CN116894292A

CN116894292A - Method and device for determining static stiffness of auxiliary frame, vehicle and storage medium

Info

Publication number: CN116894292A
Application number: CN202310347129.9A
Authority: CN
Inventors: 苏永雷
Original assignee: Xiaomi Automobile Technology Co Ltd
Current assignee: Xiaomi Automobile Technology Co Ltd
Priority date: 2023-04-03
Filing date: 2023-04-03
Publication date: 2023-10-17
Anticipated expiration: 2043-04-03
Also published as: CN116894292B

Abstract

The disclosure provides a method, a device, a vehicle and a storage medium for determining static stiffness of a subframe, and relates to the technical field of automobiles, wherein the method comprises the following steps: generating a subframe-related visual superunit model; generating a KC characteristic analysis model according to the subframe related visual superunit model, wherein the KC characteristic analysis model is used for analyzing KC characteristics; determining first association rule information between KC performance and static rigidity of the auxiliary frame; and determining a target value of the static rigidity of the auxiliary frame according to the KC characteristic analysis model and the first association rule information. Through the method and the device, the determination accuracy of the static rigidity of the auxiliary frame can be effectively improved.

Description

Method and device for determining static stiffness of auxiliary frame, vehicle and storage medium

Technical Field

The disclosure relates to the technical field of automobiles, in particular to a method and a device for determining static stiffness of a subframe, a vehicle and a storage medium.

Background

Subframe structural design is required to meet multidisciplinary performance, e.g., noise, vibration and harshness (Noise, vibration, harshness, NVH), durability, including in particular: subframe mode, dynamic stiffness, strength, fatigue and other properties. The running performance is also closely related to the structural design of the auxiliary frame. NVH performance focuses on dynamic stiffness in the frequency band above 30Hz (Hertz), while driving performance focuses on frequency band characteristics below 30Hz, and the pleasurable driving experience of vehicle driving is assessed by subframe static stiffness, which may also be referred to as subframe attachment point static stiffness.

In the related art, a standard or empirical method is generally adopted to determine the static stiffness of the auxiliary frame.

In this way, the accuracy of determining the static stiffness of the subframe is not high.

Disclosure of Invention

In order to overcome the problems in the related art, the disclosure provides a method, a device, a vehicle and a non-transitory computer readable storage medium for determining the static stiffness of a subframe, which can effectively improve the accuracy of determining the static stiffness of the subframe.

According to a first aspect of embodiments of the present disclosure, there is provided a subframe static stiffness determining method, including: generating a subframe-related visual superunit model; generating a KC characteristic analysis model according to the subframe related visual superunit model, wherein the KC characteristic analysis model is used for analyzing KC characteristics; determining first association rule information between KC performance and static rigidity of the auxiliary frame; and determining a target value of the static rigidity of the auxiliary frame according to the KC characteristic analysis model and the first association rule information.

According to a second aspect of embodiments of the present disclosure, there is provided a subframe static stiffness determining apparatus, including: the first generation unit is used for generating a subframe-related visual superunit model; the second generation unit is used for generating a KC characteristic analysis model according to the subframe related visual superunit model, wherein the KC characteristic analysis model is used for analyzing KC characteristics; the first determining unit is used for determining first association rule information between KC performance and static rigidity of the auxiliary frame; and the second determining unit is used for determining the target value of the static rigidity of the auxiliary frame according to the KC characteristic analysis model and the first association rule information.

According to a third aspect of embodiments of the present disclosure, there is provided a vehicle comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to: the method for determining the static rigidity of the auxiliary frame comprises the following steps of implementation of the method for determining the static rigidity of the auxiliary frame provided by the first aspect of the embodiment of the disclosure.

According to a fourth aspect of embodiments of the present disclosure, there is provided a non-transitory computer readable storage medium, which when executed by a processor of a mobile terminal, causes the mobile terminal to perform a subframe static stiffness determination method, the method comprising: generating a subframe-related visual superunit model; generating a KC characteristic analysis model according to the subframe related visual superunit model, wherein the KC characteristic analysis model is used for analyzing KC characteristics; determining first association rule information between KC performance and static rigidity of the auxiliary frame; and determining a target value of the static rigidity of the auxiliary frame according to the KC characteristic analysis model and the first association rule information.

