CN111891131A - Online identification method and system for tire sidewall deflection rigidity - Google Patents

Online identification method and system for tire sidewall deflection rigidity Download PDF

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CN111891131A
CN111891131A CN202010794082.7A CN202010794082A CN111891131A CN 111891131 A CN111891131 A CN 111891131A CN 202010794082 A CN202010794082 A CN 202010794082A CN 111891131 A CN111891131 A CN 111891131A
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vehicle
tire
model
cornering stiffness
formula
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CN111891131B (en
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徐昕
张兴龙
刘学卿
方强
周星
曾宇骏
施逸飞
尹昕
张慧
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National University of Defense Technology
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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
    • B60W40/10—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 vehicle motion
    • 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
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • 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
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0019—Control system elements or transfer functions
    • B60W2050/0028—Mathematical models, e.g. for simulation
    • B60W2050/0031—Mathematical model of the vehicle
    • B60W2050/0034—Multiple-track, 2D vehicle model, e.g. four-wheel model
    • 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
    • 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
    • B60W2520/12—Lateral speed
    • 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
    • B60W2540/00—Input parameters relating to occupants
    • B60W2540/18—Steering angle

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  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Human Computer Interaction (AREA)
  • Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Tires In General (AREA)

Abstract

The invention discloses a method and a system for identifying tire sidewall deflection rigidity on line, wherein the method comprises the following steps: step 1, establishing a vehicle dynamic model; step 2, simplifying the vehicle dynamics model based on the linear lateral tire force model to obtain a vehicle dynamics simplified model; step 3, discretizing the vehicle dynamics simplified model to obtain a recursion model taking the front and rear wheel side deflection rigidity of the vehicle as parameters to be estimated; and 4, identifying the lateral deflection rigidity of the front wheel and the rear wheel of the vehicle in the recursion model by adopting a limited memory recursion least square online identification method with a forgetting factor. The tire cornering stiffness online identification method based on the finite memory recursion least square with the forgetting factor has the advantages that the cornering stiffness of the front wheel and the rear wheel is identified through the finite memory recursion least square online identification method with the forgetting factor, the dimension disaster phenomenon can be avoided, the identification instantaneity is improved, the tire cornering stiffness online identification is achieved through the finite historical data, the tire cornering stiffness online identification method is suitable for most environments on the whole, the internal mechanical device is simple, the stability is high, and the algorithm efficiency is high.

Description

Online identification method and system for tire sidewall deflection rigidity
Technical Field
The invention relates to the technical field of vehicle parameter identification, in particular to an online identification method and system for tire sidewall deflection rigidity.
Background
For vehicle control, a vehicle dynamic model has properties such as strong coupling nonlinearity, and the most common modeling method at present is to establish a simplified physical model through stress and moment analysis on a vehicle. The main non-linear characteristic of the model comes from the tire, particularly when the vehicle runs on a curve, the cornering characteristic of the tire directly determines the steering stability of the vehicle, and the cornering stiffness of the tire is a very important parameter in the cornering characteristic of the tire, and the cornering stiffness of the tire can be changed correspondingly under different road environments. At present, the cornering stiffness of a tire cannot be directly measured, and how to quickly and accurately identify the cornering stiffness by using measured vehicle information and combining a certain identification algorithm is one of the problems to be solved by global researchers and engineering technicians.
The method for identifying the cornering stiffness of the tire on line proposed worldwide mainly focuses on: according to the magnitude of the current cornering angle and the load transfer amount, the current tire cornering stiffness is fitted in a segmented mode by the aid of a least square method; identifying a transfer function of an equivalent two-degree-of-freedom vehicle model of a vehicle system by using a least square method through the acquired data so as to calculate the cornering stiffness of the tire; an Electronic Stability Control System (ESC) is used, and an extended Kalman filtering algorithm is combined to perform online identification on the tire side deflection stiffness; and identifying the cornering stiffness of the tire on line by adopting a recursion least square method with a forgetting factor.
However, the above existing online identification method for the cornering stiffness of the tire mainly has the following defects: with the gradual increase of new observation data, a large amount of observation data can be accumulated and stored in the identification algorithm, and a dimension disaster phenomenon occurs; secondly, repetitive calculation is added, and the real-time performance of identification is greatly reduced; and thirdly, although a forgetting factor is added, a large amount of historical data is still used during identification.
Disclosure of Invention
Aiming at one or more defects in the prior art, the invention provides the online identification method and the online identification system for the tire cornering stiffness, which can avoid the dimension disaster phenomenon, increase the real-time performance of identification, and realize online identification of the tire cornering stiffness by using limited historical data.
