CN112721936B - Method and device for detecting road surface peak adhesion coefficient and electronic equipment - Google Patents
Method and device for detecting road surface peak adhesion coefficient and electronic equipment Download PDFInfo
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- CN112721936B CN112721936B CN202110063085.8A CN202110063085A CN112721936B CN 112721936 B CN112721936 B CN 112721936B CN 202110063085 A CN202110063085 A CN 202110063085A CN 112721936 B CN112721936 B CN 112721936B
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- 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
- B60W40/064—Degree of grip
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- 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
- B60W2520/00—Input parameters relating to overall vehicle dynamics
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- 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
- B60W2520/00—Input parameters relating to overall vehicle dynamics
- B60W2520/10—Longitudinal speed
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Abstract
The application provides a method and a device for detecting a peak adhesion coefficient of a road surface and electronic equipment, wherein the method comprises the following steps: acquiring the wheel driving force, the wheel load, the longitudinal speed and the angular speed of the tire of the current tire; according to the wheel driving force and the wheel load; determining the adhesion coefficient of the current tire; determining the longitudinal slip rate of the current tire according to the longitudinal speed and the angular speed of the tire; determining corresponding tangential stiffness according to the adhesion coefficient and the longitudinal sliding rate of the current tire; constructing a tire state matrix of the current tire by using the tangential stiffness and the adhesion coefficient of the current tire; and determining the road surface peak value adhesion coefficient of the current tire according to the tire state matrix based on a preset Gaussian distribution algorithm. According to the method provided by the scheme, the road surface peak value adhesion coefficient of the current tire is detected by monitoring the adhesion coefficient and the longitudinal sliding rate of the current tire in real time, so that the accuracy of the detection result of the road surface peak value adhesion coefficient is improved, and a foundation is laid for improving the safety of vehicles.
Description
Technical Field
The application relates to the technical field of intelligent driving, in particular to a method and a device for detecting a road surface peak adhesion coefficient and electronic equipment.
Background
With the popularization of intelligent driving, the safety requirement on an intelligent driving system is higher and higher, so that the stable boundary of the current vehicle needs to be estimated in real time when the vehicle normally runs, reasonable constraint can be generated on the current transverse and longitudinal control quantity in real time, and the peak road adhesion coefficient is a key parameter for defining the stable boundary of the vehicle.
In the prior art, a current peak road adhesion coefficient is generally collected based on a preset sensor on a vehicle, or a corresponding peak road adhesion coefficient is determined according to the current running condition of the vehicle according to historical experimental data.
However, in order to improve the accuracy of the road surface peak adhesion coefficient acquired by the sensor, it is necessary to mount a high-precision sensor on the vehicle, which increases the manufacturing cost of the vehicle. In addition, experimental data are difficult to completely cover various running conditions of the vehicle, so that the accuracy of the obtained road surface peak adhesion coefficient is low. Therefore, a method for detecting the peak adhesion coefficient of the road surface with high accuracy is urgently needed, and has important significance for improving the safety of vehicles.
Disclosure of Invention
The application provides a method and a device for detecting a peak value adhesion coefficient of a road surface and electronic equipment, which aim to overcome the defects of low accuracy and the like in the prior art.
The application provides a road surface peak adhesion coefficient detection method in a first aspect, which comprises the following steps:
acquiring the wheel driving force, the wheel load, the longitudinal speed and the angular speed of the tire of the current tire;
according to the wheel driving force and the wheel load; determining the adhesion coefficient of the current tire;
determining the longitudinal slip rate of the current tire according to the tire longitudinal speed and the tire angular speed;
determining corresponding tangential stiffness according to the adhesion coefficient and the longitudinal slip rate of the current tire;
constructing a tire state matrix of the current tire by using the tangential stiffness and the adhesion coefficient of the current tire;
and determining the road surface peak value adhesion coefficient of the current tire according to the tire state matrix based on a preset Gaussian distribution algorithm.
Optionally, before determining the peak road surface adhesion coefficient of the current tire according to the tire state matrix based on a preset gaussian distribution algorithm, the method further includes:
determining the corresponding secant stiffness according to the adhesion coefficient and the longitudinal sliding rate of the current tire;
determining the linear state of the current tire according to the tangential stiffness and the secant stiffness of the current tire based on a preset linear state determination rule;
and when the linear state of the current tire is determined to be a normal state, executing the preset Gaussian distribution algorithm, and determining the road surface peak adhesion coefficient of the current tire according to the tire state matrix.
Optionally, the method further includes:
judging whether the longitudinal sliding rate of the current tire is smaller than a preset sliding rate threshold value or not;
and when the longitudinal sliding rate of the current tire is smaller than a preset sliding rate threshold value, executing the preset Gaussian distribution algorithm, and determining the road surface peak value adhesion coefficient of the current tire according to the tire state matrix.
