CN114492078A - Method and device for determining tire sidewall deflection stiffness - Google Patents
Method and device for determining tire sidewall deflection stiffness Download PDFInfo
- Publication number
- CN114492078A CN114492078A CN202210177458.9A CN202210177458A CN114492078A CN 114492078 A CN114492078 A CN 114492078A CN 202210177458 A CN202210177458 A CN 202210177458A CN 114492078 A CN114492078 A CN 114492078A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- residual error
- determining
- cornering stiffness
- lateral
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 81
- 230000001133 acceleration Effects 0.000 claims abstract description 80
- 230000008569 process Effects 0.000 claims abstract description 24
- 230000006870 function Effects 0.000 claims description 85
- 238000012937 correction Methods 0.000 claims description 22
- 238000012986 modification Methods 0.000 claims description 6
- 230000004048 modification Effects 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 5
- 238000004364 calculation method Methods 0.000 claims description 4
- 238000010276 construction Methods 0.000 claims description 3
- 238000004422 calculation algorithm Methods 0.000 description 10
- 238000010586 diagram Methods 0.000 description 4
- 230000009471 action Effects 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000004590 computer program Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
- 230000001629 suppression Effects 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000013500 data storage Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- 230000007257 malfunction Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000000704 physical effect Effects 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 230000001052 transient effect Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/10—Noise analysis or noise optimisation
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Control Of Driving Devices And Active Controlling Of Vehicle (AREA)
- Tires In General (AREA)
Abstract
The invention relates to a method and a device for determining tire sidewall deflection rigidity, wherein the method comprises the following steps: determining a vehicle lateral acceleration estimation model taking the cornering stiffness of the front wheels and the rear wheels of the vehicle as a parameter to be identified; determining a difference value between an estimated value of the lateral acceleration of the vehicle and a true value of the current moment when the lateral stiffness is a preset initial value, and obtaining a residual error; searching the minimum value of the corrected residual error in the direction which can enable the residual error to be closest to the minimum value by utilizing the recursion relational expression until the corrected residual error is smaller than a preset residual error threshold value; and taking the cornering stiffness when the residual error is smaller than a preset residual error threshold value as the cornering stiffness of the tire in the driving process of the vehicle. The method can weaken the influence of the impulse noise on the identification result of the lateral deflection rigidity by utilizing the adjustment of the weight, thereby improving the accuracy of the identification result; and the speed of residual error approaching to the minimum value is improved, and the convergence speed is accelerated.
Description
Technical Field
The invention relates to the field of vehicle motion control, in particular to a method and a device for determining tire sidewall deflection rigidity.
Background
The cornering characteristic of the tire is particularly important to the transverse and longitudinal control accuracy and stability of the whole vehicle. With the current technology, the cornering stiffness of a running tire cannot be directly measured, so that research on a method capable of estimating the cornering stiffness of front and rear wheels of a vehicle on line has been a hot spot in which vehicle researchers compete.
The inventor finds in the research process of the prior art that a method for identifying the cornering stiffness of a tire based on a recursive least square method exists at present. According to the method, the least square method is adopted to identify the cornering stiffness of the front wheel and the rear wheel of the vehicle in the recursion model, but the convergence speed of the method is low, and the accuracy of an identification result is greatly influenced when the method is interfered by noise, particularly impulse noise.
Disclosure of Invention
The present invention is directed to solving at least one of the problems of the prior art. To this end, a first aspect of the present invention provides a method for determining tire cornering stiffness, the method comprising:
determining a discretized vehicle transverse acceleration estimation model taking the cornering stiffness of the front wheels and the rear wheels of the vehicle as a parameter to be identified according to the physical parameters of the vehicle and the driving parameters of the vehicle at the current moment;
determining a difference value between an estimated value of the lateral acceleration of the vehicle and a true value of the current moment when the lateral stiffness is a preset initial value, and obtaining a residual error;
correcting the residual error according to the relation between the residual error and a preset pulse noise threshold value to obtain a corrected residual error;
determining a recursion relational expression comprising the lateral deflection rigidity of the current moment, the lateral deflection rigidity of the next moment and the corrected residual error of the current moment, and searching the minimum value of the corrected residual error in the direction which can enable the corrected residual error to be closest to the minimum value by using the recursion relational expression until the corrected residual error is smaller than a preset residual error threshold value;
and taking the cornering stiffness when the corrected residual error is smaller than the preset residual error threshold value as the tire cornering stiffness of the vehicle in the driving process.
Optionally, before correcting the residual error, the method further includes:
constructing a cost function of the residual error, wherein the cost function is an expected value of the weight of the residual error at the current moment;
constructing a weight function of the residual error, wherein the weight function is a piecewise function, and each domain section of the weight function is divided according to the magnitude relation between the residual error and a preset impulse noise threshold value;
determining a derivative of the weight function and using the derivative as a correction function for the residual.
Optionally, the modifying the residual error according to the relationship between the residual error and a preset impulse noise threshold includes:
determining a domain interval to which the residual error belongs to obtain a target interval;
and substituting the residual error into a function expression corresponding to the target interval in the correction function to obtain a corrected residual error.
Optionally, each domain-defining interval of the weight function is determined according to the following steps:
determining N thresholds of the impulse noise according to a predetermined signal variation range without the impulse noise; n is a positive integer;
and determining N +1 domain intervals of the weight function according to the N thresholds.
Optionally, the determining a recursion relation including the yaw stiffness at the current time, the yaw stiffness at the next time, and the corrected residual error at the current time includes:
determining first partial derivatives of the cost function to the cornering stiffness of the front wheels and the cornering stiffness of the rear wheels respectively;
determining a negative gradient direction according to the first-order partial derivative, wherein the negative gradient direction is used as a direction which can enable the modified residual error to be closest to a minimum value;
and determining a recursion relational expression comprising the lateral deflection rigidity at the current moment, the lateral deflection rigidity at the next moment and the corrected residual error at the current moment according to a preset step length and the negative gradient direction.
Optionally, the searching for the minimum value of the modified residual error in the direction that the modified residual error is closest to the minimum value by using the recursive relationship includes:
and searching the minimum value of the corrected residual error in the negative gradient direction by utilizing the recursion relational expression based on the preset step length.
Optionally, before determining the discretized vehicle lateral acceleration estimation model using the cornering stiffness of the front wheel and the rear wheel of the vehicle as the parameter to be identified, the method further includes:
acquiring physical parameters of the vehicle, wherein the physical parameters comprise: the vehicle comprises a vehicle body mass, a distance from a center of mass of the vehicle to a front axle and a distance from the center of mass of the vehicle to a rear axle;
collecting vehicle running parameters at the current moment, wherein the vehicle running parameters comprise: a longitudinal velocity of the vehicle, a motion acceleration of the vehicle in a lateral direction of the vehicle, a front wheel steering angle of the vehicle, an angular velocity of a steering angle of the vehicle, a front wheel slip angle and a rear wheel slip angle of the vehicle.