In order to achieve the aim, the invention provides an online identification method for tire sidewall deflection rigidity, which comprises the following steps:
step 1, establishing a vehicle dynamic model;
step 2, simplifying the vehicle dynamics model based on the linear lateral tire force model to obtain a vehicle dynamics simplified model;
step 3, discretizing the vehicle dynamics simplified model to obtain a recursion model taking the front and rear wheel side deflection rigidity of the vehicle as parameters to be estimated;
and 4, identifying the lateral deflection rigidity of the front wheel and the rear wheel of the vehicle in the recursion model by adopting a limited memory recursion least square online identification method with a forgetting factor.
Further preferably, in step 1, the vehicle dynamics model is a two-degree-of-freedom vehicle dynamics model, and the modeling process is as follows:
considering the movement of a plane motion vehicle with 2 degrees of freedom, namely transverse movement and yaw movement, setting the longitudinal speed to be constant, and respectively obtaining a stress balance equation of a y axis and a moment balance equation around a z axis under a vehicle coordinate system according to Newton's second law, wherein the vehicle coordinate system and the tire coordinate system conform to the right hand rule:
Figure BDA0002624852990000021
in formula (1): m is the total mass of the vehicle,
Figure BDA0002624852990000022
in order to be the lateral acceleration of the vehicle,
Figure BDA0002624852990000023
as is the longitudinal speed of the vehicle,
Figure BDA0002624852990000024
as the yaw rate,
Figure BDA0002624852990000025
for yaw angular acceleration, /)fIs the distance of the center of mass to the front axis,/rIs the distance of the center of mass to the rear axis, IzFor the moment of inertia of the vehicle about the z-axis, Fyf、FyrThe force on the y axis of the vehicle coordinate system of the front and the rear tires of the vehicle.
Further preferably, in step 2, the vehicle dynamics model is simplified based on the linear lateral tire force model to obtain a simplified vehicle dynamics model, specifically:
the conversion relation between the forces received in the x and y directions of the tire coordinate system and the longitudinal and transverse forces of the vehicle coordinate system is obtained as follows:
Figure BDA0002624852990000026
in the formula (2), Fxf、FxrForce Ft, the force exerted by the front and rear tires on the x-axis of the vehicle coordinate systemxf、FtxrForce, Ft, on the x-axis of the tire coordinate system experienced by the front and rear tires of the vehicleyf、FtyrThe force which is applied to the front and the rear tires of the vehicle on the y axis of the tire coordinate system,fis a front wheel corner;
the front and rear wheel side deviation force of the vehicle is defined as follows:
Figure BDA0002624852990000031
in the formula (3), CαfFor vehicle front wheel cornering stiffness, CαrFor vehicle rear wheel cornering stiffness, αfIs a front wheel side slip angle, alpha, of the vehiclerFor the vehicle rear wheel side slip angle, based on the assumption of small slip angle, the vehicle front and rear wheel side slip angles are obtained as follows:
Figure BDA0002624852990000032
in the formula (4), vtyf、vtyrRespectively the lateral velocity of the front and rear wheels, vt, in the tyre coordinate systemxf、vtxrThe longitudinal speeds of the front wheel and the rear wheel under a tire coordinate system respectively;
the tire coordinate system lateral and longitudinal speeds are represented by the vehicle coordinate system lateral and longitudinal based on the small rotation angle assumption as follows:
Figure BDA0002624852990000033
in the formula (5), the reaction mixture is,
Figure BDA0002624852990000034
is the vehicle lateral velocity;
substituting formula (5) for formula (4) to obtain:
Figure BDA0002624852990000035
based on
Figure BDA0002624852990000036
The formula (6) is simplified as follows:
Figure BDA0002624852990000037
substituting formula (7) for formula (3) is:
Figure BDA0002624852990000041
substituting formula (8) for formula (1) to obtain a simplified model of vehicle dynamics, which is:
Figure BDA0002624852990000042
further preferably, in step 3, discretizing the simplified vehicle dynamics model to obtain a recursion model using the front and rear wheel cornering stiffness as a parameter to be estimated, specifically:
using the Euler method of the preamble, use
Figure BDA0002624852990000043
The increment at each sampling period T represents
Figure BDA0002624852990000044
Discretizing equation (9) as:
Figure BDA0002624852990000045
and (3) converting the data of the front k steps into a least square form by considering the data of the front k steps to obtain a recursion model taking the lateral deflection rigidity of the front wheel and the rear wheel of the vehicle as parameters to be estimated:
Figure BDA0002624852990000046
wherein:
Figure BDA0002624852990000047
Figure BDA0002624852990000051
in equations (10) to (13), k represents data at the k-th time,
Figure BDA0002624852990000052
indicating the longitudinal speed of the vehicle at the time k,
Figure BDA0002624852990000053
represents the lateral speed of the vehicle at the k-th time,
Figure BDA0002624852990000054
indicates the yaw rate of the vehicle at the k-th time,f(k) indicating the front wheel angle of the vehicle at the k-th time.
