CN111976731B - Road surface unevenness recognition method based on vehicle frequency domain response - Google Patents
Road surface unevenness recognition method based on vehicle frequency domain response Download PDFInfo
- Publication number
- CN111976731B CN111976731B CN202010769710.6A CN202010769710A CN111976731B CN 111976731 B CN111976731 B CN 111976731B CN 202010769710 A CN202010769710 A CN 202010769710A CN 111976731 B CN111976731 B CN 111976731B
- Authority
- CN
- China
- Prior art keywords
- vehicle
- road surface
- formula
- response
- displacement
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
- B60W40/06—Road conditions
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Mathematical Physics (AREA)
- Transportation (AREA)
- Mechanical Engineering (AREA)
- Length Measuring Devices With Unspecified Measuring Means (AREA)
- Control Of Driving Devices And Active Controlling Of Vehicle (AREA)
Abstract
The invention discloses a road surface unevenness identification method based on vehicle frequency domain response, which is a method for identifying road surface unevenness based on vehicle measurement frequency domain response and constructed vehicle measurement response frequency domain response function related to vehicle-road contact point displacement, and belongs to the technical field of road technical condition assessment. The invention has low test cost, convenient operation and easy implementation, thus having wide application prospect and practical value.
Description
Technical Field
The invention belongs to the technical field of road technical condition assessment, relates to a method suitable for identifying road surface unevenness, and in particular relates to a method for identifying road surface unevenness based on a frequency domain response of vehicle measurement and a constructed frequency domain response function of the vehicle measurement response on vehicle-road contact point displacement.
Background
In recent years, the road network skeleton of China is basically formed, and the process of ' rebuilding light-raising ' is changed into ' building and restoring. The road surface flatness is an important evaluation index of road surface running quality and running comfort, and is a key influence factor of road technical state indexes. Road surface irregularities affect the dynamic behavior of the vehicle in its travel and can be considered as external stimuli acting on the vehicle. If the deviation value of the longitudinal concave-convex amount of the road surface is large, namely the road surface flatness is not qualified, riding comfort and safety are affected. The road is not maintained in time, the damaged road surface can be further damaged, and the large-size cracking and potholes of the road surface can even cause traffic accidents. Therefore, road surface unevenness is a very important index in road quality evaluation, driving comfort evaluation, road surface flatness index calculation, vehicle power analysis, vehicle suspension design, road construction, maintenance, and the like.
The road surface state evaluation methods commonly used in the current engineering mainly comprise two types, namely a manual observation method and an accurate measurement technology by using sensors such as laser. The manual observation method has low cost, but the evaluation accuracy depends on the technical level of an observer, and has stronger subjectivity; the laser-based automatic detection equipment has high precision, but is expensive, and is not suitable for frequent detection of common roads.
The existing road surface unevenness recognition technology based on vehicle response is low in cost and can provide objective road surface condition evaluation, so that the technology is widely focused.
Disclosure of Invention
The invention solves the technical problem of rapidly identifying road surface unevenness by utilizing a frequency response function of a vehicle.
The road surface unevenness recognition method based on the vehicle frequency domain response comprises the following steps:
according to the stress analysis of the vehicle on the road surface, the motion equation of the vehicle can be obtained, and the motion equation is shown in the formula (1).
Where u (t) is a vehicle displacement matrix, M, C, K is a mass, damping and stiffness matrix of the vehicle, and F is a load applied to the vehicle due to road surface irregularities.
F can utilize the rigidity K according to the wheel t And damping C t And road surface irregularities r (T) in contact with the wheels, calculated by the formula (2), wherein T t A matrix of positions acting as wheels.
A1/2 vehicle model with 4 degrees of freedom is adopted, and the 4 degrees of freedom are respectively: vertical displacement u of vehicle body 1 (t) roll direction angular displacement u 2 (t) vertical displacement u of 2 wheels 3 (t)、u 4 (t), in FIG. 1, m is the mass of the vehicle body, J is the moment of inertia of roll of the vehicle body about the x-axis, m i (i=1, 2) mass, c of 2 wheels respectively i (i=1, 2) is the damping coefficient of 2 shock absorbers of the suspension, c i (i=3, 4) is the damping coefficient, k, of the tire i (i=1, 2) is the 2 spring rates, k, of the suspension i (i=3, 4) the vertical rigidity of 2 wheels, e 1 Representing the distance of the centroid to the rear axis e 2 Representing the distance of the centroid to the front axis. And (3) correspondingly writing out the parameter matrixes in the formula (1) and the formula (2), wherein the formula (3) is as follows:
fourier Transform (FFT) is performed on each of the two ends of the equation (1) and the equation (2), respectively, to obtain the equation (4).
