetermined relationship with the length of the decompression time t _r, Figure second brake pressure differential small amplitude detector 42 may be a master cylinder pressure, the pressure increasing time t _i and the pressure reducing time t _r constituting a table for outputting the brake pressure differential small amplitude P _v.

Next, the operation of the present embodiment will be described.

First, the reference value creation storage circuits 12D, 12D
A method of creating the reference value of the road μ gradient by S and 12I will be described.

The road surface μ gradient G _d is a gradient of the friction coefficient μ between the road surface and the wheels with respect to the slip speed ΔV, and is expressed by the equation (11) for each braking force, road surface condition, and vehicle speed.

[0068]

[Equation 11] Therefore, the road surface mu gradient G _d can be specified braking force Pc, road conditions, vehicle speed V, the sliding velocity [Delta] V, and the friction coefficient mu.

It is known that the slip speed ΔV and the braking force P have a specific relationship according to the vehicle speed as shown in FIGS. 7 to 9 according to each road surface condition.

Therefore, the reference value creation storage circuits 12D and 12D
Each of the slip speed calculation units 20 of S and 12I stores a relational expression between the braking force P and the slip speed ΔV shown in FIGS. 7 to 9 according to the vehicle speed. Accordingly, when the braking force Pc and the vehicle speed V are input, the slip speed calculation unit 20 calculates the braking force Pc and the vehicle speed V based on the relational expression, the braking force Pc and the vehicle speed V.
Is calculated, and the slip speed ΔV obtained by the calculation is output to the μ gradient reference value creation unit 22.

The vehicle speed V input to the slip speed calculating section 20 is calculated by the vehicle speed calculating section 18.
Specifically, it is obtained as follows. The vehicle speed calculation unit 18
The slip speed ΔV calculated by the slip speed calculator 20 and the wheel speed Vω from the wheel speed sensor 28 are input. The slip speed ΔV is a value obtained by subtracting the wheel speed Vω from the vehicle speed V (V−Vω). Therefore, the vehicle speed calculation unit 18
Is the input slip speed ΔV and the input wheel speed Vω
Are added to obtain the vehicle speed V, which is output to the slip speed calculator 20.

Further, it is known that the road surface μ and the braking force P have a specific relationship according to the vehicle speed as shown in FIGS. 10 to 12 according to each road surface condition.

Therefore, the reference value creation storage circuits 12D, 12D
Each μ gradient reference value creation unit 22 of S and 12I stores a relational expression between the braking force P and the road surface μ shown in FIGS. 10 to 12 according to the vehicle speed. Therefore, when the braking force Pc and the vehicle speed V are input, the μ gradient reference value creation unit 22 calculates the road surface μ corresponding to the braking force Pc and the vehicle speed V based on the above relational expression, the braking force Pc and the vehicle speed V. .

As described above, the μ gradient reference value creation unit 22 sends the braking force Pc and the vehicle speed V
Velocity ΔV (2 points, ΔV ₁ , ΔV
₂ ) is input and the road surface μ (2 points, μ ₁ , μ ₂ ) corresponding to the braking force Pc and the vehicle speed V is calculated. Therefore, the μ gradient reference value creation unit 22 calculates the slip speed Δ
The V and the road surface mu, by substituting the equation (11) calculates the reference value of the road surface mu gradient G _d. Then, from the wheel speed sensor 28 and the braking force detector 34, the wheel speed Vω and the braking force _Pc sampled at predetermined sampling times from the start of braking to the elapse of a predetermined time are input, and the μ gradient reference value generator 22 until a predetermined amount a braking force has elapsed from the start of braking calculates the reference value of the road surface μ gradient G _d each time a predetermined amount the braking force is changed, the time-series data of the reference value for each road surface μ gradient G _d, The reference value is output to the time-series data storage unit 24. The reference value time-series data storage unit 24 stores the road surface μ gradient G _d
The time series data of the reference value is stored.

The minute gain time series data storage unit 25
The time series data of the minute gain is stored in synchronization with the output timing of the time series data of the reference value from the μ gradient reference value creation unit 22 to the reference value time series data storage unit 24. That is, every time the predetermined amount of braking force changes from the start of braking until the aforementioned predetermined amount of braking force has elapsed, the minute gain filtered by the small gain filter unit 26 is stored in the minute gain time-series data storage unit 25. _GdT is input, and the input small gain _GdT is stored.