Optionally, the determining a road surface peak adhesion coefficient of the current tire according to the tire state matrix based on a preset gaussian distribution algorithm includes:
based on a preset Gaussian distribution algorithm, determining a tangent rigidity mean value of the tangent rigidity and a corresponding covariance matrix according to the tire state matrix;
performing singular decomposition on the covariance matrix to obtain a corresponding tire state characteristic matrix;
and determining the road surface peak value attachment coefficient of the current tire according to the tire state characteristic matrix and the tangent stiffness average value.
Optionally, the determining the corresponding tangential stiffness according to the adhesion coefficient and the longitudinal slip ratio of the current tire includes:
based on a preset Butterworth filter, carrying out filtering processing on the adhesion coefficient and the longitudinal sliding rate of the current tire to obtain a corresponding target adhesion coefficient and a corresponding target longitudinal sliding rate;
carrying out difference calculation on the target adhesion coefficient and the target longitudinal slip ratio to obtain corresponding difference calculation results;
and based on a preset RLS filter, carrying out filtering processing on the target attachment coefficient, the target longitudinal sliding rate and the difference calculation result to obtain corresponding tangential stiffness.
Optionally, the wheel-side driving force and the wheel-side load are used as the basis; determining a grip coefficient of the current tire, comprising:
calculating the adhesion coefficient of the current tire according to the following formula:
wherein, muxRepresents the coefficient of adhesion, FwhlRepresenting said wheel rim driving force, FzRepresenting the wheel side load.
Optionally, the determining the longitudinal slip rate of the current tire according to the tire longitudinal speed and the tire angular speed includes:
calculating the longitudinal slip ratio of the current tire according to the following formula:
wherein S isxiRepresents the longitudinal slip ratio, v, of said current tyrexiRepresenting said tire longitudinal speed, ωiIndicating angular velocity, R, of the tiregiIndicating a preset rolling radius of the current tire.
A second aspect of the present application provides a road surface peak adhesion coefficient detection apparatus, including:
the acquisition module is used for acquiring the wheel driving force, the wheel load, the longitudinal speed and the angular speed of the tire of the current tire;
a first determination module for determining the wheel driving force and the wheel load according to the wheel driving force; determining the adhesion coefficient of the current tire;
the second determination module is used for determining the longitudinal slip rate of the current tire according to the tire longitudinal speed and the tire angular speed;
the third determining module is used for determining the corresponding tangential stiffness according to the adhesion coefficient and the longitudinal slip rate of the current tire;
the building module is used for building a tire state matrix of the current tire by using the tangential stiffness and the adhesion coefficient of the current tire;
and the detection module is used for determining the road surface peak value adhesion coefficient of the current tire according to the tire state matrix based on a preset Gaussian distribution algorithm.
Optionally, the detection module is further configured to:
determining the corresponding secant stiffness according to the adhesion coefficient and the longitudinal sliding rate of the current tire;
determining the linear state of the current tire according to the tangential stiffness and the secant stiffness of the current tire based on a preset linear state determination rule;
and when the linear state of the current tire is determined to be a normal state, executing the preset Gaussian distribution algorithm, and determining the road surface peak adhesion coefficient of the current tire according to the tire state matrix.
Optionally, the detection module is further configured to:
judging whether the longitudinal sliding rate of the current tire is smaller than a preset sliding rate threshold value or not;
and when the longitudinal sliding rate of the current tire is smaller than a preset sliding rate threshold value, executing the preset Gaussian distribution algorithm, and determining the road surface peak value adhesion coefficient of the current tire according to the tire state matrix.
Optionally, the detection module is specifically configured to:
based on a preset Gaussian distribution algorithm, determining a tangent rigidity mean value of the tangent rigidity and a corresponding covariance matrix according to the tire state matrix;
performing singular decomposition on the covariance matrix to obtain a corresponding tire state characteristic matrix;
and determining the road surface peak value attachment coefficient of the current tire according to the tire state characteristic matrix and the tangent stiffness average value.
Optionally, the third determining module is specifically configured to:
based on a preset Butterworth filter, carrying out filtering processing on the adhesion coefficient and the longitudinal sliding rate of the current tire to obtain a corresponding target adhesion coefficient and a corresponding target longitudinal sliding rate;
carrying out difference calculation on the target adhesion coefficient and the target longitudinal slip ratio to obtain corresponding difference calculation results;
and based on a preset RLS filter, carrying out filtering processing on the target attachment coefficient, the target longitudinal sliding rate and the difference calculation result to obtain corresponding tangential stiffness.
Optionally, the first determining module is specifically configured to:
calculating the adhesion coefficient of the current tire according to the following formula:
wherein, muxRepresents the coefficient of adhesion, FwhlRepresenting said wheel rim driving force, FzRepresenting the wheel side load.
Optionally, the second determining module is specifically configured to:
calculating the longitudinal slip ratio of the current tire according to the following formula:
wherein S isxiRepresents the longitudinal slip ratio, v, of said current tyrexiRepresenting said tire longitudinal speed, ωiIndicating angular velocity, R, of the tiregiIndicating a preset rolling radius of the current tire.
A third aspect of the present application provides an electronic device, comprising: at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executes computer-executable instructions stored by the memory to cause the at least one processor to perform the method as set forth in the first aspect above and in various possible designs of the first aspect.
A fourth aspect of the present application provides a computer-readable storage medium having stored thereon computer-executable instructions that, when executed by a processor, implement a method as set forth in the first aspect and various possible designs of the first aspect.
This application technical scheme has following advantage:
according to the method, the device and the electronic equipment for detecting the peak road adhesion coefficient, the wheel driving force, the wheel load, the longitudinal speed and the angular speed of the tire of the current tire are obtained; according to the wheel driving force and the wheel load; determining the adhesion coefficient of the current tire; determining the longitudinal slip rate of the current tire according to the longitudinal speed and the angular speed of the tire; determining corresponding tangential stiffness according to the adhesion coefficient and the longitudinal sliding rate of the current tire; constructing a tire state matrix of the current tire by using the tangential stiffness and the adhesion coefficient of the current tire; and determining the road surface peak value adhesion coefficient of the current tire according to the tire state matrix based on a preset Gaussian distribution algorithm. According to the method provided by the scheme, the road surface peak value adhesion coefficient of the current tire is detected by monitoring the adhesion coefficient and the longitudinal sliding rate of the current tire in real time, so that the accuracy of the detection result of the road surface peak value adhesion coefficient is improved, and a foundation is laid for improving the safety of vehicles.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art according to these drawings.
Fig. 1 is a schematic structural diagram of a road surface peak adhesion coefficient detection system based on an embodiment of the present application;
fig. 2 is a schematic flow chart of a method for detecting a peak adhesion coefficient of a road surface according to an embodiment of the present disclosure;
fig. 3 is a schematic flow chart of another method for detecting a peak adhesion coefficient of a road surface according to an embodiment of the present disclosure;
fig. 4 is a schematic overall flow chart of an exemplary road surface peak adhesion coefficient detection method provided in the embodiment of the present application;
fig. 5 is a schematic structural diagram of a road surface peak adhesion coefficient detection device according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
With the above figures, there are shown specific embodiments of the present application, which will be described in more detail below. These drawings and written description are not intended to limit the scope of the disclosed concepts in any way, but rather to illustrate the concepts of the disclosure to those skilled in the art by reference to specific embodiments.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Furthermore, the terms "first", "second", etc. are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. In the description of the following examples, "plurality" means two or more unless specifically limited otherwise.
In the prior art, a current peak road adhesion coefficient is generally collected based on a preset sensor on a vehicle, or a corresponding peak road adhesion coefficient is determined according to the current running condition of the vehicle according to historical experimental data. However, in order to improve the accuracy of the road surface peak adhesion coefficient acquired by the sensor, it is necessary to mount a high-precision sensor on the vehicle, which increases the manufacturing cost of the vehicle. In addition, experimental data are difficult to completely cover various running conditions of the vehicle, so that the accuracy of the obtained road surface peak adhesion coefficient is low.
In order to solve the above problems, the method, the device and the electronic device for detecting the peak road adhesion coefficient provided by the embodiment of the present application obtain the wheel driving force, the wheel load, the longitudinal speed and the angular speed of the tire of the current tire; according to the wheel driving force and the wheel load; determining the adhesion coefficient of the current tire; determining the longitudinal slip rate of the current tire according to the longitudinal speed and the angular speed of the tire; determining corresponding tangential stiffness according to the adhesion coefficient and the longitudinal sliding rate of the current tire; constructing a tire state matrix of the current tire by using the tangential stiffness and the adhesion coefficient of the current tire; and determining the road surface peak value adhesion coefficient of the current tire according to the tire state matrix based on a preset Gaussian distribution algorithm. According to the method provided by the scheme, the road surface peak value adhesion coefficient of the current tire is detected by monitoring the adhesion coefficient and the longitudinal sliding rate of the current tire in real time, so that the accuracy of the detection result of the road surface peak value adhesion coefficient is improved, and a foundation is laid for improving the safety of vehicles.
