CN110987470B - Model iteration-based automobile quality online estimation method - Google Patents

Model iteration-based automobile quality online estimation method Download PDF

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CN110987470B
CN110987470B CN201911244005.8A CN201911244005A CN110987470B CN 110987470 B CN110987470 B CN 110987470B CN 201911244005 A CN201911244005 A CN 201911244005A CN 110987470 B CN110987470 B CN 110987470B
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longitudinal
forgetting
vehicle
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CN110987470A (en
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曾小华
钱琦峰
宋大凤
陈建新
谷京哲
苑卫松
李晓建
牛超凡
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Jilin University
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Abstract

The invention discloses an automobile quality online estimation method based on model iteration, and aims to overcome the defects of low estimation precision, poor robustness and few applicable conditions in the existing automobile quality estimation technology. The method comprises the steps of obtaining engine torque, longitudinal vehicle speed, longitudinal acceleration, lateral acceleration, yaw rate and steering wheel turning angle from a vehicle-mounted CAN bus and a sensor, combining recursive least squares and Kalman filtering algorithms, respectively estimating the whole vehicle mass by using a longitudinal model mass estimation method or a lateral model mass estimation method under different working conditions according to a model arbitration mode, and carrying out mutual iterative updating. The invention has the advantages of strong robustness, wide range of applicable conditions and high precision. The reliable finished automobile quality signal input is provided for an electronic control system of an automobile chassis.

Description

Model iteration-based automobile quality online estimation method
Technical Field
The invention relates to the technical field of automobile electronic control, in particular to a model-based automobile quality real-time estimation method.
Background
Along with the complexity and integration of the electronic control technology of the automobile chassis, the control precision of the electronic control technology is more and more sensitive to the change of the whole automobile quality. The whole vehicle mass directly or indirectly influences various dynamic performances of the vehicle, the online accurate estimation of the whole vehicle mass is realized, the electronic control effect of the vehicle can be obviously improved, and the method has important significance on an electronic control system of a vehicle chassis. For example, there is a great demand for accurate estimation of mass in automotive electronic control technologies such as adaptive cruise control, automatic transmission control, electronic stability control, and rollover prevention control.
The general mass estimation method utilizes a longitudinal dynamic balance equation of the automobile and combines a least square or Kalman filtering method to estimate the mass of the whole automobile. Since this method involves quantitative given road resistance, air resistance, etc., these mechanical parameters are difficult to estimate accurately and vary greatly with the road during the actual driving of the automobile. In addition, the output torque of the engine obtained from the vehicle-mounted CAN bus has large errors, so that the method has good estimation accuracy only under specific conditions. In addition, some methods calibrate a plurality of groups of data curves under known set conditions, and estimate the data curves by using currently acquired information and existing calibration information in the actual estimation process, and because the driving environment or driving mode of the actual and calibrated processes is greatly different, the robustness of estimation by the method is poor.
In the existing patents, for example, chinese patent publication No. CN 1940509, publication date 4/2007, entitled "a vehicle mass estimation system and method", the invention estimates the mass by using longitudinal dynamics and least square algorithm, but the influence of the accuracy of the vehicle running resistance and the engine output torque on the estimation result is not considered; chinese patent publication No. CN 103129560a, published as 2013, 6/5/titled "system and method for estimating mass of vehicle", which uses a deviation between a previously estimated acceleration and velocity and an actually measured acceleration and velocity as an index for estimating mass, but the estimated timing must occur in a specific driving state; the invention relates to an on-line estimation method for automobile quality, which is characterized in that Chinese patent publication No. CN 109030019A, publication date is 2018, 6 and 20, the name of the invention is 'an on-line estimation method for automobile quality', aiming at the acceleration process of automobile starting, the invention utilizes the existing calibration curve and the on-line measured data to carry out quality estimation, but the estimation precision can be influenced by the difference of different driving processes.
