CN114329951A - Method for calculating lateral load transfer rate of vehicle - Google Patents
Method for calculating lateral load transfer rate of vehicle Download PDFInfo
- Publication number
- CN114329951A CN114329951A CN202111611628.1A CN202111611628A CN114329951A CN 114329951 A CN114329951 A CN 114329951A CN 202111611628 A CN202111611628 A CN 202111611628A CN 114329951 A CN114329951 A CN 114329951A
- Authority
- CN
- China
- Prior art keywords
- load transfer
- transfer rate
- tractor
- semitrailer
- semi
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 19
- 238000010238 partial least squares regression Methods 0.000 claims abstract description 41
- 230000001419 dependent effect Effects 0.000 claims abstract description 35
- 238000012417 linear regression Methods 0.000 claims abstract description 18
- 238000013016 damping Methods 0.000 claims description 7
- 230000005484 gravity Effects 0.000 claims description 7
- 238000005070 sampling Methods 0.000 claims description 6
- 230000001133 acceleration Effects 0.000 claims description 4
- 230000009286 beneficial effect Effects 0.000 abstract description 3
- 230000000694 effects Effects 0.000 abstract description 3
- 239000011159 matrix material Substances 0.000 description 12
- 238000010586 diagram Methods 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 2
- 150000001875 compounds Chemical class 0.000 description 2
- 238000010219 correlation analysis Methods 0.000 description 2
- 230000008878 coupling Effects 0.000 description 2
- 238000010168 coupling process Methods 0.000 description 2
- 238000005859 coupling reaction Methods 0.000 description 2
- 238000010606 normalization Methods 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 238000000513 principal component analysis Methods 0.000 description 2
- 238000005096 rolling process Methods 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 239000000725 suspension Substances 0.000 description 2
- 230000003321 amplification Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000003199 nucleic acid amplification method Methods 0.000 description 1
Images
Landscapes
- Control Of Driving Devices And Active Controlling Of Vehicle (AREA)
Abstract
The invention discloses a method for calculating the lateral load transfer rate of a semi-trailer train based on partial least squares regression (PLS). The method comprises the following steps: dividing a semi-trailer train into a tractor and a semi-trailer, and respectively considering the state quantity and the load transfer rate of the semi-trailer train; establishing a vehicle state observer and a load transfer rate observer according to a semi-trailer train dynamics model; performing partial least squares regression by taking the vehicle state quantity from 0 to t as an independent variable and the load transfer rate as a dependent variable to obtain a multivariate linear regression equation set of the dependent variable relative to the independent variable; substituting the vehicle state at the t +1 moment into an equation set, and calculating the load transfer rate at the t +1 moment; performing partial least squares regression at the iteration time of 1 to t +1 to obtain a new multiple linear regression equation set; and continuously iterating, and calculating the load transfer rate at all the moments. The beneficial technical effects of the invention are as follows: the lateral load transfer rate can be accurately calculated without depending on a sensor as a vehicle state data source.
Description
Technical Field
The invention relates to a partial least squares regression (PLS) -based method for calculating the transverse load transfer rate of a semi-trailer train, and belongs to the field of automobile safety design.
Background
The semi-trailer train has the characteristics of high gravity center, heavy weight, narrow wheel track, complex coupling relation between the tractor and the trailer and rear amplification, so that the rollover stability threshold is low, and rollover is easy to occur. The rollover early warning method based on the static threshold value carries out rollover early warning by analyzing static indexes such as lateral acceleration, roll angle and the like of the vehicle, but the early warning accuracy is insufficient; the rollover early warning method based on the dynamic threshold value calculates the rollover state of the vehicle by considering the real-time dynamic characteristics of the vehicle, and improves the rollover early warning accuracy. The lateral load transfer rate is one of the most common dynamic rollover warning indexes, and is defined as the ratio of the difference between vertical stressed loads on the left wheel and the right wheel of the vehicle to the sum of the vertical stressed loads, but the vertical stressed loads of the wheels are difficult to directly measure, and the lateral load transfer rate cannot be directly calculated according to the definition of the vertical stressed loads, so that the method for conveniently and accurately calculating the lateral load transfer rate has great significance.
Disclosure of Invention
Aiming at the problems in the background art, the invention provides a partial least squares regression (PLS) -based method for calculating the lateral load transfer rate of a semi-trailer train, which is innovative in that: the lateral load transfer rate of the semi-trailer train can be calculated without depending on a sensor as a vehicle state data source, and accurate rollover early warning indexes are provided for vehicle rollover early warning.
