CN114611216A

CN114611216A - Method and device for optimizing vehicle body structure and electronic equipment

Info

Publication number: CN114611216A
Application number: CN202210265787.9A
Authority: CN
Inventors: 谢嘉悦; 陈有松; 沈国民; 段利斌
Original assignee: SAIC Motor Corp Ltd; Shanghai Automotive Industry Corp Group
Current assignee: SAIC Motor Corp Ltd; Shanghai Automotive Industry Corp Group
Priority date: 2022-03-17
Filing date: 2022-03-17
Publication date: 2022-06-10

Abstract

The embodiment of the application discloses a method and a device for optimizing a vehicle body structure and electronic equipment. The method comprises the following steps: determining an optimization objective of the system; determining an optimized variable of a system, an optimized variable of each subsystem and a coupling variable between each subsystem and the system, wherein the coupling variable is the same optimized variable between each subsystem and the system; determining optimization targets and constraint conditions of a system and each subsystem, wherein the constraint conditions of the system are that the numerical values of coupling variables are consistent with the subsystems; for each subsystem, the optimization objective is that the value of the coupling variable and the difference of the system are minimum, and the constraint function is the design objective of the output response of the subsystem; determining an optimization model of the system according to the optimization variables, the optimization targets and the constraint conditions of the system, and determining the optimization model of each subsystem according to the optimization variables, the optimization targets and the constraint functions of each subsystem; obtaining an optimal solution by utilizing an optimization model of the system and optimization models of all subsystems; so as to improve the degree of the optimization result meeting the optimization target.

Description

Method and device for optimizing vehicle body structure and electronic equipment

Technical Field

The invention relates to the field of vehicles, in particular to a method and a device for optimizing a vehicle body structure and electronic equipment.

Background

With the development of technology, vehicles become a part of people's lives. The vehicle body is an important component of the vehicle, so the optimization of the vehicle body structure is concerned in the vehicle design process.

At present, the optimization of the vehicle body structure is realized by using a multidisciplinary optimization mode, and usually, the multidisciplinary optimization is independently performed, and then the optimization results of each discipline are verified to obtain the optimization results of the system. However, it is difficult to obtain a more accurate optimization result by performing multidisciplinary optimization alone.

Therefore, there is a need for a method for optimizing a vehicle body structure to improve the degree of the optimization result meeting the optimization goal.

Disclosure of Invention

In view of the above, the present application provides a method and an apparatus for optimizing a vehicle body structure, and an electronic device, so as to improve accuracy of an optimization result.

In a first aspect, the present application provides a method of vehicle body structure optimization, the method comprising:

determining an optimization target of the system;

determining optimization variables of the system, optimization variables of the subsystems and coupling variables between the subsystems and the system, wherein the coupling variables are the same optimization variables between the subsystems and the system;

determining optimization targets and constraint conditions of a system and each subsystem; the constraint condition of the system is that the value of the coupling variable is consistent with that of the subsystem; for each subsystem, the optimization objective is that the value of the coupling variable and the difference of the system are minimum, and the constraint function is the design objective of the output response of the subsystem;

determining an optimization model of the system according to the optimization variables, the optimization targets and the constraint conditions of the system, and determining the optimization model of each subsystem according to the optimization variables, the optimization targets and the constraint functions of each subsystem;

and obtaining an optimized optimal solution by using the optimization model of the system and the optimization models of the subsystems.

By using the scheme of the embodiment of the application, the optimal solution is obtained through system-level optimization and subsystem-level optimization. For the optimization of the subsystem level, each subsystem can reach respective optimization target under respective constraint condition, and has certain autonomy; and by applying the idea of cooperative optimization, the optimal solution is finally obtained through the consistency of the coupling variables between the system and each subsystem. The finally obtained optimal solution can meet the optimization target of the system and the optimization target of each subsystem, so that the optimization targets of the system and each subsystem are better met.

In a possible embodiment, the optimization variables of each subsystem include:

determining an output response of each subsystem;

and determining the optimized variable of each subsystem in the design variables of each subsystem according to the output response of each subsystem.

In a possible embodiment, the determining the optimized variable of each subsystem from the design variables of each subsystem according to the output response of each subsystem includes:

and (4) carrying out sensitivity analysis of output response on the design variables of each subsystem, and determining the optimized variables of each subsystem.

In a possible implementation manner, before the determining the optimization model of each subsystem according to the optimization variables, the optimization objectives, and the constraint functions of each subsystem, the method further includes:

establishing an approximate model of each subsystem, wherein the input of the approximate model is an optimized variable of each subsystem, and the output of the approximate model is an approximate value of the output response of each subsystem;

the determining an optimization model of each subsystem according to the optimization variables, the optimization targets and the constraint functions of each subsystem includes:

and determining an optimization model of each subsystem according to the optimization variables, the optimization target, the constraint function and the approximation function of each subsystem.

In a possible implementation manner, the obtaining an optimized optimal solution by using the optimization model of the system and the optimization models of the subsystems includes:

acquiring an initial value of an optimized variable of a system;

determining initial values of coupling variables between each subsystem and the system according to the initial values of the optimization variables of the system;

obtaining an optimized value of the coupling variable between each subsystem and the system by utilizing the optimized model of each subsystem according to the initial value of the coupling variable between each subsystem and the system;

and obtaining an optimized optimal solution by utilizing an optimization model of the system according to the optimized values of the coupling variables between each subsystem and the system.

In a possible embodiment, the determining an initial value of a coupling variable between each subsystem and the system according to an initial value of an optimization variable of the system includes:

and giving the initial value amplitude of the optimized variable of the system to the coupling variable between each subsystem and the system to obtain the initial value of the coupling variable between each subsystem and the system.

In a possible implementation, after the obtaining the optimized optimal solution, the method further includes:

and verifying the optimized optimal solution.

In a second aspect, the present application provides a device for the structural optimization of a vehicle body, said device comprising

The model determining module is used for determining an optimization target of the system; determining optimization variables of the system, optimization variables of the subsystems and coupling variables between the subsystems and the system, wherein the coupling variables are the same optimization variables between the subsystems and the system; determining optimization targets and constraint conditions of a system and each subsystem; the constraint condition of the system is that the value of the coupling variable is consistent with that of the subsystem; for each subsystem, the optimization objective is that the value of the coupling variable and the difference of the system are minimum, and the constraint function is the design objective of the output response of the subsystem; the optimization model of the system is determined according to the optimization variables, the optimization targets and the constraint conditions of the system, and the optimization model of each subsystem is determined according to the optimization variables, the optimization targets and the constraint functions of each subsystem;

and the optimal solution obtaining module is used for obtaining an optimal solution by utilizing the optimization model of the system and the optimization models of the subsystems.

In a third aspect, the present application provides an electronic device for vehicle body structure optimization, the electronic device includes a processor and a memory, wherein the memory stores code, and the processor is configured to call the code stored in the memory to implement the method according to any one of the above.

In a fourth aspect, the present application provides a computer readable storage medium for storing a computer program for performing the method of any one of the above.

Drawings

FIG. 1 is a flow chart of a method for multidisciplinary optimization of a vehicle body structure provided by an embodiment of the present application;

FIG. 2A is a schematic diagram of optimization variables of a first discipline provided in an embodiment of the present application;

FIG. 2B is a graph illustrating the bending stiffness sensitivity analysis results provided in the examples of the present application;

FIG. 2C is a graph illustrating the results of a torsional stiffness sensitivity analysis provided in an embodiment of the present application;

fig. 2D is a schematic diagram of a first-order bending mode sensitivity analysis result provided in the embodiment of the present application;

fig. 2E is a schematic diagram illustrating a first-order torsional mode sensitivity analysis result according to an embodiment of the present application;

FIG. 2F is a schematic diagram of optimization variables of a second discipline provided in an embodiment of the present application;

FIG. 2G is a schematic diagram of an optimization process of a system level and two science levels provided by the embodiment of the present application;

FIG. 2H is a graphical illustration of a comparison of intrusion before and after optimization as provided by an embodiment of the present application;

FIG. 2I is a schematic diagram of comparison of intrusion velocity before and after optimization provided by embodiments of the present application;

FIG. 2J is a schematic diagram illustrating comparison of results of optimized front and rear bending stiffness curves provided in an embodiment of the present application;

FIG. 2K is a schematic diagram illustrating comparison of results of optimized front and rear torsional stiffness curves provided in an embodiment of the present application;

fig. 2L is a schematic diagram illustrating a comparison of cell deformation amounts before and after optimization of a side impact crash (AE-MDB) according to an embodiment of the present disclosure;

fig. 2M is a schematic diagram illustrating a comparison of cell deformation amounts before and after optimization of side post impact according to an embodiment of the present application;

fig. 2N is a schematic diagram of a multidisciplinary collaborative optimization model provided in the embodiment of the present application. (ii) a

FIG. 2O is a schematic diagram of a system level optimization target routine provided by an embodiment of the present application;

fig. 2P is a schematic diagram of a system constraint iteration process provided in the embodiment of the present application;

FIG. 2Q is a schematic diagram of an iterative process under another system constraint provided by an embodiment of the present application;

FIG. 3 is a schematic structural diagram of a device for optimizing a vehicle body structure provided by an embodiment of the application;

fig. 4 is a schematic structural diagram of an electronic device for optimizing a vehicle body structure according to an embodiment of the present application.

Detailed Description

Based on this, in the embodiment of the present application provided by the inventor, the method for optimizing the vehicle body structure comprises the following steps: determining an optimization target of the system; determining optimization variables of the system, optimization variables of the subsystems and coupling variables between the subsystems and the system, wherein the coupling variables are the same optimization variables between the subsystems and the system; determining optimization targets and constraint conditions of a system and each subsystem; the constraint condition of the system is that the value of the coupling variable is consistent with that of the subsystem; for each subsystem, the optimization objective is that the value of the coupling variable and the difference of the system are minimum, and the constraint function is the design objective of the output response of the subsystem; determining an optimization model of the system according to the optimization variables, the optimization targets and the constraint conditions of the system, and determining the optimization model of each subsystem according to the optimization variables, the optimization targets and the constraint functions of each subsystem; and obtaining an optimized optimal solution by using the optimization model of the system and the optimization models of the subsystems.

In addition, for the complex system design of the vehicle body structure, the optimization result of the system is obtained by combining the essence of each subsystem in the system and utilizing the processes of design, simulation analysis and the like of each subsystem, the software and the analysis method for analyzing each subsystem can be effectively integrated, and the optimization algorithm and the like of each subsystem can be effectively integrated.

In addition, each subsystem has certain autonomy, and can shorten the number of times of calculation, thereby reducing the waste of manpower, material resources and financial resources caused by serial repeated calculation.

In order to facilitate understanding and explaining technical solutions provided in the embodiments of the present application, technical terms in the embodiments of the present application will be described below.

The multidisciplinary optimization design method refers to a methodology for designing complex systems and subsystems by fully considering the coupling relationship among the systems. The multidisciplinary optimization design method can combine the essence of each discipline in the complex system design, and finally obtains the methodology of the optimal design by utilizing various discipline design and simulation analysis tools.

The multidisciplinary optimization design method originally originates from the aerospace industry, and along with the development of industrial science and technology and software technology, the multidisciplinary optimization design idea is gradually and widely applied to the fields of ships, automobiles and the like.

In the case of an electric vehicle body, the meaning of a multidisciplinary optimization design of the body is generally: based on a multidisciplinary optimization design theory, a method for organically integrating a design model, an analysis model and an optimization model of each discipline and searching an optimal design scheme.

The multidisciplinary optimization design can be simply expressed as a combination of multidisciplinary design, multidisciplinary analysis tools, and multidisciplinary optimization.

Two factors important to multidisciplinary optimization design include: analysis software, analysis methods and the like of each subject can be effectively integrated; and multidisciplinary optimization algorithms such as a collaborative optimization design method and the like can be effectively integrated.

The a-pillar refers to a connecting pillar connecting the roof and the front cabin in the front left and right, between the engine compartment and the cockpit, above the left and right rear-view mirrors. I.e. the pillar between the windscreen and the left and right front doors.

The B-pillar, which is usually located between the front and rear seats of the cabin, is the longitudinal bar between the doors on both the left and right sides of the vehicle. The B-pillar extends from the roof to the underbody. The seat belt is located on the B-pillar when viewed from the inside of the vehicle.

And the C columns are positioned on two sides of the back seat headrest.

For vehicles, the a, B and C pillars are all important parts of the structural strength supporting the vehicle.

In order to facilitate understanding of the technical solutions provided in the embodiments of the present application, a method, an apparatus, and an electronic device for optimizing a vehicle body structure provided in the embodiments of the present application are described below with reference to the accompanying drawings.

While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be embodied in various forms and should not be limited to the embodiments set forth herein. Other embodiments, which can be derived by those skilled in the art from the embodiments given herein without any inventive contribution, are also within the scope of the present application.

In the claims and specification of the present application and in the drawings accompanying the description, the terms "comprise" and "have" and any variations thereof, are intended to cover non-exclusive inclusions.

The embodiment of the application provides a method for optimizing a vehicle body structure.

Referring to fig. 1, fig. 1 is a flowchart illustrating a method for multidisciplinary optimization of a vehicle body structure according to an embodiment of the present disclosure. As shown in FIG. 1, the method for multidisciplinary optimization of the vehicle body structure in the embodiment of the application comprises S101-S105.

And S101, determining an optimization target of the system.

In S101, the system optimization target refers to a target for the vehicle body optimization design as a whole.

For example, for body weight reduction design, the system optimization objective may include body mass minimization.

S102, determining optimization variables of a system, optimization variables of subsystems and coupling variables between the subsystems and the system; the coupling variables are the same optimization variables between each subsystem and the system.

The optimization is divided into system-level optimization and subsystem-level optimization.

The system optimization variables correspond to system level optimization and the subsystem optimization variables correspond to subsystem level optimization.

A subsystem may be understood as a plurality of disciplines in a multidisciplinary design optimization. For example, different optimization disciplines, corresponding to, for example, different optimization tools and optimization methods.

The coupling variables are the same optimization variables between each subsystem and the system, that is, the coupling variables exist between the system and each subsystem respectively.

There is a coupling variable between a subsystem and a system, i.e., there is the same optimization variable between the subsystem and the system.

The coupling variables between the various subsystems and the system are not necessarily the same.

S103, determining optimization targets and constraint conditions of the system and each subsystem; the constraint condition of the system is that the value of the coupling variable is consistent with that of the subsystem; for each subsystem, the optimization objective is to minimize the value of the coupled variables and the differences of the system, and the constraint function is the design objective of the output response of the subsystem.

In the optimization process, the values of the optimization variables are the objects of optimization.

The coupling variables are the same variables between each subsystem and the system and represent the same physical meaning in the optimization process.

The constraint condition of the system is that the value of the coupling variable is consistent with that of the subsystems, which means that the coupling variables between the system and each subsystem are consistent. At the moment, the optimization result obtained under the constraint condition of the system is consistent for the system and the subsystem; and satisfies the optimization objective of the system obtained by S101.

For each subsystem, the constraint function is a design objective of the output response of the subsystem. At the moment, the optimization result obtained under the constraint condition of the system accords with the design target of the output response of the subsystem; and the optimization goal of the subsystem is that the difference between the values of the coupling variables and the system is minimal, i.e. the difference between the values of the coupling variables of the subsystem and the system is minimal while the design goal of the output response is met.

And S104, determining an optimization model of the system according to the optimization variables, the optimization targets and the constraint conditions of the system, and determining the optimization model of each subsystem according to the optimization variables, the optimization targets and the constraint functions of each subsystem.

The system optimization model is used for completing system-level optimization; each subsystem optimization model is used for completing the optimization of the subsystem level.

Optimizing a system level, wherein the optimization variables of a corresponding system, the optimization targets of the system and the constraint conditions of the system are optimized; and optimizing the subsystem level, wherein the optimization variables correspond to each subsystem, the optimization targets of each subsystem and the constraint conditions of each subsystem.

And S105, obtaining an optimized optimal solution by using the optimization model of the system and the optimization models of the subsystems.

And optimizing the subsystem level by using the optimization model of the subsystem, and optimizing the subsystem level by using the optimization model of the subsystem to obtain an optimized optimal solution, namely a final optimization result.

The following description is made with reference to specific implementations.

The application provides a method for optimizing a vehicle body structure, which can be applied to lightweight design of the vehicle body structure. The method for optimizing the structure of the vehicle body in the embodiment of the application comprises S201-S208.

S201, determining that the system optimization target is the minimum vehicle body mass.

S202, according to a system optimization target, determining that the first discipline is optimization under dynamic working condition crashworthiness, and the second discipline is optimization under static working condition rigidity performance and modal performance.

Here, the first discipline and the second discipline are subsystems in the above embodiment. S202 is the determination of the subsystem-level optimization type.

It is understood that, for the first discipline and the second discipline, the building process of the optimization model may be parallel, and the specific order of the building process does not affect the implementation of the embodiment of the present application. The present embodiment is only an example of one implementation.

S203, determining an optimization variable of the first subject.

During the design process, a number of design variables typically occur. In order to optimize the vehicle body, one or more design variables are determined as optimization variables from a plurality of design variables, usually according to preset conditions.

In one possible implementation, the output response is first determined, the selection range of the design variables is determined from the output response, and the optimization variables are then determined from the selection range of the design variables.

The first discipline is optimization under dynamic conditions crashworthiness.

And determining the output response of the first subject, and comprehensively considering various safety indexes of the vehicle body. For example, under different working conditions, the intrusion amount, the intrusion speed, the deformation displacement and the like of different positions of the vehicle body.

In order to realize optimization under the dynamic working condition crashworthiness, working conditions corresponding to the output response of the first subject comprise the following two working conditions:

the first working condition is a side movable deformation barrier collision working condition.

In a side-movable deformable barrier crash condition, outputting a response comprising: the final amount of intrusion of the B-pillar at the shoulder of the occupant is denoted as S_1,1And the final intrusion amount S of the B column at the position corresponding to the skylight_1,2And anMaximum intrusion velocity V of B-pillar at shoulder of passenger_1,1；

The second operating mode is a side column collision operating mode.

Under the side column collision working condition, the output response comprises the following steps: maximum x-direction displacement S of front seat mounting point_1,3Z-direction maximum relative deformation S of front row seat mounting point_1,4And a remaining distance S of the chest of the driver corresponding to the time when the amount of invasion is maximum_1,5。

Design variables associated with optimization under dynamic conditions crashworthiness include: one or more thickness variations, and one or more material variations.

And obtaining the design variables of the first subject by determining the selection range of the design variables under the two working conditions.

In one possible case, the selection principle for determining the selection range of the design variables may include: and respectively determining the selection range of the design variables according to momentum change analysis and absorption energy ratio analysis in the equivalent simplified model of each working condition.

For the first condition, namely the side movable deformation barrier crash condition, the selected range of design variables includes: the collision side front door, the sliding door, the side wall, the B column and the threshold beam;

for the second condition, the side impact condition, the range of design variables selected includes: the collision side front door, the side wall, the B column, the threshold beam, the upper side beam, the bottom transverse beam and the bottom longitudinal beam.

To this end, a selection range of design variables for the first discipline is obtained, and the following is the process of determining the optimized variables for the first discipline.

Considering the convenience of implementation of multidisciplinary optimization, the optimization variables of the first discipline are determined, including the following 7 thickness variables and 5 material variables:

front door anti-collision beam thickness t_1,1Thickness t of anti-collision beam of sliding door_1,2Thickness t of outer plate of roof side rail_1,3Thickness t of outer side reinforcing plate of B column_1,4Sill beam gusset thickness t_1,5Thickness t of floor side reinforcing plate_1,6Thickness t of girder_1,7And a material for the outer plate of the roof side railM_1,1B-pillar outer reinforcing plate material M_1,2Sill beam reinforcing plate material M_1,3Floor side reinforcing plate material M_1,4And girder material M_1,5Referring to fig. 2A, fig. 2A is a schematic diagram of optimization variables of a first discipline provided in the embodiment of the present application.

And S204, determining the optimization variables of the second subject.

In order to achieve optimization of the stiffness performance and modal performance for the static conditions, the conditions corresponding to the output response of the second discipline include stiffness conditions and modal conditions.

Under the stiffness condition, the output response comprises: flexural rigidity K_BAnd torsional rigidity S_T；

In modal conditions, the output response includes: first order bending mode f_BAnd a first order torsional mode f_T。

Design variables related to optimization of stiffness and modal performance, including: one or more thickness variations.

And obtaining the design variables of the second subject by determining the selection range of the design variables.

The selection range of the design variables is determined from engineering experience, taking into account the computational cost of the design and optimization process.

The design variables are determined to be design variables associated with the vehicle body and key target components of the battery pack.

For example, for a design variable under a stiffness condition, the bending stiffness design variable may include: the rear floor comprises a lower left crossbeam, a lower right crossbeam, a rear section inner plate of a left threshold beam, a rear section inner plate of a right threshold beam, a left rear side wall inner plate, a right rear side wall inner plate and the like; torsional stiffness design variables may include: left and right girder reinforcements below the middle base plate, an upper crossbeam joint of the front seat, left and right B-pillar outer plates and the like.

For example, for design variables under modal conditions, the first order bending modal design variables may include: left and right girders-1 under the rear floor, left and right threshold beam rear section inner plates, left and right rear side wall inner plates and the like; the first order torsional mode design variables may include: left and right B-pillar inner panels, left and right B-pillar outer panels, and left and right A-pillar inner panels, etc.

To this end, a selection range of design variables for the second discipline is obtained, and the following is the process of determining the optimization variables for the second discipline.

And carrying out sensitivity analysis on the output response of the second discipline on the design variables of the second discipline, and determining the optimization variables of the second discipline according to the result of the sensitivity analysis.

The output response of the second discipline includes: bending stiffness, torsional stiffness, first order bending mode, and first order torsional mode.

The bending stiffness K is respectively carried out on the design variables of the second subject_BTorsional rigidity S_TFirst order bending mode f_BAnd a first order torsional mode f_TThe sensitivity of (3).

For example, referring to fig. 2B-2E, fig. 2B is a schematic diagram of a bending stiffness sensitivity analysis result provided in the embodiment of the present application, fig. 2C is a schematic diagram of a torsional stiffness sensitivity analysis result provided in the embodiment of the present application, fig. 2D is a schematic diagram of a first-order bending mode sensitivity analysis result provided in the embodiment of the present application, and fig. 2E is a schematic diagram of a first-order torsional mode sensitivity analysis result provided in the embodiment of the present application.

Determining a suitable effective optimization variable of the second subject, for example, denoted as t, from the design variables of the second subject according to the result of the sensitivity analysis_2，1，t_2，2，...，t_2，15。

Referring to fig. 2F, fig. 2F is a schematic diagram of optimization variables of the second discipline provided in the embodiment of the present application.

S205, determining optimization variables of the system, coupling variables of the first subject and the system, and coupling variables of the second subject and the system.

The optimization variables of the system comprise the optimization variables of the first discipline and the optimization variables of the second discipline;

the coupled variables of the first discipline and the system comprise the optimized variables of the first discipline;

the coupled variables of the second discipline and the system include the optimization variables of the second discipline described above.

In summary, it is possible to obtain:

the optimization variable of the first discipline is

X₁＝{t_1，1，t_1，2，...，t_1，7，M_1，1，M_1，2，...，M_1，5}

The second subject's optimization variables are

X₂＝{t_2，1，t_2，2，...，t_2，15}

The optimization variable of the system is

S206, respectively determining constraint conditions of the system, optimization targets and constraint conditions of the first discipline and optimization targets and constraint conditions of the second discipline.

The constraint functions of the first and second disciplines are the design goals of the corresponding output responses.

For the first discipline, there is an output response:

{S_1，1，S_1，2，V_1，1，S_1，3，S_1，4，S_1，5}

for ease of illustration, the output response O of the first discipline₁Design object of (1), denoted as O₁Namely, the constraint condition of the first subject;

the design objective of the output response, for example, for the output response of the first discipline, the design objective may include that each output response meets a preset value range.

For the second discipline, there is an output response:

{K_B，S_T，f_B，f_T}

for ease of illustration, the output response O of the second discipline₂Design object of (1), denoted as O₂I.e. the constraints of the second discipline.

The optimization objective for the first and second disciplines is to minimize the difference between the values of the coupled variables and the system.

For the first discipline, the coupling variables with the system are:

X₁＝{t_1，1，t_1，2，...，t_1，7，M_1，1，M_1，2，...，M_1，5}

the optimization variables of the system corresponding to the coupling variables are as follows:

the optimization goal of the first discipline is min J₁Wherein

min J₁Is represented by J₁Has a minimum value;

for the second discipline, the coupling variables with the system are:

X₂＝{t_2，1，t_2，2，...，t_2，15}

the second subject's optimization objective is min J₂Wherein

min J₂Is represented by J₂Has a minimum value;

for the system, the constraint is

J₁＝0，J₂＝0

In practical application, to improve the convergence of optimization, J can be selected₁And J₂The upper and lower limits are set, for example, to 0.01 and-0.01, respectively.

The constraints of the system can also be understood as optimization targets of various science levels in the whole optimization framework.

It is understood that the objective function of the system is obtained in S201, i.e.

min Mass

Mass is the total quality of the optimized variable of the system, and min Mass represents that the Mass has the minimum value.

For system level optimization, the system's optimization variables are:

s207, determining a system optimization model according to the optimization variables, the optimization targets and the constraint conditions of the system; determining an optimization model of the first subject according to the optimization variables, the optimization target and the constraint function of the first subject; and determining an optimization model of the second discipline according to the optimization variables, the optimization target and the constraint function of the second discipline.

And determining a system optimization model, an optimization model of the first subject and an optimization model of the second subject according to the obtained optimization variables, optimization targets and constraint conditions to obtain an optimized optimal solution.

Because the Genetic Algorithm (GA) has better calculation stability, the method is more suitable for solving the nonlinear problem with constraints. Thus, the optimization models of the first and second disciplines may employ genetic algorithms.

For the optimization process of the system, since the solved target and the constraint condition are linear, the optimization algorithm can adopt a quadratic programming algorithm (NLPQL) to improve the calculation efficiency and hardly cause the reduction of the calculation precision.

In S201-S207, the vehicle body is optimally designed and disassembled into a system level optimization problem and two science and science level optimization problems.

In the system-level optimization process, the lightweight design of the vehicle body is mainly considered, and the aim of vehicle body lightweight is to minimize the mass of the vehicle body; in the optimization process of science and science, the crash resistance of the dynamic working condition of the whole vehicle and the rigidity performance and modal performance of the static working condition are respectively optimized.

Referring to fig. 2G, fig. 2G is a schematic diagram of an optimization process of a system level and two science levels provided by the embodiment of the present application.

As shown in fig. 2G, the sub-discipline 1 is the first discipline, and the sub-discipline 2 is the second discipline.

In fig. 2G, "s.t." is a constraint on system-level and discipline-level optimization functions. For the optimization of two science levels, specifically referring to the design objective of the output response, the specific explanation of the parameters refers to the descriptions of S203-S204, which are not repeated herein. .

For the explanation of other parameters, refer to the explanation of S205-S206, which are not described herein.

And S208, obtaining an optimized optimal solution by using the optimization model of the system, the optimization model of the first subject and the optimization model of the second subject.

In one possible implementation, determining the optimization model of the first/second disciplines according to the optimization variables, the optimization objectives and the constraints of the first/second disciplines may include:

constructing an approximate model of the first discipline, wherein the input of the approximate model is an optimization variable of the first discipline, and the output of the approximate model is an approximate value of an output response of the first discipline;

and constructing an approximate model of the second discipline, wherein the input of the approximate model is the optimization variable of the second discipline, and the output of the approximate model is the approximate value of the output response of the second discipline.

For an original model (also referred to as a detailed model) that optimizes variables and output responses, the inputs to the model are the optimized variables and the outputs of the model are the output responses. To simplify the calculation process, the output response is obtained using an approximate model, rather than directly using the original model.

In the process of solving engineering problems, numerical simulation is usually performed on experiments to evaluate constraint functions and objective functions under different design variables.

However, when the number of models used for numerical simulation is large, for example, hundreds or thousands, the amount of calculation for directly performing numerical simulation is too large to be realized. Therefore, the above approximate model is usually established, and the calculation result of the original model is estimated/predicted to reduce the calculation amount.

In the process of multidisciplinary optimization design, the above approximate model is called a proxy model or a meta model.

For different engineering problems, different methods of constructing the approximate model are generally adopted.

For example, a neural network method (RBF), a polynomial Response Surface Method (RSM), a Kriging method (Kriging), a Support Vector Machine (SVM), and the like.

The kriging approximate model obtained by the kriging method has good nonlinear fitting effect and quantized error estimation function. Therefore, the method is widely applied to numerical simulation of the collision condition.

The realization of the kriging approximate model is mainly divided into the following three parts: sampling, constructing a model and estimating variance. In order to improve the accuracy of the approximation model, effective sampling is performed in the design space.

In the aspect of approximate model construction of static working conditions, a polynomial Response Surface Method (RSM) is adopted to establish an approximate model of rigidity and modal.

In a possible case, the approximate models of the components can be respectively and uniformly sampled by an Optimal Latin Hypercube (Optimal Latin Hypercube) sampling method for comparison with the original models, so as to obtain the fitting precision of the approximate models. The precision of the approximate model is used for indicating whether the constructed approximate model can replace the original model (detailed model) to carry out subsequent multidisciplinary optimization work.

In an example of the present embodiment, the approximate model of the first discipline may include two, respectively, approximate models of a side impact and a side post impact;

since the design variables for the stiffness regime and the modal regime are the same, only one approximation model can be constructed for the second discipline.

When the optimization model of the system is determined, the quality of the variable is taken as an optimization target, and the influence of the error of the approximate model can be effectively reduced.

In a possible implementation manner, obtaining an optimized optimal solution by using the optimization model of the system, the optimization model of the first discipline, and the optimization model of the second discipline may include:

and data is transmitted between the optimization model of the system and the optimization models of all disciplines.

The manner of data transfer may be varied. For example, the definition may be implemented by assignment, or by directly adding a data stream to a line in the data stream.

In a possible implementation manner, the obtaining of the optimized optimal solution by using the optimization model of the system and the optimization models of the subsystems may be implemented in the following manner, including S301 to S304.

S301, obtaining initial values of optimization variables of the system.

The initial value of the optimized variable of the system refers to the value of the optimized variable of the system before optimization.

S302, determining initial values of coupling variables between each subsystem and the system according to the initial values of the optimization variables of the system.

In one possible implementation manner, S302 may be implemented by: and giving the initial value amplitude of the optimized variable of the system to the coupling variable between each subsystem and the system to obtain the initial value of the coupling variable between each subsystem and the system.

That is, the initial values of the coupling variables between each subsystem and the system are determined to be the initial values of the optimization variables of the system.

For example, after obtaining the initial value of the optimized variable of the system, the amplitude of the coupled variable of the first subject and the system is the initial value of the optimized variable of the system corresponding to the coupled variable; and the amplitude of the coupling variable of the second subject and the system is the initial value of the optimization variable of the system corresponding to the coupling variable.

And S303, obtaining the optimized value of the coupling variable between each subsystem and the system by utilizing the optimized model of each subsystem according to the initial value of the coupling variable between each subsystem and the system.

S304, obtaining an optimized optimal solution by using an optimization model of the system according to the optimized values of the coupling variables between each subsystem and the system.

S303-S304 means that after each subsystem completes iterative computation, the system obtains a final optimization result, namely an optimized optimal solution, according to the optimization results of each subsystem.

In a possible implementation manner, after the optimal solution is obtained in the above manner, the optimal solution may be verified.

And in some possible cases, bringing the obtained optimal solution into the original detailed model for verification. For example, crash resistance, stiffness, and modal performance verification is performed.

The verification results obtained are shown in FIG. 2K-.

FIG. 2H is a graphical illustration of a comparison of intrusion before and after optimization as provided by the examples of the present application.

In fig. 2H, the final shoulder intrusion results are compared on the left side, with time (in ms) on the abscissa and intrusion (in mm) on the ordinate, the intrusion before optimization being shown by the solid line, and the intrusion after optimization being shown by the dashed line.

The right side of fig. 2H is a graph comparing the results of the intrusion of the canopy, with time (in ms) on the abscissa and intrusion (in mm) on the ordinate, the optimized front canopy intrusion being shown by the solid line and the optimized rear canopy intrusion being shown by the dashed line.

Fig. 2I is a schematic diagram of comparison of intrusion speed before and after optimization provided by the embodiment of the present application.

The abscissa is time (in ms), the ordinate is intrusion velocity (in mm/ms), the solid line indicates the intrusion velocity of the optimized anterior dummy shoulder, and the dotted line indicates the intrusion velocity of the optimized posterior dummy shoulder.

Fig. 2J is a schematic diagram of comparison of results of bending stiffness curves before and after optimization provided in the embodiment of the present application.

The abscissa is a node ID, and different node IDs represent different positions of the vehicle body; the ordinate is the displacement (in mm), the solid line represents the original bending stiffness curve and the dashed line represents the optimized bending stiffness curve.

Fig. 2K is a schematic diagram of comparison of results of optimized front and rear torsional stiffness curves provided in the embodiments of the present application.

The abscissa is a node ID, and different node IDs represent different positions of the vehicle body; the ordinate is the displacement (in mm), the solid line represents the original torsional stiffness curve and the dashed line represents the optimized torsional stiffness curve.

And comparing the intrusion amount before and after optimization, and bringing the optimal solution into the original detailed model for verifying the collision resistance, the rigidity and the modal performance to verify the optimization result of the proxy model.

The safety performance of the battery pack can be verified, and specifically, the cell deformation before and after optimization under the side collision working condition and the cell deformation before and after optimization under the side column collision working condition are obtained.

Fig. 2L is a schematic diagram illustrating a comparison of cell deformation amounts before and after optimization of a side impact (AE-MDB) according to an embodiment of the present disclosure.

The left side in fig. 2L is a schematic diagram of the cell deformation amount before optimization, and the right side in fig. 2L is a schematic diagram of the cell deformation amount after optimization.

In the Y direction, the cell deformation before optimization is 0.3mm, and the cell deformation after optimization is 0.2 mm.

Fig. 2M is a schematic diagram of comparing deformation amounts of the cell before and after optimization of side bumping according to the embodiment of the present application.

The left side in fig. 2M is a schematic diagram of the cell deformation amount before optimization, and the right side in fig. 2M is a schematic diagram of the cell deformation amount after optimization.

In the Y direction, the cell deformation before optimization is 2.8mm, and the cell deformation after optimization is 2.1 mm.

As an example, the embodiment of the application provides a building process of a system-level optimization model and two science-level optimization models.

Referring to fig. 2N, fig. 2N is a schematic diagram of a multidisciplinary collaborative optimization model provided in the embodiment of the present application.

As shown in fig. 2N, an optimization module of the first discipline is built. "Opt _ blast" (Optimization blast collision condition Optimization) is an Optimization module of the first subject, and system level constraints and targets are set in the module; "AE-MDB (side impact)" and "SIDPole (side post impact)" are proxy models for two conditions of crashworthiness; "Calc _ J1" (Call J1) is a formula definition for the first discipline optimization objective.

As shown in fig. 2N, an optimization module for the second discipline is built. "Opt _ Static" is an Optimization module of the second discipline in which system level constraints and objectives are set; "Static" (Static working condition) is a proxy model, and because the rigidity is completely the same as the design variable of the modal working condition, only one proxy model needs to be established here; "Calc _ J2" (Calculate J2 for calculation J2) is the formula definition for the second discipline optimization objective.

As shown in FIG. 2N, "Option systems" (Optimization system Overall Optimization System) is an Optimization of an overall system-level module in which system-level constraints and goals are set, and to improve the convergence of the collaborative Optimization framework, the upper limits of J1 and J2 may be set to 0.01 and the lower limits to-0.01. "Transtorm" is the transformation of system-level and subject-level parameters.

The 'Calc _ systems' (called system level calculation) is a calculator corresponding to a system level module, a formula of a system level optimization target is defined in the module, and the quality of a variable is skillfully used as the optimization target, so that the influence of an agent model error is effectively reduced.

The calculation formula can be:

data transfer between the system and the disciplines is performed.

The method for defining data transmission in software is various, and can be defined by using an assignment method, or can be realized by directly adding a data flow direction line in a data flow.

As shown in fig. 2N, a value assignment may be used to implement the data transfer, and the calculator may transfer the system-optimized variables to the variables of each discipline, so as to perform a new round of sub-discipline iteration.

Because the Genetic Algorithm (GA) has strong computational stability and is suitable for solving the nonlinear problem with constraints, the optimization algorithms of the first and second disciplines can both adopt genetic algorithms.

The remaining related definitions of the second discipline are similar to the first discipline and will not be described further herein.

For the total system, no matter the solved target or the constraint condition is linear, so a continuous quadratic programming algorithm (NLPQL) can be adopted, the calculation efficiency can be improved, and the calculation precision cannot be reduced.

For the system level constraints and the iterative history of the optimization objective in the optimization process, the present embodiment gives the following example, see fig. 2O-2Q.

Fig. 2O is a schematic diagram of a system-level optimization target routine provided in the embodiment of the present application, fig. 2P is a schematic diagram of a system constraint iterative routine provided in the embodiment of the present application, and fig. 2Q is a schematic diagram of another system constraint iterative routine provided in the embodiment of the present application.

For the description of the parameters in fig. 2O-2Q, refer to the above description, and are not repeated here.

The system-level optimization of the whole optimization process is carried out for 236 times in total, 2237988 times in the first discipline and 3981320 times in the second discipline, and finally the optimal solution is obtained when J1 is 0.0 and J2 is 0.08.

And the problems are subjected to multidisciplinary optimization by adopting a collaborative optimization frame, and the weight of the body in white is reduced by 21kg under the condition that the performances of the body in white are basically maintained unchanged compared with the initial state, so that a better light weight effect is obtained.

The embodiment of the application also provides a device for optimizing the structure of the vehicle body.

Referring to fig. 3, fig. 3 is a schematic structural diagram of a device for optimizing a vehicle body structure according to an embodiment of the present application.

As shown in fig. 3, the apparatus 200 includes a model determining module 201 and an optimal solution obtaining module 202.

A model determination module 201 for determining an optimization objective of the system; determining optimization variables of the system, optimization variables of the subsystems and coupling variables between the subsystems and the system, wherein the coupling variables are the same optimization variables between the subsystems and the system; determining optimization targets and constraint conditions of a system and each subsystem; the constraint condition of the system is that the value of the coupling variable is consistent with that of the subsystem; for each subsystem, the optimization objective is that the value of the coupling variable and the difference of the system are minimum, and the constraint function is the design objective of the output response of the subsystem; the optimization model of the system is determined according to the optimization variables, the optimization targets and the constraint conditions of the system, and the optimization model of each subsystem is determined according to the optimization variables, the optimization targets and the constraint functions of each subsystem;

and an optimal solution obtaining module 202, configured to obtain an optimal solution by using the optimization model of the system and the optimization models of the subsystems.

The units included in the device and the connection relationship among the units can achieve the same technical effect as the method, and are not repeated here to avoid repetition.

The embodiment of the application also provides electronic equipment for optimizing the structure of the vehicle body.

Referring to fig. 4, fig. 4 is a schematic structural diagram of an electronic device for optimizing a vehicle body structure according to an embodiment of the present application.

As shown in fig. 4, the electronic device 300 comprises a processor and 301 a memory 302, wherein the memory 302 stores codes, and the processor 301 is configured to call the codes stored in the memory 302 to execute any one of the above methods.

The units included in the above device and the connection relationship between the units can achieve the same technical effects as the above method, and are not described herein again to avoid repetition.

In an embodiment of the present application, a computer-readable storage medium is further provided, where the computer-readable storage medium is used for storing a computer program, and the computer program is used for executing the method for optimizing a vehicle body structure, and can achieve the same technical effect, and in order to avoid repetition, details are not repeated here. The computer-readable storage medium may be a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk. The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method of vehicle body structure optimization, the method comprising:

determining an optimization target of the system;

2. The method of claim 1, wherein the optimization variables for each subsystem comprise:

determining an output response of each subsystem;

3. The method of claim 2, wherein determining the optimized variables for each subsystem among the design variables for each subsystem based on the output response of each subsystem comprises:

4. The method of claim 1, prior to determining the optimization model for each subsystem based on the optimization variables, the optimization objectives, and the constraint functions for each subsystem, further comprising:

and determining an optimization model of each subsystem according to the optimization variables, the optimization target, the constraint function and the approximate function of each subsystem.

5. The method of claim 1, wherein obtaining an optimized optimal solution using the optimization model of the system and the optimization models of the subsystems comprises:

acquiring an initial value of an optimized variable of a system;

6. The method of claim 5, wherein determining initial values for coupling variables between each subsystem and the system based on initial values for optimization variables for the system comprises:

7. The method of claim 1, further comprising, after said obtaining the optimized optimal solution:

and verifying the optimized optimal solution.

8. Device for the structural optimization of a vehicle body, characterized in that it comprises

9. An electronic device for vehicle body structure optimization, characterized in that the electronic device comprises a processor and a memory, wherein the memory stores code, and the processor is configured to call the code stored in the memory to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that the computer-readable storage medium is used to store a computer program for performing the method of any of claims 1 to 7.