-ω 2 MU(ω)+ωjCU(ω)+KU(ω)＝T t (ωjC t +K t )R(ω) (4)
Wherein j is 2 = -1, u (ω) is the vehicle displacement frequency domain response matrix, R (ω) is the contact point displacement frequency domain response matrix.
The finishing formula (4) can obtain an expression of U (ω), see formula (5).
U(ω)＝ (-ω 2 M+ωjC+K) -1 T t (ωjC t +K t )R(ω) (5)
And then can calculate the observed vehicle frequency domain response U m (ω) is shown in formula (6).
U m (ω)＝(ωj) n C 0 U(ω) (6)
Wherein C is 0 For an observation matrix corresponding to a vehicle layout sensor, a parameter n reflects the type of measured vehicle response, n=0 corresponds to a displacement response, n=1 corresponds to a velocity response, and n=2 corresponds to an additionAnd (5) speed response.
Deriving a vehicle system observation response U based on the above derivation m And (omega) and the contact point displacement frequency domain response R (omega) are shown in the formula (7).
Wherein H is ur (ω) is a displacement frequency response matrix of the vehicle displacement with respect to the contact point displacement, H mr For the observed frequency response matrix of the measured vehicle response with respect to the displacement of the contact point, they can be calculated from the system parameters of the vehicle.
The distance between the front wheel and the rear wheel is e 1 +e 2 Assuming that the speed of the vehicle traveling forward at a constant speed is v, when the vehicle passes through the same position on the road surface, the time for the front wheel to advance from the rear wheel is t 0 ＝(e 1 +e 2 ) And/v. The degree of irregularity of the road surface on which the front wheel is positioned at the moment t is recorded as r 2 (t) the rear wheel experiences the position at a time t later than the front wheel 0 Time, i.e. t+t 0 Time of day, therefore r 1 (t+t 0 )＝r 2 (t). According to the time shift property of Fourier transform, r 2 (t) and r 1 Fourier transform R of (t) 2 (ω) and R 1 (ω) there is a relationship of formula (8).
Then R (ω) can be Fourier transformed using the contact point displacement of the rear wheel R 1 And (omega) is shown in the formula (9).
Combining formula (7) and formula (9) to finally obtain R 1 The formula of (ω) is shown in formula (10).
Therefore, the road surface unevenness recognition process based on the vehicle frequency domain response is that, first, the observation frequency response matrix H is calculated by the formula (7) according to the vehicle parameters mr Then, the response U is observed in conjunction with the vehicle system m (ω) obtaining a contact point displacement Fourier transform R of the rear wheel by using the equation (10) 1 (ω), finally, for R 1 (omega) performing inverse Fourier transform (IFFT) to obtain the unevenness r of the road surface where the rear wheel is located 1 And (t) is shown in the formula (11), and the road surface unevenness matrix r (t) of the wheel contact is shown in the formula (12).
r 1 (t)＝IFFT(R 1 (ω)) (11)
The invention has the beneficial effects that the road surface unevenness can be rapidly identified by arranging the sensor on the vehicle, enabling the vehicle to run on the road surface at a constant speed and measuring the vibration response of the vehicle and then combining the frequency response function calculated by the physical parameters of the vehicle, and the invention provides a theoretical basis for road surface evaluation. The invention has low test cost, convenient operation and easy implementation, and has wide application prospect and practical value.
Drawings
FIG. 1 is a schematic illustration of a 1/2 vehicle model force.
Fig. 2 is a road surface unevenness curve.
Fig. 3 is an acceleration response time course of three sensors.
FIG. 4 is a frequency response plot of vehicle three sensor acceleration responses versus rear wheel displacement.
FIG. 5 is a diagram of S 1 Responsive to the identified road surface irregularities spectrum.