Optionally, the determining a discretized vehicle lateral acceleration estimation model taking the cornering stiffness of the front wheel and the rear wheel of the vehicle as a parameter to be identified according to the physical parameter of the vehicle and the driving parameter at the current moment includes:
according to a two-degree-of-freedom dynamic model of the vehicle and a Newton's second law, establishing a motion equation of the vehicle in the transverse direction by using the mass of the whole vehicle, the transverse motion acceleration of the vehicle, and the lateral force of a front wheel tire and the lateral force of a rear wheel tire of the vehicle;
respectively determining the slip angle of the front wheel and the slip angle of the rear wheel according to the steering angle of the front wheel, the speed angle of the front wheel and the speed angle of the rear wheel;
determining the lateral force of the front wheel and the lateral force of the rear wheel according to the slip angle of the front wheel, the slip angle of the rear wheel, the slip stiffness of the front wheel and the slip stiffness of the rear wheel; the slip angle of the front wheel and the slip angle of the rear wheel are parameters to be identified;
determining a speed angle of the front wheel and a speed angle of the rear wheel according to the distance from the center of mass to a front shaft, the distance from the center of mass to a rear shaft, the transverse motion acceleration and the angular velocity of the direction angle;
substituting the lateral force of the front wheels, the lateral force of the rear wheels, the speed angle of the front wheels and the speed angle of the rear wheels into a motion equation of the vehicle in the transverse direction to obtain a vehicle transverse acceleration estimation model taking the lateral deflection rigidity of the front wheels and the rear wheels of the vehicle as parameters to be identified;
and carrying out discretization processing on the vehicle transverse acceleration estimation model to obtain a discretized vehicle transverse acceleration estimation model.
Optionally, the preset initial value is the cornering stiffness at the last moment.
A second aspect of an embodiment of the present invention provides a tire cornering stiffness determining apparatus, the apparatus including:
the estimation model determining module is used for determining a discretized vehicle transverse acceleration estimation model which takes the cornering stiffness of the front wheels and the rear wheels of the vehicle as a parameter to be identified according to the physical parameters of the vehicle and the driving parameters of the vehicle at the current moment;
the residual error calculation module is used for determining a difference value between the estimated value of the lateral acceleration of the vehicle and the true value of the current moment when the lateral deflection rigidity is a preset initial value, so as to obtain a residual error;
the correction module is used for correcting the residual error according to the relation between the residual error and a preset pulse noise threshold value to obtain a corrected residual error;
the search module is used for determining a recursion relational expression comprising the lateral deflection rigidity at the current moment, the lateral deflection rigidity at the next moment and the corrected residual error at the current moment, and searching the minimum value of the corrected residual error in the direction which can enable the corrected residual error to be closest to the minimum value by using the recursion relational expression until the corrected residual error is smaller than a preset residual error threshold value;
and the cornering stiffness determining module is used for taking the cornering stiffness when the corrected residual error is smaller than the preset residual error threshold value as the tire cornering stiffness of the vehicle in the driving process.
Optionally, the apparatus further comprises:
the cost function construction module is used for constructing a cost function of the residual error, and the cost function is an expected value of the weight of the residual error at the current moment;
the weight function building module is used for building a weight function of the residual error, the weight function is a piecewise function, and each domain section of the weight function is divided according to the size relation between the residual error and a preset impulse noise threshold value;
and the modification function determining module is used for determining a derivative of the weight function and taking the derivative as a modification function of the residual error.
Optionally, the modification module is further configured to:
determining a domain interval to which the residual error belongs to obtain a target interval;
and substituting the residual error into a function expression corresponding to the target interval in the correction function to obtain a corrected residual error.
Optionally, the weight function constructing module is further configured to:
determining N thresholds of the impulse noise according to a predetermined signal variation range without the impulse noise; n is a positive integer;
and determining N +1 domain intervals of the weight function according to the N thresholds.
Optionally, the search module is further configured to:
determining first partial derivatives of the cost function to the cornering stiffness of the front wheel and the cornering stiffness of the rear wheel, respectively;
determining a negative gradient direction according to the first-order partial derivative, wherein the negative gradient direction is used as a direction which can enable the modified residual error to be closest to a minimum value;
and determining a recursion relational expression comprising the cornering stiffness at the current moment, the cornering stiffness at the next moment and the corrected residual error at the current moment according to a preset step length and the negative gradient direction.
Optionally, the search module is further configured to:
and searching the minimum value of the corrected residual error in the negative gradient direction by utilizing the recursion relational expression based on the preset step length.
Optionally, the apparatus further comprises:
a physical parameter obtaining module for obtaining physical parameters of the vehicle, the physical parameters including: the vehicle comprises a vehicle body mass, a distance from a center of mass of the vehicle to a front axle and a distance from the center of mass of the vehicle to a rear axle;
the driving parameter acquisition module is used for acquiring the driving parameters of the vehicle at the current moment, and the driving parameters of the vehicle comprise: a longitudinal velocity of the vehicle, a motion acceleration of the vehicle in a lateral direction of the vehicle, a front wheel steering angle of the vehicle, an angular velocity of a steering angle of the vehicle, a front wheel slip angle and a rear wheel slip angle of the vehicle.
Optionally, the estimation module determining module is further configured to:
according to a two-degree-of-freedom dynamic model of the vehicle and a Newton's second law, establishing a motion equation of the vehicle in the transverse direction by using the mass of the whole vehicle, the transverse motion acceleration of the vehicle, and the lateral force of a front wheel tire and the lateral force of a rear wheel tire of the vehicle;
respectively determining the slip angle of the front wheel and the slip angle of the rear wheel according to the steering angle of the front wheel, the speed angle of the front wheel and the speed angle of the rear wheel;
determining the lateral force of the front wheel and the lateral force of the rear wheel according to the slip angle of the front wheel, the slip angle of the rear wheel, the slip stiffness of the front wheel and the slip stiffness of the rear wheel; the slip angle of the front wheel and the slip angle of the rear wheel are parameters to be identified;
determining a speed angle of the front wheel and a speed angle of the rear wheel according to the distance from the center of mass to a front shaft, the distance from the center of mass to a rear shaft, the transverse motion acceleration and the angular velocity of the direction angle;
substituting the lateral force of the front wheels, the lateral force of the rear wheels, the speed angle of the front wheels and the speed angle of the rear wheels into a motion equation of the vehicle in the transverse direction to obtain a vehicle transverse acceleration estimation model taking the lateral deflection rigidity of the front wheels and the rear wheels of the vehicle as parameters to be identified;
and carrying out discretization processing on the vehicle transverse acceleration estimation model to obtain a discretized vehicle transverse acceleration estimation model.
A third aspect of embodiments of the present invention provides an electronic device comprising a processor and a memory having stored thereon at least one instruction, at least one program, set of codes, or set of instructions that is loaded into and executed by the processor to implement a method of determining cornering stiffness of a tyre according to the first aspect.
A fourth aspect of embodiments of the present invention is directed to a computer readable storage medium having at least one instruction, at least one program, a set of codes, or a set of instructions stored therein, which is loaded and executed by a processor to implement the method for determining cornering stiffness of a tyre according to the first aspect.
