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CN116588119B - A vehicle state estimation method based on tire model parameter adaptation - Google Patents

A vehicle state estimation method based on tire model parameter adaptation Download PDF

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CN116588119B
CN116588119B CN202310627273.8A CN202310627273A CN116588119B CN 116588119 B CN116588119 B CN 116588119B CN 202310627273 A CN202310627273 A CN 202310627273A CN 116588119 B CN116588119 B CN 116588119B
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vehicle
speed
wheel
tire model
longitudinal
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CN116588119A (en
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卢佳兴
陈虹
张琳
李斌
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Tongji University
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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
    • 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
    • B60W40/105—Speed
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    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/10—Geometric CAD
    • G06F30/15—Vehicle, aircraft or watercraft design
    • 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
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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
    • 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
    • B60W2050/0043—Signal treatments, identification of variables or parameters, parameter estimation or state estimation
    • 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
    • B60W2520/28—Wheel 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
    • B60W2530/00—Input parameters relating to vehicle conditions or values, not covered by groups B60W2510/00 or B60W2520/00
    • B60W2530/20—Tyre data
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    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces
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Abstract

The invention relates to a vehicle state estimation method based on tire model parameter self-adaption, which comprises the following steps: collecting real vehicle data; building a tire experience model based on the vehicle load transfer model and the wheel center speed calculation; establishing a vehicle state estimation scheme based on unscented Kalman filtering; screening and memorizing data fragments meeting the continuous excitation condition under the typical working condition; carrying out tire model parameter identification by adopting particle swarm optimization, and obtaining the relation of tire model parameters along with the change of the vehicle speed according to the fitting of optimal tire model parameters in different vehicle speed sections; substituting the relation of the tire model parameters along with the change of the vehicle speed into a vehicle state estimation scheme based on unscented Kalman filtering to obtain real-time vehicle state estimation. The tire model and parameter identification algorithm provided by the invention fully considers the nonlinear and transverse and longitudinal dynamics coupling relation of the vehicle, and can realize more reliable and accurate vehicle dynamics parameter identification by adaptively adjusting the tire model parameters through the optimization algorithm.

Description

Vehicle state estimation method based on tire model parameter self-adaption
Technical Field
The invention relates to the technical field of vehicle state estimation, in particular to a vehicle state estimation method aiming at time-varying tire model parameters.
Background
Accurate predictions and estimations of vehicle states are critical to the performance of vehicle control systems, such as automatic driving, vehicle dynamics control, and chassis control. The longitudinal speed, lateral speed and yaw rate of the vehicle directly determine the state of motion of the vehicle. The vehicle state estimation algorithm utilizes an existing vehicle model and sensor signal combination to estimate the vehicle motion state. The accuracy of the vehicle model directly affects the accuracy of the vehicle state estimation. Because of the large interference in the actual vehicle running process, the accuracy of the vehicle model is affected in many ways, for example, as the vehicle speed changes, the key parts of the vehicle model (tire model parameters) also change, so that the accuracy of the vehicle model with fixed parameters is reduced. Tire dynamics is an important component of vehicle dynamics, and the lateral forces, longitudinal forces, etc. of the ground to which the vehicle is subjected are exerted on the vehicle system by the tire. However, tires have strong nonlinearities, and their performance is easily affected by the running conditions, and reliable parameter identification is currently a difficult problem that restricts further improvement of the vehicle control performance. The main evaluation method for identifying the dynamic parameters of the vehicle at present is to obtain the true value of the parameters through experiments and compare the true value with the estimated value of the parameters, thereby realizing the optimization of the identification method. The main problem of this approach is the content of both the truth acquisition and the parameter identification algorithms:
1) And (3) true value acquisition: the parameter true value of the tire is not suitable to be obtained through a test, and the idealization of test conditions is carried out, so that the parameter identification requirement in the actual driving scene of the vehicle is eliminated, and the obtaining of the true value has great uncertainty; when obtaining the true values of the tire parameters, a large number of different types of sensors are often required, which greatly increases the cost;
2) Parameter identification algorithm: most of parameter identification algorithms do not carry out coupling modeling on transverse and longitudinal dynamics of a vehicle, nonlinear characteristics of a vehicle system are often ignored, and the accuracy of the model is not high; the parameter optimization method has the problems of easy local optimal solution, premature convergence and the like.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a vehicle state estimation method based on tire model parameter self-adaption.
The aim of the invention can be achieved by the following technical scheme:
a method of vehicle state estimation based on tire model parameter adaptation, the method comprising:
collecting real vehicle data, including measuring intrinsic parameters of the vehicle and collecting vehicle motion parameters for typical working conditions of vehicle running;
building a tire experience model by calculating a vehicle load transfer model and a wheel center speed; establishing a vehicle state estimation scheme based on unscented Kalman filtering UKF;
screening data fragments meeting the continuous excitation condition under the typical working condition and memorizing the data;
Based on the memorized data segments, performing tire model parameter identification by adopting particle swarm optimization, and fitting according to the optimal tire model parameters in different vehicle speed sections to obtain the relationship of the tire model parameters along with the change of the vehicle speed;
and substituting the relation of the tire model parameters along with the change of the vehicle speed into a vehicle state estimation scheme based on unscented Kalman filtering UKF to obtain real-time vehicle state estimation.
Further, the tire empirical model obtains the relationship between the vertical force, the longitudinal force and the transverse force of the tire according to the distribution condition of the vertical load of each wheel position, the measured longitudinal vehicle speed and the calculated wheel center speed:
Wherein r w is the rolling radius of the wheel; omega i is the rotation speed of the ith wheel; v x,i and v y,i are the longitudinal speed and the lateral speed of the ith wheel center, respectively; s i,i,sy,i is the longitudinal slip rate and the transverse slip rate respectively; s i is the comprehensive slip rate; f x,i,Fy,i is the longitudinal and transverse forces to which the tire is subjected, respectively; c 1,c2 are tire model parameters respectively; f z,i is the vertical load distribution vector of the i-th wheel.
Further, the vehicle load transfer model obtains the relation between the acceleration of the vehicle and the vertical force of each wheel according to the acquired information of the acceleration of the vehicle and the measured intrinsic parameter information of the vehicle:
Fz＝(θT·(θ·θT)-1)·[ax,hg-ay·hg g]T·m Wherein F z is the vertical load distribution vector of the wheel, θ is the vehicle size parameter matrix, a x is the vehicle longitudinal acceleration, a y is the vehicle lateral acceleration, h g is the mass center height of the vehicle, g is the gravitational acceleration, and m is the mass of the vehicle.
Further, the wheel center speeds include a longitudinal speed and a lateral speed of each wheel center,
The longitudinal speed and the transverse speed of each wheel center are calculated by measuring the front wheel rotation angle of the vehicle, the yaw rate of the vehicle, the longitudinal speed and the transverse speed of the vehicle and the vehicle size parameters.
Further, the vehicle state estimation scheme based on unscented Kalman filter UKF is established, and the specific steps are as follows:
performing UKF parameter definition, wherein the parameters comprise a state vector, a control vector, a measurement vector, an algorithm super-parameter and noise of the vehicle;
establishing a UKF vehicle state space equation;
and according to the defined UKF parameters and the established UKF vehicle state space equation, estimating the vehicle state based on the UKF, and outputting an estimated value of the longitudinal speed and an estimated value of the transverse speed of the vehicle.
Further, the building of the UKF vehicle state space equation comprises the following specific steps:
Based on the vehicle load and the measured vehicle speed information, the air resistance F a,x and the rolling resistance F f,i of the wheels are calculated in combination with the inherent parameters of the vehicle during the running process of the vehicle:
Fa,x＝0.5·ρa·cD·A·vx 2
Ff,i＝f·Fz,i
Wherein ρ a is the air density; c D is the windage coefficient; a is the front projection area of the locomotive; f is the rolling resistance coefficient; f z,i is the vertical load of the ith wheel, i is 1,2,3 and 4 respectively representing the left front wheel, the right front wheel, the left rear wheel and the right rear wheel;
According to the measured vehicle front wheel rotation angle delta, the driving moment M M,i of the wheels, the braking moment M B,i of the wheels, the vehicle inherent parameters and the calculated vehicle load, a differential equation of the vehicle state vector is obtained:
Wherein F x,i,Fy,i is the longitudinal force and the transverse force applied to the tire, i is 1,2,3 and 4 respectively representing the left front wheel, the right front wheel, the left rear wheel and the right rear wheel; delta is the front wheel corner of the vehicle; v x,vy is the longitudinal speed and the lateral speed of the vehicle, respectively; w z is the yaw rate of the vehicle; a is the distance from the center of mass of the vehicle to the front axle; b is the distance from the center of mass of the vehicle to the rear axle; b is half of the distance between the wheels on the left side and the right side of the vehicle; m M,i is the driving torque of the wheels; i z is the moment of inertia of the vehicle; j ω is the moment of inertia of the wheel.
Further, when the positioning accuracy of the high-accuracy navigation system is good, the high-accuracy navigation system is utilized to correct the tire model parameters in the vehicle speed estimation algorithm, and the specific steps are as follows:
And according to the defined vehicle state vector and UKF vehicle state estimation, evaluating a state estimation deviation function J c:
Wherein c y is the lateral velocity estimation error weight; An estimated value of a longitudinal speed and an estimated value of a lateral speed of the vehicle, which are respectively estimated from the UKF vehicle state; The longitudinal speed and the lateral speed of the vehicle are respectively output by the high-precision inertial navigation system.
Further, the screening of the data segments meeting the continuous excitation condition under the typical working condition and the data memorization are carried out, and the specific process is as follows:
selecting a uniform speed lane change working condition, segmenting the vehicle speed at set speed intervals, and setting tire model parameters in the segments as fixed values;
obtaining the distance between a vehicle and a lane line;
judging a time point T chang when the vehicle crosses the lane line according to the lateral distance change;
Taking the moment T start,Tstart when the absolute value of the lateral acceleration searched forward by the T chang is smaller than the set value as the starting point of the track-changing segment;
Taking the moment T end,Tend when the absolute value of the backward searching lateral acceleration of the T change is smaller than the set threshold as the ending point of the track changing segment;
Extracting data of a T start～Tend time segment in the historical data;
According to the extracted lane change data segment, calculating the average longitudinal speed, the maximum lateral acceleration absolute value and the moment T chang of crossing the lane line of the vehicle of the segment, and calculating the priority of each data segment;
and storing the data fragments with the highest priority in each vehicle speed section for parameter identification.