Further preferably, in step 4, the identifying the front and rear wheel cornering stiffness in the recursive model by using a finite memory recursive least square online identification method with a forgetting factor specifically comprises:
equations (12) - (13) are converted to a sample set form with a forgetting factor as:
Figure BDA0002624852990000055
Figure BDA0002624852990000056
wherein:
Figure BDA0002624852990000057
Figure BDA0002624852990000058
in the formula, A (k), b (k) represent limited memory initial sample sets at the first k times, A (k +1) and b (k +1) represent limited memory sample sets with forgetting factors at the k +1 th time, and A (k + i) and b (k + i) represent limited memory sample sets with forgetting factors at the k + i th time;
taking the first k time finite memory initial sample sets, and obtaining the cornering stiffness when the sampling time is the kth time as follows:
Figure BDA0002624852990000061
when the sampling time is greater than the kth time, that is, the sampling time is the kth + i time, where i is 1, 2, 3 …, newly adding sampling data as α (k + i) and β (k + i), removing the foremost historical data α (i +1) and β (i +1) in order to keep the finite memory length unchanged, and obtaining the cornering stiffness when the sampling time is the kth + i time as follows:
Figure BDA0002624852990000062
in formula (15), λ ═ ρ2The yaw stiffness estimate at time k + i is the total forgetting factor.
Further preferably, the total forgetting factor has a value range of: lambda is more than 0.95 and less than 1.
In order to achieve the above object, the present invention further provides an online identification system for tire sidewall stiffness, comprising:
a data acquisition module for acquiring the transverse speed of the vehicle
Figure BDA0002624852990000063
Longitudinal velocity
Figure BDA0002624852990000064
Yaw rate
Figure BDA0002624852990000065
A vehicle steering wheel angle;
the industrial personal computer is in communication connection with the data acquisition module and is used for receiving the data acquired by the data acquisition module and storing and transferring the data;
and the identification module is carried on the industrial personal computer, the identification module stores a computer program, and the identification module realizes the step 4 of the method when executing the computer program.
Further preferably, the data acquisition module comprises a positioning module and a controller area network communication module;
the positioning module is used for acquiring the transverse speed of the vehicle
Figure BDA0002624852990000066
Longitudinal velocity
Figure BDA0002624852990000067
Yaw rate
Figure BDA0002624852990000068
The system is provided by a combined navigation module combined by a GPS positioning module, an inertial navigation system and a differential correction searching module;
the controller local area network communication module is used for acquiring a controller local area network message of a steering wheel corner of the vehicle control unit, the controller local area network communication module is an Ethernet-controller local area network converter, and a controller local area network port of the Ethernet-controller local area network converter is connected with a vehicle controller local area network bus through a controller local area network line so as to acquire the steering wheel corner of the vehicle.
Preferably, the positioning module comprises two mushroom head antennas and an inertial navigation unit, the mushroom head antennas are connected with the inertial navigation unit, and the inertial navigation unit is connected with the industrial personal computer through an RS 422-COM data line so as to acquire and transmit vehicle pose information.
The method and the system for identifying the tire cornering stiffness on line provided by the invention realize the identification of the cornering stiffness of the front wheel and the rear wheel by the limited memory recursive least square online identification method with forgetting factors, can avoid dimension disaster phenomenon, increase the real-time performance of the identification, realize the online identification of the tire cornering stiffness by using limited historical data, are suitable for most environments integrally, and have the advantages of simple internal mechanical device, high stability and high algorithm efficiency.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments 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 present invention, and for those skilled in the art, other drawings can be obtained according to the structures shown in the drawings without creative efforts.
FIG. 1 is a flow chart of an online identification method for cornering stiffness of a tire according to an embodiment of the present invention;
FIG. 2 is a simplified schematic of a vehicle dynamics model according to an embodiment of the present invention;
FIG. 3 is a schematic view of a tire under stress in an embodiment of the present invention;
FIG. 4 is a schematic block diagram of an online tire cornering stiffness identification system according to an embodiment of the present invention;
FIG. 5 is a road route map during simulation according to an embodiment of the present invention;
FIG. 6 is a graph showing the results of LMFF-RLS and RLS estimated cornering stiffnesses of the front and rear wheels when the vehicle speed is 60km/h during simulation according to an embodiment of the present invention;
FIG. 7 is a graph showing the results of LMFF-RLS and RLS estimated cornering stiffnesses of the front and rear wheels at a vehicle speed of 70km/h during simulation according to an embodiment of the present invention;
FIG. 8 is a graph showing the results of LMFF-RLS and RLS estimated cornering stiffnesses of the front and rear wheels when the vehicle speed is 80km/h during simulation according to an embodiment of the present invention;
FIG. 9 is a schematic diagram of simulation time of the LMFF-RLS and RLS when the vehicle speed is 60km/h in the simulation process according to the embodiment of the invention;
FIG. 10 is a schematic diagram of simulation time of the LMFF-RLS and RLS when the vehicle speed is 70km/h in the simulation process of the embodiment of the invention;
FIG. 11 is a simulation time diagram of the LMFF-RLS and RLS when the vehicle speed is 80km/h in the simulation process according to the embodiment of the invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
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 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 invention.
