CN108984901A - A kind of automobile body crash-worthiness optimization method - Google Patents
A kind of automobile body crash-worthiness optimization method Download PDFInfo
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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 present invention relates to vehicle safety optimisation technique field more particularly to a kind of automobile body crash-worthiness optimization methods.
Background technique
Crash-worthiness is the key performance that must be taken into consideration in body of a motor car optimization design, and influence vehicle body crash-worthiness is usually vapour
For deforming component of energy-absorbing, such as front longitudinal beam, automobile front outer cover etc. when vehicle collides, the energy absorption characteristics of these components and
Deformation pattern determines power, displacement and acceleration of the automobile in collision process, plays a key effect to the protection of automobile passenger.
Current body of a motor car crash-worthiness multiple-objection optimization technology require mostly optimized variable parameter determine, in this way for
The more unobtainable uncertain parameter of sample information needs to pay higher cost to obtain great amount of samples, and the practicability is poor.And
And even if being determined that parameter can still have error due to processing and manufacturing etc. in automobile actual production process, lead to result
There is deviation, expected requirement is not achieved in vehicle safety.
Summary of the invention
In view of above-mentioned analysis, the embodiment of the present invention is intended to provide a kind of automobile body crash-worthiness optimization method, to solve
Certainly require in the prior art optimized variable parameter determine caused by it is at high cost, the practicability is poor and safety optimization be not achieved
It is expected that problem.
A kind of automobile body crash-worthiness optimization method is provided according to an aspect of the present invention, comprising:
The multiple target objects and the structure of while selecting vehicle body to collide multiple specified parts of selection vehicle crash-worthiness optimization or
Material parameter is as optimized variable, wherein the structure or material parameter of the multiple specified parts are uncertain parameter；
Vehicle is established using multidimensional parallelepiped interval model according to selected optimization aim object and optimized variable
Body crash-worthiness Model for Multi-Objective Optimization；
Optimization is carried out to Model for Multi-Objective Optimization using miniature section multi-objective genetic algorithm, obtains optimal solution
Collection；
An optimal solution is selected from the optimal solution set, and using the optimal solution selected as optimal value of the parameter.
Above-mentioned technical proposal has the beneficial effect that: the automobile body crash-worthiness optimization method of the embodiment of the present invention, considers
Parameter it is uncertain, according to the optimization aim object and optimized variable of selection, built using multidimensional parallelepiped interval model
Vertical vehicle body crash-worthiness Model for Multi-Objective Optimization, so as to the uncertain of accurate description body of a motor car specified parts attribute etc.
Property distribution and its mutual relationship, and efficient miniature section multi-objective Genetic is applied into asking for Model for Multi-Objective Optimization
Xie Zhong, the technical solution of the embodiment of the present invention are applied to after body of a motor car optimization design, and it is resistance to can to effectively improve vehicle body
The precision and efficiency of hitting property optimization, so as to reduce its development cost while improving automobile product safety.
It in the present invention, can also be combined with each other between above-mentioned each technical solution, to realize more preferred assembled schemes.This
Other feature and advantage of invention will illustrate in the following description, also, certain advantages can become from specification it is aobvious and
It is clear to, or understand through the implementation of the invention.The objectives and other advantages of the invention can by specification, claims with
And it is achieved and obtained in specifically noted content in attached drawing.
Detailed description of the invention
Attached drawing is only used for showing the purpose of specific embodiment, and is not to be construed as limiting the invention, in entire attached drawing
In, identical reference symbol indicates identical component.
Fig. 1 is the flow diagram of the automobile body crash-worthiness optimization method of one embodiment of the invention；
Fig. 2 is the body of a motor car head-on crash simplified model schematic diagram of one embodiment of the invention；
Fig. 3 is the three-dimensional model diagram of the main energy absorbing component of body of a motor car head-on crash of one embodiment of the invention；
Fig. 4 a be one embodiment of the invention body of a motor car head-on crash before threedimensional model schematic diagram；
Fig. 4 b be one embodiment of the invention body of a motor car head-on crash after threedimensional model schematic diagram；
Fig. 5 is the schematic diagram of optimal solution set obtained in one embodiment of the invention.