In summary, the existing quality estimation method rarely considers the estimation accuracy of the driving resistance, and the limit of the estimation time of the whole vehicle quality is too strict, and the adaptability and the robustness are poor, so that the online estimation method with strong robustness and high accuracy of the whole vehicle quality is rarely realized. Therefore, it is necessary to provide such an online estimation method for automobile quality based on model iteration to make up for the deficiencies of the prior art.
Disclosure of Invention
The invention aims to overcome the defects of low estimation precision, poor robustness and few applicable conditions in the prior art, and provides a quality online estimation method with strong practicability and high precision.
In order to solve the problems, the invention adopts the following technical scheme:
an automobile quality online estimation method based on model iteration comprises the following steps:
step one, estimating the lateral speed of the automobile; simplifying the whole vehicle into a two-degree-of-freedom lateral model as shown in formulas (1) to (3), discretizing the model into a formula (5), and calculating the prior estimated values of the state quantity lateral speed and the yaw angle;
Figure BDA0002307016780000021
Figure BDA0002307016780000022
Figure BDA0002307016780000023
in the formula, deltaf-the two-degree-of-freedom vehicle model front axle angle is obtained by converting the steering wheel angle
u＝δf-input of quantities
A-State transition matrix
B-control matrix
x＝[Vy r]TState quantities, respectively lateral vehicle speed and yaw angular velocity
Figure BDA0002307016780000026
First derivative of the state quantity
Figure BDA0002307016780000024
Two-degree-of-freedom vehicle model front axle lateral deflection rigidity and rear axle lateral deflection rigidity
IzTwo-degree-of-freedom vehicle model yaw moment of inertia
Figure BDA0002307016780000025
-mass of two degree of freedom vehicle model, which is the current latest mass estimation result
Yaw rate of r-two-degree-of-freedom vehicle model
lf，lr-distance between center of mass of two-degree-of-freedom vehicle model and front axle and rear axle
Vx，Vy-longitudinal speed, lateral speed of two degree of freedom vehicle model
The equation (4) is used as a measurement equation of the state lateral speed and the yaw rate, and the measurement values of the lateral speed and the yaw rate are calculated;
Figure BDA0002307016780000031
in the formula, aysen-lateral acceleration values measured by an acceleration sensor
Figure BDA0002307016780000032
-measurement of lateral vehicle speed at kth calculation step
Figure BDA0002307016780000033
-lateral velocity measurements at the k +1 th calculation step
rsen-yaw rate measured by a gyroscope
Figure BDA0002307016780000034
-yaw rate measurement
dt-running step
The estimated values of the state quantity lateral velocity and the yaw rate are obtained from the state prior value calculated by the two-degree-of-freedom lateral model and the state measurement value calculated by the sensor signal by the kalman filter algorithms (5) to (9).
Figure BDA0002307016780000035
Figure BDA0002307016780000036
Figure BDA0002307016780000037
Figure BDA0002307016780000038
Figure BDA0002307016780000039
In the formula (I), the compound is shown in the specification,
Figure BDA00023070167800000310
-prior values of state quantity lateral speed and yaw rate at kth calculation step
Figure BDA00023070167800000311
-estimated values of state quantity lateral speed and yaw rate at kth calculation step length
Figure BDA00023070167800000312
-state at kth calculation stepMeasuring lateral speed and yaw rate
G＝eAdt-discrete state transition matrix
Figure BDA00023070167800000313
-discrete control matrix
Q-state transition noise covariance
R-measurement noise covariance
H-state variable to measured value conversion matrix
Kk-Kalman gain factor at the kth calculation step
Figure BDA0002307016780000041
Covariance of the a priori estimated states at kth calculation step
Pk-computing the covariance of the estimated state at step size k.
