METHOD AND SYSTEM FOR MEASURING VERTICAL WHEEL IMPACT FORCE IN REAL-TIME BASED ON TIRE PRESSURE MONITORING

Abstract

A method and system for measuring vertical wheel impact force in real-time based on tire pressure monitoring is provided by the present invention. The system mainly includes the four modules, namely a tire pressure derotation preprocessing, a tire pressure-wheel force system identification, a calibration method, and an integrated device for tire pressure-wheel force measurement. The method uses the integrated device to collect tire pressure data in real-time. The corresponding vertical wheel impact force is obtained through the derotation preprocessing and the tire pressure-wheel force system identification, and is calibrated according to the calibration method. The present invention provides an efficient, accurate, and highly adaptable wheel force measurement solution in the theoretical aspect and device aspect, which meets the requirements for the quick evaluation of the bridge health condition. Also, the present invention has a great potential in the fields such as road safety diagnosis, automobile performance related design, etc.

Claims

1. A method for measuring vertical wheel impact force in real-time based on tire pressure monitoring, comprising: collecting, by an integrated device, real-time tire pressure data; obtaining a corresponding wheel force by performing a tire pressure derotation preprocessing and a tire pressure-wheel force system identification; and performing a calibration to the corresponding wheel force according to a calibration method, wherein the tire pressure-wheel force system identification comprises a first calculation method of a gray box model and a second calculation method of a black box model; the first calculation method of the gray box model comprises: first, describing a relationship between a vertical deformation of a tire and the vertical wheel impact force by a single-degree-of-freedom mass-spring-damper model, wherein, the formula is presented below:
c{dot over (x)}+kx=F.sub.tire wherein, c is a vertical damping of the tire, k is a vertical stiffness of the tire, x is the vertical deformation of the tire under a dynamic load, {dot over (x)} is a first-order differential of time, and F.sub.tire is the vertical wheel impact force; second, establishing a relationship between a tire pressure and the vertical deformation of the tire according to an ideal gas equation, wherein, the formula is presented below: $x=\frac{p_0.Math.V_0}{\mathrm{aA}\left(p_0+.Math..Math.p\right)}.Math.\left(1-\frac{p_0}{p_0+.Math..Math.p}\right)$ $\stackrel{.}{x}=-\frac{p_0.Math.\stackrel{.}{.Math..Math.p}.Math.V_0}{{\mathrm{aA}\left(p_0+.Math..Math.p\right)}^2}\left(1-\frac{2.Math.p_0}{p_0+.Math..Math.p}\right)$ wherein, p.sub.0 is an initial tire pressure; p is a tire pressure change under dynamic load, the derotation preprocessing should be implemented; {dot over (p)} is a first-order differential of p for time; V.sub.0 is an initial volume of an inner cavity of the tire under a static load; A is a contact area of the tire under the static load, and an effect of a tire deformation on the contact area is expressed by $\mathrm{aA}\left(1+\frac{.Math..Math.p}{P_0}\right);$ accordingly, obtaining a relationship between the tire pressure and the vertical wheel impact force, wherein the formula is presented below: $F_{\mathrm{tire}}=\frac{{\mathrm{cp}}_0.Math.V_0\left(p_0-.Math..Math.p\right)}{{\mathrm{aA}\left(p_0+.Math..Math.p\right)}^3}\stackrel{.}{.Math..Math.p}+\frac{{\mathrm{kp}}_0.Math.V_0.Math..Math..Math.p}{{\mathrm{aA}\left(p_0+.Math..Math.p\right)}^2}$ lastly, identifying unknown parameters in the formula of the tire pressure-vertical wheel impact force through a Kalman filtering according to accurate tire pressure data and accurate wheel force data obtained from a calibration test; wherein assuming that parameters to be identified include $=\frac{{\mathrm{cp}}_0.Math.V_0}{\mathrm{aA}},=\frac{k}{c},$ an output is $y=\frac{.Math..Math.p}{{\left(p_0+.Math..Math.p\right)}^2},$ and an input is u=F.sub.tire, a state variable is expressed as follows: $\left[\begin{array}{c}{x}_1\\ x_2\\ x_3\end{array}\right]=\left[\begin{array}{c}y\\ \begin{array}{c}\\ \end{array}\end{array}\right]$ a state equation is expressed as follows: $\left[\begin{array}{c}\stackrel{.}{x_1}\\ \stackrel{.}{x_2}\\ \stackrel{.}{x_3}\end{array}\right]=\left[\begin{array}{c}\stackrel{.}{y}\\ 0\\ 0\end{array}\right]=\left[\begin{array}{c}\frac{u}{x_2}-x_1.Math.x_3\\ 0\\ 0\end{array}\right]$ and an observation equation is expressed as follows: $y=\left[1,0,0\right]\left[\begin{array}{c}{x}_1\\ x_2\\ x_3\end{array}\right];$ the second calculation method of the black box model comprises: assuming that the tire pressure change and the vertical wheel impact force satisfy a linear convolution relationship, then in a frequency domain, obtaining the following formula:
F.sub.tire(w)=p(w)H(w) wherein, H(w) is a frequency response function; F.sub.tire(w) and p(w) are Fourier transforms of time-history data of F.sub.tire(t) and p(t) respectively; identifying the frequency response function according to the accurate tire pressure data and the accurate wheel force data obtained from the calibration test; wherein, both the gray box model and the black box model are functions of the tire pressure and the corresponding wheel force, and the gray box model and the black box model are configured to correct each other.

2. The method for measuring vertical wheel impact force in real-time based on tire pressure monitoring of claim 1, wherein the tire pressure derotation preprocessing eliminates periodic interferences caused by an uneven air pressure distribution during a rotation of the tire by a method of filtering, so that the real-time tire pressure data after preprocessing directly reflects effects of the vertical wheel impact force.

3. The method for measuring vertical wheel impact force in real-time based on tire pressure monitoring of claim 1, wherein in the tire pressure-wheel force system identification, a relation model of the tire pressure and the vertical wheel impact force is established according to tire vibration characteristics, and specific parameters of the relation model are identified according to the accurate tire pressure data and the accurate wheel force data obtained from the calibration test, so that the corresponding wheel force is calculated in a subsequent formal test with merely the tire pressure known.

4. The method for measuring vertical wheel impact force in real-time based on tire pressure monitoring of claim 1, wherein the integrated device comprises a tire pressure sensing system, a central signal control system, and a data analysis system; wherein, the tire pressure sensing system collects air pressure change data in the inner cavity of the tire through a tire pressure sensor, and exchanges instructions and data with the central signal control system through a local signal controller in a wired or wireless control manner; wherein, the central signal control system transmits collected data to the data analysis system, and the data analysis system uses an embedded real-time vertical wheel impact force calculation program to automatically analyze the collected data and output a visual evaluation result of the wheel force.

5. The method for measuring vertical wheel impact force in real-time based on tire pressure monitoring of claim 1, wherein the calibration method uses a set of test device and a third calculation method to obtain the accurate wheel force data, and calibrate the integrated device.

6. The method for measuring vertical wheel impact force in real-time based on tire pressure monitoring of claim 5, wherein the calibration method uses the test device for the calibration, and the test device integrates a data collection, a signal transmission, and a result analysis as a whole; wherein, the test device for calibration mainly comprises: a set of approach bridge tracks, a main bridge track, a track acceleration sensing system, a track bearing force sensing system, a central signal control system, and a data analysis system; wherein, when the tire rolls on the main bridge track, the wheel force is obtained from collected track vibration information, the tire enters the main bridge track from the approach bridge tracks, and the main bridge track only contacts with a ground through bearings; wherein, firstly the track acceleration sensing system collects a vertical acceleration of the tracks and exchanges first instructions and first data with the central signal control system in a wired or wireless manner; wherein, secondly the track bearing force sensing system collects a bearing force of the tracks and exchanges second instructions and second data with the central signal control system in the wired or wireless manner; wherein, thirdly in addition to exchange data and instructions with the the track acceleration sensing system and the track bearing force sensing system, the central signal control system also provides third data to the data analysis system; and the data analysis system uses an embedded algorithm program to analyze the third data and output a visual evaluation result of a calibrated wheel force.

7. The method for measuring vertical wheel impact force in real-time based on tire pressure monitoring of claim 6, wherein a calculation method for a force calibration of the force comprises when the tire rolls on the main bridge track, the wheel force and a structural response of the track satisfy the following formula:
F.sub.tire+G.sub.tire=F.sub.bearing+ma wherein F.sub.tire is the vertical wheel impact force; G.sub.tire is the static load of the tire; F.sub.bearing is a resultant force of the bearings after removing a weight of the main bridge track, namely, merely subjected to effects of the tire; ma is a resultant inertia force of various units of the main bridge track.

8. The method for measuring vertical wheel impact force in real-time based on tire pressure monitoring of claim 1, further comprising the following steps: installing and debugging the integrated device; obtaining the accurate tire pressure data and wheel force data through a calibration test; performing the tire pressure derotation preprocessing; obtaining the relationship between the tire pressure and the wheel force by the system identification of the grey box model or the black box model; obtaining the real-time tire pressure data from the integrated device for tire pressure-wheel force measurement in a formal test; performing the tire pressure derotation preprocessing; and calculating the wheel force according to the relationship between the tire pressure and the wheel force.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0081] FIG. 1 is a conceptual diagram of a system for measuring vertical wheel impact force in real-time based on tire pressure monitoring of the present invention;

[0082] FIG. 2 is a diagram showing a tire pressure-wheel force integrated device of the present invention;

[0083] FIG. 3 is a diagram showing an integrated device for wheel force calibration of the present invention;

[0084] FIG. 4 is a diagram showing an uneven distribution of tire pressure of the present invention;

[0085] FIG. 5 is a diagram showing a periodic variation of air pressure sensor data caused by the uneven air pressure distribution of the present invention;

[0086] FIG. 6 is a dynamic model diagram of the vertical vibration relationship between the tire and the ground of the present invention;

[0087] FIG. 7 is a flow diagram showing an implementation of a method for measuring vertical wheel impact force in real-time based on tire pressure monitoring of the present invention;

[0088] FIG. 8 is a diagram showing the calibration effect of the vertical wheel impact force of the present invention;

[0089] FIG. 9 is a diagram showing the tire pressure derotation preprocessing effect of the present invention;

[0090] FIG. 10 is a diagram showing the identification effect of the parameter of the Kalman evaluation algorithm of the present invention;

[0091] FIG. 11 is a diagram showing identification effect of the parameter of the Kalman evaluation algorithm of the present invention;

[0092] FIG. 12 is a diagram showing the identification effect of the amplitude of the frequency response function of the present invention;

[0093] FIG. 13 is a diagram showing the identification effect of the phase of the frequency response function of the present invention;

[0094] FIG. 14 is a diagram showing the tire pressure measurement value after derotation preprocessing of the present invention;

[0095] FIG. 15 is a comparison diagram of the value of the vertical wheel impact force calculated by the gray box model and the actual value;

[0096] FIG. 16 is a comparison diagram of the value of the vertical wheel impact force calculated by the black box model and the actual value.

DETAILED DESCRIPTION OF THE EMBODIMENTS

Embodiments

[0097] As shown in FIG. 7, the workflow of the system for measuring vertical wheel impact force in real-time based on tire pressure monitoring of the present embodiment is as follows.

[0098] Step 1. The integrated device for tire pressure-vertical wheel impact force measurement was installed to realize functions such as data collection, signal transmission, and result analysis, etc., and complete a series of steps from tire pressure collection to visual result display of real-time wheel force. The entire device is shown in FIG. 2.

[0099] Before the test, a tire pressure sensing system is installed on the tire. The tire pressure sensing system consists of a tire pressure sensor 1.1 and a local signal controller 1.2, which are responsible for signal collection and signal transmission, respectively. A central signal control system 2 and a data analysis system 3 can be configured in the vehicle and responsible for overall signal transmission and control, and data analysis, respectively. After the installation of the devices is completed, a complete test process includes the following steps. The data analysis system 3 controls the start of the test. The central signal control system 2 sends a data collection command to the tire pressure sensing system. The tire pressure sensing system starts to collect tire air pressure data, and the data is fed back to the central signal control system 2 after collection. Lastly, the data comes together in the data analysis system 3, the collected data is automatically analyzed to output the visual evaluation results in combination with an embedded real-time tire pressure-vertical wheel impact force calculation program. In order to ensure the accuracy of the measurement results, the tire may be filled with low-thermal-conductivity gases such as nitrogen, etc., and the gas temperature is measured before and after the tire is used to make sure that the gas temperature is stable before and after use.

[0100] Step 2. Calibration Test

[0101] First, the relative integrated test devices should be installed and debugged. As shown in FIG. 3, two parallel tracks are arranged on an appropriate place. The two parallel tracks include approach bridge track 4 and main bridge track 5. The two parallel tracks are required to be placed for two wheels to travel on. The approach bridge track 4 and the main bridge track 5 should not be in contact with each other. The main bridge track 5 is divided into multiple units with a proper number and a vertical acceleration sensor is arranged at a center position of the lower surface of each unit to form a track acceleration sensing system 6. The main bridge track 5 is fixed to the ground through bearings, and each bearing is configured with a vertical bearing force sensor to form a track bearing force sensing system 7. A central signal control system 8 and a data analysis system 9 are arranged at appropriate locations in the laboratory.

[0102] After the installation of the devices is completed, a complete calibration process includes the following steps. The data analysis system 9 controls the start of the test. The central signal control system 8 sends a data collection command to the track acceleration sensing system 6 and the track bearing force sensing system 7. The track acceleration sensing system 6 and the track bearing force sensing system 7 start collecting data simultaneously. The tire enters the main bridge track 5 from the approach bridge track 4, then gets off the bridge from the approach bridge track 4. After the tire gets off the bridge, the data collection is completed, and the track acceleration sensing system 6 and the track bearing force sensing system 7 feeds the data back to the central signal control system 8. The central signal control system 8 transmits the data to the data analysis system 9, and automatically outputs the visual result of the calibrated vertical wheel impact force in combination of the embedded calculation program. The effects are shown in FIG. 8.

[0103] The tire pressure data should be collected in synchronization with the collection of the vertical wheel impact force data to obtain the tire pressure information of the tire at the corresponding time on the approach bridge track 4 and the main bridge track 5.

[0104] The amount of the collected data and the number of samples should be sufficient and accurate enough to meet the calculation requirements of the tire system identification. The working conditions should be similar to the later formal measurement of the vertical wheel impact force (only the tire pressure data is collected). Generally, the working conditions are controlled and determined by factors such as the bearing load of the tire, the rotation speed, the initial inflation pressure, pavement surface roughness, etc.

[0105] Step 3. Tire Pressure Derotation Preprocessing

[0106] Since the position of the air pressure sensor relative to the tire is fixed, the spatial position of the sensor changes constantly as the tire rotates, which causes interferences with the collected tire air pressure data. When the tire is in a rolling state, the air pressure in the empty cavity will produce a stable uneven distribution, as shown in FIG. 4. However, the air pressure sensor observes this unevenness as the position rotates and intuitively represents the unevenness as a periodic trend variation in the data, as shown in FIG. 5. Such periodic variation can cause tire pressure data drift, thereby interfering with the relationship between tire pressure and wheel force. In order to eliminate such interferences, a filtering method is required to eliminate the effects of rotation.

[0107] The tire pressure derotation preprocessing makes the air pressure data directly reflect the vibration of the tire. The periodic trend line can be eliminated by the filtering, so as to remove the impacts of the uneven air pressure distribution. The processing effect is shown in FIG. 9.

[0108] Step 4. Tire Pressure-Vertical Wheel Impact Force System Identification.

[0109] The relationship between the tire pressure and the vertical wheel impact force is established according to the gray box model or the black box model. The unknown parameters in the tire pressure-wheel force relation formula are identified in combination with the accurate tire pressure data and wheel force data obtained through the calibration. After the complete relationship calculation between the tire pressure and the wheel force is obtained, the corresponding wheel force data can be calculated merely according to the tire pressure data.

[0110] (a) Gray Box Model Calculation Method

[0111] The unknown parameters in the tire pressure-vertical wheel impact force equation are identified according to the vertical wheel impact force data obtained from the calibration test and the tire pressure data obtained after the derotation preprocessing by using the Kalman filtering algorithm. The effects are shown in FIG. 10 and FIG. 11.

[0112] (b) Black Box Model Calculation Method

[0113] The frequency response function is identified according to the vertical wheel impact force data obtained from the calibration test and the tire pressure data obtained after the derotation preprocessing. The effects are shown in FIG. 12 and FIG. 13.

[0114] Step 5. Formal Test

[0115] After the complete relationship of the tire pressure and the vertical wheel impact force is obtained, the corresponding vertical wheel impact force can be calculated according to the tire pressure data collected in the formal test. The gray box model algorithm can acquire the real-time wheel force data according to the tire pressure data at each moment. The black box model algorithm can acquire the wheel force data in a corresponding period of time according to the tire pressure data in the period of time. The two methods can verify each other so as to improve the reliability of the calculation results. The two results can also be averaged to obtain an optimized ground vertical contact force. In order to illustrate the accuracy of the results, the results calculated by the method of the present invention are compared with the calibrated actual value as shown in FIGS. 14, 15 and 16.

[0116] The embodiments of the present invention have been described in detail above with reference to the drawings. However, the present invention is not limited to the described embodiments. Various changes, modifications, substitutions and variations of these embodiments derived by those of ordinary skill in the art without departing from the scope of the principles and technical ideas of the present invention should still be considered as falling within the scope of the present invention.