FIG. 6 is a diagram of the use of S 2 Responsive to the identified road surface irregularities spectrum.
FIG. 7 is a diagram of the use of S 1 And S is 2 Responsive to the identified road surface irregularities spectrum.
FIG. 8 is a diagram of the use of S 1 、S 2 And S is 3 Responsive to the identified road surface irregularities spectrum.
FIG. 9 is a diagram of the use of S 1 、S 2 And S is 3 Responsive to the identified road surface irregularities.
Detailed Description
The following describes the specific implementation method of the present invention in detail with reference to the technical scheme and the accompanying drawings.
Taking the half-vehicle model with 4 degrees of freedom in fig. 1 as an example, numerical simulation is performed, and 4 mass parameters of the vehicle are respectively m 1 ＝2288.9kg,m 2 ＝8394.5kg,m 3 ＝109kg,m 4 =108.1 kg, stiffness parameters k respectively 1 ＝92788.4N/m,k 2 ＝78837.82N/m,k 3 ＝1020907N/m,k 4 = 1020907N/m, damping parameters c respectively 1 ＝4269.82N·s/m,c 2 ＝4222.89N·s/m,c 3 ＝4300N·s/m,c 4 =3900n·s/m, with a front-rear wheelbase of e 1 ＝1.654m,e 2 = 1.746m. The speed of the vehicle is 10m/s, the road surface length is 1600m, the road grade is grade A, and the unevenness coefficient is 16 multiplied by 10 -6 m 3 The road surface unevenness curve r (t) is randomly generated, see fig. 2.
Three sensors are arranged, S 1 For the vertical degree of freedom acceleration response of the vehicle body, S 2 For the rear wheel acceleration response S 3 For the front wheel acceleration response, the sampling frequency is 400Hz, and the lower limit of the spatial frequency is 0.0221m -1 Upper limit 1.4142m -1 。
The acceleration response time course of the three sensors is shown in fig. 3, and first, an acceleration frequency response curve of the acceleration response with respect to the displacement of the rear wheel is obtained according to equation (7), see fig. 4. Then, the road surface unevenness spectrum R can be obtained from the formula (10) 1 (ω) respectively using S 1 、S 2 The single-group response identification obtains a road surface unevenness frequency spectrum, as shown in fig. 5 and 6; combined utilization S 1 And S is 2 The two sets of response identification result in a road surface unevenness spectrum, as shown in fig. 7; by S 1 、S 2 And S is 3 A total of three sets of response identifications resulted in a road surface irregularity spectrum, as shown in fig. 8. It can be found that the 3-group response identification is utilizedThe road surface unevenness has the highest spectral accuracy. Finally, for R 1 (omega) performing inverse Fourier transform (IFFT) to obtain the identified rear wheel contact road surface irregularity curve r 1 (t) as shown in fig. 9. Comparing with the actual road surface irregularity curve r (t), the two curves are almost completely matched, the estimated road surface irregularity is consistent with the actual road surface irregularity, and the quick, accurate and feasible road surface irregularity identification method based on the vehicle frequency domain response is verified.