The embodiment of the invention has the following beneficial effects:
in the embodiment of the invention, a vehicle transverse acceleration estimation model taking the cornering stiffness of the front wheels and the rear wheels of the vehicle as a parameter to be identified is determined according to the physical parameters of the vehicle and the driving parameters at the current moment; determining a difference value between an estimated value of the lateral acceleration of the vehicle and a true value of the current moment when the lateral stiffness is a preset initial value, and obtaining a residual error; correcting the residual error according to the relation between the residual error and a preset pulse noise threshold value to obtain a corrected residual error; determining a recursion relational expression comprising the lateral deflection rigidity of the current moment, the lateral deflection rigidity of the next moment and the corrected residual error of the current moment, and searching the minimum value of the corrected residual error in the direction which can enable the residual error to be closest to the minimum value by using the recursion relational expression until the corrected residual error is smaller than a preset residual error threshold value; and taking the cornering stiffness when the residual error is smaller than a preset residual error threshold value as the cornering stiffness of the tire in the driving process of the vehicle. In the method, the weight corresponding to the residual error is determined according to the relation between the residual error and the preset impulse noise threshold value, the iterative cornering stiffness is determined according to the weight, and the influence of the impulse noise on the identification result of the cornering stiffness can be weakened by utilizing the adjustment of the weight, so that the accuracy of the identification result is improved; in addition, the method uses the lateral deviation rigidity which can enable the residual error to be the fastest close to the minimum value in each iteration, improves the speed of enabling the residual error to be close to the minimum value, and accelerates the convergence speed of the scheme.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings used in the description of the embodiment or the prior art will be briefly described below. It is obvious that the drawings in the following description are only some embodiments of the invention, and that for a person skilled in the art it is also possible to derive other drawings from these drawings without inventive effort.
FIG. 1 is a flow chart illustrating steps of a first method for determining tire cornering stiffness according to an embodiment of the present invention;
FIG. 2 is a flowchart illustrating steps for building a discretized vehicle lateral acceleration estimation model according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a two-degree-of-freedom dynamic model of a vehicle according to an embodiment of the present invention;
FIG. 4 is a flowchart illustrating steps in a second method of determining tire cornering stiffness according to an embodiment of the invention;
FIG. 5 is a flowchart illustrating a method for determining cornering stiffness of a tire according to an embodiment of the present invention;
FIG. 6 is a block diagram of a tire sidewall stiffness determining apparatus according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
The present specification provides method steps as described in the examples or flowcharts, but more or fewer steps may be included based on routine or non-invasive labor. In actual system or server product execution, sequential execution or parallel execution (e.g., parallel processor or multithreaded processing environments) may occur according to the embodiments or methods shown in the figures.
FIG. 1 is a flowchart illustrating steps of a first method for determining tire cornering stiffness according to an embodiment of the present invention. The method may comprise the steps of:
The physical parameters of the vehicle refer to the size and physical properties of the vehicle, such as the length, width, wheelbase, track, mass, etc. of the vehicle. The running parameters of the vehicle refer to vehicle running posture information and running speed information such as longitudinal speed, lateral speed, longitudinal acceleration, lateral acceleration, steering angle, angular velocity and the like of the vehicle during running.
The physical parameters of the vehicle can be obtained by measuring or consulting the delivery parameters of the vehicle, and the driving parameters of the vehicle can be directly obtained or calculated by data collected by a sensor arranged on the vehicle.
In calculating the vehicle lateral acceleration estimation model, physical parameters such as the wheel base and mass of the vehicle, and running parameters such as the longitudinal speed, the angular velocity of the steering angle, the lateral acceleration, and the steering angle of the front wheels of the vehicle are mainly used.
Tire cornering stiffness is the ratio of tire cornering force to cornering angle, which is an important tire parameter that determines operational stability. The tire has high cornering stiffness, and can ensure good operation stability of the automobile.
According to Newton's second law, the equality relation between the mass of the automobile, the lateral acceleration of the automobile and the lateral force of the tire of the front wheel and the rear wheel of the automobile can be obtained.
The tire lateral force is expressed by the tire cornering stiffness and the cornering angle, and the tire lateral force is substituted into an equation relation obtained according to a Newton's second law, so that a vehicle transverse acceleration estimation model which takes the cornering stiffness of the front wheel and the rear wheel of the vehicle as a parameter to be identified can be obtained.
Discretizing the vehicle lateral acceleration estimation model means discretizing the estimation model into estimation models corresponding to each time.
The vehicle lateral acceleration estimation model is an equation in which the cornering stiffness of the front and rear wheels is a parameter to be identified, and other parameters in the equation may be calculated to obtain a determined value according to the physical parameters and the driving parameters of the vehicle. Therefore, cornering stiffnesses of the front and rear wheels can be calculated from the model.
And 102, determining a difference value between the estimated value of the lateral acceleration of the vehicle and the true value of the current moment when the cornering stiffness is a preset initial value, and obtaining a residual error.
The initial value of cornering stiffness may be a tire cornering stiffness parameter calibrated by a manufacturer when the tire is shipped. When the vehicle is used, the tire is worn, and the cornering stiffness is changed, so that the cornering stiffness of the tire acquired last time can be used as a preset initial value.
Substituting the preset initial value of the cornering stiffness and other parameters at the current moment into the vehicle transverse acceleration estimation model, and calculating to obtain the estimated value of the vehicle transverse acceleration at the current moment.
In addition, the real vehicle lateral acceleration at the current moment can be obtained through the data collected by the sensor.
The absolute value of the difference between the estimated value and the true value is taken as the residual.
According to the vehicle lateral acceleration estimation model, the residual error is related to the cornering stiffness, and if the error can be corrected to be the lowest or 0, the estimated value of the cornering stiffness is very close to or consistent with the real value.
And 103, correcting the residual error according to the relation between the residual error and a preset impulse noise threshold value to obtain a corrected residual error.
Impulse noise is discontinuous and consists of irregular impulses or noise spikes of short duration and large amplitude. The impulse noise is generated from various reasons, including electromagnetic interference and malfunction and defect of the communication system, and may be generated when the electrical switches and relays of the communication system change states. The interference of the impulse noise has a great influence on the accuracy of the identification result of the yaw stiffness.
The intensity of the impulse noise is different at different moments, the influence degree of the noise with different intensities on the identification result is nonlinear, and different suppression intensities can be adopted to reduce the influence of the noise on the original signal. Therefore, different weights can be set for the residuals of different sizes, and the weighted residuals can be used as the corrected residuals. The effect of the weights here is to adjust the suppression strength for impulse noise of different strengths.
Specifically, a threshold of impulse noise may be preset, and different domain sections may be determined according to a magnitude relationship between a residual and the threshold. And setting different weights for different domain intervals, and taking the product of the weights and the residual error as a modified residual error.
And 104, determining a recursion relational expression comprising the yaw stiffness at the current moment, the yaw stiffness at the next moment and the corrected residual error at the current moment, and searching the minimum value of the corrected residual error in the direction which can enable the residual error to be closest to the minimum value by using the recursion relational expression until the corrected residual error is smaller than a preset residual error threshold value.
Specifically, a recurrence relation including the cornering stiffness at the current time, the cornering stiffness at the next time, and the corrected residual at the current time may be determined. And determining the direction of the negative gradient according to the partial derivative of the corrected residual error to the lateral stiffness. The negative gradient direction is the direction that makes the modified residual approach the minimum value the fastest. And determining the yaw stiffness of the next iteration in the negative gradient direction, and determining the corrected residual error of the next iteration by taking the yaw stiffness of the next iteration as a known number.