The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present invention will be described below with reference to the accompanying drawings.
First, a description will be given of a configuration of a road surface peak adhesion coefficient detection system based on the present application:
the method and the device for detecting the road surface peak adhesion coefficient and the electronic equipment are suitable for detecting the road surface peak adhesion coefficient of a tire. As shown in fig. 1, the system is a schematic structural diagram of a road surface peak adhesion coefficient detection system based on an embodiment of the present application, and mainly includes a current tire, a data acquisition device, and a road surface peak adhesion coefficient detection device for detecting a road surface adhesion coefficient of the current tire. Specifically, the data acquisition device may be used to acquire the wheel driving force, the wheel load, the tire longitudinal speed, and the tire angular speed of the current tire, and send the acquired data to the road surface peak adhesion coefficient detection device, which is used to detect the road surface peak adhesion coefficient of the current tire and generate a corresponding detection result.
The embodiment of the application provides a road surface peak adhesion coefficient detection method, which is used for detecting the road surface peak adhesion coefficient of a tire in the running process of a vehicle. The implementation subject of the embodiment of the present application is an electronic device, such as a server, a desktop computer, a notebook computer, a tablet computer, and other electronic devices that can be used to detect peak road adhesion.
As shown in fig. 2, a schematic flow chart of a method for detecting a peak road adhesion coefficient according to an embodiment of the present application is shown, where the method includes:
Specifically, the wheel-side driving force, the wheel-side load, the tire longitudinal speed, and the tire angular speed of the present tire to be detected may be acquired using a data sensor (data acquisition device) mounted on the vehicle, such as a hall sensor or the like.
Specifically, in one embodiment, the wheel-side driving force may also be calculated based on the following formula:
wherein, FwhlRepresenting the wheel-side driving force, also called the wheel-side longitudinal force, ReiIndicating the rolling radius of the current tire, FfShows the sliding resistance of the entire vehicle, FmodelRepresenting the driving force, k, of the tyre obtained by a predetermined tyre calculation model1And k2Respectively representing the weight coefficients corresponding to the two, wherein the weight coefficients are not more than 1, and the adaptability of the wheel rim moment is improved mainly through the feedback correction of a tire calculation model.
For example, the embodiment of the application provides a method for predicting the peak road adhesion coefficient of a front-drive vehicle, so that the left front wheel is used for carrying out mumaxEstimate, left front wheel side load FzIt is mainly calculated by the following formula:
wherein m is the mass (kilogram) of the whole vehicle, LfDistance of the centroid to the front axis (m), Lr distance of the centroid to the rear axis (m), bfIs the track (meter) of the front wheel, brIs the rear wheel track (meter), axFor longitudinal acceleration, ayIs the lateral acceleration.
Specifically, in one embodiment, the adhesion coefficient of the current tire may be calculated according to the following formula:
wherein, muxDenotes the coefficient of adhesion, FwhlIndicating wheel rim driving force, FzRepresenting the wheel side load.
And step 203, determining the longitudinal slip rate of the current tire according to the longitudinal speed and the angular speed of the tire.
Specifically, in one embodiment, the longitudinal slip rate of the current tire may be calculated according to the following formula:
wherein S isxiIndicating the longitudinal slip ratio, v, of the current tirexiRepresenting the longitudinal speed, ω, of the tyreiIndicating angular velocity, R, of the tiregiIndicating a preset rolling radius of the current tire.
Illustratively, since the vehicle has a driving slip rate and a braking slip rate, respectively, in the longitudinal condition, collectively referred to as slip rates, since μ is being carried outmaxDuring identification, the driving and braking conditions of the vehicle need to be considered comprehensively, and therefore need to be considered respectively. Because the vehicle has dynamic change processes of increasing and decreasing driving force and braking force respectively, in order to cover the common working conditions of the vehicle as much as possible, logic switching is designed, namely slip ratio formula calculation is used when the vehicle is driven, slip ratio calculation is used when braking is carried out, and moving sliding average smoothing processing is carried out when switching is carried out.
The slip ratio calculation formula is as follows:
and step 204, determining the corresponding tangential stiffness according to the adhesion coefficient and the longitudinal slip rate of the current tire.
Specifically, because the vehicle running condition is complex, and there are large random errors and uncertain sudden changes in the used adhesion coefficient and slip ratio, filtering processing is required to be performed, and the filtering processing is performed firstly aiming at mu respectivelyxAnd SxiAnd filtering, respectively performing difference calculation on the filtered signals, and then performing RLS filtering on the signals before and after calculation to obtain the tangential stiffness.
Specifically, in one embodiment, the adhesion coefficient and the longitudinal slip rate of the current tire may be filtered based on a preset butterworth filter to obtain a corresponding target adhesion coefficient and a target longitudinal slip rate; carrying out differential calculation on the target adhesion coefficient and the target longitudinal slip rate to obtain corresponding differential calculation results; and based on a preset RLS filter, carrying out filtering processing on the target attachment coefficient, the target longitudinal sliding rate and the difference calculation result to obtain corresponding tangential stiffness.
It should be explained that the tangential stiffness represents μxThe slope of the tangent to the curve.
And step 205, constructing a tire state matrix of the current tire by using the tangential stiffness and the adhesion coefficient of the current tire.
Specifically, the adhesion coefficient μmay be usedxAnd
a 2x 1 matrix is constructed, that is, a tire condition matrix of the current tire. Wherein XBS represents the tangential stiffness.
And step 206, determining the road surface peak value adhesion coefficient of the current tire according to the tire state matrix based on a preset Gaussian distribution algorithm.
Specifically, in an embodiment, the tangent stiffness mean value of the tangent stiffness and the corresponding covariance matrix may be determined according to the tire state matrix based on a preset gaussian distribution algorithm; performing singular decomposition on the covariance matrix to obtain a corresponding tire state characteristic matrix; and determining the road surface peak value attachment coefficient of the current tire according to the tire state characteristic matrix and the tangent stiffness average value.
Illustratively, the assumption of a Gaussian distribution of the distribution of a two-dimensional matrix of inputs may be made
Iterative calculations based on this assumption result in the mean and variance, i.e., μ, σ2The iterative calculation formula derivation process is as follows:
specifically, after obtaining the 2X2 covariance matrix of the input matrix, the singular decomposition is performed to obtain the feature matrix U, which is a matrix of 2X2 and is also an orthogonal matrix, the essence of which is rotation, and the corresponding angle θ is obtained by conversion of the feature matrix U, so that the following common method can be usedFormula calculation of road surface peak value adhesion coefficient mux，maxCan also be recorded as mumax：
μx，max＝Xk(1，1)-Cotθ·Xk(2，1)
On the basis of the above embodiment, since the accuracy of the road surface peak adhesion coefficient obtained based on the above method is relatively low when the operating condition of the vehicle is in an unstable state, in order to improve the accuracy of the obtained test result, as an implementable manner, on the basis of the above embodiment, in an embodiment, before determining the road surface peak adhesion coefficient of the current tire according to the tire condition matrix based on a preset gaussian distribution algorithm, the method further includes:
step 301, determining corresponding secant stiffness according to the adhesion coefficient and the longitudinal sliding rate of the current tire;
step 302, determining a linear state of the current tire according to the tangent stiffness and the secant stiffness of the current tire based on a preset linear state determination rule;
and 303, when the linear state of the current tire is determined to be a normal state, executing a preset Gaussian distribution algorithm, and determining the road surface peak adhesion coefficient of the current tire according to the tire state matrix.
Exemplarily, as shown in fig. 3, a schematic flow chart of another method for detecting a peak adhesion coefficient of a road surface provided in the embodiment of the present application is shown. Where the secant stiffness represents the slope from the origin of the curve to the current tire operating point. The linear state of the current tire can be identified by fusion filtering of the two parameters.
Specifically, in order to filter out a strong nonlinear region and a working point of a low-slip-rate overlapping region and optimize the precision of an iterative solution part, the embodiments of the present application provide some iterative triggering conditions. Firstly, it is confirmed that the current clock information is correct, secondly, the slip rate threshold value needs to be judged, and because the current clock information is directed to the pure longitudinal working condition, the threshold value limitation of the steering wheel rotation angle needs to be carried out, and the same is true for the current clock informationThere is also a vehicle speed limit. Setting up
Then the judgment limit of K e R is set, if all the above judgment is correct, 1 is output, and the iterative calculation step provided by the above embodiment is triggered.
It should be explained that when the linear state of the current tire is a normal state, that is, the running state of the current tire is in a linear region, that is, the current working condition is stable.
Specifically, in one embodiment, it may be determined whether the longitudinal slip rate of the current tire is less than a preset slip rate threshold; and when the longitudinal sliding rate of the current tire is smaller than a preset sliding rate threshold value, executing a step of determining the road surface peak value adhesion coefficient of the current tire according to the tire state matrix based on a preset Gaussian distribution algorithm.
The slip rate threshold may be specifically set according to an actual application, and the embodiment of the present application is not limited.
It should be explained that, for the method for detecting the peak road adhesion coefficient provided in the embodiment of the present application, a specific theoretical derivation process of the peak road adhesion coefficient is as follows:
theoretically, the operating point rule under the mu-XBS phase diagram is more suitable for carrying out mumaxEstimation, the following derivation is continued:
will be provided with
Taking the derivative of Φ, one can obtain:
dividing the last infinite series by the number of inequality
The following can be obtained:
and:
because:
moreover, during the actual running of the vehicle, the tire force hardly exceeds the peak point, so KtopAnd KmaxThe specific calculation process of (2) is as follows:
the slope of the corresponding point is Ktop：
Wherein KtopNormalized tangential stiffness for the force peak point correspondences, combined with:
the above formula is equivalent to:
the above formula is the key point of the mu-XBS phase diagram. Under the mu-XBS phase diagram, the above formula is characterized as: the (mu, XBS) working point obtained on a section of road surface with better consistency is always between two straight lines, and the intersection point of the two boundary lines and the mu coordinate axis is the upper and lower bounds of the peak value adhesion coefficient of the section of road surface.
Exemplarily, as shown in fig. 4, an overall flow chart of an exemplary road surface peak adhesion coefficient detection method provided in the embodiments of the present application is schematically illustrated. The method shown in fig. 4 is specifically an exemplary implementation of the method shown in fig. 2, and the two implementation principles are the same and are not described again.
According to the road surface peak adhesion coefficient detection method provided by the embodiment of the application, the wheel driving force, the wheel load, the longitudinal speed and the angular speed of the tire of the current tire are obtained; according to the wheel driving force and the wheel load; determining the adhesion coefficient of the current tire; determining the longitudinal slip rate of the current tire according to the longitudinal speed and the angular speed of the tire; determining corresponding tangential stiffness according to the adhesion coefficient and the longitudinal sliding rate of the current tire; constructing a tire state matrix of the current tire by using the tangential stiffness and the adhesion coefficient of the current tire; and determining the road surface peak value adhesion coefficient of the current tire according to the tire state matrix based on a preset Gaussian distribution algorithm. According to the method provided by the scheme, the road surface peak value adhesion coefficient of the current tire is detected by monitoring the adhesion coefficient and the longitudinal sliding rate of the current tire in real time, so that the accuracy of the detection result of the road surface peak value adhesion coefficient is improved, and a foundation is laid for improving the safety of vehicles. And in addition, by combining the modern control theory and vehicle dynamics, an iterative estimation method based on Gaussian distribution is provided, and the accuracy of the detection result of the road surface peak value adhesion coefficient is further improved.
The embodiment of the application provides a road surface peak adhesion coefficient detection device, which is used for executing the road surface peak adhesion coefficient detection method provided by the embodiment.
Fig. 5 is a schematic structural diagram of a road surface peak adhesion coefficient detection device according to an embodiment of the present application. The road surface peak adhesion coefficient detection device 70 comprises an acquisition module 701, a first determination module 702, a second determination module 703, a third determination module 704, a construction module 705 and a detection module 706.
The device comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring the wheel driving force, the wheel load, the longitudinal speed and the angular speed of the tire of the current tire; a first determination module for determining a wheel-side driving force and a wheel-side load; determining the adhesion coefficient of the current tire; the second determination module is used for determining the longitudinal slip rate of the current tire according to the longitudinal speed and the angular speed of the tire; the third determining module is used for determining the corresponding tangential stiffness according to the adhesion coefficient and the longitudinal slip rate of the current tire; the building module is used for building a tire state matrix of the current tire by using the tangential stiffness and the adhesion coefficient of the current tire; and the detection module is used for determining the road surface peak value adhesion coefficient of the current tire according to the tire state matrix based on a preset Gaussian distribution algorithm.
Specifically, in an embodiment, the detection module is further configured to:
determining the corresponding secant stiffness according to the adhesion coefficient and the longitudinal sliding rate of the current tire;
determining the linear state of the current tire according to the tangential stiffness and the secant stiffness of the current tire based on a preset linear state determination rule;
and when the linear state of the current tire is determined to be a normal state, executing a preset Gaussian distribution algorithm, and determining the road surface peak adhesion coefficient of the current tire according to the tire state matrix.
Specifically, in an embodiment, the detection module is further configured to:
judging whether the longitudinal sliding rate of the current tire is smaller than a preset sliding rate threshold value or not;
and when the longitudinal sliding rate of the current tire is smaller than a preset sliding rate threshold value, executing a step of determining the road surface peak value adhesion coefficient of the current tire according to the tire state matrix based on a preset Gaussian distribution algorithm.
Specifically, in an embodiment, the detection module is specifically configured to:
determining a tangent rigidity mean value of tangent rigidity and a corresponding covariance matrix according to a tire state matrix based on a preset Gaussian distribution algorithm;
performing singular decomposition on the covariance matrix to obtain a corresponding tire state characteristic matrix;
and determining the road surface peak value attachment coefficient of the current tire according to the tire state characteristic matrix and the tangent stiffness average value.
Specifically, in an embodiment, the third determining module is specifically configured to:
based on a preset Butterworth filter, carrying out filtering processing on the adhesion coefficient and the longitudinal sliding rate of the current tire to obtain a corresponding target adhesion coefficient and a corresponding target longitudinal sliding rate;
carrying out differential calculation on the target adhesion coefficient and the target longitudinal slip rate to obtain corresponding differential calculation results;
and based on a preset RLS filter, carrying out filtering processing on the target attachment coefficient, the target longitudinal sliding rate and the difference calculation result to obtain corresponding tangential stiffness.
Specifically, in an embodiment, the first determining module is specifically configured to:
the adhesion coefficient of the current tire is calculated according to the following formula:
wherein, muxDenotes the coefficient of adhesion, FwhlIndicating wheel rim driving force, FzRepresenting the wheel side load.
Specifically, in an embodiment, the second determining module is specifically configured to:
calculating the longitudinal slip ratio of the current tire according to the following formula:
wherein S isxiIndicating the longitudinal slip ratio, v, of the current tirexiRepresenting the longitudinal speed, ω, of the tyreiIndicating angular velocity, R, of the tiregiIndicating a preset rolling radius of the current tire.
With regard to the road surface peak adhesion coefficient detection apparatus in the present embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
The device for detecting the peak road adhesion coefficient provided by the embodiment of the application is used for executing the method for detecting the peak road adhesion coefficient provided by the embodiment, and the implementation manner and the principle are the same, and are not repeated.
The embodiment of the application provides electronic equipment for executing the road surface peak adhesion coefficient detection method provided by the embodiment.
Fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application. The electronic device 80 includes: at least one processor 81 and memory 82;
the memory stores computer-executable instructions; the at least one processor executes computer-executable instructions stored in the memory, so that the at least one processor executes the road surface peak adhesion coefficient detection method provided by the above embodiments.
The electronic device provided by the embodiment of the application is used for executing the method for detecting the peak value adhesion coefficient of the road surface provided by the embodiment, and the implementation manner and the principle of the method are the same and are not repeated.
The embodiment of the application provides a computer-readable storage medium, wherein a computer executing instruction is stored in the computer-readable storage medium, and when a processor executes the computer executing instruction, the method for detecting a road surface peak adhesion coefficient provided by any one of the above embodiments is implemented.
The storage medium including the computer-executable instructions of the embodiment of the present application may be used to store the computer-executable instructions of the road surface peak adhesion coefficient detection method provided in the foregoing embodiment, and the implementation manner and the principle thereof are the same and are not described again.
It is obvious to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional modules is merely used as an example, and in practical applications, the above function distribution may be performed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to perform all or part of the above described functions. For the specific working process of the device described above, reference may be made to the corresponding process in the foregoing method embodiment, which is not described herein again.
Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present application.
Claims (9)
1. A road surface peak adhesion coefficient detection method is characterized by comprising the following steps:
acquiring the wheel driving force, the wheel load, the longitudinal speed and the angular speed of the tire of the current tire;
according to the wheel driving force and the wheel load; determining the adhesion coefficient of the current tire;
determining the longitudinal slip rate of the current tire according to the tire longitudinal speed and the tire angular speed;
determining corresponding tangential stiffness according to the adhesion coefficient and the longitudinal slip rate of the current tire;
constructing a tire state matrix of the current tire by using the tangential stiffness and the adhesion coefficient of the current tire;
determining a road surface peak value adhesion coefficient of the current tire according to the tire state matrix based on a preset Gaussian distribution algorithm;
the method for determining the road surface peak value adhesion coefficient of the current tire according to the tire state matrix based on the preset Gaussian distribution algorithm comprises the following steps:
based on a preset Gaussian distribution algorithm, determining a tangent rigidity mean value of the tangent rigidity and a corresponding covariance matrix according to the tire state matrix;
performing singular decomposition on the covariance matrix to obtain a corresponding tire state characteristic matrix;
determining the road surface peak value attachment coefficient of the current tire according to the tire state characteristic matrix and the tangent stiffness mean value;
determining the road surface peak value attachment coefficient of the current tire according to the tire state characteristic matrix and the tangent stiffness mean value, wherein the determining comprises the following steps:
calculating the road surface peak adhesion coefficient of the current tire according to the following formula:
μx，max＝Xk(1，1)-Cotθ·Xk(2，1)
wherein the content of the first and second substances,
μ denotes the mean value of the tangential stiffness, σ2Denotes the tangent stiffness covariance, XBS denotes the tangent stiffness, μxThe adhesion coefficient is expressed, and θ represents the conversion angle of the tire condition characteristic matrix.
2. The method of claim 1, wherein before determining the road surface peak adhesion coefficient of the current tire from the tire state matrix based on a preset gaussian distribution algorithm, the method further comprises:
determining the corresponding secant stiffness according to the adhesion coefficient and the longitudinal sliding rate of the current tire;
determining the linear state of the current tire according to the tangential stiffness and the secant stiffness of the current tire based on a preset linear state determination rule;
and when the linear state of the current tire is determined to be a normal state, executing the preset Gaussian distribution algorithm, and determining the road surface peak adhesion coefficient of the current tire according to the tire state matrix.
3. The method of claim 1, further comprising:
judging whether the longitudinal sliding rate of the current tire is smaller than a preset sliding rate threshold value or not;
and when the longitudinal sliding rate of the current tire is smaller than a preset sliding rate threshold value, executing the preset Gaussian distribution algorithm, and determining the road surface peak value adhesion coefficient of the current tire according to the tire state matrix.
4. The method of claim 1, wherein said determining a corresponding tangential stiffness based on the adhesion coefficient and longitudinal slip ratio of the current tire comprises:
based on a preset Butterworth filter, carrying out filtering processing on the adhesion coefficient and the longitudinal sliding rate of the current tire to obtain a corresponding target adhesion coefficient and a corresponding target longitudinal sliding rate;
carrying out difference calculation on the target adhesion coefficient and the target longitudinal slip ratio to obtain corresponding difference calculation results;
and based on a preset RLS filter, carrying out filtering processing on the target attachment coefficient, the target longitudinal sliding rate and the difference calculation result to obtain corresponding tangential stiffness.
5. The method of claim 1, wherein the wheel-side drive force and wheel-side load are based on the wheel-side drive force and the wheel-side load; determining a grip coefficient of the current tire, comprising:
calculating the adhesion coefficient of the current tire according to the following formula:
wherein, muxRepresents the coefficient of adhesion, FwhlRepresenting said wheel rim driving force, FzRepresenting the wheel side load.
6. The method of claim 1, wherein determining a longitudinal slip rate of a current tire from the tire longitudinal velocity and tire angular velocity comprises:
calculating the longitudinal slip ratio of the current tire according to the following formula:
wherein S isxiRepresents the longitudinal slip ratio, v, of said current tyrexiRepresenting said tire longitudinal speed, ωiIndicating angular velocity, R, of the tiregiIndicating a preset rolling radius of the current tire.
7. A road surface peak adhesion coefficient detection device is characterized by comprising:
the acquisition module is used for acquiring the wheel driving force, the wheel load, the longitudinal speed and the angular speed of the tire of the current tire;
a first determination module for determining the wheel driving force and the wheel load according to the wheel driving force; determining the adhesion coefficient of the current tire;
the second determination module is used for determining the longitudinal slip rate of the current tire according to the tire longitudinal speed and the tire angular speed;
the third determining module is used for determining the corresponding tangential stiffness according to the adhesion coefficient and the longitudinal slip rate of the current tire;
the building module is used for building a tire state matrix of the current tire by using the tangential stiffness and the adhesion coefficient of the current tire;
the detection module is used for determining the road surface peak value adhesion coefficient of the current tire according to the tire state matrix based on a preset Gaussian distribution algorithm;
wherein, the detection module is specifically configured to:
based on a preset Gaussian distribution algorithm, determining a tangent rigidity mean value of the tangent rigidity and a corresponding covariance matrix according to the tire state matrix; performing singular decomposition on the covariance matrix to obtain a corresponding tire state characteristic matrix; determining the road surface peak value attachment coefficient of the current tire according to the tire state characteristic matrix and the tangent stiffness mean value;
the detection module is further specifically configured to:
calculating the road surface peak adhesion coefficient of the current tire according to the following formula:
μx，max＝Xk(1，1)-Cotθ·Xk(2，1)
wherein the content of the first and second substances,
μ denotes the mean value of the tangential stiffness, σ2Denotes the tangent stiffness covariance, XBS denotes the tangent stiffness, μxThe adhesion coefficient is expressed, and θ represents the conversion angle of the tire condition characteristic matrix.
8. An electronic device, comprising: at least one processor and memory;
the memory stores computer-executable instructions;
the at least one processor executing the computer-executable instructions stored by the memory causes the at least one processor to perform the method of any of claims 1-6.
9. A computer-readable storage medium having computer-executable instructions stored thereon which, when executed by a processor, implement the method of any one of claims 1 to 6.
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