That is, the problem of the prior art is that the filtering process is performed for each sampling interval. Therefore, if the filtering process is performed only when the braking force changes by a predetermined amount, the variation in the locus in FIG. 22 is reduced. Specifically, a filtering process represented by the following equation similar to the equation (2) is performed.

[0077]

(Equation 12) Here, Tp is a constant corresponding to the time constant T in the equation (12), ΔPc is a predetermined amount of braking force, and k is a step updated every time the predetermined braking force changes by a predetermined amount. The expression (12) may be expanded and expressed as the following expression.

[0078]

(Equation 13) Here, X is a variable vector (size is n × 1) of the internal state of the filter, a11-ann, b1-bn, c11-
c1n and d1 to dn are each constant of the filter. FIG.
Next, the filter effect of Expression (12) is compared with that of Expression (2). As shown in FIG. 23, the variation is suppressed to be small as compared with the result of the filter processing of the equation (2), and at each point of the braking force,
It can be seen that the average value of the results of the equation is obtained. However, at the time of the start of the braking force (around the braking force 0), the result of (1) follows the maximum value instead of the average value. This is T
This indicates a state of an excessive response related to the magnitude of p.

Instead of the filter processing as in the equations (12) and (13), the average value during the time when the braking force changes by a predetermined amount may be obtained as in the following equation.

[0080]

[Equation 14] Here, m is the number of minute gain points sampled from k to k + 1, x (k) is the average value of the minute gain at the time point k, and j is the step that is initialized to 1 at the time point k and updated at every sampling step. It is.

Next, a collation processing routine executed by the time-series data trajectory collation unit 14 at the start of braking will be described with reference to FIG.

In step 82 of FIG. 13, a variable k representing the time at which the braking force has changed by a predetermined amount from the start of braking is initialized,
In step 84, the variable k is incremented by one.

At step 86, at the time represented by the variable k (k · τ, τ: an arbitrary multiple of the sampling period), the road surface μ gradient G _d T (k ) (Small gain). This road surface μ gradient G _d T (k) is, as described above, filtered by the small gain filter unit 26 based on the expression (12) or (13).

At step 88, a variable s for identifying the road surface condition is initialized, and at step 90, the variable s is incremented by one.

[0085] In step 92, the time of the series data of the road surface μ gradient G _d stored in the reference value time series data 24 corresponding to the road surface condition s, captures the reference value G _d s at time k (k).

In step 94, the collation value z _s is calculated from equation (15).

[0087]

Z _s = z _s + (G _d T (k) −G _d s (k)) ² (15) In step 96, the variable s is a predetermined total number of road surface conditions s ₀ ( In the present embodiment, it is determined whether 3) or more. If not the variable s ≧ total number s _0, since there is a road surface condition that is not computed verification value z _s at time k, returns to step 90 to execute the above processing (step 90 step 96). When the variable s ≧ the total number s ₀ , the collation values z _s at all times k of the road surface state have been calculated. Judge. Variable k ≧
If it is not n, since the predetermined time has not elapsed since the start of braking, the flow returns to step 84 and the above processing (step 8)
4 to step 98) are executed. If the variable k ≧ n, since a predetermined time has elapsed since the start of braking, step 10
0, the minimum collation value z of the collation values z _s (z _{1 to} z ₃ )
It is estimated that the road surface state corresponding to _s is the current road surface state.

That is, z _s is represented by equation (16).

[0089]

(Equation 16) As described above, z _s is calculated based on the time-series data from the start of braking to the lapse of a predetermined time because, as shown in FIG. 14, the road surface μ gradient G _d T ( k) is the road surface μ of the current road surface condition
It does not always coincide with the gradient G _ds (k) (in FIG. 14, the road surface state is Snow), and fluctuates around the reference value of the road surface μ gradient G _{ds of} the current road surface state. Therefore, the road surface condition is accurately estimated using the time-series data from when the braking starts to when the predetermined amount of braking force has elapsed.

[0090] Then, from among the peak values mu _MAX friction coefficient stored in advance in correspondence with each road surface condition, the peak value mu _MAX of the friction coefficients corresponding to the estimated road surface condition, not shown ABS control unit, etc. And outputs data representing the estimated road surface condition to the slip speed selection unit 16.