It should be noted that all the directional indicators (such as up, down, left, right, front, and rear … …) in the embodiment of the present invention are only used to explain the relative position relationship between the components, the movement situation, etc. in a specific posture (as shown in the drawing), and if the specific posture is changed, the directional indicator is changed accordingly.
In addition, the descriptions related to "first", "second", etc. in the present invention are only for descriptive purposes and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
In the present invention, unless otherwise expressly stated or limited, the terms "connected," "secured," and the like are to be construed broadly, and for example, "secured" may be a fixed connection, a removable connection, or an integral part; the connection can be mechanical connection, electrical connection, physical connection or wireless communication connection; they may be directly connected or indirectly connected through intervening media, or they may be connected internally or in any other suitable relationship, unless expressly stated otherwise. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In addition, the technical solutions in the embodiments of the present invention may be combined with each other, but it must be based on the realization of those skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination of technical solutions should not be considered to exist, and is not within the protection scope of the present invention.
The embodiment discloses an online identification method for tire cornering stiffness, which can realize online identification of the tire cornering stiffness of tire cornering stiffness characteristics in a linear region when a vehicle runs on a curve. Due to the limited memory, the identified cornering stiffness is approximately 0 when the vehicle is driven straight for a long time, i.e. when the vehicle enters a curve to be driven, the cornering stiffness starts to be identified; when leaving the curve and going straight, the previously identified cornering stiffness is gradually forgotten. Referring to fig. 1, the method specifically includes the following steps:
step 1, establishing a vehicle dynamic model;
step 2, simplifying the vehicle dynamics model based on the linear lateral tire force model to obtain a vehicle dynamics simplified model;
step 3, discretizing the vehicle dynamics simplified model to obtain a recursion model taking the front and rear wheel side deflection rigidity of the vehicle as parameters to be estimated;
and 4, identifying the lateral deflection rigidity of the front wheel and the rear wheel of the vehicle in the recursion model by adopting a limited memory recursion least square online identification method with a forgetting factor.
Referring to fig. 2-3, in the present embodiment, the vehicle dynamics model adopts a two-degree-of-freedom vehicle dynamics model, and the two-degree-of-freedom vehicle dynamics model is simplified based on the linear lateral tire force model.
In this embodiment, the two-degree-of-freedom vehicle dynamics model modeling process is as follows: considering a plane moving vehicle with 2 degrees of freedom of movement, i.e. lateral and yaw movement, the longitudinal speed is set constant, i.e.
Figure BDA0002624852990000096
Constant, vehicle and tire coordinate systems both conform to right hand rules.
In this example, the tire stress is defined as follows:
the force on the front and rear tires of the vehicle on the y axis of the vehicle coordinate system is Fyf、Fyr；
The force F applied to the front and rear tires on the x-axis of the vehicle coordinate systemxf、Fxr；
The force Ft applied to the front and rear tires of the vehicle on the x-axis of the tire coordinate systemxf、Ftxr；
The force Ft applied to the front and rear tires of the vehicle on the y-axis of the tire coordinate systemyf、Ftyr。
According to Newton's second law, respectively obtaining a stress balance equation of a y axis and a moment balance equation around a z axis under a vehicle coordinate system:
Figure BDA0002624852990000091
in formula (1): m is the total mass of the vehicle (service mass + passenger mass + load mass) in kg;
Figure BDA0002624852990000092
is the lateral acceleration of the vehicle, and has the unit of m/s2；
Figure BDA0002624852990000093
Is the longitudinal speed of the vehicle, and the unit is m/s;
Figure BDA0002624852990000094
is the yaw angular velocity, and the unit is rad/s;
Figure BDA0002624852990000095
is yaw angular acceleration, in rad/s2；lfIs the distance from the center of mass to the front axis in m; lrIs the distance from the center of mass to the rear axis in m; i iszIs the inertia of the vehicle rotating around the z axis and has the unit of kgm2；Fyf、FyrThe force on the vehicle coordinate system y axis of the front and rear tires of the vehicle is given by the unit of N.
In this embodiment, the vehicle dynamics model is simplified based on the linear lateral tire force model to obtain a simplified vehicle dynamics model, which specifically includes: front and rear wheel side cornering forces and longitudinal tire force Fty★And Ftx★Are front and rear wheel side slip angles alpha, respectively★And slip ratio s★Where ∈ { f, r }. The forces in the x and y directions of the tire coordinate system are transformed into longitudinal and lateral forces of the vehicle coordinate system as follows:
Figure BDA0002624852990000101
in the formula (2), Fxf、FxrForce Ft, the force exerted by the front and rear tires on the x-axis of the vehicle coordinate systemxf、FtxfForce, Ft, on the x-axis of the tire coordinate system experienced by the front and rear tires of the vehicleyf、FtyrThe force which is applied to the front and the rear tires of the vehicle on the y axis of the tire coordinate system,fis a front wheel corner;
the front and rear wheel side deviation force of the vehicle is defined as follows:
Figure BDA0002624852990000102
in the formula (3), CαfFor vehicle front wheel cornering stiffness, CαrFor vehicle rear wheel cornering stiffness, αfIs a front wheel side slip angle, alpha, of the vehiclerFor the vehicle rear wheel side slip angle, based on the assumption of small slip angle, the vehicle front and rear wheel side slip angles are obtained as follows:
Figure BDA0002624852990000103
in the formula (4), vtyf、vtyrRespectively the lateral velocity of the front and rear wheels, vt, in the tyre coordinate systemxf、vtxrThe longitudinal speeds of the front wheel and the rear wheel under a tire coordinate system respectively;
the tire coordinate system lateral and longitudinal speeds are represented by the vehicle coordinate system lateral and longitudinal based on the small rotation angle assumption as follows:
Figure BDA0002624852990000104
in the formula (5), the reaction mixture is,
Figure BDA0002624852990000105
is the vehicle lateral velocity;
substituting formula (5) for formula (4) to obtain:
Figure BDA0002624852990000111
based on
Figure BDA0002624852990000112
The formula (6) is simplified as follows:
Figure BDA0002624852990000113
substituting formula (7) for formula (3) is:
Figure BDA0002624852990000114
substituting formula (8) for formula (1) to obtain a simplified model of vehicle dynamics, which is:
Figure BDA0002624852990000115
due to the fact that
Figure BDA0002624852990000116
Cannot be measured directly, so the proud Euler method is used
Figure BDA0002624852990000117
The increment at each sampling period T represents
Figure BDA0002624852990000118
Discretizing equation (9) as:
Figure BDA0002624852990000119
in the formula, the index k represents the data at the current time and the lateral speed of the vehicle
Figure BDA00026248529900001110
Longitudinal velocity
Figure BDA00026248529900001111
Yaw rate
Figure BDA00026248529900001112
And front wheel steering anglefCan be measured by a positioning module and a local area network communication module of the vehicle-mounted controller;
and (3) further considering the data of the front k steps, converting the data (10) into a least square form, and obtaining a recursion model taking the lateral deflection rigidity of the front wheel and the rear wheel of the vehicle as parameters to be estimated:
Figure BDA0002624852990000121
wherein:
Figure BDA0002624852990000122
Figure BDA0002624852990000123
up to this point, the basic form of least squares, i.e., a recursive model with the front and rear wheel side deflection stiffness of the vehicle as the parameter to be estimated, has been established.
For convenience of description, the finite memory recursive least square online identification method with forgetting factors is adopted to identify the lateral deflection rigidity of the front wheel and the rear wheel of the vehicle in the recursive model, and specifically the method comprises the following steps:
equations (12) - (13) are converted to a sample set form with a forgetting factor as:
Figure BDA0002624852990000124
Figure BDA0002624852990000125
wherein:
Figure BDA0002624852990000131
Figure BDA0002624852990000132
in the formula, A (k), b (k) represent limited memory initial sample sets at the first k times, A (k +1) and b (k +1) represent limited memory sample sets with forgetting factors at the k +1 th time, and A (k + i) and b (k + i) represent limited memory sample sets with forgetting factors at the k + i th time;
firstly, selecting an initial sample set of limited memory at the first k moments, determining the length of the limited memory to be k-2, and obtaining the lateral deflection rigidity when the sampling moment is the kth moment as follows:
Figure BDA0002624852990000133
when the sampling time is greater than the kth time, that is, the sampling time is the kth + i time, where i is 1, 2, 3 …, newly adding sampling data as α (k + i) and β (k + i), removing the foremost historical data α (i +1) and β (i +1) in order to keep the finite memory length unchanged, and obtaining the cornering stiffness when the sampling time is the kth + i time as follows:
Figure BDA0002624852990000134
in the formula (15), the estimated yaw stiffness value at the time k + i; λ ═ ρ2The total forgetting factor has the effect of weakening the effect of historical data on identification along with the addition of new data, so that the influence of the new data on the identification effect is improved, and the value range is generally more than 0.95 and less than lambda and less than 1. Due to the limited memory length, the sampling data volume of the method does not increase with the increase of new data at each sampling moment, namely, dimensionality disaster is avoided, and the calculation cost of identification does not increase with the increase of new data. The method is shown in table 1:
TABLE 1 Limited memory recursive least squares online identification method with forgetting factor
Figure BDA0002624852990000141
Referring to fig. 4, the present embodiment further discloses an online identification system for tire sidewall deflection stiffness, including:
a data acquisition module for acquiring the transverse speed of the vehicle
Figure BDA0002624852990000142
Longitudinal velocity
Figure BDA0002624852990000143
Yaw rate
Figure BDA0002624852990000144
A vehicle steering wheel angle;
the industrial personal computer is in communication connection with the data acquisition module and is used for receiving the data acquired by the data acquisition module and storing and transferring the data;
the identification module is carried on the industrial personal computer and stores a computer program, and the limited memory recursive least square online identification method with the forgetting factor is realized when the identification module executes the computer program.
In this embodiment, the data acquisition module includes a positioning module and a controller area network communication module, specifically:
the positioning module is used for providing the transverse speed of the vehicle
Figure BDA0002624852990000145
Longitudinal velocity
Figure BDA0002624852990000146
Yaw rate
Figure BDA0002624852990000147
The combined navigation system is mainly provided by a combined navigation module combined by a GPS positioning module, an inertial navigation system and a differential correction searching module. The positioning module has high positioning precision and high frequency (namely low time delay) of output positioning data. The positioning module is composed of two mushroom head antennas and an inertial navigation unit, the mushroom head antennas are connected with the inertial navigation unit, and the inertial navigation unit is connected with the industrial personal computer through an RS 422-COM data line so as to acquire and transmit vehicle pose information.
The controller area network communication module is used for collecting steering wheel corner controller area network messages of the vehicle control unit, and the main equipment is an Ethernet-controller area network converter, namely CANET. The controller local area network port of the Ethernet-controller local area network converter is connected with a vehicle controller local area network bus through a controller local area network line so as to obtain the steering angle of the vehicle steering wheel. The Ethernet-controller LAN converter can also be connected with an industrial personal computer through an Ethernet cable, and the vehicle steering wheel corner is transmitted to the industrial personal computer through the Ethernet cable to acquire the vehicle steering wheel corner and send the controller LAN message.
In this embodiment, the industrial personal computer is used for receiving data from the data acquisition module, and processing the data by a finite memory recursive least square online identification method with forgetting factors to identify the side deflection rigidity of the front wheel and the rear wheel. That is to say, the industrial personal computer plays a role in data transfer, storage and operation of the identification method.
The method and the system for online identification of tire sidewall stiffness in the present embodiment are further described with reference to specific simulation examples.
In this embodiment, an experimental environment is constructed by using Carsim \ Simulink joint simulation, and a road route thereof is shown in fig. 5. The road adhesion coefficient is 0.85, the vehicle speed is 60km/h, 70km/h and 80km/h respectively, the sampling time is 50ms, the simulation time is 100s, and k is 200. The finite Memory Recursive Least Square online identification method (Limited Memory Recursive Least Square with forming factor, LMFF-RLS) and the Recursive Least Square (RLS) with forgetting factor in the embodiment are respectively adopted.
The comparison of the experimental results of FIGS. 6-8 shows that:
when the vehicle speed is 60km/h, the cornering stiffness of the left and right front wheels is about 90000N/rad, and the cornering stiffness of the left and right rear wheels is about 71000N/rad; the cornering stiffnesses of the front and rear wheels estimated by LMFF-RLS and RLS were about 85000N/rad and 65500N/rad, respectively;
when the vehicle speed is 70km/h, the cornering stiffness of the left and right front wheels is about 92000N/rad, and the cornering stiffness of the left and right rear wheels is about 77000N N/rad; the cornering stiffnesses of the front and rear wheels estimated by LMFF-RLS and RLS were about 84000N/rad and 65400N N/rad, respectively;
when the vehicle speed is 80km/h, the lateral deflection rigidity of the left front wheel and the right front wheel is about 92480N/rad, and the lateral deflection rigidity of the left rear wheel and the right rear wheel is about 81500N/rad; the cornering stiffnesses of the front and rear wheels estimated by LMFF-RLS and RLS were approximately 83250N/rad and 64950N/rad, respectively.
The results are obtained from the linear region of the tire cornering power, and the vehicle model is a simplified model, so the system deviation exists in the identification result, and the identification result is added into the compensation of 5000-10000N/rad to approach the true value after comparison. For RLS and LMFF-RLS, although a forgetting factor is added, the RLS still belongs to a long-term memory method and uses a large amount of historical data; however, the LMFF-RLS is a short-term memory method, and when the tire has a cornering phenomenon, the LMFF-RLS recognizes that the tire starts to be recognized in a straight line, and the LMFF-RLS gradually forgets the recognition result, which is a cause of fluctuation of the LMFF-RLS. If the vehicle is driven in a straight line for a long time, the recognition result is 0. If the k value is increased in LMFF-RLS, the fluctuation can be reduced, but the calculation cost increases.
In the embodiment, the vehicle speed is respectively 60km/h, 70km/h and 80km/h, and the simulation time of the LMFF-RLS method and the simulation time of the RLS method are compared. Referring to fig. 9-11, it can be seen that both methods have fluctuations in simulation time per sampling period, and that the RLS method as a whole is more time consuming than the LMFF-RLS method. Furthermore, the RLS method tends to increase in fluctuation, while LMFF-RLS fluctuates within a certain range, which is increasingly apparent as samples are accumulated.
In summary, the present invention first establishes a simplified vehicle dynamics model; then, vehicle information acquired through the integrated navigation and vehicle-mounted CAN communication module is substituted into a vehicle dynamics model; and finally, performing online identification on the front wheel side deflection rigidity and the rear wheel side deflection rigidity by the LMFF-RLS method provided by the patent. The system is an electromechanical integrated system which integrates a plurality of advanced technologies such as modeling technology, machinery, electronics, sensors, computer software hardware, artificial intelligence and the like.
In the present embodiment, the terms of related art are interpreted as:
CAN: a controller area network (controllerarenetwork), a bus technology for mutual communication between various sensors and actuators in an automobile;
VCU: a Vehicle Control Unit (Vehicle Control Unit) and a new energy Vehicle central Control Unit are the core of the whole Control system and can acquire Vehicle Control information;
RLS: recursive Least squares (Recursive Least Square);
LMFF-RLS: a finite memory Recursive Least Square online identification method (Limited memory Recursive Least Square with forming Factor) with Forgetting Factor.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention, and all modifications and equivalents of the present invention, which are made by the contents of the present specification and the accompanying drawings, or directly/indirectly applied to other related technical fields, are included in the scope of the present invention.

Claims (9)

1. The online identification method for the tire sidewall deflection rigidity is characterized by comprising the following steps:
step 1, establishing a vehicle dynamic model;
step 2, simplifying the vehicle dynamics model based on the linear lateral tire force model to obtain a vehicle dynamics simplified model;
step 3, discretizing the vehicle dynamics simplified model to obtain a recursion model taking the front and rear wheel side deflection rigidity of the vehicle as parameters to be estimated;
and 4, identifying the lateral deflection rigidity of the front wheel and the rear wheel of the vehicle in the recursion model by adopting a limited memory recursion least square online identification method with a forgetting factor.
2. The method for identifying the cornering stiffness of the tire on line according to claim 1, wherein in the step 1, the vehicle dynamic model is a two-degree-of-freedom vehicle dynamic model, and the modeling process is as follows:
considering the movement of a plane motion vehicle with 2 degrees of freedom, namely transverse movement and yaw movement, setting the longitudinal speed to be constant, and respectively obtaining a stress balance equation of a y axis and a moment balance equation around a z axis under a vehicle coordinate system according to Newton's second law, wherein the vehicle coordinate system and the tire coordinate system conform to the right hand rule:
Figure FDA0002624852980000011
in formula (1): m is the total mass of the vehicle,
Figure FDA0002624852980000012
in order to be the lateral acceleration of the vehicle,
Figure FDA0002624852980000013
as is the longitudinal speed of the vehicle,
Figure FDA0002624852980000014
as the yaw rate,
Figure FDA0002624852980000015
for yaw angular acceleration, /)fIs the distance of the center of mass to the front axis,/rIs the distance of the center of mass to the rear axis, IzFor the moment of inertia of the vehicle about the z-axis, Fyf、FyrThe force on the y axis of the vehicle coordinate system of the front and the rear tires of the vehicle.
3. The method for identifying the cornering stiffness of the tire on line according to claim 2, wherein in the step 2, the vehicle dynamics model is simplified based on the linear lateral tire force model to obtain a simplified vehicle dynamics model, specifically:
the conversion relation between the forces received in the x and y directions of the tire coordinate system and the longitudinal and transverse forces of the vehicle coordinate system is obtained as follows:
Figure FDA0002624852980000016
in the formula (2), Fxf、FxrFor front and rear wheels of vehicleForce, Ft, exerted by the tyre on the x-axis of the vehicle coordinate systemxf、FtxrForce, Ft, on the x-axis of the tire coordinate system experienced by the front and rear tires of the vehicleyf、FtyrThe force which is applied to the front and the rear tires of the vehicle on the y axis of the tire coordinate system,fis a front wheel corner;
the front and rear wheel side deviation force of the vehicle is defined as follows:
Figure FDA0002624852980000021
in the formula (3), CαfFor vehicle front wheel cornering stiffness, CαrFor vehicle rear wheel cornering stiffness, αfIs a front wheel side slip angle, alpha, of the vehiclerFor the vehicle rear wheel side slip angle, based on the assumption of small slip angle, the vehicle front and rear wheel side slip angles are obtained as follows:
Figure FDA0002624852980000022
in the formula (4), vtyf、vtyrRespectively the lateral velocity of the front and rear wheels, vt, in the tyre coordinate systemxf、vtxrThe longitudinal speeds of the front wheel and the rear wheel under a tire coordinate system respectively;
the tire coordinate system lateral and longitudinal speeds are represented by the vehicle coordinate system lateral and longitudinal based on the small rotation angle assumption as follows:
Figure FDA0002624852980000023
in the formula (5), the reaction mixture is,
Figure FDA0002624852980000024
is the vehicle lateral velocity;
substituting formula (5) for formula (4) to obtain:
Figure FDA0002624852980000025
based on
Figure FDA0002624852980000026
The formula (6) is simplified as follows:
