CN108984901A - A kind of automobile body crash-worthiness optimization method - Google Patents
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Abstract
The present invention relates to a kind of automobile body crash-worthiness optimization methods, belong to vehicle safety optimisation technique field.The structure of multiple specified parts or material parameter are as optimized variable when including: the multiple target objects for selecting vehicle crash-worthiness to optimize and vehicle body being selected to collide for automobile body crash-worthiness optimization method, wherein the structure or material parameter of the multiple specified parts are uncertain parameter；Vehicle body crash-worthiness Model for Multi-Objective Optimization is established using multidimensional parallelepiped interval model according to selected optimization aim object and optimized variable；Optimization is carried out to Model for Multi-Objective Optimization using miniature section multi-objective genetic algorithm, optimal solution set is obtained, an optimal solution is selected from optimal solution set, and using the optimal solution selected as optimal value of the parameter.The present invention effectively increases the precision and efficiency of vehicle body crash-worthiness optimization, and while improving automobile product safety, achievees the purpose that reduce its development cost.
Description
Technical Field
The invention relates to the technical field of automobile safety optimization, in particular to a method for optimizing the crashworthiness of a vehicle body.
Background
Crash resistance is a key performance which must be considered in the optimization design of an automobile body, and the impact resistance of the automobile body is generally influenced by components for deformation energy absorption in the collision of the automobile, such as a front longitudinal beam, an automobile front outer cover and the like, and the energy absorption characteristics and the deformation mode of the components determine the force, displacement and acceleration of the automobile in the collision process, thereby playing a key role in protecting automobile passengers.
Most of the existing automobile body crashworthiness multi-objective optimization technologies require parameter determination of optimization variables, so that for uncertain parameters of sample information which is difficult to obtain, high cost needs to be paid for obtaining a large number of samples, and the practicability is poor. Moreover, in the actual production process of the automobile, even if the parameters are determined, errors still exist due to processing, manufacturing and the like, so that the result is deviated, and the safety of the automobile cannot meet the expected requirement.
Disclosure of Invention
In view of the above analysis, the embodiment of the present invention aims to provide a vehicle body crashworthiness optimization method, which is used to solve the problems of high cost, poor practicability and unexpected safety optimization caused by the requirement of parameter determination of optimization variables in the prior art.
According to one aspect of the invention, a vehicle body crashworthiness optimization method is provided, comprising the following steps:
selecting a plurality of target objects for optimizing the crashworthiness of the vehicle and selecting the structure or material parameters of a plurality of specified components during vehicle body collision as optimization variables, wherein the structure or material parameters of the specified components are uncertain parameters;
according to the selected optimization target object and the selected optimization variable, a multi-dimensional parallelepiped interval model is utilized to establish a vehicle body crashworthiness multi-target optimization model;
carrying out optimization solution on the multi-target optimization model by adopting a micro interval multi-target genetic algorithm to obtain an optimal solution set;
and selecting an optimal solution from the optimal solution set, and taking the selected optimal solution as an optimal parameter value.
The beneficial effects of the above technical scheme are as follows: the vehicle body crashworthiness optimization method provided by the embodiment of the invention considers the uncertainty of parameters, utilizes the multidimensional parallelepiped interval model to establish the vehicle body crashworthiness multi-target optimization model according to the selected optimization target object and the optimization variable, can accurately describe the uncertainty distribution and the mutual relation of the attributes and the like of the specified parts of the vehicle body, and is applied to the solution of the efficient micro interval multi-target genetic multi-target optimization model.
In the invention, the technical schemes can be combined with each other to realize more preferable combination schemes. Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
Drawings
The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention, wherein like reference numerals are used to designate like parts throughout.
FIG. 1 is a schematic flow chart of a vehicle body crashworthiness optimization method of one embodiment of the present invention;
FIG. 2 is a simplified model of a frontal collision of a vehicle body in accordance with an embodiment of the present invention;
FIG. 3 is a three-dimensional model view of the primary energy-absorbing component of an automotive body front impact in accordance with one embodiment of the present invention;
FIG. 4a is a schematic diagram of a three-dimensional model of an automobile body before a frontal collision according to an embodiment of the invention;
FIG. 4b is a schematic representation of a three-dimensional model of an automobile body after a frontal collision according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of an optimal solution set obtained in one embodiment of the present invention.
Reference numerals:
1. fixing the rigid barrier; 2. a ground surface; 3. simplifying the model of the automobile; the automobile was driven at a speed of 56 km/h.
Detailed Description
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate preferred embodiments of the invention and together with the description, serve to explain the principles of the invention and not to limit the scope of the invention.
Most of the prior automotive body crashworthiness multi-objective optimization does not consider the uncertainty of parameters and the correlation thereof, but the uncertainty of parameters such as thickness, materials and the like is caused by the inevitable errors in the aspects of processing, manufacturing and the like in the actual production process of an automobile, and the parameters can be correlated. Uncertainty and correlation of these parameters can lead to deviation of optimized crashworthiness, and the safety of the automobile can not meet the expected requirements. Aiming at the problem, the invention provides a vehicle body crashworthiness optimization method considering uncertain parameters.
According to the vehicle body crashworthiness optimization method, uncertainty and correlation of structural or material parameters of key parts for deformation energy absorption of an automobile in collision are considered, the uncertainty parameters and the correlation of the uncertainty parameters are described by adopting a multi-dimensional parallelepiped interval model, and the correlation is processed through affine coordinate transformation. Meanwhile, establishing an approximate model based on the radial basis function; and solving the multi-target optimization problem by adopting a micro interval multi-target genetic algorithm to obtain an optimal solution set. And finally, selecting the optimal parameter value from the optimal solution set according to preference information or the current ideal solution given by the decision maker. Thus, by taking into account the uncertainty and correlation of structural or material parameters in the manufacture of automobiles, the accuracy and efficiency of vehicle body crashworthiness optimization is improved.
Fig. 1 is a schematic flow chart of a vehicle body crashworthiness optimization method according to an embodiment of the present invention, and referring to fig. 1, the vehicle body crashworthiness optimization method according to the embodiment includes the following steps:
selecting a plurality of target objects for optimizing the crashworthiness of the vehicle and selecting the structure or material parameters of a plurality of specified components during vehicle body collision as optimization variables, wherein the structure or material parameters of the specified components are uncertain parameters;
according to the selected optimization target object and the selected optimization variable, a multi-dimensional parallelepiped interval model is utilized to establish a vehicle body crashworthiness multi-target optimization model;
carrying out optimization solution on the multi-target optimization model by adopting a micro interval multi-target genetic algorithm to obtain an optimal solution set;
and selecting an optimal solution from the optimal solution set, and taking the selected optimal solution as an optimal parameter value.
Compared with the prior art, the vehicle body crashworthiness optimization method provided by the embodiment selects a plurality of target objects for vehicle crashworthiness optimization and selects uncertain parameters with unknown values of a plurality of designated parts during vehicle body collision as optimization variables. Therefore, the automobile body crashworthiness multi-target optimization of the embodiment of the invention considers the reality of parameter uncertainty, and aims at the uncertainty of parameters, a multi-dimensional parallelepiped interval model is utilized to establish an automobile body crashworthiness multi-target optimization model according to a selected optimization target object and an optimization variable, a micro interval multi-target genetic algorithm is adopted to carry out optimization solution on the multi-target optimization model to obtain an optimal solution set, an optimal solution is selected from the optimal solution set, and the selected optimal solution is used as an optimal parameter value. Therefore, on one hand, for uncertain parameters of which the sample information is difficult to obtain, the high cost brought by obtaining a large number of samples is reduced, and the method has better practicability. On the other hand, the multi-objective optimization of the collision resistance of the automobile body with uncertain considered parameters provides a reliable optimization scheme and guidance for the safety design of the automobile body, reduces the development cost and has good application prospect.
In one embodiment of the present invention, selecting a plurality of target objects for vehicle crashworthiness optimization and selecting structural or material parameters of a plurality of designated parts at the time of vehicle body collision as optimization variables includes:
the method comprises the steps of selecting an evaluation index of the vehicle body quality and the vehicle body crashworthiness, namely the displacement of a pedal of an automobile from a fixed point at the bottom of a main driver seat as an optimization target object, selecting an outer cover of the automobile, an inner cover of the automobile, a bumper, a left protective cover, a left front longitudinal beam, a left front lower support and a left frame arm of the automobile as specified components, using thickness parameters of the specified components as optimization variables, and obtaining unknown values of the thickness parameters of the specified components.
With reference to fig. 2 and 3, the present embodiment is directed to an optimized design of crashworthiness in the event of a frontal collision of a vehicle body. The simplified molding model of the front collision of the automobile body is as follows: the car 3 is placed on the ground 2 and hits the rigid barrier 1 at a speed of 56km/h (in the direction of the arrow in fig. 2). And the main deformation energy-absorbing part in the front collision of the automobile body is shown as a three-dimensional model of the main energy-absorbing part in the front collision of the automobile body in figure 3. These energy absorbing components are: an outer vehicle cover 301, an inner vehicle cover 302, a bumper 303, a left cowl 304, a left front side member 305, a left front lower bracket 306, and a left frame arm 307. In the present embodiment, these components are selected as designated components, and the structural parameters of these designated components are used as optimization variables, i.e., in the present embodiment, the thickness parameters are used as optimization variables.
FIG. 4a is a three-dimensional model of an automobile body before frontal collision according to an embodiment of the present invention, and FIG. 4b is a three-dimensional model of an automobile body after frontal collision according to an embodiment of the present invention; comparing fig. 4a and 4b, it can be seen that the vehicle body is deformed significantly and the main deformation-absorbing part is severely damaged.
In order to optimize the collision resistance of the automobile and realize the light weight of the automobile body structure, the embodiment provides the multi-objective optimization of the collision resistance of the automobile body, the meaning of the multi-objective means that the number of the optimized objective objects is multiple, and the objective objects can be one or more evaluation indexes of the collision resistance of the automobile body and the quality of the automobile body.
In one embodiment of the present invention, an evaluation index of the vehicle body mass M and the vehicle body crashworthiness, specifically, a displacement In of a vehicle pedal from a stationary point at the bottom of a main driver seat is selected as an optimization target object. That is, two optimization objectives are selected in one embodiment.
After the optimization target object and the optimization variables are selected and determined, an automobile body crashworthiness multi-target optimization model is established, and an uncertain domain gamma of an uncertain parameter U in the crashworthiness optimization problem is described by adopting a multi-dimensional parallelepiped interval model.
Determining an edge interval corresponding to uncertain parameters in the optimized variables and a correlation coefficient between the uncertain parameters; constructing a multi-dimensional parallelepiped interval model according to the edge interval and the correlation coefficient; according to the optimized target object C, the optimized target object M, the optimized variable X and the multi-dimensional parallelepiped interval model, establishing the following vehicle body crashworthiness multi-target optimization model:
XL≤X≤XR,U∈Γ(UI,ρ).
wherein, gi(X, U), i 1,2, l represents the constraint functions in the multi-objective optimization model, l is the number of the constraint functions,for the allowed interval of the ith constraint function value,are respectively asLower and upper bounds of (1), XL,XRRespectively, a lower bound and an upper bound of the optimization variable X.
In an embodiment of the present invention, before the performing the optimal solution on the multi-objective optimization model by using the micro-interval multi-objective genetic algorithm, the method shown in fig. 1 further includes: and generating a plurality of sample points, fitting the sample points by adopting a radial basis function model, and constructing an objective function and a constraint function of the multi-objective optimization model. That is to say, a Latin hypercube test design is adopted, design sample points are selected, the sample points are fitted through approximate modeling of a radial basis function model, and an objective function of a multi-objective optimization model and a radial basis function approximate model of a constraint function are constructed.
Specifically, generating a plurality of sample points and fitting the sample points by adopting radial basis function modeling, and constructing an objective function of the multi-objective optimization model and a radial basis function approximation model of a constraint function comprises: generating a plurality of sample points by adopting Latin hypercube test design, and calculating an objective function value and a constraint function value by adopting a vehicle frontal collision finite element model; and fitting the sample points by adopting a radial basis function approximation model, randomly selecting partial sample points to construct an approximation model of a target function and a constraint function of the multi-target optimization model, and performing error evaluation on fitting precision by utilizing the residual sample points.
In an embodiment of the present invention, before the performing the optimal solution on the multi-objective optimization model by using the micro-interval multi-objective genetic algorithm, the method shown in fig. 1 further includes: carrying out affine coordinate conversion on uncertain domains of uncertain parameters in the multi-target optimization model, and converting the interrelated uncertain parameters into mutually independent uncertain parameters; and converting the uncertain constraints in the multi-objective optimization model into deterministic constraints by adopting an interval probability model.
That is to say, in this embodiment, affine coordinate transformation is performed on the uncertain domain of the uncertain parameters in the optimization model, and the transformation matrix is used to transform the interrelated uncertain parameters U into the independent uncertain parameters P. Performing affine coordinate transformation on the uncertain domain of the uncertain parameter in the optimized variable comprises the following steps:
calculating a conversion matrix A according to a correlation coefficient rho and a correlation angle theta between uncertain parameters
Wherein,
in the formula, q represents the number of uncertain parameters. According to the transformation matrix A and the interval radius U of the uncertain parameter U before transformationwCalculating the section radius P of the uncertain parameter P after coordinate transformation by the following formulaw：
Pw＝[|A|]-1Uw；
Interval midpoint U according to uncertain parameter U before conversioncDetermining the mid-point P in the interval of P after conversioncLet Pc＝Uc。
In an embodiment of the present invention, the performing an optimization solution on the multi-objective optimization model by using a micro interval multi-objective genetic algorithm shown in fig. 1, and obtaining an optimal solution set includes:
step A, setting relevant parameters of an outer-layer miniature multi-target genetic algorithm, and randomly generating an initial population P with the number of individuals of 51Initializing the external population PeIn juxtaposition with PeSetting the iteration value t as 1 initially for the empty set;
step B, for the current evolution population PtFor each individual, adopting interval analysis to obtain the interval of each objective function value, and taking the weighted sum of the midpoint and the radius of the interval of the objective function value as the objective function value of the individual;
step C, merging the evolution population PtAnd an external population PeConstructing a plurality of non-dominant individual sets according to the non-dominant relationship;
step D, if the external population PeWhen the continuous same algebra reaches the preset restart maximum algebra M, the evolutionary population P is regeneratedtReturning to the step B, otherwise executing the step E;
step E, calculating the crowding distance of each individual in each non-dominant individual set;
step F, updating the external population according to the current optimal non-dominant individual set;
g, if the iteration value t reaches the preset maximum iteration number tmaxIf so, stopping iteration and outputting the current optimal solution set; otherwise, executing step H;
step H, for the regenerated evolution population PtPerforming genetic manipulation to generate offspring population Pt+1Let t be t +1 and return to step B.
In one embodiment of the present invention, the selecting an optimal solution from the optimal solution set shown in fig. 1 comprises:
selecting an optimal solution from the obtained optimal solution set according to preference information of a decision maker; or, taking the minimum value of all the optimal solutions in the optimal solution set on each target as an ideal point in the current target space, calculating the distance between each optimal solution and the ideal point in the target space, and taking the optimal solution corresponding to the minimum distance as the selected optimal solution.
It can be seen that the embodiments of the present invention provide two preferable ways of determining the optimal parameter values, one of which is selected according to the preference of the decision maker, and this way can meet the needs of the decision maker. Another is to choose according to the ideal point at present, the advantage of this way is: the information in the currently obtained optimization results can be fully utilized to select the optimal parameter values when the decision-maker is not clear or convenient to give preference.
The following description focuses on implementation steps of the method for optimizing the crashworthiness of the vehicle body according to the embodiment of the present invention with reference to a specific example.
Step one, according to the requirement of the optimized design of the collision resistance of the automobile body, selecting the evaluation index of the collision resistance of the automobile body, namely the displacement In of an automobile pedal from a fixed point at the bottom of a main driver seat and the automobile body mass M as optimization targets, and selecting the energy absorption part shown In figure 3 as the optimization targetIs a main optimization piece, and takes the thickness of the optimization pieces as optimization variables which are respectively marked as t1，t2，…，t7，t1Showing an automobile outer cover; t is t2Indicating an automobile inner cover; t is t3Represents a bumper; t is t4Showing a left shield; t is t5Showing a left front rail; t is t6Showing the left front lower cradle; t is t7Showing the left frame arm. Wherein t is1、t2And t4Has a search range of [0.5, 1.2 ]]In mm. t is t3Has a search range of [1, 2 ]]In mm. t is t5、t6And t7Has a search range of [1, 3 ]]In mm.
Step two, considering the uncertainty of the elastic modulus and the density in the material parameters of the optimized part, if the uncertainty of the parameters is described by adopting intervals, the variation intervals of the elastic modulus and the density of the bumper are respectively E1∈[2.66，2.94]GPa and rho1∈[1.14×10-6，1.26×10-6]kg/mm3The variation intervals of the elastic modulus and the density of other parts are respectively E2∈[199.5，220.5]GPa and rho2∈[7.49×10-6，8.28×10-6]kg/mm3In which E2And ρ2There is a correlation between them, and the correlation coefficient ρ thereof24Is 0.5. And describing the uncertain field gamma of the uncertain parameters by adopting a multi-dimensional parallelepiped interval model, and establishing an automobile body crashworthiness multi-target optimization model as follows:
s.t.t＝(t1,t2,t3,t4,t5,t6,t7)
0.5mm≤t1,t2,t4≤1.2mm,
1.0mm≤t3≤2.0mm,
1.0mm≤t5,t6,t7≤3.0mm,
U＝(E1,E2,ρ1,ρ2),U∈Γ(UI,ρ24),
U1 I＝[199.5,220.5]GPa,U2 I＝[2.66,2.94]GPa,
U3 I＝[7.49,8.28]×103kg/m3,U4 I＝[1.14,1.26]×103kg/m3.
and step three, adopting a Latin hypercube test for sampling, selecting 30 groups of design sample points, and adopting a finite element model of the frontal collision of the automobile to calculate an objective function value, wherein the calculation result is shown in Table 1.
Table 1 is a design sample points table.
Then, an approximate model of the multi-objective optimization objective functions In and M is constructed by adopting the radial basis functions, and the expression is as follows:
where, r ═ (t, U) is the optimized parameter vector, rifor the optimized parameter value of the ith sample point, beta is 4.2 and is the shape parameter of the radial basis function, wiIs the ith weight coefficient, nsThe number of sample points used to construct the approximation model. Randomly selecting 25 groups of sample points in the table 1, substituting the optimized parameter values and the target values of the sample points into the expression, and obtaining a weight coefficient vector:
w＝1.0e+03*[-0.6922,0.2999,-0.2045,0.4474,-0.2746,0.4047,0.6160,-0.0575,-0.3720,0.0718,-0.3902,-0.1896,-0.0206,0.5246,0.4223,0.7987,0.3714,-0.2536,1.0156,-0.0653,0.3348,-0.7503,0.1839,-0.0774,-0.3381]T
and then, the remaining 5 groups of sample points are utilized to carry out error evaluation on the fitting precision, and the calculation result is shown in table 2:
as can be seen from Table 2, the constructed radial basis function approximation model can satisfy the accuracy requirement.
Step four, carrying out affine coordinate transformation on the uncertain domain of the uncertain parameters in the optimization model, and obtaining a correlation coefficient rho of the uncertain domain24The transformation matrix can be obtained by calculation as follows, 0.5:
the related uncertain parameters U are converted into independent uncertain parameters P by the conversion matrix, and the radius P of the parameters is obtainedwAnd midpoint PcThe final interval of available P is:
P1 I＝[199.50,220.50]GPa,P2 I＝[2.65,2.95]GPa,
P3 I＝[7.49,8.28]×103kg/m3,P4 I＝[1.14,1.26]×103kg/m3.
step five, performing optimization solution by using a micro-interval multi-target genetic algorithm to obtain an optimal solution set, wherein fig. 5 is a schematic diagram of the optimal solution set obtained in one embodiment of the present invention, and referring to fig. 5, a square in the diagram represents an interval of objective function values, a black point in the middle represents an interval midpoint, and the optimal solution set of the present embodiment includes a plurality of optimal solutions.
And step six, selecting a satisfactory solution as a result according to different requirements of the two objective functions in actual conditions, and also selecting an optimal solution with the minimum distance from the current ideal point in the optimal solution set as the result. The results of selecting the optimal solution with the minimum distance from the current ideal point are listed in table 3, along with the initial solution, for comparison.
As can be seen from table 3, the range of the optimized target object displacement amount is 102.81mm to 113.16 mm. The interval range of the optimized target object mass is 102.81kg to 113.16 kg. The respective corresponding optimal parameter values for the seven optimization variables (i.e., the optimization) are: 0.51mm, 0.55mm, 1.54mm, 0.52mm, 2.89mm, 1.64mm, 1.95mm, it being understood that these parameter values are the thickness parameters of the optimized element shown in figure 3. As can be seen from Table 3, the middle point of the optimal parameter value corresponding to the target value interval is reduced by 15.7% In the displacement In of the pedal and 3.8kg In the mass M of the vehicle body compared with the initial scheme, namely after the optimization by the method of the invention, the collision resistance of the vehicle body is improved and the mass of the vehicle body is reduced.
The implementation steps and the implementation process of the vehicle body crashworthiness optimization method of the embodiment of the invention are described above, and therefore, the embodiment of the invention utilizes the multidimensional parallelepiped interval model to describe the uncertainty and the correlation of the key parameters in the vehicle collision problem, and for the uncertain parameters difficult to obtain from the sample information, the uncertain parameters can be clearly described only by knowing the value interval, so that the high cost brought by obtaining a large number of samples is reduced, and the method has better practicability. In addition, the method for optimizing the crashworthiness of the vehicle body provided by the embodiment of the invention considers the uncertainty of parameters, can provide a reliable optimization scheme and guidance for the safety design of the vehicle body, reduces the development cost of the vehicle body, and has a good application prospect.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Claims (10)
1. A vehicle body crashworthiness optimization method is characterized by comprising the following steps:
selecting a plurality of target objects for optimizing the crashworthiness of the vehicle and selecting the structure or material parameters of a plurality of specified components during vehicle body collision as optimization variables, wherein the structure or material parameters of the specified components are uncertain parameters;
according to the selected optimization target object and the selected optimization variable, a multi-dimensional parallelepiped interval model is utilized to establish a vehicle body crashworthiness multi-target optimization model;
carrying out optimization solution on the multi-target optimization model by adopting a micro interval multi-target genetic algorithm to obtain an optimal solution set;
and selecting an optimal solution from the optimal solution set, and taking the selected optimal solution as an optimal parameter value.
2. The method of claim 1, further comprising, prior to optimally solving the multiobjective optimization model using the micro-scale interval multiobjective genetic algorithm:
generating a plurality of sample points, fitting the sample points by adopting radial basis function modeling, and constructing a target function of the multi-target optimization model and a radial basis function approximate model of a constraint function;
carrying out affine coordinate conversion on uncertain domains of uncertain parameters in the multi-target optimization model, and converting the interrelated uncertain parameters into mutually independent uncertain parameters;
and converting the uncertain constraints in the multi-objective optimization model into deterministic constraints by adopting an interval probability model.
3. The method as claimed in claim 1, wherein the step of building a multi-objective optimization model for collision resistance of the vehicle body by using the multi-dimensional parallelepiped section model according to the selected optimization objective and the optimization variables specifically comprises the steps of:
determining an edge interval U corresponding to uncertain parameters in the optimized variablesIAnd a correlation coefficient ρ between the uncertain parameters;
according to the edge interval UIConstructing a multi-dimensional parallelepiped interval model according to the correlation coefficient rho;
according to the first crashworthiness index C of the optimized target object, the second vehicle body mass M of the optimized target object, the optimized variable X, the uncertain parameter U and the multidimensional parallelepiped interval model, the following vehicle body crashworthiness multi-target optimization model is established:
XL≤X≤XR,U∈Γ(UI,ρ).
wherein, gi(X, U), i 1,2, l represents the constraint functions in the multi-objective optimization model, l is the number of the constraint functions,for the allowed interval of the ith constraint function value,are respectively asLower and upper bounds of (1), XL,XRRespectively, a lower bound and an upper bound of the optimization variable X.
4. The method of claim 2, wherein the generating a plurality of sample points and fitting the sample points using a radial basis function model, and wherein the constructing the objective function and the constraint function of the multi-objective optimization model comprises:
generating a group of sample points by adopting Latin hypercube test design, and calculating an objective function value and a constraint function value by adopting a vehicle frontal collision finite element model;
and fitting the sample points by adopting a radial basis function, randomly selecting partial sample points to construct an approximate model of a target function and a constraint function of the multi-target optimization model, and performing error evaluation on the fitting precision of the established approximate model by using the residual sample points.
5. The method of claim 2, wherein performing affine coordinate transformation on uncertainty fields of uncertainty parameters in the optimized variables comprises:
calculating a conversion matrix A according to a correlation coefficient rho and a correlation angle theta between uncertain parameters
Wherein,
in the formula, q is the number of uncertain parameters and is the interval radius U of the uncertain parameters U before conversion according to the conversion matrix AwCalculating the section radius P of the uncertain parameter P after coordinate transformation by the following formulaw：
Pw＝[|A|]-1Uw；
Interval midpoint U according to uncertain parameter U before conversioncDetermining the mid-point P in the interval of P after conversioncLet Pc＝Uc。
6. The method of claim 5, wherein the performing an optimization solution on the multi-objective optimization model by using a micro-interval multi-objective genetic algorithm to obtain an optimal solution set comprises:
step A, setting relevant parameters of an outer-layer miniature multi-target genetic algorithm, and randomly generating an initial population P with the number of individuals of 51Initializing the external population PeIn juxtaposition with PeSetting the iteration value t as 1 initially for the empty set;
step B, for the current evolution population PtFor each individual, adopting interval analysis to obtain the interval of each objective function value, and taking the weighted sum of the midpoint and the radius of the interval of the objective function value as the objective function value of the individual;
step C, merging the evolution population PtAnd an external population PeConstructing a plurality of non-dominant individual sets according to the non-dominant relationship;
step D, if the external population PeWhen the continuous same algebra reaches the preset restart maximum algebra M, the evolutionary population P is regeneratedtReturning to the step B, otherwise executing the step E;
step E, calculating the crowding distance of each individual in each non-dominant individual set;
step F, updating the external population according to the current optimal non-dominant individual set;
g, if the iteration value t reaches the preset maximum iteration number tmaxIf so, stopping iteration and outputting the current optimal solution set; otherwise, executing step H;
step H, for the regenerated evolution population PtPerforming genetic manipulation to generate offspring population Pt+1Let t be t +1 and return to step B.
7. The method of claim 1, wherein selecting an optimal solution from the set of optimal solutions comprises:
and selecting an optimal solution from the obtained optimal solution set according to the preference information of the decision maker.
8. The method of claim 1, wherein selecting an optimal solution from the set of optimal solutions further comprises:
and taking the minimum value of all the optimal solutions in the optimal solution set on each target as an ideal point in the current target space, calculating the distance between each optimal solution in the target space and the ideal point, and taking the optimal solution corresponding to the minimum distance as the selected optimal solution.
9. The method of claim 1, wherein selecting a plurality of target objects for vehicle crashworthiness optimization and selecting structural or material parameters of a plurality of designated parts at the time of vehicle body collision as optimization variables comprises:
the method comprises the steps of selecting the quality of a vehicle body and one or more vehicle body crashworthiness evaluation indexes as target objects, selecting the thickness parameters of specified parts as optimization variables, wherein the values of the thickness parameters of the specified parts are unknown.
10. The method of claim 9, wherein the target objects are: the mass of the automobile body and the displacement of the automobile pedal from a fixed point at the bottom of a main driver seat;
the appointed parts comprise an automobile outer cover, an automobile inner cover, a bumper, a left protective cover, a left front longitudinal beam, a left front lower support and a left frame arm.
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