The ABS control unit which has input the peak value μ _MAX from the time-series data trajectory collation unit 14 inputs the peak value μ _MAX
The slip ratio giving _MAX is set as a reference slip ratio, and the braking force is controlled so as to follow the peak μ.

The slip speed selection unit 16 which has received the data representing the road surface condition from the time-series data trajectory collation unit 14 determines the slip speed output from the slip speed calculation unit 20 of each of the reference value creation storage circuits 12D, 12S and 12I. The slip speed ΔV corresponding to the estimated road surface state is output based on the input data representing the road surface state.

As described above, in the present embodiment, the road surface condition and the peak μ value of the road surface on which the vehicle is currently traveling can be accurately obtained, so that the vehicle stabilization control (VSC, ABS, TRC, etc.) Change, setting of a control target value), warning of a road surface condition to a driver, and estimation of various vehicle conditions such as a vehicle body slip angle and a yaw rate.

In the embodiment described above, the small gain is filtered using the equation (12) or the equation (13). However, the small gain G _d T output every sampling period is changed from the variable k to k + 1. May be averaged.

The braking force P and the road surface μ shown in FIGS.
Since the gradient has the relationship shown in FIG. 15, the relational expression shown in FIG. 15 is stored in correspondence with the vehicle speed V, and the vehicle speed V and the braking force P are stored.
_The road surface μ gradient corresponding to the vehicle speed V and the braking force _Pc may be calculated from _c and the relational expression between the braking force _Pc and the road surface μ gradient.

In the above-described embodiment, the road surface μ
Arrangement G_dT (k) and reference value G_ds (k)
Although the road surface condition is estimated using small values, the present invention
It is not limited, and the road surface
Arrangement G_dT (k) and reference value G _dTime series data of s (k)
Calculate the correlation of and estimate the road surface condition
Well.

Further, in the above-described example, the relational expression between the braking force P and the slip velocity ΔV shown in FIGS.
Relationship between the braking force P and the road surface μ shown in, or has provided to store the relationship between the braking force P and the road surface μ gradient G _d shown in FIG. 15, the present invention is not limited thereto, and the sliding velocity [Delta] V, sliding velocity [Delta] V, the road surface mu, also each of the relationship between the road surface mu gradient G _d, braking force and P, sliding velocity [Delta] V, the road surface mu,
Since the same as the respective relations of the relationship between the road surface mu gradient G _d, and the sliding velocity [Delta] V, sliding velocity [Delta] V, the road surface mu, stores a relational expression between the road surface mu gradient G _d, so as to similarly treated Is also good.

[0098] In the example described above, as the road surface μ gradient G _d, but seeking small gains, the present invention is not limited thereto, since small gain is braking torque gradient equivalent physical quantity, small gain instead of the calculation unit 36 includes a braking torque gradient calculation unit, instead of the road surface μ gradient G _d, seeking braking torque gradient may be treated similarly. Hereinafter, it will be described that the small gain is a physical quantity equivalent to the braking torque gradient.

In the characteristics shown in FIGS. 19 and 16, a functional relationship is established between the slip speed ΔV and the friction coefficient μ between the wheel and the road surface, in which the friction coefficient μ has a peak at a certain slip speed.

When the brake pressure is slightly excited,
Since the wheel speed slightly excites, the slip speed also slightly vibrates around a certain slip speed. Here, consider a change in the friction coefficient μ with respect to the slip speed ΔV when the vehicle slightly vibrates around a certain slip speed on a road surface having the characteristics shown in FIG.

At this time, the friction coefficient μ of the road surface is

[0102]

## EQU17 ## μ = μ ₀ + αRΔω (17) That is, since the change in the slip speed due to the minute vibration is small, it can be approximated by a straight line having the slope αR.

Here, when the equation (18) is substituted into the braking torque T _b = μWR generated by the friction coefficient μ between the tire and the road surface,

[0104]

The [number 18] _{T b = μWR = μ 0 WR} + αR 2 ΔωW ··· (18). Here, W is a wheel load. First-order differentiation of both sides of equation (18) with Δω gives:

[0105]

[Equation 19]Get. Therefore, the braking torque gradient is calculated by the equation (19).
(DT_b/ Δω) becomes αR ^TwoIs shown to be equal to W
Was.

On the other hand, since the brake torque T _b ′ is proportional to the brake pressure P _b , the small gain G _d is determined by the ratio of the wheel speed ω _{w to} the brake torque T _b ′ (ω _w /
T _b ′) is proportional to the vibration component of the resonance frequency ω∞. Therefore, the small gain G is obtained by the transfer characteristic of the equation (6).
_d is represented by the following equation.

[0107]

(Equation 20) However,

[0108]

J _A = J _t + J _v + J _w , J _B = J _t + J _v (21)

[0109]

(Equation 22) Generally, in equation (22),

[0110]

| A | = 0.012 << | B | = 0.1 (23) From the expressions (19) and (20),

[0111]

(Equation 24) Get. That is, the gradient (the braking torque gradient) of the braking torque T _b with respect to the slip speed ΔV is proportional to the micro-gain G _d.

Therefore, the braking torque gradient may be determined, and the same processing as described above may be performed based on the determined braking torque gradient.

Next, a method for obtaining the braking torque gradient will be described.

The wheel motion and the body motion of each wheel are represented by (2
It is described by the equations of motion of equations (5) and (26).

[0115]

(Equation 25)

[0116]

(Equation 26) Here, F _i ′ is the braking force generated on the i-th wheel, T _bi is the braking torque applied to the i-th wheel corresponding to the pedaling force, M is the vehicle mass, R _c is the effective radius of the wheel, and J is the wheel The inertia, v, is the vehicle speed (see FIG. 8). In addition, * shows differentiation with respect to time. In equations (25) and (26), F _i ′
Is shown as a function of the slip speed (v / R _c -ω _i ).

[0117] Here, the vehicle speed represented by an equivalent body of the angular velocity omega _v with ((27)), the braking torque R
_c F _i ′ is described as a linear function (slope k _i , y-intercept T _i ) of the slip speed (Equation (28)).

[0118]

V = R _c ω _v (27)

[0119]

Equation 28] _{R c F i '(ω v} -ω i) = k i × (ω v -ω i) + T i ··· (28) Further, (27), a (28), (25 ) Formula, (2
6), the wheel speed ω _i and the vehicle speed ω _v are discretized for each sample time τ, and the time series data ω _i [k], ω
_v [k] (k is a sample time in units of the sample time τ, k = 1, 2,...), the expression (29), (3
0) is obtained.

[0120]

(Equation 29)

[0121]

[Equation 30] Here, when equations (29) and (30) are simultaneously set and the equivalent angular velocity ω _{v of the} vehicle body is eliminated,

[0122]

(Equation 31) Get.

Assuming that a maximum braking torque of R _c Mg / 4 (g is gravitational acceleration) is generated under the condition of a slip speed of 3 rad / s,

[0124]

(Equation 32) Get. Here, as a specific constant, τ = 0.01 (se
c), R _c = 0.3 (m) and M = 1000 (kg)

[0125]

[Equation 33] Equation (31) can be approximated as the following equation.

[0126]

(Equation 34) However,

[0127]

(Equation 35) It is.

[0128] (35) about T _b in the formula, the brake hydraulic pressure (master cylinder pressure or wheel cylinder pressure) If can be measured, can be placed as follows.

[0129]

T _b = k _b · P _c (k) (36) where P _c is a brake oil pressure, and k _b is a conversion coefficient from the brake oil pressure to the brake torque. In this case, (34)
Equation (35) is converted to Equation (37) and Equation (38).

[0130]

(37)

[0131]

(38) However,

[0132]

[Equation 39] Here, the brake oil pressure P at the current sampling time k
_{It is} assumed that _c (k) and the brake oil pressure _Pc (k-1) at the time point k-1 one sampling before are almost equal ( _Pc (k) = _Pc (k-1)).

As the μ gradient k _j in the equation (39), the previous estimated value is used.

By organizing in this way, (37)
The equation can be described in a linear form with respect to the unknown coefficients k _i and f _i , and by applying an online parameter identification method to the equation (37), the braking torque gradient k _i with respect to the slip speed can be estimated. Can be.