The technical scheme provided by the embodiment of the disclosure can comprise the following beneficial effects:

the KC characteristic analysis model is used for analyzing KC characteristics, determining first association rule information between KC performance and static stiffness of the auxiliary frame, and determining a target value of the static stiffness of the auxiliary frame according to the KC characteristic analysis model and the first association rule information, so that the determination accuracy of the static stiffness of the auxiliary frame can be effectively improved.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure.

FIG. 1 is a flow chart of a method for determining static stiffness of a subframe according to an embodiment of the present disclosure;

FIG. 2 is a flow chart of a method for determining the static stiffness of a subframe according to another embodiment of the present disclosure;

FIG. 3 is a schematic illustration of a subframe restraint model in an embodiment of the present disclosure;

FIG. 4 is a schematic diagram of a multi-condition co-simulation workflow in an embodiment of the disclosure;

FIG. 5 is a schematic diagram of design variable definition criteria in an embodiment of the present disclosure;

FIG. 6 is a schematic diagram of a first data matrix between subframe static stiffness and KC performance in an embodiment of the present disclosure;

FIG. 7a is a diagram illustrating performance correlations identified based on confidence intervals in an embodiment of the disclosure;

FIG. 7b is a schematic diagram of identifying performance correlations based on confidence intervals in another embodiment of the disclosure;

FIG. 8 is a flow chart of a method for determining the static stiffness of a subframe according to another embodiment of the present disclosure;

FIG. 9 is a schematic diagram of an iterative process for KC performance and subframe static stiffness optimization in an embodiment of the present disclosure;

FIG. 10 is a schematic illustration of a static stiffness performance index initiation of a subframe in an embodiment of the disclosure;

FIG. 11 is a two-dimensional map of KC performance versus gauge parameters in an embodiment of the present disclosure;

FIG. 12 is a graph showing the results of optimizing subframe material thickness in an embodiment of the present disclosure;

FIG. 13 is a schematic structural view of a subframe static stiffness determination device according to an embodiment of the present disclosure;

FIG. 14 is a functional block diagram of a vehicle shown in an exemplary embodiment.

Detailed Description

Reference will now be made in detail to some embodiments of the present disclosure, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. Various changes, modifications, and equivalents of the methods, devices, and/or systems described herein will become apparent after an understanding of the present disclosure. For example, the order of operations described herein is merely an example and is not limited to those set forth herein, but may be altered as will become apparent after an understanding of the disclosure, except where necessary to perform the operations in a particular order. In addition, descriptions of features known in the art may be omitted for the sake of clarity and conciseness.

The implementations described below in some examples of the disclosure are not representative of all implementations consistent with the disclosure. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present disclosure as detailed in the accompanying claims.

Fig. 1 is a flow chart illustrating a method for determining static stiffness of a subframe according to an embodiment of the present disclosure.

The present embodiment is exemplified by the subframe static stiffness determining method being configured as the subframe static stiffness determining apparatus, and the subframe static stiffness determining method in the present embodiment may be configured in the subframe static stiffness determining apparatus, and the subframe static stiffness determining apparatus may be provided in a server, or may also be provided in an electronic device, to which the embodiment of the present disclosure is not limited.

The present embodiment takes an example in which the subframe static stiffness determination method is configured in an electronic apparatus. Among them, electronic devices such as in-vehicle devices, computer devices, vehicle performance analysis platforms, and the like have hardware devices of various operating systems.

The execution body of the embodiment of the present disclosure may be, for example, a server or a central processing unit (Central Processing Unit, CPU) in an electronic device in hardware, or may be, for example, a server or a related background service in an electronic device in software, which is not limited.

As shown in fig. 1, the method for determining the static stiffness of the auxiliary frame comprises the following steps:

s101: and generating a subframe-related visual superunit model.

The superunit model is a model obtained by modeling some components based on superunit analysis, and can be used for performing simulation analysis on the modeled components.