Claims (2)
1. The road surface unevenness recognition method based on the vehicle frequency domain response is characterized by comprising the following steps:
according to the stress analysis of the vehicle on the road surface, a motion equation of the vehicle is obtained, and the motion equation is shown in the formula (1):
where u (t) is a vehicle displacement matrix, M, C, K is a mass, damping and stiffness matrix of the vehicle, respectively, and F is the load to which the vehicle is subjected due to road surface irregularities;
f utilizing the rigidity K according to the wheels t And damping C t And road surface irregularities r (T) in contact with the wheels, calculated from formula (2), wherein T t A matrix of positions acting as wheels;
a1/2 vehicle model with 4 degrees of freedom is adopted, and the 4 degrees of freedom are respectively: vertical displacement u of vehicle body 1 (t) roll direction angular displacement u 2 (t) vertical displacement u of 2 wheels 3 (t)、u 4 (t), in FIG. 1, m is the mass of the vehicle body, J is the moment of inertia of roll of the vehicle body about the x-axis, m i (i=1, 2) mass, c of 2 wheels respectively i (i=1, 2) is the damping coefficient of 2 shock absorbers of the suspension, c i (i=3, 4) is the damping coefficient, k, of the tire i (i=1, 2) is a suspension2 spring rates, k of the frame i (i=3, 4) the vertical rigidity of 2 wheels, e 1 Representing the distance of the centroid to the rear axis e 2 Representing the distance of the centroid to the anterior axis; and (3) correspondingly writing out the parameter matrixes in the formula (1) and the formula (2), wherein the formula (3) is as follows:
fourier Transform (FFT) is performed on both ends of the above equation (1) and equation (2), respectively, to obtain equation (4):
-ω 2 MU(ω)+ωjCU(ω)+KU(ω)＝T t (ωjC t +K t )R(ω) (4)
wherein j is 2 -1, u (ω) is a vehicle displacement frequency domain response matrix, R (ω) is a contact point displacement frequency domain response matrix;
finishing formula (4) obtains an expression of U (ω), see formula (5):
U(ω)＝(-ω 2 M+ωjC+K) -1 T t (ωjC t +K t )R(ω) (5)
further, the observed vehicle frequency domain response U is obtained m (ω) see formula (6):
U m (ω)＝(ωj) n C 0 U(ω) (6)
wherein C is 0 Arranging an observation matrix corresponding to a sensor for a vehicle, wherein a parameter n reflects the type of the measured vehicle response, n=0 corresponds to a displacement response, n=1 corresponds to a speed response, and n=2 corresponds to an acceleration response;
deriving a vehicle system observation response U based on the above derivation m (ω) and the contact point displacement R (ω), see formula (7):
wherein H is ur (ω) is a displacement frequency response matrix of the vehicle displacement with respect to the contact point displacement, H mr For an observed frequency response matrix of measured vehicle responses with respect to contact point displacement,according to system parameters of the vehicle, calculating;
the distance between the front wheel and the rear wheel is e 1 +e 2 Assuming that the speed of the vehicle traveling forward at a constant speed is v, when the vehicle passes through the same position on the road surface, the time for the front wheel to advance from the rear wheel is t 0 ＝(e 1 +e 2 ) V; the unevenness of the road surface where the front wheel is positioned at the moment t is recorded as r 2 (t) the rear wheel experiences the position at a time t later than the front wheel 0 Time, i.e. t+t 0 Time of day, therefore r 1 (t+t 0 )＝r 2 (t); according to the time shift property of Fourier transform, r 2 (t) and r 1 Fourier transform R of (t) 2 (ω) and R 1 (ω) has the relationship of formula (8);
then R (ω) utilizes the contact point displacement Fourier transform R of the rear wheel 1 (ω) is represented by formula (9);
combining formula (7) and formula (9) to finally obtain R 1 A calculation formula of (omega) is shown as formula (10);
therefore, the road surface unevenness recognition process based on the vehicle frequency domain response is that, first, the observation frequency response matrix H is calculated by the formula (7) according to the vehicle parameters mr Then, the response U is observed in conjunction with the vehicle system m (ω) obtaining a contact point displacement Fourier transform R of the rear wheel by using the equation (10) 1 (ω), finally, for R 1 (omega) performing inverse Fourier transform (IFFT) to obtain the unevenness r of the road surface where the rear wheel is located 1 (t) as shown in formula (11), the road surface unevenness matrix r (t) of the wheel contact is shown in formula (12):
r 1 (t)＝IFFT(R 1 (ω)) (11)
2. the method for recognizing road surface irregularities based on frequency domain response of vehicles according to claim 1, characterized in that a sensor is disposed on the vehicle to allow the vehicle to travel on the road surface at a constant speed and measure vibration response of the vehicle, and then a frequency response function of the vehicle measurement response with respect to displacement of the vehicle-road contact point is constructed from physical parameters of the vehicle, and the road surface irregularities are recognized in combination with the vehicle measurement frequency domain response.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010769710.6A CN111976731B (en) | 2020-08-04 | 2020-08-04 | Road surface unevenness recognition method based on vehicle frequency domain response |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010769710.6A CN111976731B (en) | 2020-08-04 | 2020-08-04 | Road surface unevenness recognition method based on vehicle frequency domain response |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111976731A CN111976731A (en) | 2020-11-24 |
| CN111976731B true CN111976731B (en) | 2023-06-13 |
Family
ID=73445046
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010769710.6A Active CN111976731B (en) | 2020-08-04 | 2020-08-04 | Road surface unevenness recognition method based on vehicle frequency domain response |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111976731B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112766306B (en) * | 2020-12-26 | 2023-06-27 | 浙江天行健智能科技有限公司 | Deceleration strip area identification method based on SVM algorithm |
| CN113353085B (en) * | 2021-07-03 | 2023-03-21 | 西北工业大学 | Road surface unevenness identification method based on Kalman filtering theory |
| CN114737455B (en) * | 2022-04-21 | 2023-09-05 | 东软集团股份有限公司 | Pavement detection method, device and equipment |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104385873A (en) * | 2014-09-24 | 2015-03-04 | 湖南大学 | Multi-objective optimization method of car suspension system |
| CN108839657A (en) * | 2018-06-05 | 2018-11-20 | 吉林大学 | A method of online recognition road roughness information is responded based on automobile vibration |
| CN110781607A (en) * | 2019-11-07 | 2020-02-11 | 大连理工大学 | Bridge influence surface identification method considering vehicle wheel load spatial distribution |
-
2020
- 2020-08-04 CN CN202010769710.6A patent/CN111976731B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104385873A (en) * | 2014-09-24 | 2015-03-04 | 湖南大学 | Multi-objective optimization method of car suspension system |
| CN108839657A (en) * | 2018-06-05 | 2018-11-20 | 吉林大学 | A method of online recognition road roughness information is responded based on automobile vibration |
| CN110781607A (en) * | 2019-11-07 | 2020-02-11 | 大连理工大学 | Bridge influence surface identification method considering vehicle wheel load spatial distribution |
Non-Patent Citations (1)
| Title |
|---|
| 大跨度桥梁颤抖振分析;陈国芳;中国博士学位论文全文数据库(第2012年第06期期);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111976731A (en) | 2020-11-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111976731B (en) | Road surface unevenness recognition method based on vehicle frequency domain response | |
| JP6132304B2 (en) | Road surface evaluation apparatus and method | |
| CN104615888B (en) | Method for recognizing moving vehicle load on bridge based on generalized minimal residual algorithm | |
| CN104792937A (en) | Bridge head bump detection evaluation method based on vehicle-mounted gravitational acceleration sensor | |
| CN107894223B (en) | A kind of Road surface quality discrimination method based on inverse pseudo excitation method | |
| Wei et al. | Vehicle sideslip angle estimation based on general regression neural network | |
| CN101847270A (en) | Virtual prototype-based suspension system control simulation method of four-axle heavy truck | |
| CN104390794B (en) | Method based on drum dynamometer test data prediction tire flat road surface mechanical characteristic | |
| CN108228994A (en) | The calculation method for stress of vehicle and equipment under cross-country road arbitrary excitation | |
| CN105292121A (en) | Method for predicting stability of vehicles | |
| KR102242632B1 (en) | Method for the simulation of cornering | |
| KR100827181B1 (en) | Device and method for measuring dynamic loaded radius of tire | |
| CN111750810B (en) | Bridge deck roughness identification method based on vibration signals of two connected measuring vehicles | |
| CN111750819B (en) | Bridge deck roughness detection system | |
| CN106768234A (en) | Make the recognition methods of the bridge floor multiaxis traveling load of usage based on classical row | |
| CN116296180A (en) | Bridge damping ratio identification method based on double-shaft vehicle contact response space position relation | |
| CN109849932A (en) | A kind of the dynamic wheel load estimating system and method for pavement self-adaptive | |
| CN102057267A (en) | Method of estimating the transverse grip of a pair of tyres by comparative analysis | |
| Li et al. | Research method of tyre contact characteristics based on modal analysis | |
| CN106529118A (en) | Two-tuple linguistic AHP based intelligent automobile human-simulated steering control performance evaluation method | |
| Hu et al. | Research on heavy truck dynamic load coefficient and influence factors | |
| Hu | Road profile recovery using vertical acceleration data | |
| Sarkisov et al. | Understanding of transient tire behavior by measuring carcass deformation and contact patch shape | |
| De Mello et al. | Measurement and analysis of vehicle comfort and road quality | |
| Gobbi et al. | Motorcycle smart wheels for monitoring purposes |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |