JP3391482B2 - Tire pressure detector - Google Patents

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a tire air pressure detecting device which does not make an erroneous decision even if the signal level used for tire air pressure detection changes.

[0002]

2. Description of the Related Art Japanese Patent Laid-Open No. 63-305011 discloses
Since the wheel speed changes when the load radius of the tire changes according to the air pressure, a tire air pressure detection device that detects the wheel speed of each wheel and indirectly detects the tire air pressure is disclosed.

[0003]

However, the load radius of the tire slightly changes depending on running conditions such as tire wear, cornering, and braking. For this reason, if the tire load radius is used as a parameter for air pressure detection, there is a problem in that sufficient detection accuracy cannot be ensured. In order to solve the above problems, the inventor of the present application extracts the resonance frequency in the up-down direction or the front-rear direction of the unsprung portion, and an air-pressure determination reference value stored in advance in an electronic control unit (hereinafter referred to as an ECU) (the minimum allowable tire pressure is The inventor has applied for a device for detecting the state of the air pressure of a tire by comparing it with the corresponding resonance frequency (Japanese Patent Application No. 3-294622).

The tire pressure detecting device according to the above application is
It is excellent in that the tire air pressure can be reliably determined. However, when the road surface is rough and a large force is applied, even if the air pressure is the same, the elastic characteristics of the bushes of the suspension are non-linear, so the resonance frequency in the up-down direction or the forward-backward direction of the unsprung part is detected low Therefore, there is a problem that the tire pressure is erroneously determined. In addition, when the vehicle speed decreases, such as during braking, the signal level used to calculate the resonance frequency decreases, and the resonance point cannot be clearly detected. The present invention has been made to solve the above problems, and an object thereof is to provide a tire air pressure detection device that does not make an erroneous determination even when the signal level used for tire air pressure detection changes. is there.

[0005]

In order to solve the above problems, the tire air pressure detecting device according to the present invention uses a wheel speed signal corresponding to the rotational speed of a wheel as a signal including a vibration frequency component of the tire when the vehicle is running. A wheel speed sensor for outputting a signal of the resonance frequency component of the tire from the wheel speed signal, and a signal level of the resonance frequency component of the signal level is compared with a preset value to resonate. A signal selecting means for selecting a signal of a resonance frequency component used for frequency calculation, a calculating means for calculating a resonance frequency from the selected signal of the resonance frequency component, and a detection for detecting a tire air pressure state based on the resonance frequency. And means. In the above tire pressure detecting device, the signal level of the selected resonant frequency component of the signal by the signal selecting means, it is also possible upcoming and a signal adjusting means for adjusting the signal level set in advance. Well
In addition, the signal selecting means has a large time waveform of the wheel speed signal.
The threshold value falls within the range from the selection lower limit judgment value to the selection upper limit judgment value
Select the signal so that only the waveform data is used.
May be. Also, the signal selecting means reduces the vehicle speed.
You may choose not to use the signal when doing
Yes. In addition, the signal selection means is used on a road where the vehicle is extremely rough.
I'm going to sort out the signal when I'm driving on the surface
You may ask.

[0006]

With the above structure, the vibration frequency component of the tire is included.
The signal level of the signal of the resonance frequency component extracted by the extraction means is compared with a preset value by the signal selection means to calculate the resonance frequency component of the resonance frequency component used for the calculation of the resonance frequency. Select signals. Then, the detection means detects the tire air pressure state based on the resonance frequency calculated by the calculation means from the signal of the selected resonance frequency component. Here, the resonance frequency changes according to the tire spring constant that substantially depends only on the tire air pressure. Further, the tire air pressure-resonance frequency characteristic is
It changes according to the type of tire. Therefore, the tire air pressure state can be detected based on the extracted resonance frequency.

[0007]

【Example】

(First Embodiment) A first embodiment of the present invention will be described with reference to the drawings. FIG. 1 is a schematic configuration diagram of a tire air pressure detection device. Four tires 1a to 1 attached to the front, rear, left and right
A wheel speed sensor is installed corresponding to each d. The wheel speed sensor is a gear-shaped pulsar 2a made of a magnetic material.
2d and pickup coils 3a to 3d. The pulsars 2a to 2d are fixed to the rotating axles (not shown) of the tires 1a to 1d. Pickup coil 3a
3d are attached to the pulsars 2a to 2d at a predetermined interval, and output AC signals having a cycle corresponding to the rotation of the pulsars 2a to 2d, that is, the rotation speed of each of the tires 1a to 1d.

AC signals output from the pickup coils 3a to 3d are input to the ECU 4. The ECU 4
It is composed of a CPU, a waveform shaping circuit, a ROM, a RAM and the like, and processes various signals input according to a predetermined program. Then, the processing result is input to the display unit 5, and the display unit 5 notifies the driver of the air pressure state of each of the tires 1a to 1d. The notification mode may be such that the state of the air pressure of each tire 1a to 1d is displayed in a special manner, and when the air pressure of any one tire is lower than the reference air pressure by one warning lamp, A warning lamp may be turned on to give a warning.

The principle of tire pressure detection in this embodiment will now be described. When a vehicle travels on a paved asphalt road surface, the tire is vibrated in the vertical and longitudinal directions due to the force in the vertical and longitudinal directions due to the minute irregularities on the road surface. The frequency characteristic of the unsprung acceleration of the vehicle when the tire vibrates has peak values at points a and b as shown in FIG. Point a is the vertical resonance frequency of the vehicle under the spring, and point b is the front-rear resonance frequency of the vehicle under the spring.

When the tire air pressure changes, the spring constant of the tire rubber portion also changes, so that the resonance frequencies in the vertical direction and the front-back direction both change. For example, as shown in FIG. 3, when the tire air pressure decreases, the spring constant of the tire rubber portion also decreases, so the resonance frequencies in the up-and-down direction and the front-rear direction generally shift to the low frequency side, and the peak value a point is The peak value b point at the a'point shifts to the b'point. Therefore, if at least one of the resonance frequency in the up-down direction and the front-rear direction in the unsprung part of the vehicle is extracted from the vibration frequency of the tire, it is possible to detect the tire air pressure state based on this resonance frequency.

On the other hand, as a result of a detailed study by the present inventors, it was clarified that the detection signal of the wheel speed sensor contains the vibration frequency component of the tire. That is, it is clear that the frequency analysis result of the detection signal of the wheel speed sensor shows peak values at two points as shown in FIG. Became. For this reason, in the present embodiment, the tire air pressure is detected by extracting the vertical and longitudinal resonance frequencies under the spring of the vehicle from the detection signal of the wheel speed sensor.

As described above, according to the present embodiment, the anti-skid control device (ABS), which has been increasing in the number of vehicles equipped with it in recent years,
In a vehicle or the like equipped with the above, since each wheel is already equipped with a wheel speed sensor, the tire air pressure can be detected without adding any new sensor. In the practical range of the vehicle, the amount of change in the resonance frequency is based on the change in the spring constant of the tire rubber portion caused by the change in tire air pressure, and is stable without being affected by other factors such as tire wear. Air pressure can be detected.

The signal processing of the ECU 4, which detects that the tire air pressure has dropped below a predetermined air pressure and issues an alarm, will be described below with reference to the flowchart of FIG.
Since the ECU 4 performs the same processing for each wheel,
The flow chart only shows signal processing for one wheel. In addition, the processing content shows an example in which it is detected that the tire air pressure has dropped below a reference value and a warning is given to the driver. The above also applies to each of the following embodiments.

The ECU is turned on by turning on the ignition switch.
When the signal processing by 4 is started, in step 100, the AC signal (FIG. 6) output from the pickup coil 3 is shaped into a pulse signal, and then the pulse interval is divided by a predetermined time to obtain the wheel speed v. Is calculated. As shown in FIG. 7, the wheel speed v usually contains many high frequency components including the vibration frequency component of the tire. In step 110, a road surface state determination process is performed to determine whether the calculated fluctuation range Δv of the wheel speed v is equal to or greater than the reference value v ₀ . When it is determined that the fluctuation range Δv of the wheel speed v is equal to or greater than the reference value v ₀ , the process proceeds to step 120.

In step 120, a road surface length determination process is performed to determine whether or not the time ΔT in which the fluctuation range Δv of the wheel speed v is the reference value v ₀ or more is a predetermined time t ₀ or more. Road surface condition determination processing in step 110, and step 1
In the road surface length determination processing of 20, when the road surface on which the vehicle is traveling is
This is performed to determine whether or not the road surface is capable of accurately detecting the tire air pressure by the detection method of this embodiment. That is, in the present embodiment, the tire air pressure is detected based on the change in the resonance frequency included in the tire vibration frequency component. For this reason, the wheel speed v fluctuates to some extent and is continued, and unless extremely large fluctuations are excluded, sufficient data for accurately calculating the resonance frequency cannot be obtained. In addition, the step 1
In the determination in 20, the predetermined time Δt is set when the fluctuation width Δv of the wheel speed v becomes equal to or larger than the reference value v ₀ .
Further, within this predetermined time Δt, the fluctuation range Δ of the wheel speed v is again reduced.
When v becomes equal to or greater than v ₀ , the measurement of time ΔT is continued.

If a negative determination is made in either step 110 or step 120, step 10
Return to 0. Further, if both step 110 and step 120 are affirmatively determined, the routine proceeds to step 130, where frequency analysis (hereinafter referred to as FFT) calculation is performed on the calculated wheel speed v. However, the magnitude (amplitude) of the time waveform of the wheel speed v obtained when the vehicle actually travels on a general road is
As shown in FIG. 8, the size is not necessarily uniform depending on the degree of unevenness on the road surface. Part (A) of FIG. 8A shows a time waveform of the wheel speed v when traveling on an extremely rough road surface.
Further, part (B) shows a time waveform of the wheel speed v when the vehicle is traveling at a low speed or when the brake is actuated.

When running on a very rough road surface, not only the tire but also a suspension or the like is subjected to a shocking force, which is affected by the non-linear characteristics of bushes and anti-vibration rubber used for the support portion of the suspension and the like, and tire pressure , The resonance frequency in the up-down direction or the front-rear direction of the unsprung part is detected to be low. Further, when the vehicle speed decreases, such as during braking, the signal level (gain) used for calculating the resonance frequency decreases, and the resonance point cannot be clearly detected.

Therefore, in step 140, data selection processing is performed. In the data selection process, specifically, the selection lower limit determination value v _L and the selection upper limit determination value v _H are set for the waveform of the wheel speed v of the FFT calculation result, and the predetermined frequency range (f _{1 to} f _{2) is set.} The peak value v _P in () is compared. And v _P
When ≦ v _L (FIG. 8 (b)) or v _P ≧ v _H (FIG. 8 (c)), it is performed on the (A) part and the (B) part of FIG. 8 (a). The FFT calculation result is not used for the calculation of the resonance frequency f _K.

In step 140, a data selection process is performed using the selection lower limit and upper limit determination values v _L and v _{H. Even in} the data after selection (portion (C) in FIG. 8), as shown in FIG. Moreover, the magnitudes of the gains of the FFT calculation results are not uniform. As a result, the number of times of averaging processing in step 180 described later increases, and it takes time to calculate the resonance frequency f _K.
Therefore, in step 150, the gain (magnitude) of the wheel speed signal is adjusted. In this gain adjustment process, the value of each peak value v _P within a predetermined frequency range (f _{1 to} f ₂ ) is set to a preset value v _PK (same value) in the waveform of each FFT calculation result. Coefficients k ₁ , k ₂ ·
.. Multiply each k _i by the FFT operation result.

At step 160, the number of FFT operations N is integrated. When the FFT calculation is actually performed on the wheel speeds obtained when the vehicle actually travels on a general road, the frequency characteristics are usually very random as shown in FIG. This is because the shape (size and height) of the subtle unevenness existing on the road surface is completely irregular, and the frequency characteristic varies for each wheel speed data. Therefore,
In this embodiment, in order to reduce the variation of the frequency characteristic as much as possible, the average value of the FFT calculation results of a plurality of times is obtained.

