Complete vehicle model vibration control analysis optimization method and system

Abstract

The invention discloses a whole vehicle model vibration control analysis optimization method and system. The method comprises the following steps: solving a whole vehicle inherent frequency and a third period peak attenuation rate of a mass center vertical vibration acceleration; uncertain intervals of the suspension rigidity, the damping coefficient and the tire rigidity are divided into a plurality of sub-intervals as focal element intervals, and corresponding focal element values are given; taking the condition that the inherent frequency of the whole vehicle and the peak attenuation rate of the mass center vertical vibration acceleration in the third period meet the design requirement as a proposition to obtain a probability cumulative distribution curve, and representing the minimum probability and the maximum probability of proposition establishment by using the credibility and the likelihood; establishing an evidence theory optimization model, setting a constraint function, and carrying out optimization design on the suspension rigidity, the damping coefficient and the tire rigidity; based on the evidence theory, the probability cumulative distribution of the peak attenuation rate of the third period of the suspension stiffness, the damping coefficient and the tire stiffness under different uncertainties of the inherent frequency and the mass center vertical vibration acceleration of the whole vehicle is analyzed, and the maximum uncertain interval enabling the vibration control of the whole vehicle to have stability is determined.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle vibration control analysis and component optimization design, and in particular to a whole vehicle model vibration control analysis optimization method and system. Background Art

[0002] The vibration of road vehicles is mainly caused by the unevenness of the road surface on which the vehicle is traveling. Improving the vibration reduction performance of the whole vehicle system has always been a hot topic in the field of vehicle engineering. At present, most studies on the optimization of whole vehicle vibration control regard the vehicle structural parameters (suspension, tires) as deterministic parameters for optimization, and rarely consider the influence of parameter uncertainty. In fact, due to production tolerances and long-term operating wear and aging, the suspension stiffness and damping coefficient may change relative to the design value. Due to changes in vehicle load conditions, operating temperature, and uncertainty in the inflation pressure of poorly maintained tires, tire stiffness may also vary within the range of uncertainty.

[0003] Changes in suspension stiffness, damping and tire stiffness have an impact on the vehicle's natural frequency and the vertical vibration acceleration of the vehicle's center of mass. If the influence of parameter uncertainty is not considered in the design and development of the suspension system and tires, the optimized design scheme obtained under the deterministic assumption will be difficult to meet the actual vibration reduction design indicators, resulting in a lack of design reliability. Summary of the invention

[0004] To achieve the above-mentioned purpose and other related purposes, the present invention discloses a vehicle model vibration control analysis and optimization method, comprising:

[0005] S1. Establish the dynamic equation of the seven-degree-of-freedom vehicle model and solve the natural frequency of the vehicle and the peak attenuation rate of the third period of the vertical vibration acceleration of the center of mass;

[0006] S2. Introduce evidence variables, divide the uncertainty intervals of suspension stiffness, damping coefficient and tire stiffness into multiple sub-intervals as focal element intervals, and assign corresponding focal element values ​​to obtain the basic credibility distribution of the three uncertain parameters;

[0007] S3. Take the vehicle's natural frequency and the third period peak attenuation rate of the center-of-mass vertical vibration acceleration as the propositions that they meet the design requirements to obtain their probability cumulative distribution curves, and use credibility and plausibility to represent the minimum and maximum probabilities of the propositions being true;

[0008] S4. Establish an evidence theory optimization model, take the proposition's plausibility greater than the set probability requirement value as the constraint function, use genetic algorithm global optimization, and optimize the suspension stiffness, damping coefficient and tire stiffness;

[0009] S5. Based on the evidence theory, analyze the probability cumulative distribution of the third period peak attenuation rate of the vehicle's natural frequency and center of mass vertical vibration acceleration under different uncertainties of suspension stiffness, damping coefficient and tire stiffness, and determine the maximum uncertainty interval that makes the vehicle vibration control stable.