And carrying out multiple iterations along the negative gradient direction until the determined corrected residual error is smaller than a preset residual error threshold value, and ending the iterations.
And 105, taking the cornering stiffness when the residual error is smaller than a preset residual error threshold value as the cornering stiffness of the tire in the vehicle driving process.
When the corrected residual error is smaller than the preset residual error threshold value, the corrected residual error can be considered to obtain the minimum value, the estimated value of the transverse acceleration at the moment is closest to the true value, and then the cornering stiffness at the moment is the most accurate cornering stiffness.
The tire cornering stiffness of a vehicle may be used to control cornering, acceleration, deceleration, etc. of the vehicle during automatic driving. From the factory, the cornering stiffness of the tire is a slowly changing process, and for a certain driving, the cornering stiffness of the tire is relatively in a stable state, so that the cornering stiffness obtained through multiple iterations in a certain driving process can be used for vehicle control in the driving process.
In the embodiment of the invention, a discretization vehicle transverse acceleration estimation model taking the cornering stiffness of the front wheels and the rear wheels of the vehicle as the parameter to be identified is determined according to the physical parameters of the vehicle and the driving parameters of the vehicle at the current moment; determining a difference value between an estimated value of the lateral acceleration of the vehicle and a true value of the current moment when the lateral deflection rigidity is a preset initial value to obtain a residual error; correcting the residual error according to the relation between the residual error and a preset pulse noise threshold value to obtain a corrected residual error; determining a recursion relational expression comprising the lateral deflection rigidity of the current moment, the lateral deflection rigidity of the next moment and the corrected residual error of the current moment, and searching a minimum value of the corrected residual error in a direction which can enable the corrected residual error to be closest to the minimum value by using the recursion relational expression until the corrected residual error is smaller than a preset residual error threshold value; and taking the cornering stiffness when the corrected residual error is smaller than a preset residual error threshold value as the cornering stiffness of the tire in the driving process of the vehicle. In the method, the residual error is corrected according to the relation between the residual error and the preset impulse noise threshold value to obtain a corrected residual error, and the influence of impulse noise on the identification result of the lateral deflection rigidity can be weakened by using the correction of the residual error, so that the accuracy of the identification result is improved; and the minimum value of the corrected residual error is searched in the direction that the corrected residual error is closest to the minimum value, so that the speed of the corrected residual error approaching the minimum value is improved, and the convergence speed is accelerated.
FIG. 2 is a flowchart illustrating steps for building a discretized vehicle lateral acceleration estimation model according to an embodiment of the present invention. The method may comprise the steps of:
The front axle refers to the axle connecting the two front wheels, the rear axle refers to the axle connecting the two rear wheels, and the center of mass refers to the center of mass of the vehicle. The distance from the center of mass of the vehicle to the front axle refers to the horizontal distance from the center of mass to the midpoint of the front axle. The distance from the center of mass of the vehicle to the rear axle is the horizontal distance from the center of mass to the midpoint of the rear axle.
The longitudinal speed of the vehicle means a speed along the traveling direction of the vehicle, the lateral speed of the vehicle means a speed in a direction perpendicular to the traveling direction of the vehicle, and the lateral acceleration of the vehicle means an acceleration in a direction perpendicular to the traveling direction of the vehicle. The front wheel steering angle of the vehicle refers to an angle of the front wheel of the vehicle from the vehicle traveling direction, and the angular velocity of the rudder angle of the vehicle refers to an angular velocity of the front wheel of the vehicle from the vehicle traveling direction.
And step 203, establishing a motion equation of the vehicle in the transverse direction by utilizing the whole vehicle mass, the transverse motion acceleration of the vehicle, and the lateral force of the front wheel tire and the lateral force of the rear wheel tire of the vehicle according to a two-degree-of-freedom dynamic model of the vehicle and a Newton's second law.
Firstly, a dynamic model of a vehicle is simplified into a two-degree-of-freedom dynamic model.
Fig. 3 is a schematic diagram of a two-degree-of-freedom dynamic model of a vehicle according to an embodiment of the present invention.
As shown in fig. 3, Ψ is the vehicle's steering angle, δ is the front wheel steering angle, and u, y are the longitudinal and lateral vehicle speeds, respectively.
Neglecting the influence of the road surface gradient, according to newton's second law, the motion equation of the vehicle in the lateral direction (y-axis direction) is:
may＝Fyf+Fyr (1)
wherein: m is the total vehicle mass, ayFor transverse acceleration of the vehicle in the direction of the y-axis, FyfAnd FyrThe tire lateral forces of the front and rear wheels, respectively.
And 204, respectively determining the slip angle of the front wheel and the slip angle of the rear wheel according to the steering angle of the front wheel, the speed angle of the front wheel and the speed angle of the rear wheel.
Generally, in the case where the tire slip angle is small, the lateral force of the tire is proportional to the slip angle of the tire. The slip angle of a tire is the angle between the tire plane direction and the tire velocity vector direction. From fig. 3, it can be derived that the slip angles of the front and rear wheels are:
af＝δ-θvf (2)
ar＝-θvr (3)
wherein, afIs the slip angle of the front wheel, arIs the slip angle of the rear wheel, delta is the steering angle of the front wheel, thetavfIs the speed angle, theta, of the front wheelvrIs the speed angle of the rear wheel.
In particular, the lateral force F of the front wheelyfAnd the side force F of the rear wheelyrCan be expressed as:
Fyf＝2caf(δ-θvf) (4)
Fyr＝2car(-θvr) (5)
wherein, cafIs the cornering stiffness of the front wheel, carThe cornering stiffness of the rear wheel and the cornering stiffness of the rear wheel are unknown quantities, and are parameters to be identified in the embodiment of the invention.
And step 206, determining the speed angle of the front wheel and the speed angle of the rear wheel according to the distance from the center of mass to the front shaft, the distance from the center of mass to the rear shaft, the transverse motion acceleration and the angular speed of the direction angle.
In particular, the speed angle of the front wheels and the speed angle of the rear wheels can be expressed as:
wherein lfAnd lrRespectively the distance of the centroid to the front axis and the distance of the centroid to the rear axis,
is the acceleration of the lateral motion and psi is the angular velocity of the azimuth angle.
And step 207, substituting the lateral force of the front wheels, the lateral force of the rear wheels, the speed angle of the front wheels and the speed angle of the rear wheels into a motion equation of the vehicle in the transverse direction to obtain a vehicle transverse acceleration estimation model taking the lateral deflection rigidity of the front wheels and the rear wheels of the vehicle as parameters to be identified.
Specifically, equations (4) to (7) are substituted in equation (1), and the vehicle lateral acceleration estimation model is obtained as follows:
ay＝cafbf+carbr (8)
wherein the content of the first and second substances,
and 208, discretizing the vehicle transverse acceleration estimation model to obtain a discretized vehicle transverse acceleration estimation model.