That is, by repeating the following steps 1 and 2, time series data of the braking torque gradient is obtained from the time series data ω _i [k] of the detected wheel speed and the brake oil pressure P (i) [k]. Can be estimated (least squares estimation method).

Step 1:

[0137]

Φ _i [k] ^T · θ _i = y _i [k] (40) where

[0138]

[Equation 41]

[0139]

(Equation 42)

[0140]

Y _i [k] = − ω _i [k] + 2ω _i [k−1] −ω _i [k−2] −P _ci (k) (43) The first element of the matrix φ _i [k] in the equation (41) is a physical quantity related to the change in the wheel speed in one sample time, and the equation (43) is one sample in the change in the wheel speed in one sample time. It is a physical quantity related to change with time.

Step 2:

[0142]

[Equation 44]

[0143]

[Equation 45]

[0144]

[Equation 46] It calculates the estimated value of theta _i from recurrence formulas that are extracted as the gradient of the braking torque of the first element was estimated matrix estimate of theta _i. Here, λ is a forgetting coefficient (for example, λ = 0.98) indicating the degree of removing past data, and “ ^T ”
Indicates the transpose of a matrix.

The left side of the equation (44) is a physical quantity representing the history of the physical quantity related to the change in the wheel speed and the history of the physical quantity related to the change in the wheel speed.

As shown in FIG. 23, the trajectory of the minute gain filtered by the equation (12) has a smaller variation at each braking force point than the trajectory filtered by the equation (2). I understand.

Then, as shown in FIGS.
For each of the road surfaces y and Snow,
Equation (12) obtained by multiplying the obtained braking torque gradient by a conversion coefficient.
Road μ gradient G filtered by_dTime series of
TA G_dT₁Is the road surface μ gradient G _dTime series data of the reference value of
G_dsWith high accuracy and closer to the trajectory of the actual road surface
Therefore, the estimation accuracy of the road surface condition is improved.

In the embodiment described above, the braking force on the wheel is used as the first physical quantity representing the braking state or the driving state of the wheel of the vehicle. However, the present invention is not limited to this, and the slip speed is used. be able to.

Further, in the above-described embodiment, the fluctuation reduction processing device is applied to the road surface state estimation device. However, the present invention is not limited to this, and the fluctuation reduction processing device can be applied to the ABS device.

[0150]

As described above, according to the first aspect, the variation of the second physical quantity representing the ease of slipping of the wheel due to the change of the first physical quantity representing the braking state or the driving state of the vehicle wheel is described. Since the reduction processing is performed in accordance with the change in the first physical quantity, there is an effect that the fluctuation in the second physical quantity can be reduced.

According to a second aspect of the present invention, a second physical quantity is detected, and a variation of the detected second physical quantity according to the change of the first physical quantity is reduced according to the change of the first physical quantity. Since the trajectory of the second physical quantity subjected to the reduction processing is compared with each of the trajectories of the second physical quantity calculated corresponding to each of the plurality of road surface states, the plurality of road surface states are respectively determined based on the comparison result. If the trajectory closest to the detected trajectory is selected from among the trajectories calculated corresponding to the road, the road surface state corresponding to the selected trajectory corresponds to the actual road surface state where the wheels are traveling, and the actual road surface state This has the effect that the state can be accurately estimated.

[Brief description of the drawings]

FIG. 1 is a block diagram of a road surface state estimation device according to the present embodiment.

FIG. 2 is a block diagram of a minute gain calculation unit.

FIG. 3 is a diagram showing an equivalent model of a vibration system composed of wheels, a vehicle body, and a road surface.

FIG. 4 is a block diagram of a wheel speed minute amplitude detection unit.

FIG. 5 is a block diagram of a brake pressure minute amplitude detection unit.

FIG. 6 is a diagram showing a command to a control valve in a case where micro excitation of a brake pressure and control of an average braking force are simultaneously performed.

FIG. 7 is a diagram showing a relationship between a braking force and a slip speed at a predetermined speed in a Dry road surface state.

FIG. 8 is a diagram showing a relationship between a braking force and a slip speed at a predetermined speed in a Snow road surface state.

FIG. 9 is a diagram illustrating a relationship between a braking force and a slip speed at a predetermined speed in an Ice road surface state.

FIG. 10 is a diagram showing a relationship between a braking force and a road surface μ at a predetermined speed in a Dry road surface state.

FIG. 11 is a diagram showing a relationship between a braking force and a road surface μ at a predetermined speed in a Snow road surface state.

FIG. 12 is a diagram showing a relationship between a braking force and a road surface μ at a predetermined speed in an Ice road surface state.

FIG. 13 is a flowchart illustrating a collation processing routine executed by the time-series data trajectory collation unit at the start of braking.

FIG. 14 is a collation process (trajectory) of the time-series data trajectory collation unit;
FIG.

FIG. 15 is a diagram showing a relationship between a braking force and a road surface μ gradient.

FIG. 16 shows the change characteristics of the friction coefficient μ with respect to the slip speed, and shows that the change of μ around the center of the minute vibration can be approximated by a straight line in order to explain that the minute gain is equivalent to the braking torque gradient. FIG.

FIG. 17 is a diagram showing a relationship between a braking force and a road surface μ gradient at a predetermined speed in a Dry road surface state.

FIG. 18 is a diagram showing a relationship between a braking force and a road μ gradient at a predetermined speed in a Snow road surface state.

FIG. 19 is a graph showing characteristics of a friction coefficient μ between a tire and a road surface and a road surface μ gradient with respect to a slip speed s.

FIG. 20 is a diagram schematically showing characteristics of a friction coefficient μ between a tire and a road surface with respect to a slip speed.

FIG. 21 is a diagram illustrating characteristics of a small gain calculated by a small gain calculation unit with respect to a braking force.

FIG. 22 is a diagram showing, as a characteristic with respect to a braking force, a result obtained by filtering a minute gain calculated by a minute gain calculating unit at every constant sampling time.

FIG. 23 is a diagram illustrating, as a characteristic with respect to the braking force, a result obtained by filtering the small gain calculated by the small gain calculation unit every time the braking force changes by a predetermined amount, and comparing the result of FIG. 22;

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 10 Operation circuit 12D, 12S, 12I Reference value creation storage circuit 14 Time series data trajectory collation unit 26 Micro gain filter unit 28 Wheel speed sensor 34 Braking force detection unit 36 Micro gain calculation unit

 ──────────────────────────────────────────────────続 き Continued on the front page (72) Inventor Eiichi Ono 41-Cho, Yokomichi, Nagakute-cho, Aichi-gun, Aichi Prefecture Inside Toyota Central R & D Laboratories Co., Ltd. (41) Inventor Satoshi Sugai, Toyota Chuo R & D Center, 41, Nagakute-cho, Aichi-gun, Aichi-gun, Japan

Claims (2)

[Claims] 1. A first detecting means for detecting a first physical quantity representing a braking state or a driving state of a wheel of a vehicle; and a variance corresponding to a change in the first physical quantity, representing a slipperiness of the wheel. A second physical quantity that detects the second physical quantity to be changed, and a change in the second physical quantity that is detected by the second detection means is converted into a change in the first physical quantity that is detected by the first detection quantity. And a reduction processing means for performing reduction processing in response to the fluctuation. 2. A first detecting means for detecting a first physical quantity representing a braking state or a driving state of a wheel of the vehicle; a second detecting means for detecting a speed of the vehicle; And a third detection means for detecting a road surface state on which the wheels travel, the first physical quantity, and a second physical quantity determined according to the speed of the vehicle; and a third detection means for detecting the second physical quantity, which is determined by the third detection means. A reduction processing unit that performs a reduction process on the variation of the second physical quantity according to a change in the first physical quantity detected by the first detection means; and a first physical quantity detected by the first detection means and Based on the vehicle speed detected by the second detecting means,
Calculating means for calculating a second physical quantity for each of a plurality of predetermined road surface states; and a second processing means for reducing the first physical quantity representing the braking state or the driving state of the wheel by the reduction processing means
Comparing the trajectory of the physical quantity with each of the trajectories of the second physical quantity calculated for each of a plurality of road surface states by the arithmetic means with respect to the first physical quantity, based on a comparison result of the comparing means Estimating means for estimating a road surface state corresponding to a trajectory closest to the detected trajectory in the calculated trajectory as an actual road surface state on which the wheels are traveling. apparatus.