Appended drawing reference:
1, fixed rigid obstacle；2, ground；3, automobile simplified model；Automobile is travelled with the speed of 56km/h.
Specific embodiment
Specifically describing the preferred embodiment of the present invention with reference to the accompanying drawing, wherein attached drawing constitutes the application a part, and
Together with embodiments of the present invention for illustrating the principle of the present invention, it is not intended to limit the scope of the present invention.
Current body of a motor car crash-worthiness multiple-objection optimization does not consider the uncertainty and its correlation of parameter mostly, but by
In in automobile actual production process, processing and manufacturing etc. will necessarily have error, to cause the parameters such as its thickness, material
Uncertainty, and be also possible to be to be mutually related between these parameters.The uncertainty and correlation of these parameters will lead to
There is deviation in crash-worthiness optimum results, and expected requirement is not achieved in vehicle safety.For this problem, the invention proposes one
Kind considers the automobile body crash-worthiness optimization method of Parameter uncertainties.
The automobile body crash-worthiness optimization method of the embodiment of the present invention considers automobile and is used to deform energy-absorbing in an impact
The structure of key components and parts or the uncertainty of material parameter and its correlation, using multidimensional parallelepiped interval model to not
It determines that parameter and its mutual correlation are described, and these correlations is handled by affine coordinate conversion.
Approximate model is established based on radial basis function simultaneously；Multi-objective optimization question is carried out using miniature section multi-objective genetic algorithm again
Solution, obtain optimal solution set.The preference information or current ideal solution finally given according to policymaker, chooses from optimal solution set
Optimal value of the parameter.As a result, by considering the uncertainty and correlation of structure or material parameter in automobile manufacture, vehicle is improved
The precision and efficiency of body crash-worthiness optimization.
Fig. 1 is the flow diagram of the automobile body crash-worthiness optimization method of one embodiment of the invention, referring to Fig. 1, originally
The automobile body crash-worthiness optimization method of embodiment includes the following steps:
The multiple target objects and the structure of while selecting vehicle body to collide multiple specified parts of selection vehicle crash-worthiness optimization or
Material parameter is uncertain parameter as optimized variable, the structure or material parameter of plurality of specified parts；
Vehicle is established using multidimensional parallelepiped interval model according to selected optimization aim object and optimized variable
Body crash-worthiness Model for Multi-Objective Optimization；
Optimization is carried out to Model for Multi-Objective Optimization using miniature section multi-objective genetic algorithm, obtains optimal solution
Collection；
An optimal solution is selected from optimal solution set, and using the optimal solution selected as optimal value of the parameter.
Compared with prior art, automobile body crash-worthiness optimization method provided in this embodiment selects vehicle crash-worthiness excellent
Multiple target objects for changing and when vehicle body being selected to collide the unknown uncertain parameter of multiple specified parts values as optimized variable.
It is found that the body of a motor car crash-worthiness multiple-objection optimization of the embodiment of the present invention considers this reality of parameter uncertainty, and for ginseng
Several is uncertain, is established according to selected optimization aim object and optimized variable using multidimensional parallelepiped interval model
Vehicle body crash-worthiness Model for Multi-Objective Optimization optimizes Model for Multi-Objective Optimization using miniature section multi-objective genetic algorithm
It solves, obtains optimal solution set, an optimal solution is selected from optimal solution set, and using the optimal solution selected as optimal value of the parameter.From
And, on the one hand, uncertain parameter more unobtainable for sample information, reduce for obtain great amount of samples brought by height at
This, has preferable practicability.On the other hand, this body of a motor car crash-worthiness multiple-objection optimization for considering Parameter uncertainties is vapour
The design of vehicle bodywork safety provides reliable prioritization scheme and guidance, reduces development cost, has applications well prospect.