Further, the tire model parameter identification is carried out by adopting particle swarm optimization, and the relationship of the tire model parameter along with the change of the vehicle speed is obtained according to the fitting of the optimal tire model parameters in different vehicle speed sections, and the specific steps are as follows:
Taking tire model parameters and the distance from the mass center to the front axle as PSO optimization particles;
setting the total number of initial particles and the maximum iteration number;
initializing the initial position and speed of each particle through an initialization function;
in the process of each iteration, evaluating the fitness function according to the current position of the particle;
In each iteration, calculating an fitness function value of each particle to obtain a particle individual history optimal position and a particle swarm global optimal position of the current iteration number, and calculating the latest speed and the latest position of the particle position change of the current iteration number according to the gradient change direction of the fitness function:
When the speed is updated in each iteration, a method of variable weight coefficients is adopted to linearly change the weight coefficients;
the steps are circulated according to the steps, and when the iteration times reach the maximum iteration times, the optimal particle positions under the current working condition, namely the optimal model parameters under the working condition, are obtained;
And obtaining a relation of the tire model parameters along with the longitudinal speed by using a polynomial fitting method according to the optimal model parameters under different working conditions as identification points.
Further, when the fitness function is evaluated, parameter matching and calculation are performed according to the current particle state and the UKF vehicle state estimation algorithm, and the vehicle state deviation estimation function is used as a cost item for optimizing the tire model parameters:
Ji(j)＝p0·Jc+p1||ai(j)-areference||2
Wherein J i (J) is the fitness function value of the ith particle and the jth iteration; p 0、p1 are respectively corresponding weight coefficients; j c is the accuracy cost of parameter estimation based on the UKF; a i (j) is the centroid-to-front axis distance value of the ith particle jth round of iterations; a reference is a reference value of the centroid to front axis distance.
Compared with the prior art, the invention has the following beneficial effects:
1) When the tire model parameter identification algorithm is designed, the nonlinear, transverse and longitudinal dynamics coupling relation of the vehicle, the global searching capability of the optimization algorithm and the like are fully considered, so that more reliable and more accurate vehicle dynamics parameter identification can be realized.
2) The identification of the dynamic parameters of the vehicle is mainly used for estimating the state of the vehicle, and is oriented to the state estimation requirement in the actual running scene of the vehicle, and the multi-element sensor is adopted to realize the evaluation of the identification of the dynamic parameters, so that the manpower and material resources can be greatly saved.
3) The invention corrects the tire model parameters in the vehicle speed estimation algorithm when the high-precision navigation system has good positioning precision, thereby ensuring that the vehicle speed estimation algorithm can also provide certain vehicle speed estimation precision when the high-precision navigation system fails.
Drawings
Fig.1 is a schematic structural view of the present invention.
FIG. 2 is a graph of tire model parameter C 2 versus longitudinal speed;
FIG. 3 is a graph of lateral vehicle speed estimation effects;
FIG. 4 is a schematic diagram of a tire model change process during simulation.
Detailed Description
The invention will now be described in detail with reference to the drawings and specific examples. The present embodiment is implemented on the premise of the technical scheme of the present invention, and a detailed implementation manner and a specific operation process are given, but the protection scope of the present invention is not limited to the following examples.
Example 1
The invention combines a particle swarm optimization algorithm and a unscented Kalman filtering algorithm, aims at vehicle response state information under typical working conditions, aims at optimal vehicle state information estimation accuracy, and realizes dynamic identification of vehicle dynamics parameters, thereby providing more reliable vehicle dynamics parameters for establishment of a vehicle model and operation of a vehicle control system. The method mainly comprises the following steps of:
step one: collecting real vehicle data
First, intrinsic parameters of the vehicle are measured, including dimensional parameters, mass, centroid height, etc. Aiming at typical running conditions of a vehicle, a multi-element sensor is utilized to collect data such as triaxial speed, angular speed, acceleration (longitudinal acceleration a x and transverse acceleration a y), longitudinal speed v x, transverse speed v y, yaw rate w z, wheel speed, motor driving moment, braking moment and the like of the vehicle, and data support is carried out for follow-up research on the change relation of tire model parameters along with the vehicle speed.
Step two: vehicle state estimation module based on UKF
And (3) taking the real vehicle data in the step one as a support, and establishing a tire experience model through modeling of vehicle load transfer, wheel center speed calculation and the like. And referring to an unscented Kalman filter estimation algorithm, respectively establishing a vehicle state estimation scheme of a vehicle state vector x, a control vector u and a measurement vector y.
2.1 Building a vehicle load transfer Module
And (3) deducing the relation between the acceleration of the vehicle and the vertical force of each wheel according to the information of the acceleration of the vehicle acquired in the step one and the measured inherent parameter information of the vehicle.
Fz＝[Fz,1Fz,2Fz,3Fz,4]T
θ·Fz＝[ax·hg-ay·hg g]T·m
Fz＝(θT·(θ·θT)-1)·[ax·hg-ay·hg g]T·m
Wherein, F z is the vertical load distribution vector of the wheel, F z,i is the vertical load of the ith wheel, i is 1,2,3 and 4 respectively representing the left front wheel, the right front wheel, the left rear wheel and the right rear wheel; θ is a vehicle size parameter matrix; a is the distance from the center of mass of the vehicle to the front axle; b is the distance from the center of mass of the vehicle to the rear axle; b is half of the distance between the wheels on the left side and the right side of the vehicle; h g is the centroid height of the vehicle; g is gravity acceleration; m is the mass of the vehicle.
