CN116588119A - Vehicle state estimation method based on tire model parameter self-adaption - Google Patents
Vehicle state estimation method based on tire model parameter self-adaption Download PDFInfo
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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
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 is 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 The longitudinal speed and the transverse speed of the ith wheel center are respectively; s is(s) i,i ,s y,i The longitudinal slip rate and the transverse slip rate are respectively; s is(s) i Is the comprehensive slip rate; f (F) x,i ,F y,i The longitudinal force and the transverse force respectively applied to the tire; c 1 ,c 2 Respectively tire model parameters; f (F) z,i Is the vertical load distribution vector of the ith 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:
F z ＝(θ T ·(θ·θ T ) -1 )·[a x ，h g -a y ·h g g] T m wherein F z Is the vertical load distribution vector of the wheel, theta is the vehicle size parameter matrix, a x For vehicle longitudinal acceleration, a y For vehicle lateral acceleration, h g G is 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 of the vehicle in the running process is calculated by combining the inherent parameters of the vehicle a,x And rolling resistance F of the wheel f,i ：
F a,x ＝0.5·ρ a ·c D ·A·v x 2
F f,i ＝f·F z,i
Wherein ρ is a Is air density; c D Is the wind resistance coefficient; a is the front projection area of the locomotive; f is the rolling resistance coefficient; f (F) z,i Taking 1,2,3 and 4 as vertical loads of the ith wheel respectively to represent a left front wheel, a right front wheel, a left rear wheel and a right rear wheel;
based on the measured front wheel angle delta, the driving moment M of the wheels M,i Braking moment M of wheel B,i And obtaining a differential equation of the vehicle state vector by the vehicle intrinsic parameters and the calculated vehicle load:
wherein F is x,i ,F y,i Respectively taking 1,2,3 and 4 as 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 ，v y Longitudinal speed and transverse speed of the vehicle respectively; w (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 is M M,i Is the driving torque of the wheels; i z Is the rotational inertia of the vehicle; j (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:
based on the defined vehicle state vector and UKF vehicle state estimation, performing state estimation deviation function J c Is determined by the following steps:
wherein c y Estimating an error weight for the lateral velocity;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; />Respectively output of high-precision inertial navigation systemLongitudinal and lateral vehicle speeds of (2).
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 the time point T of the vehicle crossing the lane line according to the lateral distance change chang ；
By T chang Time T when the absolute value of the forward search lateral acceleration is smaller than the set value start ，T start The moment is the starting point of the channel changing segment;
by T change Searching backward for a time T when the absolute value of the lateral acceleration is smaller than the set threshold end ，T end The moment is the termination point of the lane change segment;
extracting T from historical data start ～T end Data of the time slice;
according to the extracted lane change data segment, calculating the average longitudinal speed, the maximum lateral acceleration absolute value and the moment T of the vehicle crossing the lane line of the segment chang 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:
J i (j)＝p 0 ·J c +p 1 ||a i (j)-a reference || 2
wherein J is i (j) The fitness function value of the jth iteration of the ith particle is the fitness function value of the jth iteration of the ith particle; p is p 0 、p 1 Respectively corresponding weight coefficients; j (J) c The cost of accuracy for parameter estimation based on UKF; a, a i (j) The centroid-to-front axis distance value for the ith particle jth round of iteration; a, a reference Is a reference value for 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 shows tire model parameters C 2 A graph of longitudinal velocity;
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. For the typical running condition of the vehicle, three-axis speed, angular speed and acceleration (longitudinal acceleration a of the vehicle are measured by using multiple sensors x Lateral acceleration a y ) Longitudinal vehicle speedv x Transverse vehicle speed v y Yaw rate w z The data such as wheel speed, motor driving torque, braking torque and the like are collected, and data support is carried out for the follow-up study of 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.