[0010] Further, step S1 includes the following steps:

[0011] S101. Perform force analysis on the vehicle model and formulate equations based on Newton's second law:

[0012]

[0013] In the formula, K d1 ,K d2 ,K d3 ,K d4 are the suspension stiffness of the right front, left front, right rear and left rear directions of the vehicle body, C d1 ,C d2 ,C d3 ,C d4 is the suspension damping coefficient corresponding to the four directions, K t1 ,K t2 ,K t3 ,K t4 are the four wheel stiffnesses, q1, q2, q3, q4 are the vertical displacements of the road surface, Z1, Z2, Z3, Z4 are the vertical displacements of the wheels, and Z d1 ,Z d2 ,Z d3 ,Z d4 is the vertical displacement at the intersection of the vehicle body and the vertical axis of the suspension, Z b is the vertical displacement of the center of mass of the vehicle body, θ is the roll angle of the vehicle, is the vehicle pitch angle, m b is the sprung mass, m1, m2, m3, m4 are the tire masses, I x is the roll moment of inertia, I y is the pitch moment of inertia;

[0014] The vertical displacement at the intersection of the body and the suspension vertical axis has the following relationship:

[0015]

[0016] Where a and b are the distances from the front and rear axles to the center of mass of the vehicle respectively; d is the wheelbase;

[0017] S102, deriving the vibration differential equation of the system according to the formula in step S101:

[0018]

[0019] Where x is the generalized coordinate vector of the vehicle vibration system; M is the mass matrix of the vehicle vibration system; C is the suspension damping matrix; K is the stiffness matrix; f is the road excitation vector, is the vertical vibration acceleration of the center of mass;

[0020] S103, according to the formula:

[0021] K{φ} i =ω i 2 M{φ} i

[0022] Calculate the natural frequency of the vehicle, where i = 1, 2, ..., 7, {φ} i is the generalized eigenvector, ω i 2 is the generalized eigenvalue, ω i is the natural frequency;

[0023] S104, according to the formula:

[0024]

[0025] Calculate the peak attenuation rate of the i-th cycle of the vertical vibration acceleration of the center of mass, where: It represents the peak value of vertical vibration acceleration of the center of mass in the ith cycle.

[0026] Furthermore, in step S2, evidence variables are introduced to process the suspension stiffness, damping coefficient and tire stiffness, and their variation range is evenly divided into several sub-intervals as focal element intervals of the corresponding evidence variables, and credibility is assigned to each focal element interval based on the probability of taking the middle focal element interval being greater than the probability of taking the edge focal element interval.

[0027] Furthermore, in step S3, according to the function definition of credibility and plausibility:

[0028]

[0029] The probability cumulative distribution of the credibility and plausibility of the third-cycle peak attenuation rate of the vehicle's natural frequency and center-of-mass vibration acceleration is obtained. In the formula, m is called the basic probability assignment, also called the focal element, Bel(A) represents the sum of the basic credibility that fully supports proposition A, Pl(A) represents the sum of the basic credibility that fully or partially supports proposition A, and Bel(A) and Pl(A) are used to represent the minimum and maximum probabilities of the proposition being true.

[0030] Furthermore, in step S4, an evidence theory optimization model is established:

[0031] minf(X,D)

[0032]

[0033] Where f(X,D) is the objective function; g i (X,D) is the constraint function; P f is the stability probability allowable value; X is the design variable, [X L ,X U ] is the nominal value X value range, x1 represents the suspension stiffness K d , x2 represents the tire stiffness K t , x3 represents the suspension damping coefficient C d ; D is the interval parameter of each design variable, [D L ,D U ] is the value interval of D, which represents the uncertainty of the parameter.

[0034] Furthermore, in step S5, the uncertainties of the suspension stiffness and the tire stiffness are taken into several groups of different values, and the probability cumulative distribution of the third period peak attenuation rate of the vehicle's natural frequency and the center of mass vertical vibration acceleration under different uncertainties is analyzed based on the evidence theory, and the maximum uncertainty interval that makes the vehicle vibration control stable is determined according to the analysis results.

[0035] Furthermore, the several groups of different uncertainty values ​​include: 10%, 15%, 20% and 30%.

[0036] Furthermore, the introduction of evidence variables to process the suspension stiffness, damping coefficient and tire stiffness, and evenly dividing their variation intervals into a number of sub-intervals as focal element intervals of the corresponding evidence variables include:

[0037] The uncertainty intervals of suspension stiffness, damping coefficient and tire stiffness are evenly divided into four equal parts as their corresponding focal element intervals.