And if k is the current moment, the discretized vehicle lateral acceleration estimation model is as follows:
ay(k)＝caf(k)bf(k)+car(k)br(k) (10)
in summary, according to the physical parameters of the vehicle and the driving parameters of the vehicle at the current moment, a discretized vehicle lateral acceleration estimation model taking the cornering stiffness of the front wheels and the rear wheels of the vehicle as the parameters to be identified is determined. The more accurate the parameter estimation of the cornering stiffness of the front and rear wheels is, the more accurate an automobile dynamic model can be obtained, and further, the more easy the accurate transverse and longitudinal motion control of the automobile is performed.
FIG. 4 is a flowchart illustrating steps of a second method for determining tire cornering stiffness according to an embodiment of the invention. The method may comprise the steps of:
In the embodiment of the present invention, step 301 may refer to step 101, and is not described herein again.
And 302, determining a difference value between the estimated value of the lateral acceleration of the vehicle and the true value of the current moment when the cornering stiffness is a preset initial value, and obtaining a residual error.
At time k, an estimated value of the front and rear wheel cornering powers is
And
an estimate of the lateral acceleration at that time can be obtained
Comprises the following steps:
estimated value and true value ay(k) The error between e (k) is:
optionally, the preset initial values are the cornering stiffness of the front wheel and the cornering stiffness of the rear wheel obtained at the last time.
In the embodiment of the present invention, the initial value of the cornering stiffness may be a tire cornering stiffness parameter calibrated by a manufacturer when the tire is shipped. When the vehicle is used, the tire is worn, and the cornering stiffness is changed, so that the cornering stiffness of the tire acquired at the last time can be used as a preset initial value.
The lateral deflection rigidity value obtained at the previous moment is used as an initial value, repeated searching processes are omitted, the searching progress can be accelerated, and correction residual errors can be converged more quickly.
The parameter identification strategy proposed by the embodiment of the invention is shown in FIG. 4, bf(k)And br(k)As the input of the identification system, multiplying the input by the corresponding front and rear wheel cornering stiffness coefficients to be identified, and taking the sum to output the estimation of the lateral acceleration of the automobile
And the error e (k) is used as the input of adaptive filter to adjust two band identification parameters
And
is obviously when
And
the error gradually returns to zero as it gradually converges to the true value.
The specific method is shown as the following steps 303-311.
And 303, constructing a cost function of the residual error, wherein the cost function is an expected value of the weight of the residual error at the current moment.
Let the residual cost function be JM(k) Then the cost function can be expressed as:
JM(k)＝E{M(e(k))} (13)
where M (.) is M estimation for E (k), M estimation for residual is used to calculate the weight of the residual, and E represents the expected value. Since there is only one residual at the current time, the expected value is equal to the residual at the current time.
And 304, constructing a weight function of the residual error, wherein the weight function is a piecewise function, and each domain defining interval of the weight function is divided according to the size relation between the residual error and a preset impulse noise threshold.
The weight function of the residual is constructed using the M estimate. The basic idea of M estimation is to adopt iterative weighted estimation regression coefficient, and determine the weight of each point according to the size of regression residual error so as to achieve the purpose of stability. To reduce the effect of "outliers", the weighting function in the present invention applies different weights to different residuals, i.e. points with small residuals are given a larger weight and points with larger residuals are given a smaller weight.
Specifically, the expression of the weight function M (e (k)) is as follows:
wherein M (e (k)) represents M estimation of residual e (k), t1、t2、t3Is a threshold used to suppress impulse noise. The definition domain interval of the weight function is determined according to the residual error | e (k) | and the impulse noise threshold t1、t2、t3The size relationship of (1) is divided.
Optionally, each domain-defining interval of the objective function is determined according to steps 3041-3042:
step 3041, determining N thresholds of impulse noise according to a predetermined signal variation range without impulse noise; n is a positive integer.
The signal variation range in the absence of impulse noise is determined in advance to be δ (k), and N thresholds are set according to the signal variation range. In the embodiment of the invention, t is taken1、t2、t33 thresholds. t is t1、t2、t3It can be determined by equation (15) and equation (16):
wherein, delta2(k)＝C1δ2(k-1)+(1-C1)C2med(e2(k),e2(k-1),...,e2(k-Lw-1)) (16)
C1＝1.483(1+5/(Lw-1)), (C1. gtoreq.1) and (2)<C2<3) C1 and C2 are the active forgetting factor and correction factor of the algorithm, respectively. L iswTo estimate the window width, 2 is taken in the embodiment of the present invention.
Step 3042, determining N +1 domain intervals of the objective function according to the N thresholds.
N +1 domain intervals can be divided according to N thresholds, and in the embodiment of the invention, the domain intervals are divided according to t1、t2、t3The 3 thresholds divide the 4 domain intervals.
It can be understood that the number of the threshold values may be set according to requirements, and the embodiment of the present invention does not limit this.
And 305, determining a derivative of the weight function, and taking the derivative as a correction function of the residual error.
The partial derivative of e (k) is calculated for the weighting function M (e (k)), and the correction function S (e (k)) is obtained as follows:
and step 306, determining a domain interval to which the residual error belongs to obtain a target interval.
And (4) calculating to obtain residual errors e (k) according to the formula (12), determining a domain interval to which the residual errors e (k) belong according to the domain interval in the formula (17), and taking the domain interval as a target interval.
And 307, substituting the residual error into a function expression corresponding to the target interval in the correction function to obtain a corrected residual error.
The residual e (k) is substituted for the functional expression corresponding to the target interval in the formula (17), and the value of the correction function is obtained and used as the correction residual.
As a result ofThis function JM(k) When the minimum value is obtained, the parameter to be estimated can also obtain the optimal solution at the same time. The cost function treats the estimated parameter (i.e., cornering stiffness of the front wheels)
And cornering stiffness of said rear wheel
) The first partial derivative of (d) can be expressed as:
and 309, determining a negative gradient direction according to the first-order partial derivative, wherein the negative gradient direction is used as a direction which can enable the modified residual error to be closest to a minimum value.
The direction of the negative gradient, i.e. the opposite of the first partial derivative, S (e (k)) (-b)f(k) Has a negative gradient direction of S (e) (k) x (b)f(k))，S(e(k))*(-br(k) Has a negative gradient direction of S (e) (k) x (b)r(k))。
The direction of the negative gradient is the direction in which the function value changes most quickly, that is, the direction in which the modified residual error is closest to the minimum value.
And 310, determining a recursion relational expression comprising the lateral deflection rigidity at the current moment, the lateral deflection rigidity at the next moment and the corrected residual error at the current moment according to a preset step length and the negative gradient direction.
The step length is the adjustment step length of the lateral deflection rigidity and needs to be preset in advance. Setting the preset step length of the front wheel as ufThe preset step length of the rear wheel is urThen the recurrence relation can be expressed as:
and 311, searching the minimum value of the corrected residual error in the negative gradient direction by using the recursion relational expression based on the preset step length.
The preset step length is used for adjusting the correction residual error at the moment k, and b can be obtained by calculation according to the vehicle running parameters at the moment kf(k) And br(k) From the residual s (e (k), b)f(k) And br(k) A negative gradient direction can be obtained. And after the correction residual error is adjusted by adopting a preset step length, searching the moment which can enable the difference between the lateral deflection rigidity at the current moment and the lateral deflection rigidity at the next moment to be minimum in the negative gradient direction, wherein the moment is the moment at which the correction residual error is minimum.