F z ＝[F z,1 F z,2 F z,3 F z,4 ] T
θ·F z ＝[a x ·h g -a y ·h g g] T ·m
F z ＝(θ T ·(θ·θ T ) -1 )·[a x ·h g -a y ·h g g] T ·m
Wherein F is z Is the vertical load distribution vector of the wheel, F z，i Taking 1,2,3 and 4 as vertical loads of the ith wheel respectively to represent a left front wheel, a right front wheel, a left rear wheel and a 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 is a 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
According to the front wheel rotation angle delta of the vehicle measured in the step one, the yaw rate w of the vehicle z Longitudinal speed v of vehicle x Transverse velocity v y And the applied dimension parameters of step 2.1, calculating the longitudinal velocity v of each wheel center x，i And transverse velocity v y，i 。
Wherein v is x，i Taking 1,2,3 and 4 as the longitudinal speed of the wheel center, wherein i represents a left front wheel, a right front wheel, a left rear wheel and a 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 is w Is the rolling radius of the wheel; omega i Is the rotation speed of the ith wheel; s is(s) x,i ,s y,i The longitudinal slip rate and the transverse slip rate are respectively; s is(s) i Is the comprehensive slip rate; f (F) x,i ,F y,i The longitudinal force and the transverse force respectively applied to the tire; c 1 ,c 2 The parameters are respectively tire model parameters, are 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＝[v x v y w z ω 1 ω 2 ω 3 ω 4 ]
u＝[δ M M，1 M M，2 M M，3 M M，4 F z，1 F z，2 F z，3 F z，4 ]
y＝[a x a y w z ω 1 ω 2 ω 3 ω 4 ]
wherein M is M,i Is the motor moment 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(n x +κ)-n x
Wherein n is 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 wheel vertical load obtained in the step 2.1 and obtained in the step one measurementVehicle speed information and inherent parameters of the vehicle model in the first step are combined to calculate air resistance F of the vehicle in the running process a,x Rolling resistance F of wheel f,i 。
F a,x ＝0.5·ρ a ·c D ·A·v x 2
F f,i ＝f·F z,i
Wherein ρ is a Is air density; c D Is the wind resistance 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 and the driving moment M of the wheels obtained in the step one M,i The method comprises the steps of carrying out a first treatment on the surface of the Braking moment M of wheel B,i The method comprises the steps of carrying out a first treatment on the surface of the 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 is z Is the rotational inertia of the vehicle; j (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 state vector of the vehicle defined in the step 2.4, UKF vehicle state estimation in the step 2.6 is carried out to carry out a state estimation deviation function J c Is determined by the following steps:
wherein c y Estimating error weight for the transverse speed, wherein 10000 is taken by the weight because of different orders of magnitude of the change ranges 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. Data memory module designs data updating machineAnd (3) comprehensively sequencing according to the expression and the data recording time of the data segment vehicles, and reserving three data segments with highest priority in each vehicle speed interval. The embodiment purposefully researches the vehicle speed versus tire model parameter c 2 According to the influence of the continuous excitation condition, the data segments meeting the continuous excitation condition are screened out in 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, the distance between a vehicle and a lane line is obtained based on a sensor system, and the time point T of the vehicle crossing the lane line can be judged according to the lateral distance change change The method comprises the steps of carrying out a first treatment on the surface of the Then at T change Searching forward for lateral acceleration absolute value less than 0.2m/s 2 Time T start ，T start The moment is the starting point of the channel changing segment; then at T change Searching backward for lateral acceleration absolute value less than 0.2m/s 2 Time T end ，T end The time is the end point of the lane change segment.
Next, T is found in the history data start ～T end The data of the time slice is extracted.
3.3 channel changing data memory module
According to the channel changing data segment extracted in the step 3.2, calculating the average longitudinal speed v of the segment x，mean Absolute value of maximum lateral acceleration a y || max And T change Time when the vehicle crosses lane line, etc
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 the priority value of each data segment, S 1 Weight of maximum lateral velocity, S 2 Weights for the track change times of the data segments. 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 is among the tire model parameters 1 Essentially constant, vehicle speed versus tire model parameter c 2 The influence is larger. At the same time, the invention considers that the centroid position of the vehicle can change during the movement process, so the invention uses the tire model parameter c 2 And centroid to front axis distance a as optimized particles of PSO, i.e., x p ＝[c 2 a]. The total number of initial particles is set to N, and the maximum iteration number is set to j max 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 estimating the function J based on the vehicle state deviation in the step 2.7 c As a cost term for optimization of tire model parameters:
J i (j)＝p 0 ·J c +p 1 ||a i (j)-a reference || 2
wherein J is i (j) The fitness function value of the jth iteration of the ith particle is the fitness function value of the jth iteration of the ith particle; p is p 0 、p 1 Respectively corresponding weight coefficients; j (J) c The cost of accuracy for parameter estimation based on UKF; a, a i (j) The centroid-to-front axis distance value for the ith particle jth round of iteration; a, a reference Is a reference value for 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 particleGlobal maximum of particle swarmOptimal position G best (j) According to the gradient change direction of the fitness function, calculating the latest speed and the latest position of the particle position change of the current iteration times:
wherein, the liquid crystal display device comprises a liquid crystal display device,the position of the ith particle at the jth, j+1 wheel; v i (j),v i (j+1) is the speed of the ith particle at the jth, j+1 wheel; p (j) is an inertial factor; k (k) 1 ,k 2 Is a learning factor; r is (r) 1 ,r 2 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 is p max 、p min The maximum value and the minimum value of the weight coefficient are obtained; j (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, when the iteration number j reaches the maximum iteration number j max When the particle position is obtained under the current working condition, namely the optimal model parameter x under the working condition p,best ＝[c 2 a]。
4.6 as shown in figure 2, according to the optimal model parameters under different working conditions, the optimal model parameters are used as the identification points in the graph, and then a polynomial fitting method is used to obtain the tire modelParameter c 2 A relation with longitudinal velocity.
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. To compare the advantages of the proposed method, two UKF algorithms with fixed vehicle model parameters are selected, wherein the tire model parameters C of UKF1 2 Tire model parameter C of UKF2, 12 2 As shown in fig. 3, the accuracy of the lateral vehicle speed estimation of the method of the invention is found to be significantly better than that of the 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 (10)
1. A method for estimating a vehicle state 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.
2. The vehicle state estimation method based on the tire model parameter adaptation according to claim 1, wherein the tire empirical model obtains the relationship between the vertical force and the longitudinal force, the lateral force of the tire according to the distribution of the vertical load of each wheel position, the measured longitudinal vehicle speed and the calculated wheel center speed:
wherein r is 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 The longitudinal speed and the transverse speed of the ith wheel center are respectively; s is(s) x,i ,s y,i The longitudinal slip rate and the transverse slip rate are respectively; s is(s) i Is the comprehensive slip rate; f (F) x,i ,F y,i The longitudinal force and the transverse force respectively applied to the tire; c 1 ,c 2 Respectively tire model parameters; f (F) z,i Is the vertical load distribution vector of the ith wheel.
3. The vehicle state estimation method based on tire model parameter adaptation according to claim 2, wherein the vehicle load transfer model obtains the relationship 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:
F z ＝(θ T ·(θ·θ T )-1)·[a x ·h g -a y ·h g g] T ·m
wherein F is z Is the vertical load distribution vector of the wheel, theta is the vehicle size parameter matrix, a x For vehicle longitudinal acceleration, a y For vehicle lateral acceleration, h g G is gravitational acceleration, and m is the mass of the vehicle.
4. A vehicle condition estimation method based on tire model parameter adaptation as in claim 2, wherein 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.
5. The vehicle state estimation method based on tire model parameter adaptation according to claim 1, wherein the building of the vehicle state estimation scheme based on unscented kalman filter UKF comprises the following specific steps:
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.
6. The method for estimating a vehicle state based on tire model parameter adaptation according to claim 5, wherein 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 of the vehicle in the running process is calculated by combining the inherent parameters of the vehicle a,x And rolling resistance F of the wheel f,i ：
F a,x ＝0.5·ρ a ·c D ·A·v x 2
F f,i ＝f·F z,i
Wherein ρ is a Is air density; c D Is the wind resistance coefficient; a is the front projection area of the locomotive; f is the rolling resistance coefficient; f (F) z,i Taking 1,2,3 and 4 as vertical loads of the ith wheel respectively to represent a left front wheel, a right front wheel, a left rear wheel and a right rear wheel;
based on the measured front wheel angle delta, the driving moment M of the wheels M,i Braking moment M of wheel B,i And obtaining a differential equation of the vehicle state vector by the vehicle intrinsic parameters and the calculated vehicle load:
wherein F is x,i ,F y,i Respectively taking 1,2,3 and 4 as 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 ，v y Longitudinal speed and transverse speed of the vehicle respectively; w (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 is M M,i Is the driving torque of the wheels; i z Is the rotational inertia of the vehicle; j (J) ω Is the moment of inertia of the wheel.
7. The method for estimating a vehicle state based on adaptive tire model parameters according to claim 5, wherein when the positioning accuracy of the high-accuracy navigation system is good, the tire model parameters in the vehicle speed estimation algorithm are corrected by the high-accuracy navigation system, comprising the following steps:
based on the defined vehicle state vector and UKF vehicle state estimation, performing state estimation deviation function J c Is determined by the following steps:
wherein c y Estimating an error weight for the lateral velocity;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.
8. The vehicle state estimation method based on tire model parameter adaptation according to claim 1, wherein the screening of the data segments meeting the continuous excitation condition under the typical working condition and the data memorization are performed comprises the following specific processes:
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 the time point T of the vehicle crossing the lane line according to the lateral distance change change ；
By T chang Time T when the absolute value of the forward search lateral acceleration is smaller than the set value start ，T start The moment is the starting point of the channel changing segment;
by T change Searching backward for a time T when the absolute value of the lateral acceleration is smaller than the set threshold end ，T end The moment is the termination point of the lane change segment;
extracting T from historical data start ～T end Data of the time slice;
according to the extracted lane change data segment, calculating the average longitudinal speed, the maximum lateral acceleration absolute value and the moment T of the vehicle crossing the lane line of the segment change 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.
9. The vehicle state estimation method based on tire model parameter adaptation according to claim 7, wherein the tire model parameter identification is performed 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 optimal tire model parameter fitting 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.
10. A vehicle state estimation method based on tire model parameter adaptation as in claim 9,
when the fitness function is evaluated, parameter matching and calculation are carried out 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:
J i (j)＝p 0 ·J c +p 1 ||a i (j)-a reference || 2
wherein J is i (j) The fitness function value of the jth iteration of the ith particle is the fitness function value of the jth iteration of the ith particle; p is p 0 、p 1 Respectively corresponding weight coefficients; j (J) c The cost of accuracy for parameter estimation based on UKF; a, a i (j) The centroid-to-front axis distance value for the ith particle jth round of iteration; a, a reference Is a reference value for the centroid to front axis distance.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105946858A (en) * | 2016-06-08 | 2016-09-21 | 吉林大学 | Method for optimizing parameters of four-driving electric car state observer based on genetic algorithm |
| US20170010184A1 (en) * | 2015-07-08 | 2017-01-12 | The Goodyear Tire & Rubber Company | Tire sensor-based vehicle state estimation system and method |
| CN111547059A (en) * | 2020-04-23 | 2020-08-18 | 上海大学 | Distributed driving electric automobile inertia parameter estimation method |
| CN111959486A (en) * | 2020-07-01 | 2020-11-20 | 武汉理工大学 | Longitudinal and transverse coupling control method and system for motor-driven vehicle and storage medium |
| CN113650621A (en) * | 2021-08-16 | 2021-11-16 | 东南大学 | Distributed driving electric vehicle state parameter estimation method facing complex working conditions |
| CN115202341A (en) * | 2022-06-16 | 2022-10-18 | 同济大学 | Transverse motion control method and system for automatic driving vehicle |
| CN115946707A (en) * | 2023-03-14 | 2023-04-11 | 北京理工大学 | Four-hub motor drive full-line control electric vehicle tire force estimation method and system |
-
2023
- 2023-05-30 CN CN202310627273.8A patent/CN116588119B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170010184A1 (en) * | 2015-07-08 | 2017-01-12 | The Goodyear Tire & Rubber Company | Tire sensor-based vehicle state estimation system and method |
| CN105946858A (en) * | 2016-06-08 | 2016-09-21 | 吉林大学 | Method for optimizing parameters of four-driving electric car state observer based on genetic algorithm |
| CN111547059A (en) * | 2020-04-23 | 2020-08-18 | 上海大学 | Distributed driving electric automobile inertia parameter estimation method |
| CN111959486A (en) * | 2020-07-01 | 2020-11-20 | 武汉理工大学 | Longitudinal and transverse coupling control method and system for motor-driven vehicle and storage medium |
| CN113650621A (en) * | 2021-08-16 | 2021-11-16 | 东南大学 | Distributed driving electric vehicle state parameter estimation method facing complex working conditions |
| CN115202341A (en) * | 2022-06-16 | 2022-10-18 | 同济大学 | Transverse motion control method and system for automatic driving vehicle |
| CN115946707A (en) * | 2023-03-14 | 2023-04-11 | 北京理工大学 | Four-hub motor drive full-line control electric vehicle tire force estimation method and system |
Non-Patent Citations (1)
| Title |
|---|
| 邱香;吴晓建;周兵;: "融合状态观测及优化方法的纯侧偏轮胎模型辨识", 振动与冲击, no. 13, 15 July 2020 (2020-07-15) * |
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