[0038] In another aspect, the present invention provides a vehicle model vibration control analysis and optimization system, comprising:

[0039] The dynamic equation building module is used to build the dynamic equation of the seven-degree-of-freedom vehicle model and solve the vehicle's natural frequency and the peak attenuation rate of the third period of the vertical vibration acceleration of the center of mass;

[0040] The basic credibility allocation module is used to introduce evidence variables, divide the uncertainty intervals of suspension stiffness, damping coefficient and tire stiffness into multiple sub-intervals as focal element intervals, and assign corresponding focal element values ​​to obtain the basic credibility allocation of the three uncertain parameters;

[0041] The probability cumulative distribution curve module is used to obtain the probability cumulative distribution curve based on the proposition that the natural frequency of the whole vehicle and the peak attenuation rate of the third period of the vertical vibration acceleration of the center of mass meet the design requirements, and use credibility and plausibility to represent the minimum and maximum probability of the proposition being true;

[0042] The module for establishing the evidence theory optimization model is used to establish the evidence theory optimization model. The likelihood of the proposition being greater than the set probability requirement value is used as the constraint function. The genetic algorithm is used for global optimization to optimize the suspension stiffness, damping coefficient and tire stiffness.

[0043] The data analysis module is used to analyze the probability cumulative distribution of the third period peak attenuation rate of the vehicle's natural frequency and center of mass vertical vibration acceleration under different uncertainties based on the evidence theory, and determine the maximum uncertainty interval that makes the vehicle vibration control stable.

[0044] By adopting the above technical solution, the present invention has the following technical effects:

[0045] 1) The method of the present invention takes parameter uncertainty into account in the whole vehicle vibration control analysis and optimization process, which is consistent with the situation that suspension and tire parameters may change relative to the design values ​​in actual production and operation;

[0046] 2) The method of the present invention introduces evidence theory to effectively handle incomplete or conflicting information of suspension and tire parameters, and can measure and evaluate the uncertainty of the stability of the vehicle vibration control system under uncertain conditions, providing a reference for the optimization design of suspension and tire parameters;

[0047] 3) The method of the present invention establishes an evidence theory optimization model to obtain the optimal parameter range, which can effectively control the vibration of the whole vehicle;

[0048] 4) The method of the present invention takes into account the volatility of the parameter uncertainty interval, and applies the evidence theory to obtain the maximum uncertainty interval that makes the vibration control of the whole vehicle stable, providing a selection space for determining the suspension and tire parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. The accompanying drawings are used to better understand the present solution and do not constitute a limitation of the present disclosure. In the accompanying drawings, the same or similar reference numerals represent the same or similar elements, among which:

[0050] Figure 1 It is a schematic diagram of the structure of the seven-degree-of-freedom vibration model of the whole vehicle;

[0051] Figure 2 is a cumulative distribution diagram of the natural frequency probability of the whole vehicle before optimization, where Figure 2(a) is the cumulative distribution diagram of the natural frequency probability of the vertical direction of the vehicle body; Figure 2(b) is the cumulative distribution diagram of the natural frequency probability of the vehicle body roll; Figure 2(c) is the cumulative distribution diagram of the natural frequency probability of the vehicle body pitch;

[0052] Figure 3 To optimize the probability cumulative distribution diagram of the third period peak attenuation rate of the vertical vibration acceleration of the front center of mass;

[0053] Figure 4 It is the probability cumulative distribution diagram of the vertical natural frequency of the optimized vehicle body;

[0054] Figure 5 It is the probability cumulative distribution of the peak attenuation rate of the third period of the vertical vibration acceleration of the center of mass after optimization;

[0055] Figure 6 The cumulative distribution diagram of the vertical natural frequency probability of the vehicle body under different uncertainty values;

[0056] Figure 7 The probability cumulative distribution diagram of the third period peak attenuation rate of the vertical vibration acceleration of the center of mass under different uncertainty values;

[0057] Figure 8 The present invention is a flow chart of a whole vehicle model vibration control analysis and optimization method. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0059] Reference Figure 1 The embodiment of the present invention provides a vehicle model vibration control analysis and optimization method, comprising the following steps:

[0060] S1. Establish the dynamic equation of the seven-degree-of-freedom vehicle model and solve the vehicle's natural frequency and the peak attenuation rate of the third period of the vertical vibration acceleration of the center of mass.

[0061] Wherein, S1 comprises the following steps:

[0062] Step S101, perform force analysis on the vehicle model, and formulate an equation according to Newton's second law:

[0063]

[0064] In the formula, K d1 ,K d2,K d3 ,K d4 are the suspension stiffness of the right front, left front, right rear and left rear directions of the vehicle body, C d1 ,C d2 ,C d3 ,C d4 is the suspension damping coefficient corresponding to the four directions, K t1 ,K t2 ,K t3 ,K t4 are the four wheel stiffnesses, q1, q2, q3, q4 are the vertical displacements of the road surface, Z1, Z2, Z3, Z4 are the vertical displacements of the wheels, and Z d1 ,Z d2 ,Z d3 ,Z d4 is the vertical displacement at the intersection of the vehicle body and the vertical axis of the suspension, Z b is the vertical displacement of the center of mass of the vehicle body, θ is the roll angle of the vehicle, is the vehicle pitch angle, m b is the sprung mass, m1, m2, m3, m4 are the tire masses, I x is the roll moment of inertia, I y is the pitch moment of inertia.

[0065] The vertical displacement at the intersection of the body and the suspension vertical axis has the following relationship:

[0066]

[0067] Where a and b are the distances from the front and rear axles to the center of mass of the vehicle respectively; d is the wheelbase.

[0068] Step S102: Substitute the equation group in step S101 into the vibration differential equation of the system:

[0069]

[0070] Where x is the generalized coordinate vector of the vehicle vibration system; M is the mass matrix of the vehicle vibration system; C is the suspension damping matrix; K is the stiffness matrix; f is the road excitation vector, is the vertical vibration acceleration of the center of mass.

[0071] Step S103, according to the formula:

[0072] K{φ} i =ω i 2 M{φ} i

[0073] Calculate the natural frequency of the vehicle, where i = 1, 2, ..., 7, {φ} i is the generalized eigenvector, ωi 2 is the generalized eigenvalue, ω i is the natural frequency.

[0074] Step S104, according to the formula:

[0075]

[0076] Calculate the peak attenuation rate of the vertical vibration acceleration period at the center of mass, where: It represents the peak value of vertical vibration acceleration of the center of mass in the ith cycle.

[0077] Taking a certain vehicle model as an example, the vehicle parameters required to calculate the vehicle's natural frequency and the peak attenuation rate of the center of mass vertical vibration acceleration period are shown in Table 1 below.

[0078] Table 1 Vehicle parameters

[0079]

[0080] S2. Introduce evidence variables, divide the uncertainty intervals of suspension stiffness, damping coefficient and tire stiffness into multiple sub-intervals as focal element intervals, and assign corresponding focal element values ​​to obtain the basic credibility allocation (BPA) of the three uncertain parameters.

[0081] According to engineering experience, the uncertainty of suspension stiffness and tire stiffness is set to 10%, and the uncertainty of suspension damping coefficient is set to 33%. d , damping coefficient C d and tire stiffness K t The uncertainty interval is evenly divided into four equal parts as its corresponding focal element intervals, and the BPA is assigned to each focal element interval based on the probability of taking the middle focal element interval being greater than the probability of taking the edge focal element interval.

[0082] In this embodiment, the uncertainty values ​​and basic credibility distribution of suspension and tire parameters are shown in Tables 2 and 3 below.

[0083] Table 2 Uncertain values ​​of suspension stiffness, tire stiffness and suspension damping coefficient

[0084]

[0085] Table 3 Basic credibility distribution of evidence variables

[0086]

[0087]

[0088] S3. Take the vehicle's natural frequency and the third period peak attenuation rate of the center-of-mass vertical vibration acceleration meeting the design requirements as the proposition to obtain its probability cumulative distribution curve, and use credibility and plausibility to represent the minimum and maximum probabilities of the proposition being true.

[0089] The proposition that the vertical natural frequency f1 of the vehicle body is less than or equal to 1.2 Hz, the roll natural frequency f2 is less than or equal to 1.6 Hz, and the pitch natural frequency f3 is less than or equal to 1.9 Hz is denoted as S, E, and H. The credibility and plausibility of proposition S are Bel(S)=Bel(f1≤1.2) and Pl(S)=Pl(f1≤1.2), respectively, where Bel and Pl represent the minimum and maximum probability that the value of the vertical natural frequency f1 of the vehicle body is less than or equal to 1.2, that is, the possibility that the vertical natural frequency of the vehicle body meets the design requirements under the condition of evidence uncertainty. Similarly, the credibility and plausibility of proposition E are Bel(E)=Bel(f2≤1.6) and Pl(E)=Pl(f2≤1.6), respectively, and the credibility and plausibility of proposition H are Bel(H)=Bel(f3≤1.9) and Pl(H)=Pl(f3≤1.9), respectively.