Step two, model arbitration; and (3) according to the formula (10), acquiring the longitudinal acceleration of the automobile according to the longitudinal speed difference, acquiring the lateral acceleration, the longitudinal speed and the steering wheel angle through a CAN bus and a sensor, and judging whether all the switching conditions are met by using the table 1 in combination with the lateral speed estimated in the first step so as to judge whether the longitudinal model quality estimation method or the lateral model quality estimation method is met. If the method accords with the longitudinal model quality estimation method, entering a third step; if the lateral model quality estimation method is satisfied, entering a fourth step; if both models do not meet the conditions, the quality estimation result keeps the latest estimation value, and the fifth step is carried out;
Figure BDA0002307016780000042
in the formula, axLongitudinal acceleration of the vehicle itself
vi-representing the vehicle longitudinal speed of the ith sample
TABLE 1 two mode switching rules
Figure BDA0002307016780000043
Thirdly, estimating the mass by using a longitudinal dynamics model; the longitudinal dynamic equilibrium equation (11) is used to transform the longitudinal dynamic equilibrium equation into a form suitable for the least square algorithm, as shown in equations (12) to (15).
Ft＝Fi+Fj+Fse (11)
In the formula (I), the compound is shown in the specification,
Figure BDA0002307016780000044
longitudinal driving force of automobile
FiMgsin alpha gradient resistance
Alpha-road inclination
Fj＝max-acceleration resistance
Fse＝Ff+FwThe sum of air resistance and rolling resistance
y＝φ·θ (12)
θ＝[m Fse]T (13)
φ＝[axsen 1] (14)
y＝Ft (15)
In the formula, theta is a multiple forgetting least squares estimator, i.e. the total vehicle mass, longitudinal forces other than acceleration and slope resistance
Phi-multiple forgetting least square coefficient
y-multiple forgetting least square output
axsen-the longitudinal acceleration measured by the acceleration sensor is equal to gsnα + ax
And (4) jointly estimating the total vehicle mass and the longitudinal force except the acceleration resistance and the gradient resistance according to multiple forgetting least square method equations (16) -20, and then entering step five.
Figure BDA0002307016780000051
Figure BDA0002307016780000052
Figure BDA0002307016780000053
Figure BDA0002307016780000054
Figure BDA0002307016780000055
In the formula (I), the compound is shown in the specification,
Figure BDA0002307016780000056
-estimate of θ at kth calculation step
λ1，λ2Quantities m and F to be estimatedseForgetting factor of
φ1(k) -computing the size of the first element in phi at step size k
φ2(k) -the size of the second element in phi at the kth calculation step
L(k)＝[L1(k) L2(k)]T-multiple forgetting least squares weighting gains at kth calculation step
P1(k) -error covariance matrix of multiple forgetting least squares to be estimated m at kth calculation step
P2(k) -multiple forgetting of least squares under kth calculation step to estimate quantity FseThe error covariance matrix of (2).
Step four, estimating mass by lateral dynamics; calculating a wheel slip angle by using an equation (21) according to the lateral vehicle speed estimated in the first step;
Figure BDA0002307016780000061
in the formula, alphaf，αr-two-degree-of-freedom vehicle model front wheel side slip angle and rear wheel side slip angle
Calculating the lateral force applied to the whole vehicle by using a formula (22) according to the mechanical property of the tire;
Figure BDA0002307016780000062
in the formula, Fyf，FyrTwo-degree-of-freedom vehicle model front axle lateral force and rear axle lateral force
According to a lateral force balance equation (23) borne by the whole vehicle, the lateral force balance equation is simplified into a least square form, and the equations are shown as formulas (24) to (27);
Fyfcosδf+Fyr＝may (23)
Figure BDA0002307016780000063
Figure BDA0002307016780000064
β＝m (26)
z＝Fyfcosδf+Fyr (27)
in the formula, beta-forgetting least square estimator, namely the mass of the whole vehicle
Figure BDA0002307016780000065
Forgetting the least squares coefficient, i.e. lateral acceleration
z-forgetting least square output, i.e. resultant lateral force experienced by the vehicle
And estimating the mass of the whole vehicle according to forgetting least square arithmetic expressions (28) to (30), and then entering step five.
Figure BDA0002307016780000071
Figure BDA0002307016780000072
Figure BDA0002307016780000073
In the formula, lambda-forgetting factor of forgetting least square
Figure BDA0002307016780000074
At kth calculation step
Figure BDA0002307016780000075
Size of (2)
N (k) -error covariance matrix of forgetting least squares at k-th calculation step
Gamma (k) -the gain matrix of forgetting least squares at the kth calculation step
Figure BDA0002307016780000076
-computing the estimate of β at step size k.
Step five, judging iterative convergence; and (4) counting the estimated values of the latest N times according to the results of the multiple iterations, calculating the variance and the mean value, outputting the mean value as an estimation result if the variance is smaller than a specified threshold value, and returning to the step one if the variance is not smaller than the specified threshold value.
Compared with the prior art, the invention has the beneficial effects that:
1. the method is based on iteration of longitudinal dynamics and lateral dynamics models, and estimation is carried out through combination of the longitudinal dynamics and the lateral dynamics, so that the accuracy, adaptability and robustness of the estimated quality are guaranteed;
2. the multiple forgetting least square algorithm is combined with a longitudinal kinetic equation, different forgetting factors are adopted for the quality and the resistance to be estimated, the method is more suitable for the change degree of the parameter to be estimated along with time, the error caused by resistance change under complex working conditions is avoided, and the estimation precision is higher;
3. by utilizing a model arbitration mode, different estimation methods are adopted under different working condition characteristics, the running working condition characteristics of the automobile are fully utilized, and the estimation result is faster and better converged to a true value;
drawings
The invention is further described with reference to the accompanying drawings in which:
FIG. 1 is an overall logic block diagram of an automobile quality online estimation method based on model iteration according to the present invention;
FIG. 2 is a flowchart illustrating the overall estimation of an online estimation method for vehicle mass based on model iteration according to the present invention;
FIG. 3 is a simplified two-degree-of-freedom dynamics analysis diagram of a vehicle;
FIG. 4 is a simplified vehicle longitudinal dynamics analysis chart;
the specific implementation mode is as follows:
the invention is described in detail below with reference to the attached drawing figures:
referring to fig. 1, the online estimation method for the mass of the automobile based on model iteration firstly obtains the longitudinal acceleration, the lateral acceleration, the longitudinal speed, the yaw angular velocity, the engine output torque and the steering wheel angle required by the estimation of the mass of the whole automobile through an acceleration sensor, a CAN bus and an angular velocity sensor, and then carries out mutual iterative computation according to a longitudinal model and a lateral model to estimate the mass of the whole automobile. The method comprises the steps that the longitudinal model estimates the whole vehicle mass based on longitudinal dynamics and combined with a multiple forgetting least square algorithm, the vehicle mass is estimated, the lateral model estimates the whole vehicle mass based on a Kalman filter and a two-degree-of-freedom model, and then the vehicle mass is estimated based on the forgetting least square algorithm and a lateral dynamics equation.
Referring to fig. 2, in the overall quality estimation flow chart, signals of a sensor and a CAN bus are collected first, then model arbitration is performed, and the current state is judged to belong to the following three states, which are respectively: longitudinal model calculations, lateral model calculations, or neither. According to the three states, respectively outputting and counting results of the longitudinal model, the maintenance result and the lateral model, finally judging whether the estimated result reaches a convergence condition, and if not, continuing iterative computation; and if the condition is met, outputting a final estimation result.
The specific execution flow comprises the following steps:
step one, estimating the lateral speed of the automobile; referring to fig. 3, the whole vehicle is simplified into a two-degree-of-freedom lateral model as shown in formulas 31 to 33, and is discretized into a formula (35), and prior estimated values of the state quantity lateral speed and the yaw angle are calculated;
Figure BDA0002307016780000081
Figure BDA0002307016780000082
Figure BDA0002307016780000083
in the formula, deltaf-the two-degree-of-freedom vehicle model front axle angle is obtained by converting the steering wheel angle
u＝δf-input of quantities
A-State transition matrix
B-control matrix
x＝[Vy r]TState quantities, respectively lateral vehicle speed and yaw angular velocity
Figure BDA0002307016780000084
First derivative of the state quantity
Figure BDA0002307016780000085
Two-degree-of-freedom vehicle model front axle lateral deflection rigidity and rear axle lateral deflection rigidity
IzTwo-degree-of-freedom vehicle model yaw moment of inertia
Figure BDA0002307016780000091
-mass of two degree of freedom vehicle model, which is the current latest mass estimation result
Yaw rate of r-two-degree-of-freedom vehicle model
lf，lr-distance between center of mass of two-degree-of-freedom vehicle model and front axle and rear axle
Vx，Vy-longitudinal speed, lateral speed of two degree of freedom vehicle model
The equation (4) is used as a measurement equation of the state lateral speed and the yaw rate, and the measurement values of the lateral speed and the yaw rate are calculated;
Figure BDA0002307016780000092
in the formula, aysen-lateral acceleration values measured by an acceleration sensor
Figure BDA0002307016780000093
-measurement of lateral vehicle speed at kth calculation step
Figure BDA0002307016780000094
-lateral velocity measurements at the k +1 th calculation step
rsen-yaw rate measured by a gyroscope
Figure BDA0002307016780000095
-yaw rate measurement
dt-running step
The estimated values of the state quantity lateral velocity and the yaw rate are obtained from the state prior value calculated by the two-degree-of-freedom lateral model and the state measurement value calculated by the sensor signal by the kalman filter arithmetic expressions (35) to (39).
Figure BDA0002307016780000096
Figure BDA0002307016780000097
Figure BDA0002307016780000098
Figure BDA0002307016780000099
Figure BDA00023070167800000910
In the formula (I), the compound is shown in the specification,
Figure BDA00023070167800000911
-prior values of state quantity lateral speed and yaw rate at kth calculation step
Figure BDA00023070167800000912
-estimated values of state quantity lateral speed and yaw rate at kth calculation step length
Figure BDA00023070167800000913
-measuring values of state quantity lateral speed and yaw rate at kth calculation step length
G＝eAdt-discrete state transition matrix
Figure BDA0002307016780000101
-discrete control matrix
Q-state transition noise covariance
R-measurement noise covariance
H-state variable to measured value conversion matrix
Kk-Kalman gain factor at the kth calculation step
Figure BDA0002307016780000102
Covariance of the a priori estimated states at kth calculation step
Pk-computing the covariance of the estimated state at step size k.
Step two, model arbitration; and (3) acquiring the longitudinal acceleration of the automobile according to the longitudinal speed difference according to the formula (40), acquiring the lateral acceleration, the longitudinal speed and the steering wheel angle through a CAN bus and a sensor, and judging whether all the switching conditions are met by using the table 2 in combination with the lateral speed estimated in the first step so as to judge whether the longitudinal model quality estimation method or the lateral model quality estimation method is met. If the method accords with the longitudinal model quality estimation method, entering a third step; if the lateral model quality estimation method is satisfied, entering a fourth step; if both models do not meet the conditions, the quality estimation result keeps the latest estimation value, and the fifth step is carried out;
Figure BDA0002307016780000103
in the formula, axLongitudinal acceleration of the vehicle itself
vi-representing the vehicle longitudinal speed of the ith sample
TABLE 2 two mode switching rules
Figure BDA0002307016780000104
Thirdly, estimating the mass by using a longitudinal dynamics model; referring to fig. 4, the longitudinal dynamic equilibrium equation (41) is modified to a form suitable for the least-squares algorithm, as shown in equations (42) to (45).