The visualized superunit model related to the auxiliary frame can be, for example, a visualized superunit model of the auxiliary frame and a superunit model related to the auxiliary frame, and the related component related to the auxiliary frame can be, for example, a suspension control arm, a swing arm, a fork arm and the like, without limitation.

In some embodiments, in the process of determining the static stiffness of the auxiliary frame, the auxiliary frame related visual superunit model may be first generated, and modeling analysis processing of the static stiffness of the auxiliary frame may be assisted according to the auxiliary frame related visual superunit model, which is not limited.

In some embodiments, the method may directly determine the type of the subframe required by the vehicle according to the type of the vehicle based on the visual superunit analysis modeling application program, and generate a corresponding subframe-related visual superunit model according to the type of the subframe.

In some embodiments of the present disclosure, a subframe reference finite element model and a subframe related component finite element model may be first established, and a subframe visual superunit model may be generated according to the subframe reference finite element model, and a subframe related component visual superunit model may be generated according to the subframe related component finite element model, where the subframe visual superunit model and the subframe related component visual superunit model are used together as a subframe related visual superunit model, which may accurately model and analyze a subframe, and may also effectively adapt to a personalized design condition of a subframe, and effectively promote flexibility of subframe modeling analysis.

In some embodiments, modeling analysis may be performed on the subframe and the suspension rod system (the suspension rod system may be an optional example of a subframe related component) based on a superunit analysis method, for example, a visual superunit analysis condition of the subframe and the suspension rod system may be established, which is not limited.

In some embodiments, a subframe reference finite element MODEL may be first established, in the process of generating a subframe visual superunit MODEL according to the subframe reference finite element MODEL, nodes of a subframe in the subframe reference finite element MODEL, which are connected with a vehicle body, a suspension, an electric drive and the like through bushings or suspensions, may be set as boundary nodes, an optigruct application program (optigruct application program, which is a finite element structure analysis and optimization software, contains an accurate and fast finite element solver for performing conceptual design and refinement design) is utilized to perform boundary node definition, the PLOT units can be sequentially connected with boundary nodes, an output result is defined through MODEL=PLOTEL, the PLOT units and auxiliary nodes in the subframe reference finite element MODEL can be used for carrying out post-processing and displaying on files of the PLOT units and facilitating construction of a high-precision KC characteristic analysis MODEL, then a dual coordination dynamic substructure (CC) algorithm can be used for carrying out modal analysis to obtain multiple modes of the subframe, modal reduction is carried out to obtain a superunit MODEL of an MNF format (the MNF format is a format of a modal file), and the superunit MODEL can be called as a subframe visual superunit MODEL without limitation.

In other embodiments, in generating the sub-frame-related-component visual superunit model from the sub-frame-related-component finite-element model, the sub-frame-related-component finite-element model may be processed in the same manner as the sub-frame-visual superunit model is generated to generate the sub-frame-related-component visual superunit model, without limitation.

In other embodiments, the finite element models of the suspension control arm, swing arm, yoke, etc. (the finite element models of the suspension control arm, swing arm, yoke, etc., are one optional example of the subframe related component finite element model) are all processed as MNF-format superunit models, which may be referred to as subframe related component visualization superunit models, without limitation.

In other embodiments, the batch header file may be further compiled to implement the invocation of the NASTRAN application solution module, where the NASTRAN application solution module may automatically solve the subframe reference finite element model or the subframe related component visual superunit model to obtain the MNF-format subframe visual superunit model and the subframe related component visual superunit model, which is not limited.

In some embodiments, the constructed sub-frame visualization superunit model and sub-frame related visualization component superunit model may be used to support the formation of a high-precision KC characteristic analysis model, without limitation.

S102: and generating a KC characteristic analysis model according to the subframe related visual superunit model, wherein the KC characteristic analysis model is used for analyzing KC characteristics.

The KC characteristic is a direct influence characteristic of the vehicle handling stability, among others. The characteristics can be classified into K (kinetic) characteristics and C (company) characteristics: the K characteristic is suspension kinematic characteristic, which is the characteristic that angular displacement and linear displacement change are generated between a wheel plane and a wheel center point due to the action of a suspension guide mechanism in the process of reciprocating motion of a wheel in the vertical direction; the characteristic C, suspension elasto-kinematic, is the characteristic of angular and linear displacement variations in the plane and centre of the wheel caused by the forces and moments exerted on the tyre by the ground.