In one embodiment of the invention, multiple target objects of vehicle crash-worthiness optimization are selected and vehicle body is selected to collide
When multiple specified parts structure or material parameter as optimized variable include:
Selection body quality and vehicle body crash-worthiness evaluation index be specifically auto pedal apart from main driving seat bottom not
The displacement of dynamic point selects automobile outer cover, automobile inner cover, bumper, left shield, left front indulges as optimization aim object
Beam, left front lower bracket and left frame boom are as specified parts, and using the thickness parameter of specified parts as optimized variable, multiple fingers
The value for determining the thickness parameter of component is unknown.
In conjunction with Fig. 2 and Fig. 3, the optimization that crash-worthiness in the case of body of a motor car head-on crash is directed in the present embodiment is set
Meter.It is as follows that body of a motor car head-on crash is simplified to model: automobile 3 is arranged on ground 2, with the speed of 56km/h (arrow in Fig. 2
Direction) hit rigid obstacle 1.And the main component for deforming energy-absorbing in Automobile head-on crash, see the Automobile in Fig. 3
The threedimensional model of the main energy absorbing component of body head-on crash is illustrated.These energy absorbing components are: automobile outer cover 301, automobile inner cover 302,
Bumper 303, left shield 304, left front girder 305, left front lower bracket 306 and left frame boom 307.This is chosen in the present embodiment
A little components are as specified parts, and using the structural parameters of these specified parts as optimized variable, that is, thickness ginseng in the present embodiment
Number is used as optimized variable.
Fig. 4 a be one embodiment of the invention body of a motor car head-on crash before three-dimensional model diagram, Fig. 4 b be the present invention one
Threedimensional model after the body of a motor car head-on crash of embodiment；Comparison diagram 4a and Fig. 4 b can see body of a motor car and obviously deforms, main
The deformation energy absorbing component wanted is impaired serious.
In order to while can be carried out optimization to vehicle collision resistant, realize the lightweight of automotive body structure, the present embodiment
Body of a motor car crash-worthiness multiple-objection optimization is provided, the meaning of multiple target refers to that optimization aim number of objects is multiple, target pair
As can be one or more vehicle body Impact Resisting Capability evaluation indexes and body quality.
In one embodiment of the invention, the evaluation index for selecting body quality M and vehicle body crash-worthiness is specifically vapour
Displacement In of the vehicle pedal apart from main driving seat bottom fixed point is as optimization aim object.That is, in one embodiment
Two optimization aims are selected.
After having selected and determined optimization aim object and optimized variable, body of a motor car crash-worthiness multiple-objection optimization mould is established
Type describes the uncertain domain Γ of uncertain parameter U in crash-worthiness optimization problem using multidimensional parallelepiped interval model.
Determine the phase relation in the optimized variable between the corresponding edge interval of uncertain parameter and uncertain parameter
Number；According to the edge interval and the related coefficient, multidimensional parallelepiped interval model is constructed；According to optimization aim object
C, optimization aim object M and optimized variable X and multidimensional parallelepiped interval model establish such as under body crash-worthiness multiple target
Optimized model:
XL≤X≤XR,U∈Γ(UI,ρ).
Wherein, gi(X, U), i=1,2 ..., l indicate that the constraint function in the Model for Multi-Objective Optimization, l are it
Number,For the permission section of i-th of constraint function value,RespectivelyLower bound and the upper bound, XL,XRIt is respectively described excellent
Change lower bound and the upper bound of variable X.
In one embodiment of the invention, using miniature section multi-objective genetic algorithm to Model for Multi-Objective Optimization into
Before row optimization, method shown in Fig. 1 further include: generate multiple sample points and using radial basis function model to the sample
This point is fitted, and constructs the objective function and constraint function of the Model for Multi-Objective Optimization.That is, super vertical using Latin
Square experimental design is chosen design sample point, and is fitted using radial basis function model approximate modeling to sample point, constructs more
The objective function of objective optimization model and the radial basis function approximate model of constraint function.
Specifically, generating multiple sample points and being fitted using radial basis function modeling to the sample point, institute is constructed
The radial basis function approximate model of the objective function and constraint function of stating Model for Multi-Objective Optimization includes: to be tried using Latin hypercube
It tests design and generates multiple sample points, and using vehicle frontal collision finite element model calculating target function value and constraint functional value；
The sample point is fitted using radial basis function approximate model, randomly selects part sample point building multiple-objection optimization mould
The objective function of type and the approximate model of constraint function, and error assessment is carried out to fitting precision using remaining sample point.
In one embodiment of the invention, using miniature section multi-objective genetic algorithm to Model for Multi-Objective Optimization into
Before row optimization, method shown in Fig. 1 further include: to the uncertain domain of uncertain parameter in the Model for Multi-Objective Optimization
Affine coordinate conversion is carried out, the uncertain parameter that will be mutually related is converted into mutually independent uncertain parameter；And to described
Constraint is not known in Model for Multi-Objective Optimization to be converted using section possibility degree model, and certainty constraint is converted into.
That is, affine coordinate transformation is carried out to the uncertain domain of uncertain parameter in Optimized model in the present embodiment,
Mutually independent uncertain parameter P is converted to using the transition matrix uncertain parameter U that will be mutually related.The optimization is become
The uncertain domain progress affine coordinate of uncertain parameter, which is converted, in amount includes:
According to the correlation coefficient ρ and related angle θ between uncertain parameter, transition matrix A is calculated
Wherein,
In formula, q is the number of uncertain parameter.According to the section radius of uncertain parameter U before transition matrix A and conversion
Uw, the section radius P of uncertain parameter P after being converted by following formula coordinates computedsw:
Pw=[| A |]-1Uw；
According to the interval midpoint U of uncertain parameter U before convertingcDetermine the interval midpoint P of P after convertingc, enable Pc=Uc。
In one embodiment of the invention, shown in FIG. 1 excellent to multiple target using miniature section multi-objective genetic algorithm
Change model and carry out optimization, obtaining optimal solution set includes:
The relevant parameter of outer layer Micro Multi-objective Genetic Algorithm is arranged in step A, and initial kind that number of individuals is 5 is randomly generated
Group P1, initialize external population Pe, juxtaposition PeFor empty set, iterative value t is enabled to be initially 1；
Step B, to current Advanced group species PtIn each individual, the area of each of which target function value is acquired using interval analysis
Between, and using the weighted sum of the midpoint in target function value section and radius as the target function value of the individual；
Step C merges Advanced group species PtAnd external population Pe, according to non-dominant relationship, construct multiple non-dominant individual collection；
Step D, if external population PeConsecutive identical algebra reaches default and restarts maximum algebra M, then regenerate into
Change population Pt, return step B is no to then follow the steps E；
Step E collects for each non-dominant individual, calculates the crowding distance of wherein each individual；
Step F is updated external population according to current optimal non-dominant individual collection；
Step G, if iterative value t reaches default greatest iteration number tmax, then stop iteration, export current optimal solution set；It is no
Then, step H is executed；
Step H, to the Advanced group species P regeneratedtMake genetic manipulation, generates progeny population Pt+1, t=t+1 is enabled, is returned
To step B.
An optimal solution is selected from optimal solution set shown in Fig. 1 in one embodiment of the invention includes:
An optimal solution is selected in the optimal solution set of acquisition according to the preference information of policymaker；Alternatively, taking described
All optimal solutions calculate each optimal solution in the minimum value in each target as the ideal point in current goal space in optimal solution set
At a distance from object space between the ideal point, and using the minimum corresponding optimal solution of distance as the optimal solution selected.
It follows that one is bases the embodiment of the invention provides two kinds of preferred modes for determining optimal value of the parameter
The preference of policymaker selects, and this mode can satisfy the demand of policymaker.Another kind is selected according to current ideal point
It selects, the advantages of this mode is: when policymaker does not know or inconvenience provides preference, can make full use of currently obtained
Optimum results in information select optimal value of the parameter.
The realization of the automobile body crash-worthiness optimization method of the embodiment of the present invention is walked below in conjunction with a specific example
Suddenly it is stressed.
Step 1 selects the evaluation index of vehicle body crash-worthiness according to the requirement of vehicle body crash-worthiness optimization design, i.e. automobile is stepped on
Displacement In and body quality M of the plate apart from main driving seat bottom fixed point select energy-absorbing portion shown in Fig. 3 as optimization aim
For part as main optimization part, and using the thickness of these optimization parts as optimized variable, optimized variable is denoted as t respectively1, t2...,
t7, t1Indicate automobile outer cover；t2Indicate automobile inner cover；t3Indicate bumper；t4Indicate left shield；t5Indicate left front girder；t6
Indicate left front lower bracket；t7Indicate left frame boom.Wherein t1、t2And t4Search range be [0.5,1.2], unit is mm.t3's
Search range is [1,2], unit mm.t5、t6And t7Search range be [1,3], unit is mm.
Step 2 considers the uncertainty of above-mentioned optimization part material parameter Elastic Modulus and density, comes according to section
The uncertainty of these parameters is described, then the elasticity modulus of bumper and the constant interval of density are respectively E1∈ [2.66,
2.94] GPa and ρ1∈[1.14×10-6, 1.26 × 10-6]kg/mm3, the elasticity modulus of other parts and the constant interval of density divide
It Wei not E2∈ [199.5,220.5] GPa and ρ2∈[7.49×10-6, 8.28 × 10-6]kg/mm3, wherein E2And ρ2Between there are phases
Guan Xing, correlation coefficient ρ24It is 0.5.The uncertain of above-mentioned uncertain parameter is described using multidimensional parallelepiped interval model
It is as follows then to establish body of a motor car crash-worthiness Model for Multi-Objective Optimization by domain Γ:
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.
Step 3 chooses 30 groups of design sample points using Latin hypercube test sampling, and using frontal crash of vehicles
Finite element model calculating target function value, calculated result are as shown in table 1.
Table 1 is design sample point table.
Then, the approximate model of multiple-objection optimization objective function In and M is constructed using radial basis function, expression formula is such as
Under:
Wherein, r=(t, U) is Optimal Parameters vector, riFor the Optimal Parameters value of i-th of sample point, β=4.2 are radial
The shape parameter of basic function, wiFor i-th of weight coefficient, nsFor the sample point number for constructing approximate model.It randomly selects in table 1
25 groups of sample points, the Optimal Parameters value and target value of sample point are brought into above-mentioned expression formula, weight coefficient vector can be acquired:
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
Remaining 5 groups of sample points are recycled to carry out error assessment to fitting precision, calculated result is as shown in table 2:
As shown in Table 2, constructed radial basis function approximate model can meet required precision.
Step 4 carries out affine coordinate transformation to the uncertain domain of uncertain parameter in Optimized model, by its related coefficient
ρ24=0.5, as follows by calculating available transition matrix:
It is converted to mutually independent uncertain parameter P using the above-mentioned transition matrix uncertain parameter U that will be mutually related,
Acquire its radius PwWith midpoint Pc, can finally obtain the section of P are as follows:
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 5 is optimized using miniature section multi-objective genetic algorithm, obtains optimal solution set, Fig. 5 is this hair
The schematic diagram of optimal solution set obtained in bright one embodiment, referring to Fig. 5, what box indicated is the area of target function value in figure
Between, what intermediate stain indicated is interval midpoint, includes multiple optimal solutions in the optimal solution set of the present embodiment.
Step 6, according to the actual situation in two objective function demands it is different choose satisfied solutions as a result,
The smallest optimal solution at a distance from current ideal point in optimal solution set can be chosen as a result.Selection is listed in table 3 and is worked as
Preceding ideal point is resulting apart from the smallest optimal solution as a result, also listing initial solution, simultaneously to compare.
As known from Table 3, the interval range of optimization aim object displacement amount is 102.81mm to 113.16mm.Optimization aim pair
As the interval range of quality is 102.81kg to 113.16kg.Seven optimized variables (that is, optimization part) it is corresponding optimal
Parameter value are as follows: 0.51mm, 0.55mm, 1.54mm, 0.52mm, 2.89mm, 1.64mm, 1.95mm, it will be understood that these parameter values
It is the thickness parameter of optimization part shown in Fig. 3.As shown in Table 3, the midpoint in this optimal value of the parameter corresponding target value section and initial
Scheme is compared, and 15.7% is reduced on the displacement In of pedal, and body quality M alleviates 3.8kg, that is, uses the method for the present invention
After optimization, while the Impact Resisting Capability of vehicle body is improved, body quality is also mitigated.
The realization step and realization process of the automobile body crash-worthiness optimization method of the embodiment of the present invention is explained above, by
Above it is found that the embodiment of the present invention describes the key parameter in car crass problem using multidimensional parallelepiped interval model
Uncertain and correlation, uncertain parameter more unobtainable for sample information, it is only necessary to know that its value interval can be by it
It is explicitly described out, reduces to obtain high cost brought by great amount of samples, there is preferable practicability.Moreover, this hair
The considerations of automobile body crash-worthiness optimization method of bright embodiment parameter uncertainty, can mention for body of a motor car safety Design
For reliable prioritization scheme and guidance, its development cost is reduced, is had a good application prospect.
The foregoing is only a preferred embodiment of the present invention, but scope of protection of the present invention is not limited thereto,
In the technical scope disclosed by the present invention, any changes or substitutions that can be easily thought of by anyone skilled in the art,
It should be covered by the protection scope of the present invention.
Claims (10)
1. a kind of automobile body crash-worthiness optimization method, characterized in that include:
The structure or material of multiple specified parts when selecting multiple target objects of vehicle crash-worthiness optimization and vehicle body being selected to collide
Parameter is as optimized variable, wherein the structure or material parameter of the multiple specified parts are uncertain parameter；
It is resistance to establish vehicle body using multidimensional parallelepiped interval model according to selected optimization aim object and optimized variable
Hitting property Model for Multi-Objective Optimization；
Optimization is carried out to Model for Multi-Objective Optimization using miniature section multi-objective genetic algorithm, obtains optimal solution set；
An optimal solution is selected from the optimal solution set, and using the optimal solution selected as optimal value of the parameter.
2. according to the method described in claim 1, it is characterized in that, using miniature section multi-objective genetic algorithm it is excellent to multiple target
Change model carries out before optimization further include:
It generates multiple sample points and the sample point is fitted using radial basis function modeling, construct the multiple-objection optimization
The objective function of model and the radial basis function approximate model of constraint function；
Affine coordinate conversion is carried out to the uncertain domain of uncertain parameter in the Model for Multi-Objective Optimization, will be mutually related not
Determine Parameter Switch at mutually independent uncertain parameter；
It is converted to constraint is not known in the Model for Multi-Objective Optimization using section possibility degree model, is converted into determination
Property constraint.
3. according to the method described in claim 1, it is characterized in that, it is described according to selected optimization aim object and optimization become
Amount, using multidimensional parallelepiped interval model, establishes vehicle body crash-worthiness Model for Multi-Objective Optimization and specifically includes:
Determine the corresponding edge interval U of uncertain parameter in the optimized variableIAnd the correlation coefficient ρ between uncertain parameter；
According to the edge interval UIWith the correlation coefficient ρ, multidimensional parallelepiped interval model is constructed；
According to one of optimization aim object crash-worthiness index C, two body quality M of optimization aim object, optimized variable X and not really
Determine parameter U and multidimensional parallelepiped interval model, establish such as under body crash-worthiness Model for Multi-Objective Optimization:
XL≤X≤XR,U∈Γ(UI,ρ).
Wherein, gi(X, U), i=1,2 ..., l indicate that the constraint function in the Model for Multi-Objective Optimization, l are its number,For
The permission section of i-th of constraint function value,RespectivelyLower bound and the upper bound, XL,XRThe respectively described optimized variable X
Lower bound and the upper bound.
4. according to the method described in claim 2, it is characterized in that, it is described to generate multiple sample points and use radial basis function model
The sample point is fitted, the objective function and constraint function for constructing the Model for Multi-Objective Optimization include:
One group of sample point is generated using Latin hypercube experimental design, and target is calculated using vehicle frontal collision finite element model
Functional value and constraint functional value；
The sample point is fitted using radial basis function, randomly selects part sample point building Model for Multi-Objective Optimization
The approximate model of objective function and constraint function, and error is carried out using fitting precision of the remaining sample point to built approximate model
Evaluation.
5. according to the method described in claim 2, it is characterized in that, to the uncertain domain of uncertain parameter in the optimized variable into
Row affine coordinate is converted
According to the correlation coefficient ρ and related angle θ between uncertain parameter, transition matrix A is calculated
Wherein,
In formula, q is the number of uncertain parameter, according to the section radius U of uncertain parameter U before transition matrix A and conversionw, pass through
The section radius P of uncertain parameter P after following formula coordinates computed conversionsw:
Pw=[| A |]-1Uw；
According to the interval midpoint U of uncertain parameter U before convertingcDetermine the interval midpoint P of P after convertingc, enable Pc=Uc。
6. according to the method described in claim 5, it is characterized in that, it is described using miniature section multi-objective genetic algorithm to multiple target
Optimized model carries out optimization, obtains optimal solution set and includes:
The relevant parameter of outer layer Micro Multi-objective Genetic Algorithm is arranged in step A, and the initial population P that number of individuals is 5 is randomly generated1,
Initialize external population Pe, juxtaposition PeFor empty set, iterative value t is enabled to be initially 1；
Step B, to current Advanced group species PtIn each individual, the section of each of which target function value is acquired using interval analysis, and
Using the weighted sum of the midpoint in target function value section and radius as the target function value of the individual；
Step C merges Advanced group species PtAnd external population Pe, according to non-dominant relationship, construct multiple non-dominant individual collection；
Step D, if external population PeConsecutive identical algebra reaches default and restarts maximum algebra M, then regenerates Advanced group species
Pt, return step B is no to then follow the steps E；
Step E collects for each non-dominant individual, calculates the crowding distance of wherein each individual；
Step F is updated external population according to current optimal non-dominant individual collection；
Step G, if iterative value t reaches default greatest iteration number tmax, then stop iteration, export current optimal solution set；Otherwise, it holds
Row step H；
Step H, to the Advanced group species P regeneratedtMake genetic manipulation, generates progeny population Pt+1, t=t+1 is enabled, step is back to
B。
7. according to the method described in claim 1, it is characterized in that, an optimal solution is selected from the optimal solution set includes:
An optimal solution is selected in the optimal solution set of acquisition according to the preference information of policymaker.
8. according to the method described in claim 1, it is characterized in that, an optimal solution is selected from the optimal solution set further include:
Take in the optimal solution set all optimal solutions in the minimum value in each target as the ideal point in current goal space, meter
Each optimal solution is calculated at a distance from object space between the ideal point, and using distance minimum corresponding optimal solution as selecting
Optimal solution.
9. according to the method described in claim 1, it is characterized in that, select vehicle crash-worthiness optimization multiple target objects simultaneously select
The structure of multiple specified parts or material parameter include: as optimized variable when vehicle body collides
Body quality and one or more vehicle body Impact Resisting Capability evaluation indexes are selected as target object and selects specified parts
Thickness parameter as optimized variable, the value of the thickness parameter of the multiple specified parts is unknown.
10. according to the method described in claim 9, it is characterized in that, the target object are as follows: body quality and auto pedal away from
Displacement from main driving seat bottom fixed point；
The specified parts include automobile outer cover, automobile inner cover, bumper, left shield, left front girder, left front lower bracket and a left side
Frame arm.
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