Figure FDA0002624852980000027
substituting formula (7) for formula (3) is:
Figure FDA0002624852980000031
substituting formula (8) for formula (1) to obtain a simplified model of vehicle dynamics, which is:
Figure FDA0002624852980000032
4. the method for identifying the cornering stiffness of the tire according to claim 3, wherein in step 3, the simplified vehicle dynamics model is discretized to obtain a recursion model taking the cornering stiffness of the front and rear wheels of the vehicle as a parameter to be estimated, and specifically, the method comprises the following steps:
using the Euler method of the preamble, use
Figure FDA0002624852980000033
The increment at each sampling period T represents
Figure FDA0002624852980000034
Discretizing equation (9) as:
Figure FDA0002624852980000035
and (3) converting the data of the front k steps into a least square form by considering the data of the front k steps to obtain a recursion model taking the lateral deflection rigidity of the front wheel and the rear wheel of the vehicle as parameters to be estimated:
Figure FDA0002624852980000036
wherein:
Figure FDA0002624852980000037
Figure FDA0002624852980000041
in equations (10) to (13), k represents data at the k-th time,
Figure FDA0002624852980000042
indicating the longitudinal speed of the vehicle at the time k,
Figure FDA0002624852980000043
represents the lateral speed of the vehicle at the k-th time,
Figure FDA0002624852980000044
indicates the yaw rate of the vehicle at the k-th time,f(k) indicating the front wheel angle of the vehicle at the k-th time.
5. The method for identifying the cornering stiffness of the tire according to claim 4, wherein in the step 4, the identifying the cornering stiffness of the front wheel and the rear wheel in the recursive model by using a finite memory recursive least square online identification method with a forgetting factor specifically comprises:
equations (12) - (13) are converted to a sample set form with a forgetting factor as:
Figure FDA0002624852980000045
Figure FDA0002624852980000046
wherein:
Figure FDA0002624852980000047
Figure FDA0002624852980000048
in the formula, A (k), b (k) represent limited memory initial sample sets at the first k times, A (k +1) and b (k + i) represent limited memory sample sets with forgetting factors at the k +1 th time, and A (k + i) and b (k + i) represent limited memory sample sets with forgetting factors at the k + i th time;
taking the first k time finite memory initial sample sets, and obtaining the cornering stiffness when the sampling time is the kth time as follows:
Figure FDA0002624852980000051
when the sampling time is greater than the kth time, that is, the sampling time is the kth + i time, where i is 1, 2, 3 …, newly adding sampling data as α (k + i) and β (k + i), removing the foremost historical data α (i +1) and β (i +1) in order to keep the finite memory length unchanged, and obtaining the cornering stiffness when the sampling time is the kth + i time as follows:
Figure FDA0002624852980000052
in formula (15), λ ═ ρ2The total forgetting factor is the sum of the factors,
Figure FDA0002624852980000053
and the estimated value of the cornering stiffness at the k + i moment is obtained.
6. The method for identifying the cornering stiffness of the tire according to claim 5, wherein the total forgetting factor has a value range of: lambda is more than 0.95 and less than 1.
7. The utility model provides a tire lateral stiffness online identification system which characterized in that includes:
a data acquisition module for acquiring the transverse speed of the vehicle
Figure FDA0002624852980000054
Longitudinal velocity
Figure FDA0002624852980000055
Yaw rate
Figure FDA0002624852980000056
A vehicle steering wheel angle;
the industrial personal computer is in communication connection with the data acquisition module and is used for receiving the data acquired by the data acquisition module and storing and transferring the data;
an identification module, which is mounted on an industrial personal computer and stores a computer program, wherein the identification module realizes the step 4 of the method of any one of claims 1 to 6 when executing the computer program.
8. The system for on-line identification of tire cornering stiffness according to claim 7, wherein the data acquisition module comprises a positioning module and a controller area network communication module;
the positioning module is used for acquiring the transverse speed of the vehicle
Figure FDA0002624852980000057
Longitudinal velocity
Figure FDA0002624852980000058
Yaw rate
Figure FDA0002624852980000059
The system is provided by a combined navigation module combined by a GPS positioning module, an inertial navigation system and a differential correction searching module;
the controller local area network communication module is used for acquiring a controller local area network message of a steering wheel corner of the vehicle control unit, the controller local area network communication module is an Ethernet-controller local area network converter, and a controller local area network port of the Ethernet-controller local area network converter is connected with a vehicle controller local area network bus through a controller local area network line so as to acquire the steering wheel corner of the vehicle.
9. The system for on-line identification of the tire cornering stiffness according to claim 8, wherein the positioning module is composed of two mushroom head antennas and an inertial navigation unit, the mushroom head antennas are connected with the inertial navigation unit, and the inertial navigation unit is connected with an industrial personal computer through an RS 422-COM data line so as to acquire and transmit vehicle pose information.
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