2.2 Wheel center speed calculation
The longitudinal speed v x,i and the lateral speed v y,i of each wheel center are calculated according to the front wheel rotation angle δ of the vehicle measured in the step one, the yaw rate w z of the vehicle, the longitudinal speed v x of the vehicle, the lateral speed v y of the vehicle, and the dimensional parameters applied in the step 2.1.
V x,i is the longitudinal speed of the wheel center, i is 1,2,3 and 4 respectively representing the left front wheel, the right front wheel, the left rear wheel and the right rear wheel; v y,i is the lateral speed of the wheel;
2.3 building tire empirical model
And (3) obtaining the relationship between the vertical force and the longitudinal force and the transverse force of the tire according to the distribution condition of the vertical load of each wheel position obtained in the step (2.1), the longitudinal vehicle speed obtained in the step (I) and the speed information of the wheels obtained in the step (2.2).
Wherein r w is the rolling radius of the wheel; omega i is the rotation speed of the ith wheel; s x,i,sy,i is the longitudinal slip rate and the transverse slip rate respectively; s i is the comprehensive slip rate; f x,i,Fy,i is the longitudinal and transverse forces to which the tire is subjected, respectively; c 1,c2 are tire model parameters, mainly related to running conditions, and are vehicle dynamics parameters to be optimized by the invention.
2.4 UKF parameter definition
Establishing a state vector x, a control vector u and a measurement vector y of the vehicle:
x＝[vx vy wz ω1 ω2 ω3 ω4]
u＝[δ MM,1 MM,2 MM,3 MM,4 Fz,1 Fz,2 Fz,3 Fz,4]
y＝[ax ay wz ω1 ω2 ω3 ω4]
Wherein M M,i is the motor torque of the wheel.
Setting a hyper-parameter alpha=0.7, kappa=3 and beta=2 of sigma point allocation; and thereby calculate the superparameter
λ＝α·α(nx+κ)-nx
Where n x is the dimension of the state vector. And then calculating the weight coefficient of the sigma point and the weight coefficient of the sigma point distribution covariance based on the above super-parameters.
Setting noise of state transition and noise of measurement process:
R＝diag([0.01 0.01 0.01 0.1 0.1 0.1 0.1])2
Q＝diag([0.00001 0.0001 0.0001 0.0001 0.0001 0.0001 0.0001])2
2.5 UKF vehicle state space equation
Firstly, based on the vertical load of the wheels obtained in the step 2.1 and the vehicle speed information obtained in the step one, the air resistance F a,x and the rolling resistance F f,i of the wheels are calculated in the running process of the vehicle by combining the inherent parameters of the vehicle model in the step one.
Fa,x＝0.5·ρa·cD·A·vx 2
Ff,i＝f·Fz,i
Wherein ρ a is the air density; c D is the windage coefficient; a is the front projection area of the locomotive; f is the rolling resistance coefficient.
According to the front wheel steering angle delta of the vehicle obtained in the step one, the driving moment M M,i of the wheels is obtained; braking torque M B,i of the wheel; the inherent parameters of the vehicle, the wheel stress calculated in the step 2.3, can calculate the differential equation of the vehicle state vector:
Wherein, I z is the rotational inertia of the vehicle; j ω is the moment of inertia of the wheel.
2.6 UKF vehicle state estimation algorithm
According to the state space equation and UKF algorithm parameters in the steps, estimating the state of the vehicle based on UKF, and outputting the estimated value of the longitudinal speed of the vehicleAnd an estimate of lateral velocity
2.7 Evaluation of State estimation deviation
According to the vehicle state vector defined in the step 2.4, estimating the UKF vehicle state in the step 2.6, and evaluating a state estimation deviation function J c:
Wherein c y is a transverse speed estimation error weight, and 10000 is taken because of different orders of magnitude of the longitudinal speed and the transverse speed; the longitudinal speed and the lateral speed of the vehicle are output by the high-precision inertial navigation system.
Step three: establishing a typical working condition screening and data memory module
The optimal values of the vehicle parameters are different under different conditions. In order to obtain the change rule of the tire model parameters under different working conditions, the embodiment takes the influence of the vehicle speed on the tire model parameters as an example, and illustrates that the method can realize the identification of the optimal parameters of the tire model under different working conditions and establish the change relation between the working conditions and the tire model.
Typical parameter identification methods require an external input of an excitation signal, and then perform a parameter identification algorithm according to the law of variation of the input and output signals. In view of safety and comfort during driving of the vehicle, no excitation signal can be added to the vehicle system. Meanwhile, in order to meet the continuous excitation condition, the invention designs a typical working condition screening and data memory algorithm, and the algorithm extracts typical working condition fragments meeting the continuous excitation condition according to vehicle-mounted sensor signals (including longitudinal acceleration, lateral acceleration, yaw rate, vehicle speed and the like) and stores the typical working condition fragments in a data memory module. The data memory module designs a data updating mechanism, performs comprehensive sorting according to the expression and the data recording time of the data segment vehicles, and reserves three data segments with highest priority in each vehicle speed interval. In the embodiment, the influence of the vehicle speed on the tire model parameter c 2 is purposefully studied, and the data segments meeting the continuous excitation condition are screened out according to the actual running process of the vehicle. The specific process is as follows:
3.1 longitudinal speed segmentation
In order to fit the optimal values of the tire model parameters of different vehicle speeds, the uniform speed lane change working condition is selected as much as possible, however, the speed cannot be ensured to maintain a constant value in the driving process. The present embodiment thus segments the vehicle speed at 10km/h speed intervals. And assuming the tire model parameters within the segment are constant values. Thus, lane-change segments at different vehicle speeds are separated into different longitudinal vehicle speed segments.