In order to define the difference value, a preset residual threshold value is set, and when the difference value is smaller than the preset residual threshold value, the modified residual is considered to be minimum.
When the corrected residual error is minimum, the difference between the calculated cornering stiffness and the real cornering stiffness is minimum, and then the calculated cornering stiffness can be used as the tire cornering stiffness in the driving process.
And step 312, taking the cornering stiffness when the corrected residual error is smaller than a preset residual error threshold value as the cornering stiffness of the tire in the vehicle driving process.
In the embodiment of the present invention, step 312 may refer to step 105, which is not described herein again.
FIG. 5 is a flowchart illustrating a method for determining cornering stiffness of a tire according to an embodiment of the present invention.
Referring to FIG. 5, firstly, the parameter C to be identifiedafAnd CarSetting an initial value, and calculating a coefficient b of the current time according to a related signal acquired by a sensor and an equation (9)fAnd brCalculating a transverse acceleration error e (k) at the current moment according to the formula (12), determining whether the current e (k) is within an allowable range, if so, stopping iteration, and ending the process; if not, the parameter to be identified at the next moment is calculated according to the formula (19).
To illustrate the superiority of the proposed solution of this embodiment, the conventional RLS (recursive Least squares) and LMS (Least-Mean-Square) are selected and compared with the solution through simulation experiments.
With the Matlab/Simulink tool, the car is assumed to make a circular arc motion around a circle of radius R of 250m at a longitudinal speed of 30 km/h. The mass of the automobile is 1412kg, and the distances lf and lr from the center of mass to the front and rear axes are 1.015m and 1.895m, respectively.
Cornering stiffness c of front wheel set in simulationaf74485N/rad, rear wheel cornering stiffness car41102N/rad. The accuracy and stability of the algorithm can be verified by comparing the result identified by the algorithm with the set true value.
The comparison of the identification results of the LMM and the RLS parameters shows that the identification speed of the parameters based on the LMM is faster than that of the RLS algorithm, the LMM algorithm converges after 50 iterations, but the RLS algorithm gradually converges after more than 300 iterations. From the perspective of identification errors, the accuracy of the LMM algorithm is around 3%, while the accuracy of the RLS algorithm is significantly lower.
The comparison between the LMM and the LMS parameter identification result shows that the tire cornering stiffness parameter identification scheme provided by the embodiment has a significant advantage in that the influence of impulse noise on the identification result can be suppressed. At the number of iterations 200,400,600,800, an impulse noise of 0.5 was injected into the recognition error, respectively. After the influence of impulse noise, the transient identification result based on the LMS algorithm is poor, and the parameter identification result based on the LMM algorithm is not influenced by the impulse noise. This is due to the fact that the proposed method can effectively identify and suppress the influence of impulse noise on the identification result.
In summary, the tire cornering stiffness determining method in fig. 4 has the beneficial effects of the tire cornering stiffness determining method in fig. 1, and also adopts the cornering stiffness value obtained at the previous moment as an initial value, so that a repeated search process is omitted, the search progress can be accelerated, and the correction residual error can be converged faster; and each domain interval of the weight function is divided according to the magnitude relation between the residual error and a preset impulse noise threshold value, so that the residual error is effectively corrected according to the influence of impulse noise with different magnitudes on the residual error, and the influence of abnormal points with noise on an identification result is reduced; and the minimum value of the residual error is searched in the negative gradient direction, so that the convergence speed of the residual error is higher, and the execution efficiency of the scheme is improved.
FIG. 6 is a block diagram of a tire sidewall stiffness determining apparatus according to an embodiment of the present invention. The tire cornering stiffness determination apparatus 400 includes:
an estimation model determining module 401, configured to determine a discretized vehicle lateral acceleration estimation model using cornering stiffnesses of front wheels and rear wheels of a vehicle as parameters to be identified according to physical parameters of the vehicle and driving parameters of the vehicle at a current moment;
a residual error calculation module 402, configured to determine a difference between an estimated value of the lateral acceleration of the vehicle and a true value of the current time when the cornering stiffness is a preset initial value, so as to obtain a residual error;
a correction module 403, configured to correct the residual error according to a relationship between the residual error and a preset impulse noise threshold, to obtain a corrected residual error;
a searching module 404, configured to determine a recurrence relation including a yaw stiffness at a current moment, a yaw stiffness at a next moment, and the corrected residual at the current moment, and search, by using the recurrence relation, a minimum value of the corrected residual in a direction that the corrected residual is closest to the minimum value until the corrected residual is smaller than a preset residual threshold;
and an cornering stiffness determining module 405, configured to use the cornering stiffness when the corrected residual is smaller than the preset residual threshold as a tire cornering stiffness of the vehicle in the current driving process.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In yet another embodiment provided by the present invention, there is also provided an apparatus comprising a processor and a memory having stored therein at least one instruction, at least one program, set of codes, or set of instructions that is loaded and executed by the processor to implement the tire cornering stiffness determination method according to an embodiment of the present invention.
In yet another embodiment provided by the present invention, a computer-readable storage medium having stored therein at least one instruction, at least one program, set of codes, or set of instructions, which is loaded and executed by a processor to implement the method for determining cornering stiffness of a tyre as described in the embodiments of the present invention is also provided.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
All the embodiments in the present specification are described in a related manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.
Claims (19)
1. A method of determining tire cornering stiffness, the method comprising:
determining a discretized vehicle transverse acceleration estimation model taking the cornering stiffness of the front wheels and the rear wheels of the vehicle as a parameter to be identified according to the physical parameters of the vehicle and the driving parameters of the vehicle at the current moment;
determining a difference value between an estimated value of the lateral acceleration of the vehicle and a true value of the current moment when the lateral stiffness is a preset initial value, and obtaining a residual error;
correcting the residual error according to the relation between the residual error and a preset pulse noise threshold value to obtain a corrected residual error;
determining a recursion relational expression comprising the lateral deflection rigidity of the current moment, the lateral deflection rigidity of the next moment and the corrected residual error of the current moment, and searching the minimum value of the corrected residual error in the direction which can enable the corrected residual error to be closest to the minimum value by using the recursion relational expression until the corrected residual error is smaller than a preset residual error threshold value;
and taking the cornering stiffness when the corrected residual error is smaller than the preset residual error threshold value as the tire cornering stiffness of the vehicle in the driving process.
2. The method of claim 1, further comprising, prior to modifying the residual error:
constructing a cost function of the residual error, wherein the cost function is an expected value of the weight of the residual error at the current moment;
constructing a weight function of the residual error, wherein the weight function is a piecewise function, and each domain section of the weight function is divided according to the magnitude relation between the residual error and a preset impulse noise threshold value;
determining a derivative of the weight function and using the derivative as a correction function for the residual.
3. The method of claim 2, wherein the modifying the residual according to the relation between the residual and a preset impulse noise threshold comprises:
determining a domain interval to which the residual error belongs to obtain a target interval;
and substituting the residual error into a function expression corresponding to the target interval in the correction function to obtain a corrected residual error.
