Suspension parameter optimization method and system

Abstract

The invention discloses a suspension parameter optimization method and system. The method comprises the following steps: determining characteristic parameters of the system and establishing a seven-degree-of-freedom vehicle mathematical model; calculating the inherent characteristics of the vehicle body suspension vibration system, wherein the inherent characteristics comprise an inherent frequency omega n and two performance evaluation indexes: a vehicle body vertical acceleration root-mean-square value # imgabs0 # and a vertical acceleration periodic attenuation rate Q (x); uncertainty theory optimization based on parameter interval is carried out on the suspension system; an original uncertain optimization problem is converted into a single-target deterministic optimization problem by adopting a method of optimizing target quantity conversion and converting an uncertain problem into a deterministic problem; optimizing the initial suspension parameters by using an interval uncertainty optimization theory; obtaining an optimization model of the interval uncertainty model of the suspension system by combining the interval theory optimization model, the characteristic parameters of the suspension system and the performance evaluation indexes; and carrying out optimization design on structural parameters of the suspension system based on the optimization model of the interval uncertainty model of the suspension system.

Description

Technical Field

[0001] The invention relates to the field of optimization design of automobile suspension structure parameters, and in particular to a suspension parameter optimization method and system. Background Art

[0002] The main role of vehicle vibration reduction is played by the vehicle suspension system composed of elastic elements and damping elements. The suspension is one of the most important systems in vehicle driving performance. The quality of suspension performance directly affects the smoothness (ride comfort) and driving safety (handling stability) of the car. Due to many problems in terms of cost and reliability, passive suspension systems are still widely used. The main function of passive suspension is to reduce the impact transmitted from the road surface to the vehicle body and attenuate the vibration of the load-bearing system caused by the road surface impact. In order to reduce the vibration of the suspension system and improve the smoothness of the car, on the one hand, the road quality can be improved and the source of vibration can be reduced; on the other hand, the vibration isolation characteristics of the suspension system to the uneven road surface input can be improved. Since there are many uncontrollable factors in the former, existing research is mostly focused on the study of suspension performance.

[0003] Most of the existing vehicle suspension structure optimization research is based on many assumptions and simplifications, that is, it is assumed that most of the key parameters (stiffness, damping, mass, etc.) in the vehicle suspension system analysis can be given precise values, so that it is treated as a deterministic system for mechanical performance analysis and solved using conventional deterministic optimization design methods. This method can simplify the design and calculation process to a certain extent. However, in actual engineering, due to the influence of manufacturing and measurement deviations and complex vehicle operating conditions, many key factors affecting suspension performance are often uncertain, such as damping ratio, suspension stiffness, etc. Among these factors, a small deviation in a certain parameter may not lead to a large fluctuation in the performance of the suspension system, but the coupling of multiple parameters may lead to a large deviation in the performance of the suspension system, making it difficult for the optimization design scheme obtained under the deterministic assumption to meet the actual suspension performance design indicators, resulting in a lack of reliability in the design. Summary of the invention

[0004] To achieve the above-mentioned purpose and other related purposes, the present invention discloses a suspension parameter optimization method, comprising:

[0005] S1. Clarify the characteristic parameters of the system and establish a mathematical model of the seven-degree-of-freedom vehicle;

[0006] S2. Calculate the inherent characteristics of the vehicle body suspension vibration system, including the natural frequency ω n And two performance evaluation indicators: body vertical acceleration root mean square value and vertical acceleration periodic decay rate Q(x);

[0007] S3, optimizing the suspension system based on uncertainty theory of parameter interval;

[0008] S4. Use the method of transforming the number of optimization targets and the method of transforming uncertainty problems into deterministic problems to transform the original uncertain optimization problem into a single-objective deterministic optimization problem;

[0009] S5. Optimize the initial suspension parameters using interval uncertainty optimization theory;

[0010] S6. Combine the interval theory optimization model with the characteristic parameters and performance evaluation indexes of the suspension system to select the front suspension stiffness k s1 , rear suspension stiffness k s2 , damping ratio ζ is the optimization design variable, tire stiffness k t is the interval parameter, and the optimization model of the interval uncertainty model of the suspension system is obtained;

[0011] S7. Based on the optimization model of the interval uncertainty model of the suspension system obtained in step S6, the structural parameters of the suspension system are optimized.

[0012] Furthermore, the step S1 specifically includes:

[0013] The parameters that affect the suspension performance are stiffness K and damping C. The seven-degree-of-freedom vehicle mathematical model takes into account the vertical, pitch, and roll degrees of freedom of the vehicle body mass and the four vertical degrees of freedom of the four wheel masses, characterizing the changes in the acceleration and velocity of the vehicle body center of mass and the changes in the angular acceleration and angular velocity of the vehicle body around its center of mass axis. Select:

[0014] Q = [q1, q2, q3, q4] T As input vector,

[0015] As output vector;

[0016] The motion differential equation for establishing the mathematical model of the seven-degree-of-freedom vehicle is:

[0017]

[0018] Where Q = [q1, q2, q3, q4] T is the input vector, Z, are displacement, velocity, and acceleration vectors, respectively; M is the mass matrix, C is the damping matrix, K is the stiffness matrix, and K t is the tire stiffness matrix.

[0019] Furthermore, the step S2 specifically includes:

[0020] Natural frequency ω n :

[0021] The natural frequency of the multi-degree-of-freedom system is obtained according to the undamped free vibration equation of the system, that is:

[0022]

[0023] Assume the solution of the equation is: Where A is the amplitude vector of the system during free vibration, then the main vibration mode equation is:

[0024]

[0025] Let its coefficient matrix The determinant of is zero, that is:

[0026]

[0027] It is called the characteristic equation, and solving it yields The n positive real roots greater than zero are called the system eigenvalues. After taking the square root of these eigenvalues, we get n ω n , that is, the n natural frequencies of the multi-degree-of-freedom system are obtained;

[0028] RMS value of vertical acceleration of vehicle body

[0029] The RMS value is calculated by squaring, summing, averaging, and then taking the square root, that is:

[0030]

[0031] Calculate the vertical acceleration values ​​corresponding to all values ​​of the interval parameter and substitute them into the formula to get the root mean square value of the vertical acceleration of the vehicle body:

[0032] Vertical acceleration period attenuation rate Q(x):

[0033] The vertical acceleration period attenuation rate Q(x), that is, the attenuation degree of the vertical acceleration peak value of each period relative to the peak value of the first period, is defined as follows:

[0034]

[0035] In the formula, is the peak acceleration of the ith cycle.

[0036] Furthermore, the step S3 specifically includes:

[0037] The formulation of the system uncertainty optimization problem based on interval analysis is as follows:

[0038]

[0039] X∈[X L ,X H ]

[0040]

[0041] Where f is the objective function including design variables and uncertain parameters; g i is the ith uncertain constraint function; k is the number of uncertain constraints; X is the n-dimensional design variable; U is the uncertainty vector; the superscripts L and H represent the upper and lower bounds of the interval; Represents the parameters U i The variation interval, lower bound and upper bound of the interval;

[0042] definition and The parameters U i The midpoint and radius of the interval U i The uncertainty level ε(U i )for:

[0043]

[0044] Further, the step S4 includes:

[0045]

[0046] X∈[X L ,X H ]

[0047]

[0048] Where α is the weighting coefficient, and its value range is [0, 1].

[0049] Furthermore, the step S5 comprises:

[0050] The front and rear suspension stiffness, tire stiffness and damping ratio are selected as design variables, the minimum root mean square value of vertical acceleration is selected as the optimization target, and the structural parameters of the suspension system are optimized with the vertical acceleration third period attenuation rate greater than a certain value and the natural frequency constraint within the specified range as the constraint conditions;

[0051] Design variables:

[0052] x=[k s1 ,k s2 ,ζ]

[0053] Where: k s1 ,k s2 are the front and rear suspension stiffness respectively; ζ is the damping ratio of the suspension;

[0054] Objective function:

[0055] The root mean square value of the vertical acceleration of the vehicle body center of mass is used as the interval objective function f(X) for ride comfort optimization:

[0056]

[0057] In the formula, X is the design variable; U is the uncertain parameter, which is used to characterize the parameter uncertainty; is the root mean square value of the vertical acceleration of the vehicle body center of mass;

[0058] The interval optimization method is used to transform the interval uncertain objective function into a certain objective function:

[0059]

[0060] In the formula, α is the weighting coefficient, and its value range is [0, 1]. In the interval uncertainty optimization method, the midpoint of the interval and the interval radius are considered equally important, so the weighting coefficient α in the formula is 0.5;

[0061] Constraints:

[0062] System frequency constraints:

[0063]

[0064] In the formula, f H and f L are the upper and lower constraints of the natural frequency respectively;

[0065] Vertical acceleration attenuation rate constraint for the third period:

[0066] Q(X,U)≥95%

[0067] Where X is the design variable and U is the uncertain parameter.

[0068] Further, the step S6 includes:

[0069]

[0070] stQ(X,U)≥95%

[0071] 19.15≤k s1 ≤23.41

[0072] 22.80≤k s2 ≤27.86

[0073] 162≤k t ≤198

[0074] 0.2≤ζ≤0.4

[0075] st1≤f≤1.5

[0076] Among them, k s1 , k s2, k t are the front and rear suspension and tire stiffnesses respectively, ζ is the damping ratio, f is the vertical frequency constraint, X is the design variable, and U is the interval parameter used to characterize the interval uncertainty.

[0077] Further, the step S7 includes:

[0078] S71: Input the initial design parameters of the suspension system: front suspension stiffness k s1 , rear suspension stiffness k s2 , tire stiffness k t , damping ratio ζ, calculate the root mean square value of the vertical acceleration of the suspension system and the initial value of the vertical acceleration attenuation rate of the third period;

[0079] S72: According to the formula Calculate the attenuation degree of the peak value of vertical acceleration of each cycle of the suspension system relative to the peak value of the first cycle, where is the peak acceleration value of the ith cycle, Q(x) is the vertical acceleration cycle attenuation rate;

[0080] S73: changing the value of the design parameter within the optimization range of the design parameter;

[0081] S74: Substituting the iterated structural parameters into step S72, calculating the vertical acceleration root mean square value of the vertical acceleration of the suspension system after iteration at a third period attenuation rate higher than 95%;

[0082] S75: If the root mean square value of the vertical acceleration of the suspension system reaches a minimum value after a certain number of iterations and Q(X,U)≥95% is guaranteed, the iteration is stopped and the corresponding structural parameter value is output.

[0083] In another aspect, the present invention provides a suspension parameter optimization system, comprising:

[0084] The freedom model building module is used to clarify the characteristic parameters of the system and establish a seven-degree-of-freedom vehicle mathematical model;

[0085] The inherent characteristics calculation module is used to calculate the inherent characteristics of the body suspension vibration system, including the natural frequency ω n And two performance evaluation indicators: body vertical acceleration root mean square value and

[0086] and vertical acceleration periodic decay rate Q(x);

[0087] Theoretical optimization module, used to optimize the suspension system based on uncertainty theory of parameter interval;

[0088] A problem conversion module is used to convert the original uncertain optimization problem into a single-objective deterministic optimization problem by using the method of converting the number of optimization targets and converting the uncertain problem into a deterministic problem;

[0089] Parameter optimization module, used to optimize the initial suspension parameters using interval uncertainty optimization theory;

[0090] The optimization model acquisition module is used to combine the interval theory optimization model with the characteristic parameters and performance evaluation indicators of the suspension system to select the front suspension stiffness k s1 , rear suspension stiffness k s2 , damping ratio ζ is the optimization design variable, tire stiffness k t is the interval parameter, and the optimization model of the interval uncertainty model of the suspension system is obtained;

[0091] The optimization design module is used to optimize the structural parameters of the suspension system based on the optimization model of the suspension system interval uncertainty model obtained by the optimization model acquisition module.

[0092] By adopting the above technical solution, the present invention has the following technical effects:

[0093] 1) The present invention constructs an uncertainty optimization model for the smoothness of automobile suspension through the interval analysis method, ensuring the reliability and stability of the vehicle smoothness design. By using the interval method, it is only necessary to obtain the upper and lower boundaries of all uncertainty parameters affecting the smoothness of the vehicle instead of their precise probability distribution, which better avoids the dependence of traditional probability methods on large sample sizes. On the basis of parameter uncertainty, combined with interval analysis theory and optimization technology, genetic algorithms are applied to combine uncertain parameters with the suspension model, while considering the influence of the entire body system including tire characteristics on the suspension performance, matching the suspension design with tire characteristics, and optimizing the relevant parameters of the suspension.

[0094] 2) The parameter optimization method of the present invention not only optimizes the design parameters of the suspension system, but also takes into account the uncertainty of some parameters under actual working conditions, making the uncertainty modeling and optimization design of the automobile suspension system more convenient and efficient. To a certain extent, it can avoid the calculation error caused by the probability distribution assumption to the uncertainty optimization, so that the optimized solution obtained by the design can better meet the actual requirements of the project, and provides a new method and idea for the analysis and optimization design of similar suspension structures in the future.

[0095] 3) In the analysis of suspension system stability based on interval analysis theory, this method not only improves the system smoothness by optimizing the stiffness and damping ratio parameters, but also allows the uncertainty parameter value interval to fluctuate around the optimized design value, and the suspension system stability can still meet the design requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0096] 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:

[0097] Figure 1 It is a whole vehicle model of the seven-degree-of-freedom system;

[0098] Figure 2 is the time domain diagram of vertical acceleration after damping ratio sampling;

[0099] Figure 3 is the time domain diagram of vertical acceleration after sampling of the front suspension stiffness;

[0100] Figure 4 is the time domain diagram of vertical acceleration after sampling of rear suspension stiffness;

[0101] Figure 5 It is the comparison of vertical acceleration results before and after optimization;

[0102] Figure 6 is the result diagram of interval optimization of suspension system under different uncertainty levels;

[0103] Figure 7 It is a flow chart of a suspension parameter optimization method of the present invention. DETAILED DESCRIPTION

[0104] 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.

[0105] Reference Figure 1 , an embodiment of the present invention provides a suspension parameter optimization method, comprising the following steps:

[0106] S1. Clarify the characteristic parameters of the system and establish a mathematical model of the seven-degree-of-freedom vehicle.

[0107] Specifically, we must first clarify the relevant parameters of the vehicle system: the vehicle suspension system consists of suspension springs, shock absorbers, guide mechanisms, etc. Its main function is to reduce the impact transmitted from the road surface to the vehicle body and attenuate the vibration of the load-bearing system caused by the road surface impact. The main parameters that affect the suspension performance are stiffness K and damping C. The specific vehicle body mass parameters and suspension parameters are shown in Tables 1 and 2.

[0108] Table 1 Body mass parameters

[0109]

[0110] Table 2 Suspension parameters

[0111]

[0112] Figure 1 The structural diagram of the whole vehicle model of the seven-degree-of-freedom system of the automobile is given in the figure. According to the diagram, the mathematical model of the whole vehicle with seven degrees of freedom is established. Assuming that the vehicle body is a rigid body, when the vehicle moves in a uniform linear motion on the horizontal plane, the seven-degree-of-freedom space model mainly considers the three degrees of freedom of vertical, pitch and roll of the vehicle body mass and the four vertical degrees of freedom of the four wheel masses, which can characterize the changes of the acceleration and velocity of the center of mass of the vehicle body and the changes of the angular acceleration and angular velocity of the vehicle body around its center of mass axis, and can more comprehensively describe the vertical, pitch, roll and other motion conditions of the vehicle.

[0113] According to the seven-degree-of-freedom vehicle vibration model, select:

[0114] Q = [q1, q2, q3, q4] T As input vector,

[0115] as the output vector.

[0116] Apply Newton's law to the four wheels and the body respectively, and establish the motion differential equation of the seven-degree-of-freedom vehicle vibration model, which is summarized as follows:

[0117]

[0118] Where: Q = [q1, q2, q3, q4] T is the input vector, Z, are displacement, velocity, and acceleration vectors, respectively; M is the mass matrix, C is the damping matrix, K is the stiffness matrix, and K t is the tire stiffness matrix.

[0119] S2. Calculate the inherent characteristics of the vehicle body suspension vibration system, including the natural frequency ω n And two performance evaluation indicators: body vertical acceleration root mean square value And the vertical acceleration periodic attenuation rate Q(x).

[0120] 1) Natural frequency ω n Calculation method:

[0121] The natural frequency of the multi-degree-of-freedom system is obtained according to the undamped free vibration equation of the system, that is:

[0122]

[0123] Assume the solution of the equation is: Where A is the amplitude vector of the system during free vibration, then the main vibration mode equation is:

[0124]

[0125] Let its coefficient matrix The determinant of is zero, that is:

[0126]

[0127] It is called the characteristic equation, and its solution is The n positive real roots greater than zero are called the system eigenvalues. After taking the square root of these eigenvalues, we get n ω n , that is, the n natural frequencies of the multi-degree-of-freedom system are obtained;

[0128] 2) RMS value of vertical acceleration of vehicle body The calculation method of:

[0129] The RMS value is calculated by squaring, summing, averaging, and then taking the square root, that is:

[0130]

[0131] Calculate the vertical acceleration values ​​corresponding to all values ​​of the interval parameter and substitute them into the formula to get the root mean square value of the vertical acceleration of the vehicle body:

[0132] 3) Calculation method of vertical acceleration period attenuation rate Q(x):

[0133] The vertical acceleration period attenuation rate Q(x), that is, the attenuation degree of the vertical acceleration peak value of each period relative to the peak value of the first period, is defined as follows:

[0134]

[0135] In the formula, is the peak acceleration of the ith cycle.

[0136] The calculation results of the inherent characteristics of the vehicle suspension system are given in Table 3 below:

[0137] Table 3 Calculation results of the natural frequencies of the suspension and the third period attenuation rate of acceleration

[0138]

[0139] Analysis of the natural frequency and vibration response of the suspension under initial parameters based on the Monte Carlo interval sampling method:

[0140] The suspension stiffness, tire stiffness, and damping ratio are taken as interval parameters, and the Monte Carlo simulation method is used to perform 20,000 random samplings. Finally, the simulation results of the natural frequency, vertical acceleration, and the third period attenuation rate of the vehicle suspension system based on the interval uncertainty parameter model are obtained as follows:

[0141] The front and rear suspension stiffness are taken as interval variables, and the tire stiffness is taken as a certain value. The simulation results of the vehicle body natural frequency are shown in Table 4:

[0142] Table 4 Sampling results of front and rear suspension stiffness intervals

[0143] parameter unit Interval value Uncertainty Level f1 Hz [1.22，1.34] 4.36％ f2 Hz [1.53，1.67] 4.32％ f3 Hz [1.89，2.06] 4.32％ f4 Hz [15.19，15.52] 1.08％ f5 Hz [15.19，15.51] 1.03％ f6 Hz [15.18，15.51] 1.05％ f7 Hz [15.18，15.52] 1.10％

[0144] Taking tire stiffness as interval variable and front and rear suspension stiffness as fixed value, the simulation results of vehicle body natural frequency are shown in Table 5:

[0145] Table 5 Sampling results of tire stiffness interval values

[0146] parameter unit Interval value Uncertainty Level f1 Hz [1.27，1.29] 0.61％ f2 Hz [1.58，1.61] 0.57％ f3 Hz [1.96，1.99] 0.59％ f4 Hz [14.73，16.08] 4.37％ f5 Hz [14.72，16.07] 4.38％ f6 Hz [14.57，15.93] 4.46％ f7 Hz [14.56，15.92] 4.47％

[0147] Taking the damping ratio as the interval variable, the tire stiffness and suspension stiffness take certain values, and the simulation results of the vertical acceleration of the vehicle body are obtained as follows: Figure 2 As shown in the figure, the front and rear suspension stiffness are taken as interval variables, the tire stiffness and damping ratio are taken as certain values, and the simulation results of the vertical acceleration of the vehicle body are obtained as shown in the figure. Figure 3 , Figure 4 shown.

[0148] Through the above analysis of the calculation results and simulation sampling results, under the values ​​of the initial stiffness and damping ratio parameters of the vehicle suspension system, the vertical natural frequency and vertical acceleration attenuation rate of the system need to be optimized and improved, because for a car, the vertical acceleration largely determines the quality of the vehicle's ride smoothness and the comfort of the occupants.

[0149] S3. Optimize the suspension system based on uncertainty theory of parameter interval.

[0150] Specifically, the system uncertainty optimization problem based on interval analysis is expressed as follows:

[0151]

[0152] X∈[X L ,X H ]

[0153]

[0154] Where f is the objective function including design variables and uncertain parameters; g iis the ith uncertain constraint function; k is the number of uncertain constraints; X is the n-dimensional design variable; U is the uncertainty vector; the superscripts L and H represent the upper and lower bounds of the interval; Represents the parameters U i The variation interval, lower bound and upper bound of the interval;

[0155] definition and The parameters U i The midpoint and radius of the interval U i The uncertainty level ε(U i )for,

[0156]

[0157] S4. The original uncertain optimization problem is transformed into a single-objective deterministic optimization problem by using the method of transforming the number of optimization objectives and transforming uncertain problems into deterministic problems.

[0158] Specifically, they include:

[0159]

[0160] X∈[X L ,X H ]

[0161]

[0162] Where α is the weighting coefficient, and its value range is [0, 1].

[0163] S5. Use interval uncertainty optimization theory to optimize the initial suspension parameters.

[0164] Specifically, the front and rear suspension stiffness, tire stiffness and damping ratio are selected as design variables, the minimum root mean square value of vertical acceleration is selected as the optimization target, and the vertical acceleration third period attenuation rate is greater than a certain value and the natural frequency is constrained within a specified range as constraints to optimize the suspension system structural parameters;

[0165] Design variables:

[0166] x=[k s1 ,k s2 ,ζ]

[0167] Where: k s1 ,k s2 are the front and rear suspension stiffness respectively; ζ is the damping ratio of the suspension;

[0168] Objective function:

[0169] The root mean square value of the vertical acceleration of the vehicle body center of mass is used as the interval objective function f(X) for ride comfort optimization:

[0170]

[0171] In the formula, X is the design variable; U is the uncertain parameter, which is used to characterize the parameter uncertainty; is the root mean square value of the vertical acceleration of the vehicle body center of mass;

[0172] The interval optimization method is used to transform the interval uncertain objective function into a certain objective function:

[0173]

[0174] In the formula, α is the weighting coefficient, and its value range is [0, 1]. In the interval uncertainty optimization method, the midpoint of the interval and the interval radius are considered equally important, so the weighting coefficient α in the formula is 0.5;

[0175] Constraints:

[0176] System frequency constraints:

[0177]

[0178] In the formula, f H and f L are the upper and lower constraints of the natural frequency respectively;

[0179] Vertical acceleration attenuation rate constraint for the third period:

[0180] Q(X,U)≥95%

[0181] Where X is the design variable and U is the uncertain parameter.

[0182] S6. Combine the interval theory optimization model with the characteristic parameters and performance evaluation indexes of the suspension system to select the front suspension stiffness k s1 , rear suspension stiffness k s2 , damping ratio ζ is the optimization design variable, tire stiffness k t is the interval parameter, and the optimization model of the interval uncertainty model of the suspension system is obtained.

[0183] Specifically, they include:

[0184]

[0185] stQ(X,U)≥95%

[0186] 19.15≤k s1 ≤23.41

[0187] 22.80≤k s2 ≤27.86

[0188] 162≤kt ≤198

[0189] 0.2≤ζ≤0.4

[0190] st1≤f≤1.5

[0191] Among them, k s1 , k s2 , k t are the front and rear suspension and tire stiffnesses respectively, ζ is the damping ratio, f is the vertical frequency constraint, X is the design variable, and U is the interval parameter used to characterize the interval uncertainty.

[0192] S7. Based on the optimization model of the interval uncertainty model of the suspension system obtained in step S6, the structural parameters of the suspension system are optimized.

[0193] Specifically, step S7 includes:

[0194] S71: Input the initial design parameters of the suspension system: front suspension stiffness k s1 , rear suspension stiffness k s2 , tire stiffness k t , damping ratio ζ, calculate the root mean square value of the vertical acceleration of the suspension system and the initial value of the vertical acceleration attenuation rate of the third period;

[0195] S72: According to the formula Calculate the attenuation degree of the peak value of vertical acceleration of each cycle of the suspension system relative to the peak value of the first cycle, where is the peak acceleration value of the ith cycle, Q(x) is the vertical acceleration cycle attenuation rate;

[0196] S73: changing the value of the design parameter within the optimization range of the design parameter;

[0197] S74: Substituting the iterated structural parameters into step S72, calculating the vertical acceleration root mean square value of the vertical acceleration of the suspension system after iteration at a third period attenuation rate higher than 95%;

[0198] S75: If the root mean square value of the vertical acceleration of the suspension system reaches a minimum value after a certain number of iterations and Q(X,U)≥95% is guaranteed, the iteration is stopped and the corresponding structural parameter value is output.

[0199] In this embodiment, the initial value k of a certain automobile suspension design parameter is s1 =21283N / m, k s2 =25328N / m, ζ=0.2 are substituted into the optimization model, and the genetic algorithm is used for iterative optimization to complete the parameter iterative design of the suspension system.

[0200] When the vertical acceleration attenuation rate of the suspension system in the third period is higher than 95% and the vertical acceleration root mean square value reaches the minimum, the calculation is stopped. The optimization results are shown in Table 7. The optimized parameters are updated, and the natural frequency and the vertical acceleration attenuation rate in the third period are calculated. The vertical acceleration comparison results before and after optimization are shown in Figure 5 shown.

[0201] Table 7 Optimized front and rear suspension stiffness, damping ratio and objective function value

[0202]

[0203] In the stability analysis of the suspension system based on interval analysis theory, for the automobile suspension system studied in this study, different parameter uncertainty levels in the range of 1 to 6% are considered, and the interval optimization results are as follows: Figure 6 shown.

[0204] An embodiment of the present invention further provides a system, comprising:

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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 suspension parameter optimization method, characterized in that: include: S1. Clarify the characteristic parameters of the system and establish a mathematical model of the seven-degree-of-freedom vehicle; S2. Calculate the inherent characteristics of the vehicle body suspension vibration system, including the natural frequency ω n And two performance evaluation indicators: body vertical acceleration root mean square value and vertical acceleration periodic decay rate Q(x); S3, optimizing the suspension system based on uncertainty theory of parameter interval; S4. Use the method of transforming the number of optimization targets and the method of transforming uncertainty problems into deterministic problems to transform the original uncertain optimization problem into a single-objective deterministic optimization problem; S5. Optimize the initial suspension parameters using interval uncertainty optimization theory; S6. Combine the interval theory optimization model with the characteristic parameters and performance evaluation indexes of the suspension system to select the front suspension stiffness k s1 , rear suspension stiffness k s2 , damping ratio ζ is the optimization design variable, tire stiffness k t is the interval parameter, and the optimization model of the interval uncertainty model of the suspension system is obtained; S7. Based on the optimization model of the interval uncertainty model of the suspension system obtained in step S6, the structural parameters of the suspension system are optimized.

2. The method according to claim 1, characterized in that: The step S1 specifically includes: The parameters that affect the suspension performance are stiffness K and damping C. The seven-degree-of-freedom vehicle mathematical model takes into account the vertical, pitch, and roll degrees of freedom of the vehicle body mass and the four vertical degrees of freedom of the four wheel masses, characterizing the changes in the acceleration and velocity of the vehicle body center of mass and the changes in the angular acceleration and angular velocity of the vehicle body around its center of mass axis. Select: Q = [q1, q2, q3, q4] T As input vector, As output vector; The motion differential equation for establishing the mathematical model of the seven-degree-of-freedom vehicle is: Where Q = [q1, q2, q3, q4] T is the input vector, Z, are displacement, velocity, and acceleration vectors, respectively; M is the mass matrix; C is the damping matrix; K is the stiffness matrix; and K t is the tire stiffness matrix.

3. The method according to claim 2, characterized in that The step S2 specifically includes: Natural frequency ω n : The natural frequency of the multi-degree-of-freedom system is obtained according to the undamped free vibration equation of the system, that is: Assume the solution of the equation is: Where A is the amplitude vector of the system during free vibration, then the main vibration mode equation is: Let its coefficient matrix The determinant of is zero, that is: It is called the characteristic equation, and its solution is The n positive real roots greater than zero are called the system eigenvalues. After taking the square root of these eigenvalues, we get n ω n , that is, the n natural frequencies of the multi-degree-of-freedom system are obtained; RMS value of vertical acceleration of vehicle body The RMS value is calculated by squaring, summing, averaging, and then taking the square root, that is: Calculate the vertical acceleration values ​​corresponding to all values ​​of the interval parameter and substitute them into the formula to get the root mean square value of the vertical acceleration of the vehicle body: Vertical acceleration period attenuation rate Q(x): The vertical acceleration period attenuation rate Q(x), that is, the attenuation degree of the vertical acceleration peak value of each period relative to the peak value of the first period, is defined as follows: In the formula, is the peak acceleration of the ith cycle.

4. The method according to claim 1, characterized in that The step S3 specifically includes: The formulation of the system uncertainty optimization problem based on interval analysis is as follows: X∈[X L ,X H ] Where f is the objective function including design variables and uncertain parameters; g i is the ith uncertain constraint function; k is the number of uncertain constraints; X is the n-dimensional design variable; U is the uncertainty vector; the superscripts L and H represent the upper and lower bounds of the interval; Represents the parameters U i The variation interval, lower bound and upper bound of the interval; definition and The parameters U i The midpoint and radius of the interval U i The uncertainty level ε(U i )for:

5. The method according to claim 4, characterized in that The step S4 comprises: X∈[X L ,X H ] Where α is the weighting coefficient, and its value range is [0, 1].

6. The method according to claim 1, characterized in that The step S5 comprises: The front and rear suspension stiffness, tire stiffness and damping ratio are selected as design variables, the minimum root mean square value of vertical acceleration is selected as the optimization target, and the structural parameters of the suspension system are optimized with the vertical acceleration third period attenuation rate greater than a certain value and the natural frequency constraint within the specified range as the constraint conditions; Design variables: x=[k s1 ,k s2 ,ζ] Where: k s1 ,k s2 are the front and rear suspension stiffness respectively; ζ is the damping ratio of the suspension; Objective function: The root mean square value of the vertical acceleration of the vehicle body center of mass is used as the interval objective function f(X) for ride comfort optimization: In the formula, X is the design variable; U is the uncertain parameter, which is used to characterize the parameter uncertainty; is the root mean square value of the vertical acceleration of the vehicle body center of mass; The interval optimization method is used to transform the interval uncertain objective function into a certain objective function: In the formula, α is the weighting coefficient, and its value range is [0, 1]. In the interval uncertainty optimization method, the midpoint of the interval and the interval radius are considered equally important, so the weighting coefficient α in the formula is 0.5; Constraints: System frequency constraints: In the formula, f H and f L are the upper and lower constraints of the natural frequency respectively; Vertical acceleration attenuation rate constraint for the third period: Q(X,U)≥95% Where X is the design variable and U is the uncertain parameter.

7. The method according to claim 1, characterized in that The step S6 comprises: stQ(X,U)≥95% 19.15≤k s1 ≤23.41 22.80≤k s2 ≤27.86 162≤k t ≤198 0.2≤ζ≤0.4 st1≤f≤1.5 Among them, k s1 , k s2 , k s are the front and rear suspension and tire stiffnesses respectively, ζ is the damping ratio, f is the vertical frequency constraint, X is the design variable, and U is the interval parameter used to characterize the interval uncertainty.

8. The method according to claim 7, characterized in that The step S7 comprises: S71: Input the initial design parameters of the suspension system: front suspension stiffness k s1 , rear suspension stiffness k s2 , tire stiffness k t , damping ratio ζ, calculate the root mean square value of the vertical acceleration of the suspension system and the initial value of the vertical acceleration attenuation rate of the third period; S72: According to the formula Calculate the attenuation degree of the peak value of vertical acceleration of each cycle of the suspension system relative to the peak value of the first cycle, where is the peak acceleration value of the ith cycle, Q(x) is the vertical acceleration cycle attenuation rate; S73: changing the value of the design parameter within the optimization range of the design parameter; S74: Substituting the iterated structural parameters into step S72, calculating the vertical acceleration root mean square value of the vertical acceleration of the suspension system after iteration at a third period attenuation rate higher than 95%; S75: If the root mean square value of the vertical acceleration of the suspension system reaches a minimum value after a certain number of iterations and Q(X,U)≥95% is guaranteed, the iteration is stopped and the corresponding structural parameter value is output.

9. A suspension parameter optimization system, characterized in that: include: The freedom model building module is used to clarify the characteristic parameters of the system and establish a seven-degree-of-freedom vehicle mathematical model; The inherent characteristics calculation module is used to calculate the inherent characteristics of the body suspension vibration system, including the natural frequency ω n And two performance evaluation indicators: body vertical acceleration root mean square value and vertical acceleration periodic decay rate Q(x); Theoretical optimization module, used to optimize the suspension system based on uncertainty theory of parameter interval; A problem conversion module is used to convert the original uncertain optimization problem into a single-objective deterministic optimization problem by using the method of converting the number of optimization targets and converting the uncertain problem into a deterministic problem; Parameter optimization module, used to optimize the initial suspension parameters using interval uncertainty optimization theory; The optimization model acquisition module is used to combine the interval theory optimization model with the characteristic parameters and performance evaluation indicators of the suspension system to select the front suspension stiffness k s1 , rear suspension stiffness k s2 , damping ratio ζ is the optimization design variable, tire stiffness k t is the interval parameter, and the optimization model of the interval uncertainty model of the suspension system is obtained; The optimization design module is used to optimize the structural parameters of the suspension system based on the optimization model of the suspension system interval uncertainty model obtained by the optimization model acquisition module.

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