[0090] In order to more intuitively show the changes of Bel(S) and Pl(S) with the suspension and tire parameters, the control parameter γ is introduced to specifically analyze Bel(f1≤1+γ) and Pl(f1≤1+γ). For each γ, there are corresponding Bel and Pl values. By connecting each Bel and Pl value, the probability cumulative distribution curves of credibility and plausibility can be obtained. Similarly, the probability cumulative distribution curves of credibility and plausibility of propositions E and H can also be obtained. In particular, when γ=0.2, the corresponding Bel and Pl values ​​are the probability intervals for proposition S to be true, when γ=0.6, the Bel and Pl values ​​are the probability intervals for proposition E to be true, and when γ=0.9, the corresponding Bel and Pl values ​​are the probability intervals for proposition H to be true.

[0091] The cumulative probability distribution of the vehicle's natural frequency is shown in Figure 2. From the figure, it can be analyzed that the probability interval of the vehicle body's vertical natural frequency reaching the design requirement range f1≤1.2 is [19%, 56%], which is relatively small and does not meet the design requirements, so further optimization is needed.

[0092] For the vibration control analysis of the third period peak attenuation rate of the vertical vibration acceleration of the center of mass, the attenuation rate is greater than or equal to 97% as the proposition, denoted as T. Similar to the analysis of the natural frequency of the vehicle body, the control parameter χ is also introduced to analyze the changes of Bel (Pdr3 ≥ 97-τ) and Pl (Pdr3 ≥ 97-τ). The values ​​of Bel and Pl when τ = 0 are the probability intervals for which the proposition T is true.

[0093] The probability cumulative distribution of the third period peak attenuation rate of the vertical vibration acceleration of the center of mass is as follows: Figure 3As shown in the figure, it can be seen that the probability value of the third period peak attenuation rate of the center of mass vertical vibration acceleration reaching the design requirement range Pdr3≥97 does not meet the design requirements.

[0094] S4. Establish an evidence theory optimization model, take the proposition's plausibility greater than the set probability requirement value as the constraint function, use genetic algorithm global optimization, and optimize the suspension stiffness, damping coefficient and tire stiffness.

[0095] According to the analysis results of the third step, the evidence theory optimization model of this embodiment is established to calculate the suspension stiffness K d , damping coefficient C d and tire stiffness K t To optimize:

[0096] minf(X,D)

[0097]

[0098] The probability cumulative distribution diagrams of the optimized vehicle body vertical natural frequency and the third period peak attenuation rate of the center of mass vertical vibration acceleration are shown in Figures 4 and Figure 5 As shown in the figure, the probability values ​​of reaching the design interval meet the requirements, which verifies the effectiveness of the optimization design method.

[0099] S5. Based on the evidence theory, analyze the probability cumulative distribution of the third period peak attenuation rate of the vehicle's natural frequency and center of mass vertical vibration acceleration under different uncertainties of suspension stiffness, damping coefficient and tire stiffness, and determine the maximum uncertainty interval that makes the vehicle vibration control stable.

[0100] Set the suspension stiffness K d and tire stiffness K t The uncertainty of is taken as 10%, 15%, 20% and 30% respectively. Based on the evidence theory, the probability cumulative distribution of the third period peak attenuation rate of the vehicle natural frequency and the center of mass vertical vibration acceleration under different uncertainties is analyzed, as shown in Figure 6 and Figure 7 As shown, according to the analysis results, it can be determined that the maximum uncertainty that makes the vibration control of the whole vehicle stable is 20%, which means that when the uncertainty of the suspension stiffness and the tire stiffness varies within 20%, the stability of the vibration control of the whole vehicle is better.

[0101] An embodiment of the present invention further provides a vehicle model vibration control analysis and optimization system, comprising:

[0102] The dynamic equation building module is used to build the dynamic equation of the seven-degree-of-freedom vehicle model and solve the vehicle's natural frequency and the peak attenuation rate of the third period of the vertical vibration acceleration of the center of mass;

[0103] The basic credibility allocation module is used to introduce evidence variables, divide the uncertainty intervals of suspension stiffness, damping coefficient and tire stiffness into multiple sub-intervals as focal element intervals, and assign corresponding focal element values ​​to obtain the basic credibility allocation of the three uncertain parameters;

[0104] The probability cumulative distribution curve module is used to obtain the probability cumulative distribution curve based on the proposition that the natural frequency of the whole vehicle and the peak attenuation rate of the third period of the vertical vibration acceleration of the center of mass meet the design requirements, and use credibility and plausibility to represent the minimum and maximum probability of the proposition being true;

[0105] The module for establishing the evidence theory optimization model is used to establish the evidence theory optimization model. The likelihood of the proposition being greater than the set probability requirement value is used as the constraint function. The genetic algorithm is used for global optimization to optimize the suspension stiffness, damping coefficient and tire stiffness.

[0106] The data analysis module is used to analyze the probability cumulative distribution of the third period peak attenuation rate of the vehicle's natural frequency and center of mass vertical vibration acceleration under different uncertainties based on the evidence theory, and determine the maximum uncertainty interval that makes the vehicle vibration control stable.

[0107] For the convenience of description, the above devices are described in terms of functions and are divided into various units and modules. Of course, when implementing the present application, the functions of each unit and module can be implemented in the same or multiple software and / or hardware.

[0108] Those skilled in the art will appreciate that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by those skilled in the art in the art to which the present invention belongs. It should also be understood that terms such as those defined in general dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless specifically defined.

[0109] For the method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0110] It can be known from the description of the above implementation modes that those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application can be essentially or partly contributed to the prior art in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which can be a personal computer, a server or a network device, etc.) to execute the methods described in the various implementation modes of the present application or certain parts of the implementation modes.

[0111] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A vehicle model vibration control analysis and optimization method, characterized in that: include: S1. Establish the dynamic equation of the seven-degree-of-freedom vehicle model and solve the natural frequency of the vehicle and the peak attenuation rate of the third period of the vertical vibration acceleration of the center of mass; S2. Introduce evidence variables, divide the uncertainty intervals of suspension stiffness, damping coefficient and tire stiffness into multiple sub-intervals as focal element intervals, and assign corresponding focal element values ​​to obtain the basic credibility distribution of the three uncertain parameters; S3. Take the vehicle's natural frequency and the third period peak attenuation rate of the center-of-mass vertical vibration acceleration as the propositions that they meet the design requirements to obtain their probability cumulative distribution curves, and use credibility and plausibility to represent the minimum and maximum probabilities of the propositions being true; S4. Establish an evidence theory optimization model, take the proposition's plausibility greater than the set probability requirement value as the constraint function, use genetic algorithm global optimization, and optimize the suspension stiffness, damping coefficient and tire stiffness; S5. Based on the evidence theory, analyze the probability cumulative distribution of the third period peak attenuation rate of the vehicle's natural frequency and center of mass vertical vibration acceleration under different uncertainties of suspension stiffness, damping coefficient and tire stiffness, and determine the maximum uncertainty interval that makes the vehicle vibration control stable.

2. The method according to claim 1, characterized in that Step S1 includes the following steps: S101, performing force analysis on the whole vehicle model, and formulating equations according to Newton's second law: In the formula, K d1 , K d2 , K d3 , K d4 are the suspension stiffness of the right front, left front, right rear and left rear directions of the vehicle body, C d1 , C d2 , C d3 , C d4 is the suspension damping coefficient corresponding to the four directions, K t1 , K t2 , K t3 , K t4 are the four wheel stiffnesses, q1, q2, q3, q4 are the vertical displacements of the road surface, Z1, Z2, Z3, Z4 are the vertical displacements of the wheels, and Z d1 , Z d2 , Z d3 , Z d4 is the vertical displacement at the intersection of the vehicle body and the vertical axis of the suspension, Z b is the vertical displacement of the center of mass of the vehicle body, θ is the roll angle of the vehicle, is the vehicle pitch angle, m b is the sprung mass, m1, m2, m3, m4 are the tire masses, I x is the roll moment of inertia, I y is the pitch moment of inertia; The vertical displacement at the intersection of the body and the suspension vertical axis has the following relationship: Where a and b are the distances from the front and rear axles to the center of mass of the vehicle respectively; d is the wheelbase; S102, deriving the vibration differential equation of the system according to the formula in step S101: Where x is the generalized coordinate vector of the vehicle vibration system; M is the mass matrix of the vehicle vibration system; C is the suspension damping matrix; K is the stiffness matrix; f is the road excitation vector, is the vertical vibration acceleration of the center of mass; S103, according to the formula: K{φ} i =ω i 2 M{φ} i Calculate the natural frequency of the vehicle, where i = 1, 2, ..., 7, {φ} i is the generalized eigenvector, ω i 2 is the generalized eigenvalue, ω i is the natural frequency; S104, according to the formula: Calculate the peak attenuation rate of the i-th cycle of the vertical vibration acceleration of the center of mass, where: It represents the peak value of vertical vibration acceleration of the center of mass in the ith cycle.

3. The method according to claim 1, characterized in that: In step S2, evidence variables are introduced to process the suspension stiffness, damping coefficient and tire stiffness, and their variation range is evenly divided into a number of sub-intervals as focal element intervals of the corresponding evidence variables, and credibility is assigned to each focal element interval based on the probability of taking the middle focal element interval being greater than the probability of taking the edge focal element interval.

4. The method according to claim 1, characterized in that: In step S3, according to the function definition of credibility and plausibility: The probability cumulative distribution of the credibility and plausibility of the third-cycle peak attenuation rate of the vehicle's natural frequency and center-of-mass vibration acceleration is obtained. In the formula, m is called the basic probability assignment, also called the focal element, Bel(A) represents the sum of the basic credibility that fully supports proposition A, Pl(A) represents the sum of the basic credibility that fully or partially supports proposition A, and Bel(A) and Pl(A) are used to represent the minimum and maximum probabilities of the proposition being true.

5. The method according to claim 1, characterized in that In step S4, an evidence theory optimization model is established: min f(X, D) Where f(X, D) is the objective function; g i (X, D) is the constraint function; P f is the stability probability allowable value; X is the design variable, [X L , X U ] is the nominal value X value range, x1 represents the suspension stiffness K d , x2 represents the tire stiffness K t , x3 represents the suspension damping coefficient C d ; D is the interval parameter of each design variable, [D L , D U ] is the value interval of D, which represents the uncertainty of the parameter.

6. The method according to claim 1, characterized in that In step S5, the uncertainty of the suspension stiffness and the tire stiffness is taken into several groups of different values, and the probability cumulative distribution of the third period peak attenuation rate of the vehicle's natural frequency and the center of mass vertical vibration acceleration under different uncertainties is analyzed based on the evidence theory, and the maximum uncertainty interval that makes the vehicle vibration control stable is determined according to the analysis results.

7. The method according to claim 6, characterized in that The several groups of different uncertainty values ​​include: 10%, 15%, 20% and 30%.

8. The method according to claim 3, characterized in that The introduction of evidence variables to process the suspension stiffness, damping coefficient and tire stiffness, and evenly dividing their variation intervals into several sub-intervals as focal intervals of the corresponding evidence variables include: The uncertainty intervals of suspension stiffness, damping coefficient and tire stiffness are evenly divided into four equal parts as their corresponding focal element intervals.

9. A vehicle model vibration control analysis and optimization system, characterized in that: include: The dynamic equation building module is used to build the dynamic equation of the seven-degree-of-freedom vehicle model and solve the vehicle's natural frequency and the peak attenuation rate of the third period of the vertical vibration acceleration of the center of mass; The basic credibility allocation module is used to introduce evidence variables, divide the uncertainty intervals of suspension stiffness, damping coefficient and tire stiffness into multiple sub-intervals as focal element intervals, and assign corresponding focal element values ​​to obtain the basic credibility allocation of the three uncertain parameters; The probability cumulative distribution curve module is used to obtain the probability cumulative distribution curve based on the proposition that the natural frequency of the whole vehicle and the peak attenuation rate of the third period of the vertical vibration acceleration of the center of mass meet the design requirements, and use credibility and plausibility to represent the minimum and maximum probability of the proposition being true; The module for establishing the evidence theory optimization model is used to establish the evidence theory optimization model. The likelihood of the proposition being greater than the set probability requirement value is used as the constraint function. The genetic algorithm is used for global optimization to optimize the suspension stiffness, damping coefficient and tire stiffness. The data analysis module is used to analyze the probability cumulative distribution of the third period peak attenuation rate of the vehicle's natural frequency and center of mass vertical vibration acceleration under different uncertainties based on the evidence theory, and determine the maximum uncertainty interval that makes the vehicle vibration control stable.

Images