The technical scheme adopted by the invention is as follows:
step 1: dividing a semi-trailer train into a tractor and a semi-trailer, and respectively considering the state quantity and the transverse load transfer rate;
step 2: establishing a semi-trailer train dynamic model, and designing a vehicle state observer and a transverse load transfer rate observer;
and step 3: taking the vehicle state quantity as an independent variable and the transverse load transfer rate as a dependent variable, taking the independent variable and the dependent variable from 0 to T as PLS data sets, performing partial least squares regression on the PLS data sets to obtain a multivariate linear regression equation set of the dependent variable relative to the independent variable, substituting the vehicle state quantity at the T +1 moment into the equation set, and calculating the transverse load transfer rate at the T +1 moment;
and 4, step 4: and (3) taking the independent variable and the dependent variable from 1 to T +1 as a new PLS data set, performing partial least squares regression on the new PLS data set to obtain a new dependent variable-independent multiple linear regression equation set, continuously iterating the time, and calculating the transverse load transfer rate at all the time.
In the step 1, the considered vehicle state quantity is the mass center deflection angle beta of the tractor1Yaw angular velocity ωr1Side inclination angle
And the yaw rate omega of the semitrailerr2Side inclination angle
The lateral load transfer rates considered for the five state quantities are the tractor lateral load transfer rate LTRC and the semitrailer lateral load transfer rate LTRT.
In the step 2, the semi-trailer train dynamics model is as follows:
convert it to the standard form of the equation of state:
in the formula:
V＝[-2k1 0 -2a1k1 0 0 0 0 0]T
in the above formulae, m1、m2The quality of a tractor and a semitrailer; omegar1、ωr2Yaw angular velocity of a tractor and a semitrailer; m iss1、ms2Spring-loaded masses of tractors and semitrailers;
the side inclination angles of the tractor and the semitrailer are set; h iss1、hs2The distance from the center of mass of the tractor and the semitrailer to a roll axis; i isx1、Ix2The moment of inertia of the tractor and the semitrailer around the x axis; i isxz1、Ixz2The spring-loaded mass of the tractor and the semitrailer has the transverse-swinging and side-tilting inertia product around the gravity center; i isz1、Iz2The moment of inertia of the tractor and the semitrailer around the z axis; delta is the corner of the front wheel of the tractor; g is the acceleration of gravity;
and
the roll stiffness of the front axle, the rear axle and the semitrailer axle of the tractor;
and
the front axle, the rear axle and the semitrailer axle roll damping is realized; h is1、h2The distance from a traction saddle to the roll axis of a tractor and a semitrailer; a is1、b1And c1The distance from the center of mass of the tractor to the front and rear shafts and the traction saddle; b2、c2The distance from the center of mass of the semitrailer to the trailer axle and the traction saddle; k is a radical of1、k2And k3Is used for the front part of a tractor,Rear axle and semi-trailer axle unilateral tire cornering stiffness.
In the step 2, according to the standard form of the state equation of the modern control theory:
y＝Cx+Du
order:
D＝0
then:
thus, the vehicle state observer can be formed to observe five state quantities of the vehicle.
In the step 2, a vehicle moment balance equation is established on the basis of considering the roll stiffness and the roll damping coefficient of the suspension:
simultaneous:
Fzl+Fzr＝Mg
finishing to obtain:
wherein M is the mass of the vehicle, B is the track width,
for tilting the vehicle bodyThe angle of the corner is such that,
in order to provide the roll rigidity of the axle,
for axle roll damping, FzlIs the vertical load on the left wheel of the vehicle; fzrIs the vertical load on the right wheel of the vehicle.
Considering the lateral load transfer rate LTRC of the tractor and the lateral load transfer rate LTRT of the semitrailer separately, as two indexes, the rollover indexes of the tractor and the semitrailer can be expressed as:
wherein B is1、B2The track is the track of tractor and semitrailer.
According to the standard form of the state equation of modern control theory:
y＝Cx+Du
constructing a transverse load transfer rate observer, and ordering:
y＝[LTRC LTRT]T
the following can be obtained:
D＝0
thus forming a lateral load transfer rate observer for observing the lateral load transfer rate of the tractor and the semitrailer.
In the step 3, the independent variables are as follows:
the dependent variables are:
Y＝[LTRC LTRT]T
the independent variable data source is the vehicle state observer constructed in the step 2, the dependent variable data source from 0 to T is the transverse load transfer rate observer constructed in the step 2, and the data source after T is obtained by calculation according to a multiple linear regression equation set obtained by partial least square regression.
In the steps 3 and 4, the independent variable data and the dependent variable data have the same sampling period, and one time represents one sampling period of the independent variable and the dependent variable.
In the steps 3 and 4, the partial least squares regression modeling process is as follows:
normalizing X and Y to obtain normalized independent variable matrix E0And dependent variable matrix F0。
In the formula (I), the compound is shown in the specification,
is XjMean value of (1), sjIs XjThe standard deviation of (a) is determined,
is YkMean value of (1), skIs YkStandard deviation of (2).
From E0Extracting a main component t1＝E0w1From F0Extracting a main component u1＝F0c1If it is to be t1And u1The data variation information in X and Y can be represented well respectively, and according to the principle of principal component analysis, the method comprises the following steps:
var(t1)→max
var(u1)→max
t is required due to the need for regression modeling1For u is paired1Has great explanatory power, t is the idea of typical correlation analysis1And u1Should reach a maximum value, i.e.:
r(t1，u1)→max
therefore, t is required in partial least squares regression1And u1The covariance of (a) is maximized, i.e.:
the mathematical expression should be to solve the following optimization problem:
max<E0w1，F0c1>
available according to the lagrange multiplier method: wherein w1Is corresponding to the matrix
Unit feature vector of maximum feature value, c1Is corresponding to the matrix
A unit eigenvector of the largest eigenvalue;
finding w1And c1The components can be obtained:
respectively solve for E0And F0At t1The regression equation above:
in the formula, p1，r1Are regression coefficients, i.e.:
recording a residual matrix:
③ using residual matrix E1And F1By substitution of E0And F0The loop flow (c) can obtain a regression equation:
in the formula, p2，r2Are regression coefficients, i.e.:
if the rank of X is a, then:
due to t1，t2，…，taIs a normalized variable
So that a multi-linear regression equation set of the normalized dependent variable with respect to the normalized independent variable can be obtained, and the inverse normalization processing is performed to obtain each dependent variable yiWith respect to the independent variable x1，x2，…，xpThe system of multiple linear regression equations.
In the steps 3 and 4, the form of a multiple linear regression equation set obtained by partial least squares regression is as follows:
wherein a is0、b0Is a constant term of1，a2，…，a5，b1，b2，…，b5Are regression coefficients.
The invention has the beneficial effects that: the lateral load transfer rate of the semi-trailer train can be accurately calculated without depending on a sensor as a vehicle state data source, accurate rollover early warning indexes are provided for vehicle rollover early warning, the number of vehicle-mounted sensors is reduced, the cost is saved, and the accuracy of the lateral load transfer rate is higher than that of a simple state observer observation value.
Drawings
FIG. 1 is a flow chart of a method for calculating a lateral load transfer rate of a semi-trailer train based on partial least squares regression (PLS)
FIG. 2 is a plane motion model diagram of a semi-trailer train
FIG. 3 is a diagram of a model of a lateral rolling motion of a towing vehicle
FIG. 4 is a diagram of a model of a side-tipping motion of a semitrailer
FIG. 5 is a simplified roll moment balance model diagram for a vehicle
Detailed Description
The technical solution of the present invention is further described in detail below with reference to the accompanying drawings.
The invention provides a partial least squares regression (PLS) -based method for calculating the transverse load transfer rate of a semi-trailer train, which adopts the following technical scheme:
step 1: dividing a semi-trailer train into a tractor and a semi-trailer, and respectively considering the state quantity and the transverse load transfer rate;
step 2: establishing a semi-trailer train dynamic model, and designing a vehicle state observer and a transverse load transfer rate observer;
and step 3: taking the vehicle state quantity as an independent variable and the transverse load transfer rate as a dependent variable, taking the independent variable and the dependent variable from 0 to T as PLS data sets, performing partial least squares regression on the PLS data sets to obtain a multivariate linear regression equation set of the dependent variable relative to the independent variable, substituting the vehicle state quantity at the T +1 moment into the equation set, and calculating the transverse load transfer rate at the T +1 moment;
and 4, step 4: and (3) taking the independent variable and the dependent variable from 1 to T +1 as a new PLS data set, performing partial least squares regression on the new PLS data set to obtain a new dependent variable-independent multiple linear regression equation set, continuously iterating the time, and calculating the transverse load transfer rate at all the time.
The technical scheme is shown in a flow chart in figure 1.
In the step 1, the considered vehicle state quantity is the mass center deflection angle beta of the tractor1Yaw angular velocity ωr1Side inclination angle
And the yaw rate omega of the semitrailerr2Side inclination angle
The lateral load transfer rates considered for the five state quantities are the tractor lateral load transfer rate LTRC and the semitrailer lateral load transfer rate LTRT.
In the step 2, the established semi-trailer train model is assumed as follows:
neglecting aerodynamic effects;
a tractor driving shaft and a trailer shaft are equivalent to a single-shaft model;
neglecting the rolling and slope resistance of the ground to the wheels;
neglecting the influence of the tire load change on the tire aligning moment;
constant longitudinal speed and small articulation angle;
neglecting the rotational inertia moment effect.
According to the stress analysis conditions of the semi-trailer train in fig. 2, 3 and 4, the dynamic differential equations of the lateral direction, the side-tipping direction and the yaw direction of the tractor and the semi-trailer are respectively as follows:
differential equation of lateral motion of tractor
Differential equation of roll motion of tractor
Differential equation of yaw motion of tractor
Differential equation of lateral motion of semitrailer
Differential equation of side-tipping motion of semitrailer
Differential equation of semi-trailer yaw motion
Assuming a rigid connection between the traction saddle of the tractor and the traction saddle of the trailer, the longitudinal speeds of the tractor and the trailer are equal, i.e. vx1＝vx2Then the force coupling equation of the tractor and the trailer at the contact point is as follows:
the articulation angle theta of the connection between the tractor and the trailer satisfies:
the mass center slip angle of the trailer meets the following requirements:
wherein, Fyil、FyirAnd (i is 1, 2 and 3) the cornering powers of the left and right tires of the tractor and the semitrailer respectively, and when a linear tire mechanics model is adopted, the cornering power can be processed to be in a linear relation between the cornering angle and the cornering power when the tire cornering angle is small, so that the cornering powers of the tires of the axles are respectively expressed as follows:
in the above formulae, m1、m2The quality of a tractor and a semitrailer; v. ofy1、vy2The lateral speed of a tractor and a semitrailer; omegar1、ωr2Yaw angular velocity of a tractor and a semitrailer; m iss1、ms2Spring-loaded masses of tractors and semitrailers;
the side inclination angles of the tractor and the semitrailer are set; h iss1、hs2The distance from the center of mass of the tractor and the semitrailer to a roll axis; i isx1、Ix2The moment of inertia of the tractor and the semitrailer around the x axis; i isxz1、Ixz2The spring-loaded mass of the tractor and the semitrailer has the transverse-swinging and side-tilting inertia product around the gravity center; i isz1、Iz2The moment of inertia of the tractor and the semitrailer around the z axis; delta is the corner of the front wheel of the tractor; fAx、FAyLongitudinal force and lateral force applied to a traction saddle of the tractor; fTx、FTyLongitudinal force and lateral force applied to a traction pin of the semitrailer; g is the acceleration of gravity;
and
the roll stiffness of the front axle, the rear axle and the semitrailer axle of the tractor;
and
the front axle, the rear axle and the semitrailer axle roll damping is realized; h is1、h2The distance from a traction saddle to the roll axis of a tractor and a semitrailer; a is1、b1And c1The distance from the center of mass of the tractor to the front and rear shafts and the traction saddle; b2、c2The distance from the center of mass of the semitrailer to the axle and the traction saddle of the semitrailer; k is a radical of1、k2And k3The lateral deflection rigidity of the front and rear axles and the semi-trailer axle unilateral tires is provided; alpha is alpha1、α2And alpha3The slip angles of the front and rear axles and the semi-trailer axle are the slip angles of the front and rear axles of the tractor.
The differential equation is arranged and solved and F is eliminatedAy、FTyThe obtained semi-trailer train dynamics model is as follows:
convert it to the standard form of the equation of state:
in the formula:
V＝[-2k1 0 -2a1k1 0 0 0 0 0]T
in the step 2, according to the standard form of the state equation of the modern control theory:
y＝Cx+Du
order:
D＝0
then:
thus, the vehicle state observer can be formed to observe five state quantities of the vehicle.
In the step 2, as shown in fig. 5, a vehicle moment balance equation is established in consideration of the roll stiffness and the roll damping coefficient of the suspension:
simultaneous:
Fzl+Fzr＝Mg
finishing to obtain:
wherein M is the mass of the vehicle, B is the track width,
the inclination angle of the vehicle body is the inclination angle,
in order to provide the roll rigidity of the axle,
for axle roll damping, FzlIs the vertical load on the left wheel of the vehicle; fzrIs the vertical load on the right wheel of the vehicle.
Considering the lateral load transfer rate LTRC of the tractor and the lateral load transfer rate LTRT of the semitrailer separately, as two indexes, the rollover indexes of the tractor and the semitrailer can be expressed as:
wherein B is1、B2The track of the tractor and the semitrailer;
according to the standard form of the state equation of modern control theory:
y＝Cx+Du
constructing a transverse load transfer rate observer, and ordering:
y＝[LTRC LTRT]T
the following can be obtained:
D＝0
thus forming a load transfer rate observer for observing the load transfer rate of the tractor and the semitrailer.
In the step 3, the independent variables are as follows:
the dependent variables are:
Y＝[LTRC LTRT]T
the independent variable data source is the vehicle state observer constructed in the step 2, the dependent variable data source from 0 to T is the transverse load transfer rate observer constructed in the step 2, and the data source after T is obtained by calculation according to a multiple linear regression equation set obtained by partial least square regression.
In the steps 3 and 4, the independent variable data and the dependent variable data have the same sampling period, and one time represents one sampling period of the independent variable and the dependent variable.
In the steps 3 and 4, the partial least squares regression modeling process is as follows:
normalizing X and Y to obtain normalized independent variable matrix E0And dependent variable matrix F0。
In the formula (I), the compound is shown in the specification,
is XjMean value of (1), sjIs XjThe standard deviation of (a) is determined,
is YkMean value of (1), skIs YkStandard deviation of (2).
From E0Extracting a main component t1＝E0w1From F0Extracting a main component u1＝F0c1If it is to be t1And u1The data variation information in X and Y can be represented well respectively, and according to the principle of principal component analysis, the method comprises the following steps:
var(t1)→max
var(u1)→max
t is required due to the need for regression modeling1For u is paired1Has great explanatory power, t is the idea of typical correlation analysis1And u1Should reach a maximum value, i.e.:
r(t1，u1)→max
therefore, t is required in partial least squares regression1And u1The covariance of (a) is maximized, i.e.:
the mathematical expression should be to solve the following optimization problem:
max<E0w1，F0c1>
available according to the lagrange multiplier method: wherein w1Is corresponding to the matrix
Unit feature vector of maximum feature value, c1Is corresponding to the matrix
The unit eigenvector of the largest eigenvalue.
Finding w1And c1The components can be obtained:
respectively solve for E0And F0At t1The regression equation above:
in the formula, p1，r1Are regression coefficients, i.e.:
recording a residual matrix:
③ using residual matrix E1And F1By substitution of E0And F0The loop flow (c) can obtain a regression equation:
in the formula, p2，r2Are regression coefficients, i.e.:
if the rank of X is a, then:
due to t1，t2，…，taIs a normalized variable
So that a system of multiple linear regression equations of the normalized dependent variable with respect to the normalized independent variable can be obtained, and the system is subjected to an anti-normalization process, thereby finally obtaining each dependent variable yi with respect to the independent variable x1，x2，…，xpThe system of multiple linear regression equations.
In the steps 3 and 4, the form of a multiple linear regression equation set obtained by partial least squares regression is as follows:
wherein a is0、b0Is a constant term of1，a2，…，a5，b1，b2，…，b5Are regression coefficients.
The invention has the beneficial effects that: the lateral load transfer rate of the semi-trailer train can be calculated without depending on a sensor as a vehicle state data source, accurate rollover early warning indexes are provided for vehicle rollover early warning, the number of vehicle-mounted sensors is reduced, the cost is saved, and the accuracy of the lateral load transfer rate is higher than that of a simple state observer observation value.
Claims (8)
1. A method for calculating the lateral load transfer rate of a semi-trailer train based on partial least squares regression (PLS) adopts the technical scheme that:
step 1: dividing a semi-trailer train into a tractor and a semi-trailer, and respectively considering the state quantity and the transverse load transfer rate;
step 2: establishing a semi-trailer train dynamic model, and designing a vehicle state observer and a transverse load transfer rate observer;
and step 3: taking the vehicle state quantity as an independent variable and the transverse load transfer rate as a dependent variable, taking the independent variable and the dependent variable from 0 to T as PLS data sets, performing partial least squares regression on the PLS data sets to obtain a multivariate linear regression equation set of the dependent variable relative to the independent variable, substituting the vehicle state quantity at the T +1 moment into the equation set, and calculating the transverse load transfer rate at the T +1 moment;
and 4, step 4: and (3) taking the independent variable and the dependent variable from 1 to T +1 as a new PLS data set, performing partial least squares regression on the new PLS data set to obtain a new dependent variable-independent multiple linear regression equation set, continuously iterating, and calculating the transverse load transfer rate at all times.
2. Step 1 according to claim 1, wherein the considered vehicle state quantity is the deviation angle β of the mass center of the tractor1Yaw angular velocity ωr1Side inclination angle
And the yaw rate omega of the semitrailerr2Side inclination angle
The lateral load transfer rates considered for the five state quantities are the tractor lateral load transfer rate LTRC and the semitrailer lateral load transfer rate LTRT.
3. The step 2 of claim 1, wherein the established semi-trailer train dynamics model is:
convert it to the standard form of the equation of state:
in the formula:
A＝T-1M；B＝T-1V；u＝δ。
V＝[-2k1 0 -2a1k1 0 0 0 0 0]T
in the above formulae, m1、m2The quality of a tractor and a semitrailer; omegar1、ωr2Yaw angular velocity of a tractor and a semitrailer; m iss1、ms2Spring-loaded masses of tractors and semitrailers;
the side inclination angles of the tractor and the semitrailer are set; h iss1、hs2The distance from the center of mass of the tractor and the semitrailer to a roll axis; i isx1、Ix2The moment of inertia of the tractor and the semitrailer around the x axis; i isxz1、Ixz2The spring-loaded mass of the tractor and the semitrailer has the transverse-swinging and side-tilting inertia product around the gravity center; i isz1、Iz2The moment of inertia of the tractor and the semitrailer around the z axis; delta is the corner of the front wheel of the tractor; g is the acceleration of gravity;
and
the roll stiffness of the front axle, the rear axle and the semitrailer axle of the tractor;
and
the front axle, the rear axle and the semitrailer axle roll damping is realized; h is1、h2The distance from a traction saddle to the roll axis of a tractor and a semitrailer; a is1、b1And c1The distance from the center of mass of the tractor to the front and rear shafts and the traction saddle; b2、c2The distance from the center of mass of the semitrailer to the trailer axle and the traction saddle; k is a radical of1、k2And k3The lateral deflection rigidity of the front and rear axles and the semi-trailer axle unilateral tires is provided.
4. Step 2 according to claim 1, the vehicle state observer is designed in the form of:
y＝Cx+Du
wherein:
D＝0
A. x, B, u are the same as described in claim 3, and five observed state quantities of the vehicle are:
5. step 2 according to claim 1, the lateral load transfer rate observer is designed in the form of:
y＝Cx+Du
wherein:
D＝0
A. x, B, u are the same as described in claim 3, and the observed lateral load transfer rate of the vehicle is:
y＝[LTRC LTRT]T。
6. step 3 according to claim 1, the arguments of which are:
the dependent variables are:
Y＝[LTRC LTRT]T
the independent variable data source is the vehicle state observer constructed in the step 2, the dependent variable data source from 0 to T is the transverse load transfer rate observer constructed in the step 2, and the data source after T is the transverse load transfer rate calculated by using a multiple linear regression equation system obtained by partial least squares regression.
7. According to the steps 3 and 4 of claim 1, the independent variable data and the dependent variable data have the same sampling period, and one time represents one sampling period of the independent variable and the dependent variable.