3.2 Lane change condition segment extraction
Firstly, obtaining the distance between a vehicle and a lane line based on a sensor system, and judging the time point T change of the vehicle crossing the lane line according to the lateral distance change; then, searching forward by using a T change, wherein the absolute value of the lateral acceleration is smaller than 0.2m/s 2 moment T start,Tstart as a channel changing segment starting point; and then taking the moment T end,Tend when the absolute value of the backward searching lateral acceleration of the T change is smaller than 0.2m/s 2 as the end point of the track change segment.
And secondly, extracting the data of the T start～Tend time slice from the historical data.
3.3 Channel changing data memory module
According to the lane change data segment extracted in the step 3.2, calculating the average longitudinal speed v x,mean, the maximum lateral acceleration absolute value I a y||max, the moment when the T change vehicle crosses the lane line and the like of the segment
Because the storage space is limited and all data cannot be stored, the embodiment comprehensively considers the maximum lateral acceleration absolute value and the moment when the vehicle crosses the lane line, and screens out 3 data segments with highest priority of each vehicle speed segment.
Wherein R is a priority value of each data segment, S 1 is a weight of a maximum value of the lateral velocity, and S 2 is a weight of a channel switching time of the data segment. Each data segment may calculate a priority according to the above formula, and only the 3 data segments with the highest priority in each speed segment are saved for parameter identification.
Step four: tire model parameter identification based on particle swarm optimization
And step three, accumulating data fragments meeting the continuous excitation conditions under different working conditions in the actual running process of the vehicle. And (3) taking the tire model parameters as particles optimized by the particle swarm, wherein the change of the tire model parameters is represented by the position and the speed of the particles, initializing the position and the speed of the particles, and establishing an adaptability function by combining the vehicle state estimation effect in the step two and the reference value of the vehicle dynamics parameters under the working condition. In each iteration, the fitness function of each particle needs to be calculated, so that the historical optimal value and the global optimal value of the particle swarm of the particle individual are obtained, and the latest speed and the latest position of the particle position change of the current iteration number are calculated according to the gradient change direction of the fitness function, so that the iteration round by round is realized. And when the maximum iteration number is reached, the identification of the optimal dynamic parameters under the working condition is completed. And then, fitting according to the optimal tire model parameters in different vehicle speed sections to obtain the relationship of the tire model parameters along with the change of the vehicle speed.
4.1 Particle Swarm Optimization (PSO) initialization
When the road surface condition is unchanged, then c 1 in the tire model parameters is basically constant, and the vehicle speed has a larger influence on the tire model parameters c 2. While considering that the position of the centroid may change during movement of the vehicle, the present invention uses the tire model parameters c 2 and the centroid-to-front axis distance a as optimized particles of PSO, i.e., x p＝[c2 a ]. Setting the total number of initial particles as N, setting the maximum iteration number as j max, and initializing the initial position and speed of each particle by an initialization function to obtain Is the initial position of the ith particle; Is the initial velocity of the ith particle.
4.2 Calculating fitness function
In each iteration, the fitness function needs to be evaluated according to the current position of the particle. When evaluating the fitness function of the tire model parameter optimization, performing parameter matching and calculation according to the current particle state and the UKF vehicle state estimation algorithm in the step 2.6, and then using the vehicle state deviation estimation function J c in the step 2.7 as a cost item of the tire model parameter optimization:
Ji(j)＝p0·Jc+p1||ai(j)-areference||2
Wherein J i (J) is the fitness function value of the ith particle and the jth iteration; p 0、p1 are respectively corresponding weight coefficients; j c is the accuracy cost of parameter estimation based on the UKF; a i (j) is the centroid-to-front axis distance value of the ith particle jth round of iterations; a reference is a reference value of the centroid to front axis distance.
4.3 Updating particle position, velocity
In each iteration, the individual particle historic optimal position of the current iteration number can be obtained by calculating the fitness function value of each particleThe global optimal position G best (j) of the particle swarm is used for calculating the latest speed and the latest position of the particle position change of the current iteration number according to the gradient change direction of the fitness function:
Wherein, The position of the ith particle at the jth, j+1 wheel; v i(j),vi (j+1) is the speed of the ith particle at the jth, j+1 wheel; p (j) is an inertial factor; k 1,k2 is a learning factor; r 1,r2 is a random number between (0, 1).
4.4 Linear variable weight coefficient
When the speed is updated in each iteration, in order to solve the problem of early maturity of the algorithm, a method of variable weight coefficients is adopted to linearly change the weight coefficients:
Wherein p (j) represents a weight coefficient at the j-th round of iteration; p max、pmin is the maximum and minimum of the weight coefficients; j max is the maximum number of iterations.