Ft＝Fi+Fj+Fse (41)
In the formula (I), the compound is shown in the specification,
Figure BDA0002307016780000111
longitudinal driving force of automobile
FiMgsin alpha gradient resistance
Fj＝max-acceleration resistance
Fse＝Ff+FwThe sum of air resistance and rolling resistance
y＝φ·θ (42)
θ＝[m Fse]T (43)
φ＝[axsen 1] (44)
y＝Ft (45)
In the formula, theta is a multiple forgetting least square estimation quantity and comprises the mass of the whole vehicle and longitudinal force except acceleration resistance and gradient resistance
Phi-multiple forgetting least square coefficient
y-multiple forgetting least square output
axsen-the longitudinal acceleration measured by the acceleration sensor is equal to gsnα + ax
And (4) combining multiple forgetting least square method expressions (46) to (50), carrying out joint estimation on the total vehicle mass and the longitudinal force except the acceleration resistance and the gradient resistance, and then entering the step five.
Figure BDA0002307016780000112
Figure BDA0002307016780000113
Figure BDA0002307016780000114
Figure BDA0002307016780000115
Figure BDA0002307016780000121
In the formula (I), the compound is shown in the specification,
Figure BDA0002307016780000122
-estimate of θ at kth calculation step
λ1，λ2Quantities m and F to be estimatedseForgetting factor of
φ1(k) -computing the size of the first element in phi at step size k
φ2(k) -the size of the second element in phi at the kth calculation step
L(k)＝[L1(k) L2(k)]T-multiple forgetting least squares weighting gains at kth calculation step
P1(k) -error covariance matrix of multiple forgetting least squares to be estimated m at kth calculation step
P2(k) -multiple forgetting of least squares under kth calculation step to estimate quantity FseThe error covariance matrix of (2).
Step four, estimating mass by lateral dynamics; calculating a wheel slip angle by using an equation (51) according to the lateral vehicle speed estimated in the first step;
Figure BDA0002307016780000123
in the formula, alphaf，αr-two-degree-of-freedom vehicle model front wheel side slip angle and rear wheel side slip angle
Calculating the lateral force applied to the whole vehicle by using a formula (52) according to the mechanical property of the tire;
Figure BDA0002307016780000124
in the formula, Fyf，FyrTwo-degree-of-freedom vehicle model front axle lateral force and rear axle lateral force
According to a lateral force balance equation (53) of the whole vehicle, the lateral force balance equation is simplified into a least square form, and the equations are shown as formulas (54) to (57);
Fyfcosδf+Fyr＝may (53)
Figure BDA0002307016780000125
Figure BDA0002307016780000126
β＝m (56)
z＝Fyfcosδf+Fyr (57)
in the formula, beta-forgetting least square estimator, namely the mass of the whole vehicle
Figure BDA0002307016780000131
Forgetting the least squares coefficient, i.e. lateral acceleration
z-forgetting least square output, i.e. resultant lateral force experienced by the vehicle
And estimating the mass of the whole vehicle according to forgetting least square arithmetic expressions (58) to (60), and then entering step five.
Figure BDA0002307016780000132
Figure BDA0002307016780000133
Figure BDA0002307016780000134
In the formula, lambda-forgetting factor of forgetting least square
Figure BDA0002307016780000135
At kth calculation step
Figure BDA0002307016780000136
Size of (2)
N (k) -error covariance matrix of forgetting least squares at k-th calculation step
Gamma (k) -the gain matrix of forgetting least squares at the kth calculation step
Figure BDA0002307016780000137
-computing the estimate of β at step size k.
Step five, judging iterative convergence; and (4) counting the estimated values of the latest N times according to the results of the multiple iterations, calculating the variance and the mean value, outputting the mean value as an estimation result if the variance is smaller than a specified threshold value, and returning to the step one if the variance is not smaller than the specified threshold value.

Claims (1)

1. An automobile quality online estimation method based on model iteration is characterized by comprising the following steps:
step one, estimating the lateral speed of the automobile, wherein the estimation method of the lateral speed comprises the following steps:
acquiring steering wheel corners, longitudinal acceleration and lateral acceleration from a CAN bus and an acceleration sensor, simplifying the whole automobile into a two-degree-of-freedom lateral model according to inherent parameters of the automobile, as shown in formulas (1) to (3), and discretizing into a formula (5), and calculating prior estimated values of state quantity lateral speed and yaw angle;
Figure FDA0002847212010000011
Figure FDA0002847212010000012
Figure FDA0002847212010000013
in the formula, deltaf-the two-degree-of-freedom vehicle model front axle angle is obtained by converting the steering wheel angle
u＝δf-input of quantities
A-State transition matrix
B-control matrix
x＝[Vy r]TState quantities, respectively lateral vehicle speed and yaw angular velocity
Figure FDA0002847212010000014
First derivative of the state quantity
Figure FDA0002847212010000015
Two-degree-of-freedom vehicle model front axle lateral deflection rigidity and rear axle lateral deflection rigidity
IzTwo-degree-of-freedom vehicle model yaw moment of inertia
Figure FDA0002847212010000016
-mass of two degree of freedom vehicle model, estimating junction for current latest massFruit
Yaw rate of r-two-degree-of-freedom vehicle model
lf，lr-distance between center of mass of two-degree-of-freedom vehicle model and front axle and rear axle
Vx，Vy-longitudinal speed, lateral speed of two degree of freedom vehicle model
The equation (4) is used as a measurement equation of the state lateral speed and the yaw rate, and the measurement values of the lateral speed and the yaw rate are calculated;
Figure FDA0002847212010000021
in the formula, aysen-lateral acceleration values measured by an acceleration sensor
Figure FDA0002847212010000022
-measurement of lateral vehicle speed at kth calculation step
Figure FDA0002847212010000023
-lateral velocity measurements at the k +1 th calculation step
rsen-yaw rate measured by a gyroscope
Figure FDA0002847212010000024
-yaw rate measurement
dt-running step
According to a state prior value calculated by a two-degree-of-freedom lateral model and a state measurement value calculated by a sensor signal, calculating estimated values of a state lateral speed and a yaw angular speed through the Kalman filtering arithmetic expressions (5) to (9);
Figure FDA0002847212010000025
Figure FDA0002847212010000026
Figure FDA0002847212010000027
Figure FDA0002847212010000028
Figure FDA0002847212010000029
in the formula (I), the compound is shown in the specification,
Figure FDA00028472120100000210
-prior values of state quantity lateral speed and yaw rate at kth calculation step
Figure FDA00028472120100000211
-estimated values of state quantity lateral speed and yaw rate at kth calculation step length
Figure FDA00028472120100000212
-measuring values of state quantity lateral speed and yaw rate at kth calculation step length
G＝eAdt-discrete state transition matrix
Figure FDA00028472120100000213
-discrete control matrix
Q-state transition noise covariance
R-measurement noise covariance
H-state variable to measured value conversion matrix
Kk-Kalman gain factor at the kth calculation step
Figure FDA0002847212010000031
Covariance of the a priori estimated states at kth calculation step
PkCovariance of estimated states at kth calculation step
Step two, model arbitration; judging whether the current working condition conditions accord with a longitudinal model quality estimation method or a lateral model quality estimation method according to the lateral vehicle speed, the longitudinal acceleration, the lateral acceleration, the longitudinal vehicle speed and the steering wheel rotation angle, and if the current working condition conditions accord with the longitudinal model quality estimation method, entering the third step; if the lateral model quality estimation method is satisfied, entering a fourth step; if both models do not meet the conditions, the quality estimation result keeps the latest estimation value, and the fifth step is carried out;
step three, estimating the quality of the longitudinal model, and then entering step five; the longitudinal model quality estimation method comprises the following steps: the longitudinal dynamic equilibrium equation (10) is used to transform the equation into a form suitable for the least square algorithm, as shown in equations (11) to (14):
Ft＝Fi+Fj+Fse (10)
in the formula (I), the compound is shown in the specification,
Figure FDA0002847212010000032
longitudinal driving force of automobile
FiMgsin alpha gradient resistance
Alpha-road inclination
Fj＝max-acceleration resistance
Fse＝Ff+FwThe sum of air resistance and rolling resistance
y＝φ·θ (11)
θ＝[m Fse]T (12)
φ＝[axsen 1] (13)
y＝Ft (14)
In the formula, theta is a multiple forgetting least squares estimator, i.e. the total vehicle mass, longitudinal forces other than acceleration and slope resistance
Phi-multiple forgetting least square coefficient
y-multiple forgetting least square output
axsen-the longitudinal acceleration measured by the acceleration sensor is equal to gsnα + ax
Jointly estimating the total vehicle mass and the longitudinal force except the acceleration resistance and the gradient resistance according to multiple forgetting least square method expressions (15) to (19);
Figure FDA0002847212010000041
Figure FDA0002847212010000042
Figure FDA0002847212010000043
Figure FDA0002847212010000044
Figure FDA0002847212010000045
in the formula (I), the compound is shown in the specification,
Figure FDA0002847212010000046
-estimate of θ at kth calculation step
λ1，λ2Quantities m and F to be estimatedseForgetting factor of
φ1(k) -computing the size of the first element in phi at step size k
φ2(k) -the size of the second element in phi at the kth calculation step
L(k)＝[L1(k) L2(k)]T-multiple forgetting least squares weighting gains at kth calculation step
P1(k) -error covariance matrix of multiple forgetting least squares to be estimated m at kth calculation step
P2(k) -multiple forgetting of least squares under kth calculation step to estimate quantity FseError covariance matrix of
Step four, estimating the quality of the lateral model, and then entering step five; the lateral model quality estimation method comprises the following steps: calculating a wheel slip angle using equation (20) based on the lateral vehicle speed estimated in step one as follows:
Figure FDA0002847212010000047
in the formula, alphaf，αr-two-degree-of-freedom vehicle model front wheel side slip angle and rear wheel side slip angle
Calculating the lateral force applied to the whole vehicle by using a formula (21) according to the mechanical property of the tire;
Figure FDA0002847212010000051
in the formula, Fyf，Fyr-two degree of freedom vehicle model frontAxial and lateral forces, rear axial and lateral forces
According to a lateral force balance equation (22) borne by the whole vehicle, the lateral force balance equation is simplified into a least square form, and the equations are shown as formulas (23) to (26);
Fyfcosδf+Fyr＝may (22)
Figure FDA0002847212010000052
Figure FDA0002847212010000053
β＝m (25)
z＝Fyfcosδf+Fyr (26)
in the formula, beta-forgetting least square estimator, namely the mass of the whole vehicle
Figure FDA0002847212010000054
Forgetting the least squares coefficient, i.e. lateral acceleration
z-forgetting least square output, i.e. resultant lateral force experienced by the vehicle
Estimating the whole vehicle mass according to forgetting least square arithmetic expressions (27) to (29);
Figure FDA0002847212010000055
Figure FDA0002847212010000056
Figure FDA0002847212010000057
in the formula, lambda-forgetting factor of forgetting least square
Figure FDA0002847212010000058
At kth calculation step
Figure FDA0002847212010000059
Size of (2)
N (k) -error covariance matrix of forgetting least squares at k-th calculation step
Gamma (k) -the gain matrix of forgetting least squares at the kth calculation step
Figure FDA00028472120100000510
-estimate of β at kth calculation step
Step five, judging iterative convergence; and (4) counting the estimated values of the latest N times according to the iteration result, calculating the variance and the mean value, outputting the mean value as the estimation result if the variance is smaller than a specified threshold value, and returning to the step one if the variance is not smaller than the specified threshold value.
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