Therefore, in step 170, this FF
It is determined whether or not the number of times N of T operations has reached a predetermined number of times n ₀ . If not reached, the processes of steps 100 to 160 are repeated. When the number of calculations N reaches the predetermined number of times n ₀ , in the subsequent step 180, the equalization process is performed. This averaging process, as shown in FIG.
The average value of the FT calculation results is obtained, and the average value of the gain of each frequency component is calculated. By this averaging process, it is possible to reduce the fluctuation of the FFT calculation result due to the road surface.

However, with the above averaging process alone, the gain of the resonance frequency in the vertical direction and the front-back direction of the unsprung part of the vehicle does not always reach the maximum peak value as compared with the gain of the frequency in the vicinity thereof due to noise or the like. There is a problem that it is not limited. Therefore, following the averaging process described above, the moving average process is performed in step 190. This moving average processing is performed by obtaining the gain Y _n of the _nth frequency by the following arithmetic expression.

[0023]

## EQU1 ## Y _n = (y _{n + 1} + Y _n-1 ) / 2 That is, in the moving average processing, the gain Y of the nth frequency is obtained.
_n is the (n + 1) th gain y in the previous calculation result
_{n + 1} and the gain Y of the n-1th frequency already calculated
_It is an average value with _n-1 . As a result, the FFT calculation result shows a waveform that changes smoothly. FIG. 12 shows the calculation result obtained by this moving average processing.
The waveform processing here is not limited to the above moving average processing, but the differential calculation of the wheel speed v is performed before the FFT calculation of step 105, and the differential calculation result is FF.
You may implement T calculation.

In the following step 200, the resonance frequency f _K of the unsprung vehicle in the front-rear direction is calculated on the basis of the FFT calculation result smoothed by the moving average processing. Subsequent step 210 determines an initial resonant frequency f _K0 that is set to correspond to the previously normal tire pressure, decrease the deviation between the resonance frequency f _K is sequentially calculates (f _K0 -f _K), wherein f _K0 And tire pressure drop warning pressure (eg 1.
The resonance frequency f _L corresponding to 4 kg / cm ² ) and the judgment deviation Δf = (f _K0 −f _L ) are compared. If (f _K0 −f _K ) <Δf, the processing from step 100 onward is performed. Also,
If (f _K0 −f _K ) ≧ Δf, the _routine proceeds to step 220, where it is determined that the tire pressure is below the allowable value, and the display unit 5 displays a warning to the driver. In the above embodiment,
Based only on the unsprung resonance frequency of the vehicle,
Although the example in which the decrease in the tire air pressure is detected is shown, the decrease in the tire air pressure can be detected based on only the resonance frequency in the vertical direction instead of this. It is also possible to detect based on both the front-rear direction and the vertical resonance frequency.

(Second Embodiment) The second embodiment is different from the case of the first embodiment in that data selection processing and gain adjustment processing are performed on the time waveform of the wheel speed v. FIG. 13 is a flowchart showing the outline of the processing contents. Note that the processing from step 120 onward except steps 140 and 150 is the same as that in the first embodiment, so a detailed description thereof will be omitted. In the data selection process of step 111, the selection lower limit determination value |
v _L ′ | and the selection upper limit determination value | v _H ′ | are set, and the magnitude of the time waveform of the wheel speed v is in the range of (−v _H ′ to −v _L ′) and (v _L ′ to v _H). Only waveform data that falls within the range of ′) is used.

In the gain adjusting process in step 112, each peak value v within a constant time Δt 'set as shown in FIG. 15 is applied to the time waveform of the wheel speed v after the data selecting process.
Each coefficient k _i ′ is multiplied by the value of the wheel speed v within a fixed time Δt ′ so that the value of _P ′ becomes equal to a preset value (same value). Then, the processing from step 120 onward is performed to detect a decrease in tire air pressure.

In each of the above embodiments, the resonance frequency f for detecting the tire air pressure is detected by performing the data selection process.
The value of _K is reliably calculated and calculated, and the resonance frequency f _K
Is not calculated too low. Further, since the gain adjustment processing is performed, even if the frequency characteristic varies for each wheel speed data, the magnitude of the gain of each FFT calculation result does not become uneven, and the average value of the FFT calculation results of a plurality of times is obtained. The number of averaging processes can be reduced, and it becomes possible to quickly detect a decrease in tire air pressure.

[0036]

The tire air pressure detecting device of the present invention has the above-mentioned structure, and the signal level of the signal of the resonance frequency component extracted by the extracting means is compared with a preset value by the signal selecting means. The resonance frequency component signal used for calculating the resonance frequency, and the detection means detects the tire air pressure state based on the resonance frequency calculated by the calculation means from the selected resonance frequency component signal. Even if the signal level used for tire pressure detection changes, there is no erroneous determination. Further, by providing the signal adjusting means for adjusting the signal level of the signal of the resonance frequency component selected by the signal selecting means to a preset signal level,
There is an excellent effect that the time interval for detecting the tire pressure state can be shortened.

[Brief description of drawings]

FIG. 1 is a schematic configuration diagram of a tire air pressure detection device according to the present invention.

FIG. 2 is a characteristic diagram showing frequency characteristics of unsprung acceleration of a vehicle.

FIG. 3 is a characteristic diagram showing how the resonance frequencies of the unsprung part of the vehicle change in the up-down direction and the front-rear direction with changes in tire air pressure.

FIG. 4 is an explanatory diagram showing the principle of tire pressure detection.

FIG. 5 is a flowchart showing the processing contents of the ECU of the first embodiment.

FIG. 6 is a waveform diagram showing an output voltage waveform of a wheel speed sensor.

FIG. 7 is a waveform diagram showing a variation state of a wheel speed v calculated based on a detection signal of a wheel speed sensor.

FIG. 8 is an explanatory diagram showing a data selection process of the first embodiment.

FIG. 9 is an explanatory diagram showing a gain adjustment process of the first embodiment.

FIG. 10 is a characteristic diagram showing an FFT calculation result for a wheel speed v time waveform.

FIG. 11 is an explanatory diagram illustrating an averaging process.

FIG. 12 is a characteristic diagram showing an FFT calculation result after performing a moving average process.

FIG. 13 is a flowchart showing a part of the processing contents of the ECU of the second embodiment.

FIG. 14 is an explanatory diagram showing a data selection process of the second embodiment.

FIG. 15 is an explanatory diagram showing a gain adjustment process of the second embodiment.

[Explanation of symbols]

1a-1d ... tires 2a ~ 2d ... Pulsa 3a-3d ... pickup coil 4 ... ECU (electronic control unit) 5 ... Display

Continued front page    (72) Inventor Kenji Fujiwara               1-1, Showa-cho, Kariya city, Aichi Japan               Denso Co., Ltd.                (56) References JP-A-62-149503 (JP, A)                 "Automotive Technology Handbook" Volume 1                 Basics / Theory, 1st edition, incorporated association               Automotive Engineering Society December 1, 1990 p264-               288

Claims (5)

(57) [Claims] 1. A wheel speed sensor that outputs a wheel speed signal corresponding to a rotation speed of a wheel as a signal including a vibration frequency component of a tire when a vehicle is running, and a signal of a resonance frequency component of a tire from the wheel speed signal. Extraction means for extracting,
The signal level of the signal of the resonance frequency component is compared with a preset value, and the signal selection means for selecting the signal of the resonance frequency component used for the calculation of the resonance frequency, and the signal of the selected resonance frequency component A tire air pressure detection device comprising: a calculation unit that calculates a resonance frequency; and a detection unit that detects a tire air pressure state based on the resonance frequency. 2. The tire air pressure according to claim 1, further comprising signal adjusting means for adjusting the signal level of the signal of the resonance frequency component selected by the signal selecting means to a preset signal level. Detection device. 3. The signal selecting means, when the wheel speed signal
The size of the waveform between the selection lower limit judgment value and the selection upper limit judgment value
Performs signal selection so that only waveform data within the range is used
The tag according to claim 1 or 2, wherein
Ear pressure detection device. 4. The signal selecting means reduces the vehicle speed when
The feature is that it is sorted so that the signal when it is used is not used
Tire air according to any one of claims 1 to 3.
Pressure detector. 5. The signal selecting means ensures that the vehicle is extremely rough.
Select not to use the signal when driving on a dry road surface
Any one of claims 1 to 4 characterized in that
The tire pressure detection device according to.