4.5 In the iteration process, the loop is carried out according to the steps 4.2-4.4, and when the iteration number j reaches the maximum iteration number j max, the optimal particle position under the current working condition, namely the optimal model parameter x p,best＝[c2 a under the working condition, is obtained.
4.6 As shown in fig. 2, according to the optimal model parameters under different working conditions, the optimal model parameters are identification points in the graph, and then a polynomial fitting method is utilized to obtain a relational expression of the tire model parameters c 2 along with the longitudinal speed.
Step five: real-time estimation of vehicle state
Substituting the relationship of the tire model parameters obtained in the fourth step with the change of the vehicle speed into the second step, and further improving the accuracy of real-time estimation of the vehicle state. The tire model parameter variation process in the simulation process is shown in fig. 4. In order to compare the advantages of the method provided by the invention, two UKF algorithms with fixed vehicle model parameters are selected, wherein the tire model parameter C 2 of the UKF1 is 12, the tire model parameter C 2 of the UKF2 is 30, as shown in a simulation effect figure 3, the lateral vehicle speed estimation accuracy of the method of the invention can be found to be obviously superior to that of other two estimation algorithms.
The foregoing describes in detail preferred embodiments of the present invention. It should be understood that numerous modifications and variations can be made in accordance with the concepts of the invention by one of ordinary skill in the art without undue burden. Therefore, all technical solutions which can be obtained by logic analysis, reasoning or limited experiments based on the prior art by the person skilled in the art according to the inventive concept shall be within the scope of protection defined by the claims.

Claims (3)

1.一种基于轮胎模型参数自适应的车辆状态估计方法，其特征在于，所述方法包括：1. A vehicle state estimation method based on tire model parameter adaptation, characterized in that the method comprises: 采集实车数据，包括测量车辆的固有参数以及针对车辆行驶的典型工况采集车辆运动参数；Collect real vehicle data, including measuring the inherent parameters of the vehicle and collecting vehicle motion parameters based on typical driving conditions; 通过对车辆载荷转移模型与车轮中心速度计算，建立轮胎经验模型；建立基于无迹卡尔曼滤波UKF的车辆状态估计方案；所述轮胎经验模型根据每个车轮位置的垂向载荷的分布情况、测得的纵向车速与计算得到的车轮中心速度，得到轮胎所受的垂向力和纵向力、横向力之间的关系：By calculating the vehicle load transfer model and the wheel center speed, a tire empirical model is established; a vehicle state estimation scheme based on the unscented Kalman filter (UKF) is established; the tire empirical model obtains the relationship between the vertical force, longitudinal force and lateral force on the tire according to the distribution of the vertical load at each wheel position, the measured longitudinal vehicle speed and the calculated wheel center speed: 其中，rw为车轮的滚动半径；ωi为第i个车轮的转速；vx,i与vy,i分别为第i个车轮中心的纵向速度与横向速度；sx,i,sy,i分别为纵向滑移率和横向滑移率；si为综合滑移率；Fx,i,Fy,i分别为轮胎所受的纵向力和横向力；c1,c2分别为轮胎模型参数；Fz,i为第i个车轮的垂向载荷分布向量；Wherein, rw is the rolling radius of the wheel; ωi is the rotation speed of the i-th wheel; vx,i and vy,i are the longitudinal velocity and lateral velocity of the i-th wheel center respectively; sx,i and sy,i are the longitudinal slip rate and lateral slip rate respectively; si is the comprehensive slip rate; Fx,i and Fy,i are the longitudinal force and lateral force on the tire respectively; c1 and c2 are the tire model parameters respectively; Fz ,i is the vertical load distribution vector of the i-th wheel; 所述建立基于无迹卡尔曼滤波UKF的车辆状态估计方案，具体步骤如下：The specific steps of establishing a vehicle state estimation scheme based on unscented Kalman filter UKF are as follows: 进行UKF参数定义，所述参数包括车辆的状态向量、控制向量、测量向量、算法超参数以及噪声；Defining UKF parameters, including the vehicle's state vector, control vector, measurement vector, algorithm hyperparameters, and noise; 建立UKF车辆状态空间方程，具体步骤如下：Establish the UKF vehicle state space equation. The specific steps are as follows: 基于车辆载荷、测得的车速信息，结合车辆固有参数，计算车辆在行驶过程中所受的空气阻力Fa,x和车轮的滚动阻力Ff,i：Based on the vehicle load, the measured speed information, and the vehicle's inherent parameters, the air resistance F a,x and the wheel rolling resistance F f,i that the vehicle is subject to during driving are calculated: Fa,x＝0.5·ρa·cD·A·vx 2 F a,x = 0.5·ρ a ·c D ·A ·v x 2 Ff,i＝f·Fz,i F f,i = f·F z,i 其中，ρa为空气密度；cD为风阻系数；A为车头正面投影面积；f为滚动阻力系数；Fz,i为第i个车轮的垂向载荷，i分别取1,2,3,4代表左前轮、右前轮、左后轮、右后轮；Wherein, ρ a is the air density; c D is the drag coefficient; A is the front projection area of the vehicle; f is the rolling resistance coefficient; F z,i is the vertical load of the i-th wheel, i is 1, 2, 3, 4 respectively representing the left front wheel, right front wheel, left rear wheel, and right rear wheel; 根据测得的车辆前轮转角δ、车轮的驱动力矩MM,i、车轮的制动力矩MB,i、车辆固有参数以及计算得到的车辆载荷，得到对车辆状态向量的微分方程：According to the measured vehicle front wheel steering angle δ, the wheel driving torque M M,i , the wheel braking torque M B,i , the vehicle inherent parameters and the calculated vehicle load, the differential equation for the vehicle state vector is obtained: 其中，Fx,i,Fy,i分别为轮胎所受的纵向力和横向力，i分别取1,2,3,4代表左前轮、右前轮、左后轮、右后轮；δ为车辆的前轮转角；υx，υy分别为车辆的纵向速度与横向速度；ωz为车辆的横摆角速度；a为车辆的质量中心距离前轴的距离；b为车辆的质量中心距离后轴的距离；B为车辆左右两侧车轮距离的一半；MM,i为车轮的驱动力矩；Iz为车辆的转动惯量；Jω为车轮的转动惯量；Wherein, Fx ,i , Fy,i are the longitudinal force and lateral force on the tire respectively, i is 1, 2, 3, 4 to represent the left front wheel, right front wheel, left rear wheel and right rear wheel respectively; δ is the front wheel turning angle of the vehicle; υx , υy are the longitudinal velocity and lateral velocity of the vehicle respectively; ωz is the yaw angular velocity of the vehicle; a is the distance from the mass center of the vehicle to the front axle; b is the distance from the mass center of the vehicle to the rear axle; B is half of the distance between the left and right wheels of the vehicle; M M,i is the driving torque of the wheel; Iz is the moment of inertia of the vehicle; Jω is the moment of inertia of the wheel; 根据上述定义的UKF参数与建立的UKF车辆状态空间方程，进行基于UKF的车辆状态估计，输出车辆的纵向速度的估计值和横向速度的估计值；According to the UKF parameters defined above and the established UKF vehicle state space equation, a vehicle state estimation based on UKF is performed, and an estimated value of the longitudinal velocity and an estimated value of the lateral velocity of the vehicle are output; 筛选典型工况下满足持续激励条件的数据片段并进行数据记忆，具体过程如下：Filter the data segments that meet the continuous excitation conditions under typical working conditions and memorize the data. The specific process is as follows: 选择匀速换道工况，将车速以设定速度间隔进行分段，并设定分段内的轮胎模型参数为定值；Select the uniform lane-changing condition, divide the vehicle speed into segments according to the set speed intervals, and set the tire model parameters in the segments to fixed values; 获得车辆距离车道线距离；Get the distance between the vehicle and the lane line; 根据侧向距离变化判断出车辆跨过车道线的时间点Tchange；Determine the time point T change when the vehicle crosses the lane line based on the change in lateral distance; 以Tchange向前搜索侧向加速度绝对值小于设定值的时刻Tstart，Tstart时刻为换道片段起始点；Search forward from T change to the time T start when the absolute value of the lateral acceleration is less than the set value, and the time T start is the starting point of the lane change segment; 以Tchange向后搜索侧向加速度绝对值小于设定阈值的时刻Tend，Tend时刻为换道片段终止点；Search backward from T change to the time T end when the absolute value of the lateral acceleration is less than the set threshold, and the time T end is the end point of the lane change segment; 提取历史数据中Tstart～Tend时间片段的数据；Extract the data of the time segment T start ~ T end in the historical data; 根据提取出的换道数据片段，计算出该片段的平均纵向速度、最大侧向加速度绝对值和车辆跨过车道线时刻Tchange，并计算每个数据片段的优先级；According to the extracted lane-changing data segment, the average longitudinal speed, the maximum lateral acceleration absolute value and the time T change when the vehicle crosses the lane line of the segment are calculated, and the priority of each data segment is calculated; 保存每个车速段内优先级最高的设定数量数据片段用于参数识别；Saving a set number of data segments with the highest priority in each vehicle speed segment for parameter identification; 基于记忆的数据片段，采用粒子群优化进行轮胎模型参数识别，并根据不同车速段下的最优轮胎模型参数拟合得到轮胎模型参数随着车速变化的关系，具体步骤如下：Based on the memorized data fragments, particle swarm optimization is used to identify tire model parameters, and the relationship between tire model parameters and vehicle speed is obtained according to the optimal tire model parameters under different vehicle speed ranges. The specific steps are as follows: 将轮胎模型参数和质心到前轴距离作为PSO的优化粒子；The tire model parameters and the distance from the center of mass to the front axle are used as the optimization particles of PSO; 设置初始粒子的总数量与最大迭代次数；Set the total number of initial particles and the maximum number of iterations; 通过初始化函数对每个粒子的初始位置和速度进行初始化；Initialize the initial position and velocity of each particle through the initialization function; 在每次迭代的过程中，根据粒子的当前位置，对适应度函数进行评价；During each iteration, the fitness function is evaluated according to the current position of the particle; 在每一轮迭代中，通过对每个粒子进行适应度函数值的计算，获得当前迭代次数的粒子个体历史最优位置、粒子群全局最优位置，根据适应度函数的梯度变化方向，计算当前迭代次数的粒子位置变化的最新速度和最新位置：In each round of iteration, the fitness function value is calculated for each particle to obtain the individual historical optimal position of the particle at the current iteration and the global optimal position of the particle group. According to the gradient change direction of the fitness function, the latest speed and latest position of the particle position change at the current iteration are calculated: 在每次迭代中进行速度的更新时，采用可变权重系数的方法，对权重系数进行线性变化；When updating the speed in each iteration, the variable weight coefficient method is used to linearly change the weight coefficient; 按照上述步骤进行循环，当迭代次数达到最大迭代次数时，获得当前工况下的最优粒子位置，即该工况下的最优模型参数；Follow the above steps to loop, and when the number of iterations reaches the maximum number of iterations, the optimal particle position under the current working condition is obtained, that is, the optimal model parameters under the working condition; 根据不同工况下的最优模型参数为识别点，利用多项式拟合方法得到轮胎模型参数随着纵向速度的关系式；The optimal model parameters under different working conditions are used as identification points, and the relationship between tire model parameters and longitudinal speed is obtained by using polynomial fitting method; 在进行所述适应度函数的评价时，根据当前的粒子状态与UKF车辆状态估计算法进行参数匹配和计算，以车辆状态偏差估计函数作为轮胎模型参数优化的代价项：When evaluating the fitness function, parameters are matched and calculated based on the current particle state and the UKF vehicle state estimation algorithm, and the vehicle state deviation estimation function is used as the cost item for tire model parameter optimization: Ji(j)＝p0·Jc+p1||ai(j)-areference||2 Ji (j)= p0 · Jc + p1 || ai (j) -areference || 2 其中，Ji(j)为第i个粒子第j轮迭代的适应度函数值；p0、p1分别为对应的权重系数；Jc为基于UKF进行参数估计的准确性代价；ai(j)为第i个粒子第j轮迭代的质心到前轴距离值；areference为质心到前轴距离的参考值；Wherein, Ji (j) is the fitness function value of the i-th particle in the j-th iteration; p0 and p1 are the corresponding weight coefficients respectively; Jc is the accuracy cost of parameter estimation based on UKF; ai (j) is the distance from the center of mass to the front axis of the i-th particle in the j-th iteration; areference is the reference value of the distance from the center of mass to the front axis; 利用轮胎模型参数随着车速变化的关系，代入到基于无迹卡尔曼滤波UKF的车辆状态估计方案中，得到车辆状态实时估计；The relationship between tire model parameters and vehicle speed is used to substitute them into the vehicle state estimation scheme based on the unscented Kalman filter (UKF) to obtain a real-time estimation of the vehicle state. 所述方法在高精度导航系统定位精度良好时，利用高精度导航系统校正车速估计算法中的轮胎模型参数，具体步骤如下：The method uses the high-precision navigation system to correct the tire model parameters in the vehicle speed estimation algorithm when the positioning accuracy of the high-precision navigation system is good. The specific steps are as follows: 根据定义的车辆的状态向量以及UKF车辆状态估计，进行状态估计偏差函数Jc的评价：According to the defined vehicle state vector and UKF vehicle state estimation, the state estimation deviation function J c is evaluated: 其中cy为横向速度估计误差权重；分别为UKF车辆状态估计的车辆的纵向速度的估计值与横向速度的估计值；分别为高精度惯性导航系统输出的车辆纵向速度和侧向速度。Where c y is the lateral velocity estimation error weight; are respectively the estimated value of the longitudinal velocity and the estimated value of the lateral velocity of the vehicle estimated by UKF vehicle state; They are the vehicle longitudinal velocity and lateral velocity output by the high-precision inertial navigation system. 2.根据权利要求1所述的一种基于轮胎模型参数自适应的车辆状态估计方法，其特征在于，所述车辆载荷转移模型根据采集得到的车辆加速度的信息以及测量得到的车辆的固有参数信息，得出车辆的加速度同各个车轮垂向力之间的关系：2. The vehicle state estimation method based on tire model parameter adaptation according to claim 1 is characterized in that the vehicle load transfer model obtains the relationship between the vehicle acceleration and the vertical force of each wheel according to the collected vehicle acceleration information and the measured vehicle inherent parameter information: Fz＝(θT·(θ·θT)-1)·[ax·hg -ay·hg g]T·m Fz ＝( θT ·(θ· θT ) -1) ·[ax · hg - ay · hgg ] T ·m 其中，Fz为车轮的垂向载荷分布向量，θ为车辆尺寸参数矩阵，ax为车辆纵向加速度，ay为车辆横向加速度，hg为车辆的质心高度，g为重力加速度，m为车辆的质量。Among them, Fz is the vertical load distribution vector of the wheel, θ is the vehicle size parameter matrix, ax is the vehicle longitudinal acceleration, ay is the vehicle lateral acceleration, hg is the height of the vehicle's center of mass, g is the acceleration of gravity, and m is the mass of the vehicle. 3.根据权利要求1所述的一种基于轮胎模型参数自适应的车辆状态估计方法，其特征在于，所述车轮中心速度包括每个车轮中心的纵向速度和横向速度，3. The vehicle state estimation method based on tire model parameter adaptation according to claim 1, characterized in that the wheel center speed includes the longitudinal speed and the lateral speed of each wheel center, 所述每个车轮中心的纵向速度和横向速度通过测量得到的车辆的前轮转角、车辆的横摆角速度、车辆的纵向速、横向速度以及车辆尺寸参数计算得到。The longitudinal speed and lateral speed of each wheel center are calculated by measuring the front wheel turning angle of the vehicle, the yaw rate of the vehicle, the longitudinal speed, the lateral speed and the vehicle size parameters.
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