4. The method of claim 2, wherein each domain interval of the weighting function is determined by:
determining N thresholds of the impulse noise according to a predetermined signal variation range without the impulse noise; n is a positive integer;
and determining N +1 domain intervals of the weight function according to the N thresholds.
5. The method of claim 2, wherein determining a recurrence relation including a cornering stiffness at a current time, a cornering stiffness at a next time, and the modified residual at the current time comprises:
determining first partial derivatives of the cost function to the cornering stiffness of the front wheels and the cornering stiffness of the rear wheels respectively;
determining a negative gradient direction according to the first-order partial derivative, wherein the negative gradient direction is used as a direction which can enable the modified residual error to be closest to a minimum value;
and determining a recursion relational expression comprising the lateral deflection rigidity at the current moment, the lateral deflection rigidity at the next moment and the corrected residual error at the current moment according to a preset step length and the negative gradient direction.
6. The method of claim 5, wherein searching for the minimum value of the modified residual in a direction that would bring the modified residual to the minimum value fastest using the recurrence relation comprises:
and searching the minimum value of the corrected residual error in the negative gradient direction by utilizing the recursion relational expression based on the preset step length.
7. The method of claim 1, further comprising, prior to determining the discretized vehicle lateral acceleration estimation model having cornering stiffnesses of front and rear wheels of the vehicle as a parameter to be identified:
acquiring physical parameters of the vehicle, wherein the physical parameters comprise: the vehicle comprises a vehicle body mass, a distance from a center of mass of the vehicle to a front axle and a distance from the center of mass of the vehicle to a rear axle;
collecting vehicle running parameters at the current moment, wherein the vehicle running parameters comprise: a longitudinal speed of the vehicle, a motion acceleration of the vehicle in a lateral direction of the vehicle, a front wheel steering angle of the vehicle, an angular speed of a steering angle of the vehicle, a front wheel slip angle and a rear wheel slip angle of the vehicle.
8. The method according to claim 7, wherein the determining a discretized vehicle lateral acceleration estimation model using cornering stiffnesses of front and rear wheels of the vehicle as a parameter to be identified according to a physical parameter of the vehicle and a driving parameter of a current time comprises:
according to a two-degree-of-freedom dynamic model of the vehicle and a Newton's second law, establishing a motion equation of the vehicle in the transverse direction by using the mass of the whole vehicle, the transverse motion acceleration of the vehicle, and the lateral force of a front wheel tire and the lateral force of a rear wheel tire of the vehicle;
respectively determining the slip angle of the front wheel and the slip angle of the rear wheel according to the steering angle of the front wheel, the speed angle of the front wheel and the speed angle of the rear wheel;
determining the lateral force of the front wheel and the lateral force of the rear wheel according to the slip angle of the front wheel, the slip angle of the rear wheel, the slip stiffness of the front wheel and the slip stiffness of the rear wheel; the slip angle of the front wheel and the slip angle of the rear wheel are parameters to be identified;
determining a speed angle of the front wheel and a speed angle of the rear wheel according to the distance from the center of mass to a front shaft, the distance from the center of mass to a rear shaft, the transverse motion acceleration and the angular velocity of the direction angle;
substituting the lateral force of the front wheels, the lateral force of the rear wheels, the speed angle of the front wheels and the speed angle of the rear wheels into a motion equation of the vehicle in the transverse direction to obtain a vehicle transverse acceleration estimation model taking the lateral deflection rigidity of the front wheels and the rear wheels of the vehicle as parameters to be identified;
and carrying out discretization processing on the vehicle transverse acceleration estimation model to obtain a discretized vehicle transverse acceleration estimation model.
9. The method of claim 1, wherein the preset initial value is the cornering stiffness at the last moment.
10. A tire cornering stiffness determination apparatus, the apparatus comprising:
the estimation model determining module is used for determining a discretized vehicle transverse acceleration estimation model which takes the cornering stiffness of the front wheels and the rear wheels of the vehicle as a parameter to be identified according to the physical parameters of the vehicle and the driving parameters of the vehicle at the current moment;
the residual error calculation module is used for determining a difference value between the estimated value of the lateral acceleration of the vehicle and the true value of the current moment when the lateral deflection rigidity is a preset initial value, so as to obtain a residual error;
the correction module is used for correcting the residual error according to the relation between the residual error and a preset pulse noise threshold value to obtain a corrected residual error;
the search module is used for determining a recursion relational expression comprising the lateral deflection rigidity at the current moment, the lateral deflection rigidity at the next moment and the corrected residual error at the current moment, and searching the minimum value of the corrected residual error in the direction which can enable the corrected residual error to be closest to the minimum value by using the recursion relational expression until the corrected residual error is smaller than a preset residual error threshold value;
and the cornering stiffness determining module is used for taking the cornering stiffness when the corrected residual error is smaller than the preset residual error threshold value as the tire cornering stiffness of the vehicle in the driving process.
11. The apparatus of claim 10, further comprising:
the cost function construction module is used for constructing a cost function of the residual error, and the cost function is an expected value of the weight of the residual error at the current moment;
the weight function building module is used for building a weight function of the residual error, the weight function is a piecewise function, and each domain section of the weight function is divided according to the size relation between the residual error and a preset impulse noise threshold value;
and the modification function determining module is used for determining a derivative of the weight function and taking the derivative as a modification function of the residual error.
12. The apparatus of claim 11, wherein the correction module is further configured to:
determining a domain interval to which the residual error belongs to obtain a target interval;
and substituting the residual error into a function expression corresponding to the target interval in the correction function to obtain a corrected residual error.
13. The apparatus of claim 11, wherein the weight function construction module is further configured to:
determining N thresholds of the impulse noise according to a predetermined signal variation range without the impulse noise; n is a positive integer;
and determining N +1 domain intervals of the weight function according to the N thresholds.
14. The apparatus of claim 11, wherein the search module is further configured to:
determining first partial derivatives of the cost function to the cornering stiffness of the front wheel and the cornering stiffness of the rear wheel, respectively;
determining a negative gradient direction according to the first-order partial derivative, wherein the negative gradient direction is used as a direction which can enable the modified residual error to be closest to a minimum value;
and determining a recursion relational expression comprising the lateral deflection rigidity at the current moment, the lateral deflection rigidity at the next moment and the corrected residual error at the current moment according to a preset step length and the negative gradient direction.
15. The apparatus of claim 14, wherein the search module is further configured to:
and searching the minimum value of the corrected residual error in the negative gradient direction by utilizing the recursion relational expression based on the preset step length.
16. The apparatus of claim 10, further comprising:
a physical parameter obtaining module for obtaining physical parameters of the vehicle, the physical parameters including: the vehicle comprises a vehicle body mass, a distance from a center of mass of the vehicle to a front axle and a distance from the center of mass of the vehicle to a rear axle;
the driving parameter acquisition module is used for acquiring the driving parameters of the vehicle at the current moment, and the driving parameters of the vehicle comprise: a longitudinal speed of the vehicle, a motion acceleration of the vehicle in a lateral direction of the vehicle, a front wheel steering angle of the vehicle, an angular speed of a steering angle of the vehicle, a front wheel slip angle and a rear wheel slip angle of the vehicle.
17. The apparatus of claim 16, wherein the estimation module determination module is further configured to:
according to a two-degree-of-freedom dynamic model of the vehicle and a Newton second law, establishing a motion equation of the vehicle in the transverse direction by using the mass of the whole vehicle, the transverse motion acceleration of the vehicle, and the lateral force of a front wheel tire and the lateral force of a rear wheel tire of the vehicle;
respectively determining the slip angle of the front wheel and the slip angle of the rear wheel according to the steering angle of the front wheel, the speed angle of the front wheel and the speed angle of the rear wheel;
determining the lateral force of the front wheel and the lateral force of the rear wheel according to the slip angle of the front wheel, the slip angle of the rear wheel, the slip stiffness of the front wheel and the slip stiffness of the rear wheel; the slip angle of the front wheel and the slip angle of the rear wheel are parameters to be identified;
determining a speed angle of the front wheel and a speed angle of the rear wheel according to the distance from the center of mass to a front shaft, the distance from the center of mass to a rear shaft, the transverse motion acceleration and the angular velocity of the direction angle;
substituting the lateral force of the front wheels, the lateral force of the rear wheels, the speed angle of the front wheels and the speed angle of the rear wheels into a motion equation of the vehicle in the transverse direction to obtain a vehicle transverse acceleration estimation model taking the lateral deflection rigidity of the front wheels and the rear wheels of the vehicle as parameters to be identified;
and carrying out discretization processing on the vehicle transverse acceleration estimation model to obtain a discretized vehicle transverse acceleration estimation model.
18. An electronic device comprising a processor and a memory having stored therein at least one instruction, at least one program, a set of codes, or a set of instructions, which is loaded and executed by the processor to implement the tire cornering stiffness determination method according to any one of claims 1-9.
19. A computer readable storage medium having stored therein at least one instruction, at least one program, a set of codes, or a set of instructions, which is loaded and executed by a processor to implement the tire cornering stiffness determination method according to any one of claims 1-9.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210177458.9A CN114492078B (en) | 2022-02-25 | 2022-02-25 | Tire cornering stiffness determination method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210177458.9A CN114492078B (en) | 2022-02-25 | 2022-02-25 | Tire cornering stiffness determination method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114492078A true CN114492078A (en) | 2022-05-13 |
| CN114492078B CN114492078B (en) | 2024-05-31 |
Family
ID=81483492
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210177458.9A Active CN114492078B (en) | 2022-02-25 | 2022-02-25 | Tire cornering stiffness determination method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114492078B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117657175A (en) * | 2023-12-01 | 2024-03-08 | 小米汽车科技有限公司 | Method and device for determining wear degree of vehicle wheel, medium and vehicle |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050065666A1 (en) * | 2003-09-19 | 2005-03-24 | Naoshi Miyashita | Tire parameter deriving method, tire cornering characteristic calculating method, tire designing method, vehicle dynamics analyzing method, and program |
| JP2008008882A (en) * | 2006-06-01 | 2008-01-17 | Yokohama Rubber Co Ltd:The | Evaluation method and device of cornering characteristics of tire |
| CN110116732A (en) * | 2019-04-09 | 2019-08-13 | 吉林大学 | A kind of lateral stable control method of vehicle considering tire cornering stiffness variation |
| CN113609586A (en) * | 2021-07-30 | 2021-11-05 | 东风商用车有限公司 | Joint identification method and system for lateral deflection rigidity and rotational inertia parameters |
-
2022
- 2022-02-25 CN CN202210177458.9A patent/CN114492078B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050065666A1 (en) * | 2003-09-19 | 2005-03-24 | Naoshi Miyashita | Tire parameter deriving method, tire cornering characteristic calculating method, tire designing method, vehicle dynamics analyzing method, and program |
| JP2008008882A (en) * | 2006-06-01 | 2008-01-17 | Yokohama Rubber Co Ltd:The | Evaluation method and device of cornering characteristics of tire |
| CN110116732A (en) * | 2019-04-09 | 2019-08-13 | 吉林大学 | A kind of lateral stable control method of vehicle considering tire cornering stiffness variation |
| CN113609586A (en) * | 2021-07-30 | 2021-11-05 | 东风商用车有限公司 | Joint identification method and system for lateral deflection rigidity and rotational inertia parameters |
Non-Patent Citations (1)
| Title |
|---|
| 吴房胜;李如平;陈业慧;谢晓敏;: "基于最小二乘法的汽车轮胎侧偏刚度及质心侧偏角设计与仿真", 宜宾学院学报, no. 12, 30 August 2017 (2017-08-30) * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117657175A (en) * | 2023-12-01 | 2024-03-08 | 小米汽车科技有限公司 | Method and device for determining wear degree of vehicle wheel, medium and vehicle |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114492078B (en) | 2024-05-31 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| JP6884276B2 (en) | Systems and methods for controlling vehicles | |
| JP6815519B2 (en) | Systems and methods for calibrating vehicle tires | |
| CN104182991A (en) | Vehicle running state estimation method and vehicle running state estimation device | |
| Lian et al. | Cornering stiffness and sideslip angle estimation based on simplified lateral dynamic models for four-in-wheel-motor-driven electric vehicles with lateral tire force information | |
| CN109204458B (en) | Steering angle tracking method for steering wheel of automatic driving automobile with unknown EPS (electric power steering) characteristics | |
| KR20140093961A (en) | Sensor system for independently evaluating the accuracy of the data of the sensor system | |
| WO2007105077A2 (en) | Trajectory tracking control system and method for mobile unit | |
| JP5955465B2 (en) | Automobile, system and method for determining steering angle of vehicle steering column | |
| CN114492078A (en) | Method and device for determining tire sidewall deflection stiffness | |
| CN103279675A (en) | Method for estimating tire-road adhesion coefficients and tire slip angles | |
| CN113771857B (en) | Longitudinal speed estimation method and system for vehicle control | |
| JP3271952B2 (en) | Road surface friction coefficient estimation device for vehicles | |
| Huang et al. | An improved adaptive unscented Kalman filter for estimating the states of in‐wheel‐motored electric vehicle | |
| CN114228721A (en) | Method, device and system for calculating road adhesion coefficient | |
| Yu et al. | Automatic vehicle trajectory tracking control with self-calibration of nonlinear tire force function | |
| CN116409327A (en) | Road surface adhesion coefficient estimation method considering transient characteristics of tire under lateral working condition | |
| CN116527515A (en) | Remote state estimation method based on polling protocol | |
| Fang et al. | Robust adaptive control of automatic guidance of farm vehicles in the presence of sliding | |
| CN112758109B (en) | Transverse tracking steady state deviation compensation method and device | |
| CN114435371A (en) | Road slope estimation method and device | |
| WO2020261584A1 (en) | Ground load estimation device, control device, and ground load estimation method | |
| Qi et al. | Maximum correntropy extended Kalman filter for vehicle state observation | |
| JP2000043745A (en) | Road surface state judging device | |
| CN117807703B (en) | Method for estimating key parameters of scooter chassis vehicle with mutually corrected dynamic and static parameters | |
| CN111310303B (en) | Sine wave parameter identification method for amplitude exponential decay |
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 |