8. Steps 3, 4 according to claim 1, a system of multiple linear regression equations for five vehicle state quantities with respect to two lateral load transfer rates is obtained using partial least squares regression (PLS), of the form:
wherein a is0、b0Is a constant term of1，a2，…，a5，b1，b2，…，b5Are regression coefficients.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111611628.1A CN114329951A (en) | 2021-12-27 | 2021-12-27 | Method for calculating lateral load transfer rate of vehicle |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111611628.1A CN114329951A (en) | 2021-12-27 | 2021-12-27 | Method for calculating lateral load transfer rate of vehicle |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114329951A true CN114329951A (en) | 2022-04-12 |
Family
ID=81012927
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111611628.1A Pending CN114329951A (en) | 2021-12-27 | 2021-12-27 | Method for calculating lateral load transfer rate of vehicle |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114329951A (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2630152A1 (en) * | 2007-05-02 | 2008-11-02 | Paccar Inc | Rollover prediction and warning method |
| CN108622105A (en) * | 2018-04-16 | 2018-10-09 | 吉林大学 | Vehicle bend safe speed prediction based on multiple regression analysis and early warning system |
| CN112733325A (en) * | 2020-12-21 | 2021-04-30 | 东南大学 | Comprehensive and accurate evaluation method for AEBS of semi-trailer train |
-
2021
- 2021-12-27 CN CN202111611628.1A patent/CN114329951A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2630152A1 (en) * | 2007-05-02 | 2008-11-02 | Paccar Inc | Rollover prediction and warning method |
| CN108622105A (en) * | 2018-04-16 | 2018-10-09 | 吉林大学 | Vehicle bend safe speed prediction based on multiple regression analysis and early warning system |
| CN112733325A (en) * | 2020-12-21 | 2021-04-30 | 东南大学 | Comprehensive and accurate evaluation method for AEBS of semi-trailer train |
Non-Patent Citations (2)
| Title |
|---|
| YAO, JL 等: "Automobile active tilt control based on active suspension", ADVANCES IN MECHANICAL ENGINEERING, vol. 10, no. 10, 4 October 2018 (2018-10-04), pages 1 - 9 * |
| 杨鄂川 等: "基于模型预测控制的半挂汽车主动防侧倾控制方法", 汽车安全与节能学报, vol. 11, no. 01, 15 March 2020 (2020-03-15), pages 61 - 70 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111845775B (en) | Joint estimation method for driving state and inertia parameters of distributed driving electric automobile | |
| CN105667520B (en) | A kind of front-wheel side force method of estimation of distributed driving electric car | |
| CN104077459B (en) | A kind of automobile chassis handling stability energy analysis model method for building up based on suspension KC characteristics | |
| CN104354700B (en) | A kind of vehicle parameter On-line Estimation method based on Unscented kalman filtering | |
| CN108482379A (en) | Wheel-hub motor driven vehicle coefficient of road adhesion and road gradient synchronize real-time estimation system and method | |
| Darling et al. | An experimental investigation of car—trailer high-speed stability | |
| CN110573362A (en) | vehicle state estimating device, control device, suspension control device, and suspension device | |
| CN113799783B (en) | Road transverse gradient measuring method and system applied to vehicle | |
| CN108241773A (en) | A kind of improved vehicle running state method of estimation | |
| CN113602279B (en) | Method for estimating mass center slip angle and tire lateral force of distributed driving electric automobile | |
| CN115675524A (en) | Self-adaptive control method for transverse track tracking of automatic driving semi-trailer truck | |
| CN112270039A (en) | Distributed asynchronous fusion-based nonlinear state estimation method for drive-by-wire chassis vehicle | |
| CN114329951A (en) | Method for calculating lateral load transfer rate of vehicle | |
| CN106476809A (en) | A kind of method of estimation of automobile attachment state and Special testing device | |
| CN113449378B (en) | Transverse load transfer rate calculation method based on deformation of vehicle suspension | |
| CN112287289A (en) | Vehicle nonlinear state fusion estimation method for cloud control intelligent chassis | |
| Ehlers et al. | State and parameter estimation in a semitrailer for different loading conditions only based on trailer signals | |
| CN117073939A (en) | Tire rigidity calculation method | |
| US20230303082A1 (en) | Method for controlling vehicle performance by estimating the center of gravity of a loaded vehicle | |
| Alatorre et al. | Sideslip estimation algorithm comparison between Euler angles and quaternion approaches with black box vehicle model | |
| Salaani | The application of understeer gradient in stability analysis of articulated vehicles | |
| CN116756935A (en) | Braking performance control analysis method based on multi-axis heavy vehicle joint simulation model | |
| CN115809590A (en) | Estimation method for main dynamic parameters of vehicle | |
| Khemoudj et al. | Variable gain sliding mode observer for heavy duty vehicle tyre forces estimation | |
| CN115406669A (en) | Multi-axle special vehicle rollover index optimization method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |