A method for judging the stability of vehicle angular modules based on multidimensional random parameters and vehicle

Abstract

This invention relates to a method for determining the stability of a vehicle corner module based on multidimensional stochastic parameters, and a vehicle in the field of vehicle control technology. The method for determining the stability of the vehicle corner module includes: determining the stability based on the lateral stiffness uncertainty of the vehicle corner module. r 1. Uncertainty of torsional stiffness r 2. Driving speed v Calculate the maximum Lyapunov exponent of the vehicle angle module. L s ;like L s If the value is ≤0, the vehicle corner module is considered stable; otherwise, it is considered unstable. This invention, by considering a multidimensional stochastic parameter-driven vehicle corner module dynamics model, simultaneously takes into account the uncertainties in the lateral and torsional stiffness of the vehicle corner module. This makes the constructed vehicle corner module dynamics model more consistent with actual conditions, laying a model foundation for the stability analysis and evaluation of the vehicle corner module. It reduces and eliminates the impact of uncertainties, thereby enabling effective analysis and evaluation of vehicle corner module instability.

Description

Technical Field

[0001] This invention relates to the field of vehicle control technology, and in particular to a method for determining the stability of a vehicle's angular module based on multidimensional random parameters, and a vehicle thereof. Background Technology

[0002] The vehicle corner module highly integrates chassis actuators such as drive, steering, suspension, and braking, improving its controllable degrees of freedom. However, by eliminating the steering tie rod, the vehicle corner module is highly susceptible to steering yaw after initial external disturbances during driving, severely impacting vehicle stability. Furthermore, factors such as modeling errors, material property uncertainties, and measurement errors lead to significant uncertainties in the torsional and lateral stiffness of the vehicle corner module, posing new challenges to revealing its yaw dynamics.

[0003] In the prior art, for example, Chinese invention patent with patent application number CN202311296571X, entitled "Sway Judgment Method and Device, Storage Medium, Electronic Equipment", discloses: collecting vehicle steering system data, front axle data, front wheel data, and speed data; establishing a vibration calculus model and a sway discrimination model; using the sway discrimination model to process vehicle driving data and the weight value of each vehicle driving data to obtain the sway discrimination value of the sway system, thus solving the problems of complex operation and low accuracy of sway judgment methods. Chinese invention patent with patent application number CN202311358125.7, entitled "A Vehicle High-Speed ​​Driving Sway System and Method Based on Multi-Sample Analysis", discloses: inputting random excitation loads of tires into a whole-vehicle simulation analysis model; based on the excitation load of a single tire, using standard sensitivity conditions and whole-vehicle sensitivity analysis, finally obtaining the sway response magnitude analysis, thereby being used for the forward development and control of steering wheel sway problems. However, current technical solutions mainly target traditional vehicle chassis architectures, and the research has not yet focused on vehicle corner module architectures. At the same time, it should be noted that the torsional stiffness and lateral stiffness of the vehicle corner module system have a significant impact on the occurrence of shimmy. Current technical solutions all use deterministic structural parameters to establish vehicle shimmy dynamic models, without considering the impact of multidimensional parameter uncertainties in torsional stiffness and lateral stiffness caused by factors such as modeling errors, material property uncertainties, and measurement errors. This leads to difficulties in the analysis and evaluation of instability of vehicle corner modules. Summary of the Invention

[0004] To address the technical problem that existing vehicle stability assessment methods fail to consider the impact of uncertainties caused by factors such as modeling errors, material property uncertainties, and measurement errors, thus leading to difficulties in analyzing and assessing instability in vehicle corner modules, this invention provides a vehicle corner module stability judgment method and vehicle based on multidimensional random parameter excitation.

[0005] This invention is achieved through the following technical solution:

[0006] In a first aspect, the present invention proposes a method for determining the stability of a vehicle angle module based on multidimensional random parameter excitation, which includes:

[0007] Based on the lateral stiffness uncertainty of the vehicle angle module r 1. Uncertainty of torsional stiffness r 2. Driving speed v Calculate the maximum Lyapunov exponent of the vehicle angle module. L s ;like L s If the value is less than or equal to 0, the vehicle corner module is considered stable; otherwise, it is considered unstable.

[0008] in, L s satisfy:

[0009] .

[0010] .

[0011] .

[0012] In the formula, m ( r ) is the drift coefficient. r The magnitude is in polar coordinates. s ( r ) represents the diffusion coefficient. F 1. F All three are intermediate variables; a ij These are the coefficients of the corresponding matrix elements in the two-dimensional central manifold of the vehicle corner module. i , j All are the indexes of the matrix elements; D The intensity of Gaussian white noise.

[0013] Secondly, this invention also proposes a stability judgment method for four-wheel steering vehicles, which uses the vehicle corner module stability judgment method based on multidimensional random parameter excitation from the first aspect to judge at least one vehicle corner module of the four-wheel steering vehicle. If at least one vehicle corner module is judged to be unstable, the four-wheel steering vehicle is judged to be unstable.

[0014] Thirdly, the present invention also proposes a four-wheel steering vehicle, which adopts the stability judgment method for four-wheel steering vehicles in the second aspect to determine whether the four-wheel steering vehicle is stable during driving.

[0015] Fourthly, the present invention also proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the vehicle angle module stability judgment method based on multidimensional random parameter excitation as described in the first aspect.

[0016] The beneficial effects of this invention are as follows:

[0017] 1. This invention, by considering a multidimensional stochastic parameter-driven vehicle corner module dynamics model, simultaneously takes into account the influence of uncertainties in the lateral and torsional stiffness of the vehicle corner module. This makes the constructed vehicle corner module dynamics model more consistent with actual conditions, laying a model foundation for the stability analysis and evaluation of the vehicle corner module, reducing and eliminating the influence of uncertainties, and thus enabling effective analysis and evaluation of vehicle corner module instability.

[0018] 2. This invention first establishes a dynamic model of the vehicle corner module considering multidimensional random parameters, then solves the two-dimensional central flow of the vehicle corner module under multidimensional random parameters, and obtains the analytical expressions of the probability density function and the maximum Lyapunov exponent of the vehicle corner module based on the stochastic averaging method, thereby enabling the analysis and evaluation of the instability of the vehicle corner module. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart of a vehicle angle module stability judgment method based on multidimensional random parameter excitation;

[0021] Figure 2 yes v A schematic diagram showing the solution results of the probability density function at a speed of 30km / h;

[0022] Figure 3 yes v A schematic diagram showing the result of solving the probability density function at a speed of 45 km / h. Detailed Implementation

[0023] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] It should be noted that when a component is said to be "installed on" another component, it can be directly on the other component or it may be in a component that is centered on it. When a component is said to be "set on" another component, it can be directly set on the other component or it may also be in a component that is centered on it. When a component is said to be "fixed to" another component, it can be directly fixed to the other component or it may also be in a component that is centered on it.

[0025] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the specification of this invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "or / and" as used herein includes any and all combinations of one or more of the associated listed items.

[0026] This embodiment proposes a method for determining the stability of a vehicle corner module based on multidimensional stochastic parameter excitation. First, the uncertainties of the torsional stiffness and lateral stiffness of the vehicle corner module are obtained, and the vehicle corner module's travel speed is collected. Then, a two-degree-of-freedom dynamic model of the vehicle corner module considering multidimensional stochastic parameter excitation is established. Next, the two-dimensional central flow theorem of the vehicle corner module is solved using the central flow theorem, and the average Iton stochastic differential equation of the vehicle corner module is solved based on the stochastic averaging method. Finally, the maximum Lyapunov exponent of the vehicle corner module is obtained, thereby determining the stability of the vehicle corner module system under multidimensional stochastic parameter excitation, providing technical support for better stability control of vehicle corner modules. Specifically, please refer to... Figure 1 The vehicle angle module stability judgment method based on multidimensional random parameter excitation includes the following steps:

[0027] Based on the lateral stiffness uncertainty of the vehicle angle module r 1. Uncertainty of torsional stiffness r 2. Driving speed v Calculate the maximum Lyapunov exponent of the vehicle angle module. L s .like L s If the value is ≤0, the vehicle corner module is considered stable; otherwise, it is considered unstable. L s satisfy:

[0028] .

[0029] .

[0030] .

[0031] In the formula, m ( r ) is the drift coefficient. r The magnitude is in polar coordinates. s ( r ) represents the diffusion coefficient. F 1. F All three are intermediate variables; a ij These are the coefficients of the corresponding matrix elements in the two-dimensional central manifold of the vehicle corner module. i , j All are the indexes of the matrix elements; D The intensity of Gaussian white noise.

[0032] One of the design focuses of this invention is the lateral stiffness uncertainty. r 1. Uncertainty of torsional stiffness r The solution to problem 2, by considering a multidimensional stochastic parameter-excited vehicle corner module dynamics model, simultaneously takes into account the uncertainties in the lateral and torsional stiffness of the vehicle corner module. This makes the constructed vehicle corner module dynamics model more consistent with actual conditions, laying the model foundation for the stability analysis and evaluation of the vehicle corner module. This vehicle corner module dynamics model is a two-degree-of-freedom dynamic model that includes the rotation angle and lateral displacement of the vehicle corner module.

[0033] .

[0034] .

[0035] .

[0036] In the formula, m It is the quality of the vehicle corner module. m 2 is the frame quality. b The distance from the center of mass of the vehicle corner module to the center of the kingpin axis. For vehicle corner module turning angle, c 1 is the lateral damping of the frame. y It is the lateral displacement of the vehicle's corner module. k 1 is the lateral stiffness of the frame. for t Gaussian white noise at any given time. F y The lateral force acting on the vehicle's corner module. J It is the moment of inertia of the vehicle's angular module. c 2 is the frame torsional damping. k 2 is the frame torsional stiffness. M z It is the tire return torque, and M z = Fy t p , t p This indicates the tire's mechanical trail distance. k t1 This is the linear stiffness coefficient of the tire. k t2 This represents the cubic stiffness coefficient of the tire. α This refers to the tire slip angle. s This refers to the tire slack length.

[0037] After constructing the dynamic model of the vehicle corner module, the two-dimensional central flow of the vehicle corner module under multidimensional stochastic excitation is solved. The analytical expressions for the probability density function and the maximum Lyapunov exponent of the vehicle corner module are obtained based on the stochastic averaging method, enabling analytical analysis and evaluation of the vehicle corner module's instability. The processing procedure is as follows:

[0038] First, we introduce state variables. Transform the vehicle corner module dynamics model into state equations:

[0039] .

[0040] In the formula, A The Jacobian matrix of the vehicle angular module dynamics model:

[0041] .

[0042] F The matrix containing nonlinear terms:

[0043] .

[0044] U The coefficient matrix is ​​based on random perturbations:

[0045] .

[0046] intermediate variables m t for: Intermediate variables J t for: . x 1~ x 5 is a state variable. x The components in.

[0047] Next, the Jacobian matrix A The speed of the first bifurcation is obtained using Hope's bifurcation theory. v 1, and set the bifurcation parameters m for m = vv 1, that is v = m + v 1. Then introduce new state variables. Y : Y ∈ R 5 , so that: x = pY and will v = m + v 1 and x = pY Substituting into the equations transforms them into:

[0048] .

[0049] in, p The transformation matrix can be obtained by recombination of the real and imaginary parts of the eigenvectors in the state equation.

[0050] Then let the central manifold function be:

[0051] .

[0052] In the formula, i =3, 4, 5. That is, Y =( y 1, y 2, y 3, y 4, y 5), y 1~ y 5 is a state variable. Y The components in. h i (·) represents the central flow function. h i1 ~ h i8 These are the coefficients of the corresponding matrix elements in the central manifold function. The initial condition for this central manifold function is that the values ​​of the central manifold function at the origin are all zero. This is Gaussian white noise. Furthermore, according to the differential theorem, the differentiated central manifold function can be expressed as:

[0053] .

[0054] Substituting the differentiated central manifold function into the transformed state equation, we obtain the initial two-dimensional central manifold of the vehicle angle module:

[0055] .

[0056] a ijLet be the coefficients of the corresponding matrix elements in the two-dimensional central manifold. i , j These are all indexes of matrix elements. Let... After substituting into the initial two-dimensional central manifold and simplifying, we obtain the two-dimensional central manifold of the vehicle corner module:

[0057] .

[0058] .

[0059] In the formula, f 11 , f 21 Both represent intermediate variables. r The magnitude is in polar coordinates. i The phase angle is in polar coordinates. g ij (·) represents the coefficients of the corresponding matrix elements in the average Itoh stochastic differential equation. i , j All are the indexes of the matrix elements.

[0060] Furthermore, by combining the stochastic averaging method, the two-dimensional central manifold is transformed into an averaged Itō stochastic differential equation:

[0061] .

[0062] .

[0063] .

[0064] In the formula, m ( r ) represents the drift coefficient. s ( r ) represents the diffusion coefficient. W ( t ) represents the standard Wiener process.

[0065] After solving, we get:

[0066] ;

[0067] ;

[0068] ;

[0069] ;

[0070] Furthermore, the analytical expression for the maximum Lyapunov exponent of the vehicle angle module can be obtained as follows:

[0071] .

[0072] When the maximum Lyapunov index L s If the value is ≤0, the vehicle corner module is considered stable. When the maximum Lyapunov exponent... L s If the value is greater than 0, the vehicle corner module is considered unstable. The stability of all four vehicle corner modules is assessed using this method to analyze and evaluate instability under multidimensional random parameter excitation.

[0073] On the other hand, by solving the average Iton stochastic differential equation using the stochastic averaging method, the probability density function for analyzing vehicle corner module instability can be obtained. P st ( y 1, y 2):

[0074] .

[0075] In the formula, C is the normalization coefficient, and exp(·) is the exponential function.

[0076] To verify the effectiveness of the vehicle angle module stability judgment method based on multidimensional random parameters, a simulation experiment was conducted. The simulation parameters were first set as follows:

[0077] m =15kg, m 2 = 4kg J =0.48kg·m 2 , c 1 = 220 N·s / m, c 2 = 60 N·s·m / rad, k 1 = 150000 N / m k 2 = 25000 N·m / rad, D =0.15, b =0.2m, r 1 = 2510 N / m r 2 = 2510 N / rad.

[0078] When the vehicle corner module travels at speed v for v At a speed of 30 km / h, the maximum Lyapunov exponent can be obtained using the vehicle angle module stability judgment method based on multidimensional stochastic excitation of the present invention. L s : L s =-0.1705<0, indicating that the vehicle corner module is stable, and its corresponding probability density function is...P st ( y 1, y 2) The solution results are as follows Figure 2 As shown, the system response of the vehicle corner module converges to the origin. When the vehicle corner module's speed... v for v At a speed of 45 km / h, the maximum Lyapunov exponent is obtained. L s : L s =0.0862>0, indicating that the vehicle corner module is unstable, and its corresponding probability density function is... P st ( y 1, y 2) The solution results are as follows Figure 3 As shown, the system response converges to the origin only probabilistically, and there is a certain probability of oscillation. This demonstrates that the vehicle corner module stability judgment method proposed in this invention based on multidimensional random parameter excitation is effective.

[0079] In another embodiment, a stability assessment method for a four-wheel steering vehicle is proposed. This method employs the multidimensional random parameter-based vehicle corner module stability assessment method described in the previous embodiment to assess at least one vehicle corner module of the four-wheel steering vehicle. The four-wheel steering vehicle includes four vehicle corner modules that serve as wheels. If at least one vehicle corner module is determined to be unstable, the four-wheel steering vehicle is determined to be unstable.

[0080] In another embodiment, a four-wheel steering vehicle is also proposed, which uses the stability judgment method of the four-wheel steering vehicle in the above embodiment to determine whether the four-wheel steering vehicle is stable during driving.

[0081] In another embodiment, a computer-readable storage medium storing a computer program is also proposed. When the computer program is executed by a processor, it implements the steps of the vehicle angle module stability determination method based on multidimensional random parameters in the above embodiments. The computer-readable storage medium may include, but is not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof.

[0082] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0083] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A method for judging the stability of a vehicle corner module based on multi-dimensional random excitation, characterized in that It comprises: According to lateral stiffness uncertainty of vehicle corner module ρ 1. torsional stiffness uncertainty ρ 2. driving speed v , calculating maximum lyapunov exponent of vehicle corner module L s ; if L s , judging that the vehicle corner module is stable, otherwise, judging that the vehicle corner module is unstable; wherein L s satisfies: ； ； ； wherein m r is a drift coefficient, r is an amplitude in polar coordinates, σ r denotes a diffusion coefficient, F 1, F 3 are intermediate variables; a ij is a coefficient of the corresponding matrix element in the two-dimensional central manifold of the vehicle angle module, i , j are the indices of the matrix elements; D is the intensity of the Gaussian white noise;​​ wherein obtaining ρ 1、 ρ 2 comprises: A two-degree-of-freedom vehicle corner module dynamics model is constructed, which contains vehicle corner module cornering angle and lateral displacement and considers multi-dimensional random excitation; Introducing state variables Transforming the vehicle angle module dynamics model into a state equation: ； wherein A J is the Jacobian matrix of the vehicle angle module dynamics model, F N is a nonlinear term matrix containing nonlinear terms, U C is a coefficient matrix based on random perturbations; A two-dimensional center manifold of the vehicle corner module is solved by a state equation: ； wherein f 11 , f 21 denote intermediate variables, r is the amplitude in polar coordinates, θ is the phase angle in polar coordinates, g ij (·) is the coefficient of the corresponding matrix element in the mean Itô stochastic differential equation; The two-dimensional center manifold is transformed into an averaged It stochastic differential equation, and the solution is obtained as ρ 1、 ρ 2; The two-dimensional center manifold of the vehicle corner module is obtained by differentiating the center manifold function and substituting it into the converted state equation and simplifying; wherein the center manifold function is: ； wherein i = 3, 4, 5, y 1 ~ y 5 are components in the state variable Y h i (·) denotes a central moment function, h i1 ~ ~ h i8 is a coefficient of the corresponding matrix element in the central moment function, is a Gaussian white noise;​ First, Jacobian matrix A The first bifurcation speed is obtained by Hopf bifurcation theory v 1; bifurcation parameter μ is μ = v-v 1, and an intermediate conversion variable is introduced in the state equation: The converted state equation is obtained as follows: ； p is a transformation matrix, Y denotes the new state variable.

2. The method of claim 1, wherein, The vehicle corner module dynamics model is: ； ； ； wherein, m is the vehicle corner module mass, m 2 is the frame mass, b is the distance from the vehicle corner module mass center to the kingpin axis center, Φ is the vehicle corner module rotation angle, c 1 is the frame lateral damping, y is the vehicle corner module lateral displacement, k 1 is the frame lateral stiffness, is the t Gaussian white noise at time instant, F y is the lateral force on the vehicle corner module, J is the vehicle corner module moment of inertia, c 2 is the frame torsional damping, k 2 is the frame torsional stiffness, M z is the tire aligning torque, k t1 is the linear stiffness coefficient of the tire, k t2 is the cubic stiffness coefficient of the tire, α is the tire slip angle, σ is the tire slack length.

3. The method of claim 1, wherein, The initial two-dimensional center manifold is obtained by differentiating the center manifold function and substituting it into the converted state equation: ； Let and substituting into the reduced 2D central manifold in the initial vehicle angle module.

4. The method of claim 1, wherein, In the equation of state, the Jacobian matrix A is: ； Nonlinear term matrix F is: ； coefficient matrix U is: ； wherein the intermediate variable m t is: ; the intermediate variable J t is: ; t p represents the mechanical tire drag distance; and / or, in a two-dimensional central manifold of the vehicle corner module, f 11 , f 21 The expression is: 。 5. The method of claim 4, wherein, The average It random differential equation is: ； ； ； wherein m ( r ) denotes the drift coefficient, σ ( r ) denotes the diffusion coefficient, W ( t ) denotes the standard Wiener process; The solution is: ； ； ； ； The average It stochastic differential equation is solved by random average method, and the probability density function for the vehicle angle module instability analysis is obtained P st ( y 1, y 2) ； In the formula, C is a normalization coefficient, and exp( ) is an exponential function.

6. A stability determination method for a four-wheel steering vehicle, characterized by comprising: a step of determining a stability of the vehicle based on a yaw rate of the vehicle and a lateral acceleration of the vehicle. A stability judgment method for a four-wheel steering vehicle is provided, which is based on multi-dimensional random excitation of a vehicle corner module. If at least one vehicle corner module is judged to be unstable, and the four-wheel steering vehicle is judged to be unstable.

7. A four-wheel steering vehicle characterized by comprising: The stability judgment method for the four-wheel steering vehicle is used to judge whether the four-wheel steering vehicle is stable during driving.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to realize the steps of the stability judgment method for the vehicle corner module based on multi-dimensional random excitation of the vehicle corner module.

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