Among them, a model for modeling analysis of KC characteristics may be referred to as a KC characteristic analysis model.

In some embodiments, the KC characteristic analysis model can be generated by combining the sub-frame visual superunit model and the sub-frame related component visual superunit model, so that the analysis precision of the KC characteristic analysis model can be effectively improved.

In some embodiments, the sub-frame visual superunit model and the sub-frame related component visual superunit model can provide information such as positions, masses, inertia, rigidity and the like of a suspension control arm, a swing arm, a fork arm and the like, and can effectively avoid the technical problem that a direct modeling method in related technologies cannot obtain information such as accurate rigidity, inertia and the like, so that an established KC characteristic analysis model is more accurate.

In some embodiments of the present disclosure, it may be that subframe-related component performance parameters are determined, and a C-characteristic analysis model is built from the subframe-related component performance parameters, the subframe-visual superunit model, and the subframe-related component visual superunit model, wherein the C-characteristic analysis model includes: the C characteristic analysis model is used for analyzing the C characteristic, a constraint relation is established for each elastic element in the C characteristic analysis model, and the established C characteristic analysis model is used as a K characteristic analysis model, wherein the K characteristic analysis model is used for analyzing the K characteristic, and the C characteristic analysis model and the K characteristic analysis model are jointly used as a KC characteristic analysis model, so that modeling accuracy and modeling efficiency of the KC characteristic analysis model can be effectively improved.

In some embodiments, a C-characteristic analysis model of the suspension may be established based on an Adams application, where the C-characteristic analysis model of the suspension is an optional example of the C-characteristic analysis model, and the subframe related component may be, for example, an elastic component related to the subframe, and the performance parameter may be, for example, stiffness, damping, etc. of each direction of each elastic component, and then, a kinematic relationship between each elastic component and each rod connected thereto may be established, and the C-characteristic analysis model of the suspension is completed based on the kinematic relationship, which is not limited.

In other embodiments, after the C-characteristic analysis model of the suspension is built, the K-characteristic analysis model may be built on the basis of the C-characteristic analysis model, which is not limited.

In other embodiments, a constraint relationship may be established for the elastic element in the C-characteristic analysis model, and the original attribute may be subjected to a suppression process, where the analysis model obtained by the process may be regarded as an alternative example of the K-characteristic analysis model of the suspension, which is not limited thereto.

In other embodiments, after the KC characteristic analysis model is constructed, a batch header file may be further compiled to implement the call to the Adams application solution module, and when the Adams application solution module is called, the established KC characteristic analysis model may be automatically solved, and the solution result may be converted into a txt format file that may be conveniently read, which is not limited thereto.

S103: first association rule information between KC performance and subframe static stiffness is determined.

The first association rule information refers to information that can be used to describe an association rule between KC performance and static stiffness of the subframe, for example, a rule of association change between KC performance and static stiffness of the subframe, for example, when KC performance changes, the static stiffness of the subframe changes along with the change of KC performance, which is not limited.

S104: and determining a target value of the static rigidity of the auxiliary frame according to the KC characteristic analysis model and the first association rule information.

In some embodiments, a coupling association rule of KC performance and static stiffness of the subframe can be analyzed, and the coupling association rule can be an optional example of first association rule information, and a target value of the static stiffness of the subframe is determined by combining the KC characteristic analysis model and the first association rule information, so that the target value of the static stiffness of the subframe is formulated in an auxiliary and accurate manner according to the KC performance, and the determination accuracy of the static stiffness of the subframe can be improved effectively.

In this embodiment, a KC characteristic analysis model is generated by generating a subframe-related visual superunit model and according to the subframe-related visual superunit model, where the KC characteristic analysis model is used to analyze KC characteristics, determine first association rule information between KC performance and subframe static stiffness, and determine a target value of subframe static stiffness according to the KC characteristic analysis model and the first association rule information, so that determination accuracy of subframe static stiffness can be effectively improved.

Fig. 2 is a flow chart illustrating a method for determining static stiffness of a subframe according to another embodiment of the present disclosure.

As shown in fig. 2, the method for determining the static stiffness of the auxiliary frame comprises the following steps:

s201: and generating a subframe-related visual superunit model.

S202: and generating a KC characteristic analysis model according to the subframe related visual superunit model, wherein the KC characteristic analysis model is used for analyzing KC characteristics.

The descriptions of S201 to S202 may be referred to the above embodiments, and are not repeated here.

S203: equivalent modeling is carried out on connection rigidity of the auxiliary frame and the vehicle body in the auxiliary frame reference finite element model so as to obtain an auxiliary frame constraint model, wherein the connection rigidity comprises the following components: the suspension rigidity and/or the auxiliary frame attachment point rigidity are in series connection, and the auxiliary frame constraint model is used for defining auxiliary frame static rigidity analysis working conditions and boundary information.

Wherein, the subframe reference finite element model can be pre-established.

In some embodiments, the connection rigidity between the subframe and the vehicle body in the subframe reference finite element model can be equivalently modeled to obtain a subframe constraint model, and the subframe constraint model is used for defining the subframe static rigidity analysis working condition and boundary information, so that the method is not limited.

In some embodiments, the connection stiffness includes a suspension stiffness, or may include a subframe attachment point stiffness, or may further include a suspension stiffness and a subframe attachment point stiffness, where the suspension stiffness and the subframe attachment point stiffness are in a serial connection relationship based on an elastic unit, which is not limited.

In some embodiments, a subframe restraint model and an attachment point static stiffness condition may be first established, which may be performed to determine subframe static stiffness without limitation.

In some embodiments, in the process of establishing the subframe constraint model, the subframe reference finite element model may be established first, including: the connection of the sheet metal and the cast part, the welding line, the bolt and the like, and further, the equivalent modeling is carried out on the connection of the auxiliary frame and the vehicle body in the auxiliary frame reference finite element model, and the connection rigidity is simulated through a BUSH unit (the BUSH unit is a common connecting unit which is a common three-way spring-damper unit, and the rigidity and the damping of up to six directions (three translational directions and three rotational directions) can be defined) so as to establish an auxiliary frame constraint model, as shown in fig. 3, and fig. 3 is a schematic diagram of the auxiliary frame constraint model in the embodiment of the disclosure.

In some embodiments, the subframe static stiffness analysis conditions may determine the subframe static stiffness, for example, as follows:

the first step: boundary conditions for the static stiffness analysis may be set.

Taking auxiliary frame and suspension attachment points in the auxiliary frame constraint model as shown in fig. 3 as loading points, respectively loading 1000N (newton) force along the direction of a local coordinate system X, Y, Z at the loading points, performing auxiliary frame static stiffness simulation analysis, and performing post-processing to obtain a displacement value D of the loading points in the acting force direction _total 。

And a second step of: and executing an attachment point static stiffness algorithm.

Based on the loading analysis of the auxiliary frame constraint model, one part of the displacement value is contributed by the auxiliary frame body, and the other part is contributed by the auxiliary frame bodyThe contribution of the subframe to the body, i.e. D _total ＝D _subframe +D _connection 。

In order to eliminate the displacement contributed by the connection of the auxiliary frame and the vehicle body in the embodiment of the disclosure, the auxiliary frame can be processed into a rigid body (for example, the elastic modulus of the material can be amplified by 1000 times), and the corresponding displacement value is calculated and recorded as D _rigid Static rigidity K of mounting point of auxiliary frame _subframe ＝1000/(D _total -D _rigid ) Static rigidity K of mounting point of auxiliary frame _subframe As an alternative example of the static stiffness of the subframe, this is not limiting.

And a third step of: and compiling a batch processing header file to realize the call of the Optifruct application program solving module, wherein the Optifruct application program solving module can automatically solve the established static stiffness working condition when being called, and convert the solved displacement result into a PCH file (PCH file is a pre-compiling header file) format.

S204: and determining a first data matrix between the static stiffness and the KC performance of the auxiliary frame according to the auxiliary frame constraint model.

In the embodiment of the disclosure, the first association rule information between the KC performance and the static stiffness of the auxiliary frame can be integrated into the determination process of the static stiffness of the auxiliary frame, so that the determination accuracy of the static stiffness of the auxiliary frame is improved.

That is, in the embodiment of the disclosure, a multidisciplinary information association process is supported to be established, a static stiffness analysis working condition, a superunit analysis working condition (the superunit analysis working condition can be used for generating a high-precision KC characteristic analysis model) and a KC analysis working condition can be integrated, the superunit analysis working condition and the KC analysis working condition are realized to be in a serial calculation mode, and meanwhile, the superunit analysis working condition and the static stiffness analysis working condition are in a parallel calculation mode, as shown in fig. 4, fig. 4 is a multi-task joint simulation working flow schematic diagram in the embodiment of the disclosure, wherein the static stiffness simulation analysis (i.e. the KC analysis working condition) of the flexible subframe and the superunit analysis working condition share one simulation model, the static stiffness simulation analysis (i.e. the static stiffness analysis working condition) of the rigid subframe independently uses one simulation model, the three working conditions are automatically driven to operate by an integrated working flow, and in each sample operation process, the displacement result of the static stiffness analysis can be automatically extracted, and the KC analysis result is automatically extracted through a txt format file without limitation.

The first data matrix may include some optional value data of static stiffness performance and optional value data of KC performance, which is not limited.

In some embodiments of the present disclosure, in performing the determining of the first data matrix between the static stiffness and the KC performance of the subframe according to the subframe constraint model, it may be, without limitation, determining an experimental design data matrix and extracting the first data matrix from the experimental design data matrix.

The manner of constructing the data matrix for the experimental design can be exemplified as follows:

in some embodiments, simulation debugging can be performed based on the simulation workflow shown in fig. 4, and a single sample (the sample refers to a defined design variable, and the defined design variable may include a material thickness parameter) is analyzed and calculated, so as to ensure that an analysis result is consistent with a direct calculation result of an Adams application program; and further performing analysis and calculation on a plurality of samples to ensure that the association flow can drive the automatic iterative updating of the design variables, and the method is not limited to the automatic iterative updating.

In some embodiments, the variables may be designed to define criteria and linkage execution experiment design, without limitation.

In some embodiments, the defined design variables may be thickness parameters, and the values of the different thickness parameters may reflect changes in the static stiffness of the attachment points, e.g., to accurately characterize the impact of the static stiffness of the attachment points on KC performance, the impact of the sub-frame body stiffness on KC performance may be avoided and reduced. As shown in fig. 5, fig. 5 is a schematic diagram of design variable definition criteria in the embodiment of the disclosure, if the attachment point area is a single bracket, such as the type of point 101, point 102, point 104, and point 114, the bracket thickness (the bracket thickness is an optional example of a material thickness parameter) can be directly modified, that is, the change of the rigidity of the attachment point can be reflected, without causing the change of the overall rigidity of the subframe; if the attachment point area is a through beam, the areas near the attachment points of the beam can be individually grouped as an equivalent local bracket, and the local bracket thickness (the local bracket thickness is an optional example of a material thickness parameter) can be changed, so that the change of the rigidity of the attachment points can be reflected, and the change of the overall rigidity of the auxiliary frame is not caused.

In some embodiments, to effectively reduce the number of design variables, for a bilateral symmetric bracket, one design variable may be shared to implement automatic association, and the total weight of the component with the changed material thickness parameter (referred to as the component with the changed material thickness parameter) is extracted as the performance output in the workflow, which is not limited.

In some embodiments, the above designed material thickness parameters may be used to construct a matrix of experimental design data, without limitation.

In the embodiment of the disclosure, in defining the thickness parameter, a design range of the thickness parameter (the design range includes a plurality of possible parameter values of the thickness parameter, the parameter values may be an optional example of candidate design variables), for example, an upper limit of the design range of the thickness parameter may be set to 12mm (millimeters) to obtain a significant change of static stiffness of the attachment point, an initial experiment sample number is 500, a limit range of each performance associated with a KC analysis condition is input into the integrated workflow according to a performance requirement, an experiment design matrix may be established by using a latin hypercube method, a joint simulation workflow is performed, and multiple simulation condition parallel analysis is automatically performed to form an experiment design data matrix, where the experiment design data matrix may include the thickness parameter, a static stiffness performance (for example, some optional value data of the static stiffness performance), and KC performance data information (for example, optional value data of the KC performance), which is not limited.

In some embodiments of the present disclosure, the determining the performance associated with the KC analysis conditions may be based on a subframe constraint model, where the associated performance includes at least: the static stiffness and the KC performance of the auxiliary frame are determined, constraint range information of the associated performance is determined, and a first data matrix between the static stiffness and the KC performance of the auxiliary frame is determined according to the constraint range information, so that an independent experimental design data matrix between the static stiffness and the KC performance of the auxiliary frame is accurately determined, and the independent experimental design data matrix can be an optional example of the first data matrix.

The constraint range information represents optional range information of the corresponding performance under the constraint of the subframe constraint model, for example, the optional range information of KC performance and the optional range information of the subframe static stiffness can be contained, and the constraint range information is not limited.

In some embodiments, the first data matrix between the static stiffness and the KC performance of the auxiliary frame can be used for modeling and analyzing the correlation rule between the static stiffness and the KC performance of the auxiliary frame, and the method is not limited.

S205: and generating a first approximate model set according to the first data matrix, wherein the first approximate model set comprises a first approximate model corresponding to each KC performance, and the first approximate model is used for carrying out equivalent modeling on the association rule between the static stiffness of the auxiliary frame and the KC performance.

After the first data matrix between the static stiffness and the KC performance of the subframe is formed, a first approximate model set may be generated according to the first data matrix, where the first approximate model set includes a first approximate model corresponding to each KC performance, and the first approximate model is used for performing equivalent modeling on a correlation rule between the static stiffness and the KC performance of the subframe, which is not limited.

The approximation model is a mathematical model in the theory, which refers to a model obtained by performing approximation simulation modeling on a reference object, and in the embodiment of the present disclosure, the reference object may be KC performance, which is not limited.

In some embodiments, the types of KC performance may be multiple, and then a corresponding approximation model may be established for each KC performance, and an approximation model corresponding to the KC performance may be established, and may be referred to as a first approximation model, where the established first approximation model is used for equivalently modeling a correlation rule between the static stiffness of the subframe and the KC performance, and the first approximation model corresponding to each of the multiple KC performances forms a first approximation model set, which is not limited thereto.

In some embodiments of the present disclosure, in performing the generating of the first approximation model set according to the first data matrix, it may be that a plurality of approximation models corresponding to each KC performance in the first data matrix are determined, wherein the approximation models have corresponding model precision values, a value maximum model precision value is selected from the plurality of model precision values, and a first approximation model corresponding to the value maximum model precision value is determined, wherein the first approximation model belongs to the plurality of approximation models, and the first approximation model set is generated according to the plurality of first approximation models, so that the generating accuracy of the first approximation model set is improved, and the effect of performing equivalent modeling on the association rule between the static stiffness of the subframe and the KC performance is effectively improved.

In some embodiments, the experimental design data matrix may be converted to obtain a first data matrix between the static stiffness and KC performance of the subframe, and a first approximate model set may be formed based on the first data matrix, which is not limited.

In some embodiments, the static stiffness of the subframe may be redefined as a new design variable input, the KC performance as an output, and a separate experimental data matrix between the static stiffness of the subframe and the KC performance may be formulated according to the experimental design data matrix described above, where the separate experimental data matrix may be an alternative example of the first data matrix, as shown in fig. 6, and fig. 6 is a schematic diagram of the first data matrix between the static stiffness of the subframe and the KC performance in the embodiment of the disclosure.

In some embodiments, as shown in tables 1 and 2 below, tables 1 and 2 show a first approximation model set construction process schematic. In table 1, an approximation model of a corresponding approximation model type may be constructed based on some approximation model modeling methods, where the approximation model type may be, for example, radial basis function neural network (Radial Basis Function Neural Network, RBF neural network), kriging (kriging) model, optimal response surface optimization (Optimal RSM) model, deep neural network (Deep Neural Networks) model, finite weight neural network (Limited Weights Neural Networks, LWNN), random forest regression (Random Forest Regression, RFR) model, relevance Vector Regression (RVR) model, red pool information volume criterion (Akaike information criterion, AIC), and taylor polynomial (Taylor Polynomail), without limitation.

TABLE 1

TABLE 2

In some embodiments, an approximation model set I (which may be an alternative example of the first approximation model set) between the static stiffness and KC performance of the subframe may be constructed, error analysis may be performed using a K-time cross-check method, and the approximation model accuracy is measured by an R2press accuracy assessment index. For each specific KC performance of the KC characteristics, constructing an approximation model of a corresponding type by adopting a plurality of approximation model construction methods respectively to obtain each approximation model and the precision thereof, selecting the approximation model with the highest model precision as the optimal approximation model (which can be an optional example of a first approximation model) of the KC performance, and establishing an optimal approximation model combination of all KC performances, namely an optimal approximation model set I, as the basis of the next optimization. If the accuracy of the first approximation model of the performance of any KC in the optimal approximation model set I is lower than 95%, the number of experimental samples may be increased, and the experimental design and the construction of the approximation model set I may be performed again, which is not limited.

S206: and determining first association rule information between KC performance and static rigidity of the auxiliary frame according to the first approximate model set.

In some embodiments, after the first approximate model set is obtained by the above construction, the first association rule information between KC performance and subframe static stiffness may be analyzed based on the first approximate model set, which is not limited.

In some embodiments of the present disclosure, an adjustment amount corresponding to KC performance may be determined, the KC performance may be adjusted according to the adjustment amount, and in the process of adjusting the KC performance, index change information corresponding to the static stiffness of the subframe may be determined, where the index change information satisfies an association relationship constrained by a first approximation model set, and first association rule information may be generated according to the adjustment amount and the index change information, and since the first approximation model set includes an optimal first approximation model corresponding to each KC performance, when analyzing the first association rule information between the KC performance and the static stiffness of the subframe based on the optimal first approximation model corresponding to each KC performance, accuracy of analysis of the first association rule information may be effectively improved, and accuracy of determining the static stiffness of the subframe may be further improved.

For example, performance correlation analysis may be performed based on the optimal approximation model set I (may be an optional example of the first approximation model set), the optimal approximation model set I is called, confidence interval analysis is adopted to perform performance correlation identification based on big data driving, as shown in fig. 7a, fig. 7a is a schematic diagram of performance correlation based on confidence interval identification in an embodiment of the disclosure, as shown in fig. 7b, fig. 7b is a schematic diagram of performance correlation based on confidence interval identification in another embodiment of the disclosure, where performance correlation is an optional example of the first correlation rule information, fig. 7a represents a process of adjusting KC performance (for example, KC performance adjustment may be performed based on an adjustment amount determined experimentally), index change information of corresponding static stiffness of a subframe is dynamically identified, fig. 7b shows index change information of static stiffness of a subframe, and also, according to distribution probability of a confidence interval feasible design domain, correlation of KC performance and static stiffness of a subframe is identified (may be an optional example of the first correlation information), meanwhile, the performance correlation may be identified as an optional example of the static stiffness of a subframe is difficult to achieve in order to do not achieve limitation in the process of determining relative KC performance.

S207: and determining a target value of the static rigidity of the auxiliary frame according to the KC characteristic analysis model and the first association rule information.

The description of S207 may be specifically referred to the above embodiments, and will not be repeated here.

In this embodiment, a KC characteristic analysis model is generated by generating a subframe-related visual superunit model and according to the subframe-related visual superunit model, where the KC characteristic analysis model is used to analyze KC characteristics, determine first association rule information between KC performance and subframe static stiffness, and determine a target value of subframe static stiffness according to the KC characteristic analysis model and the first association rule information, so that determination accuracy of subframe static stiffness can be effectively improved. Equivalent modeling is carried out on the connection rigidity of the auxiliary frame and the vehicle body in the auxiliary frame reference finite element model so as to obtain an auxiliary frame constraint model, wherein the connection rigidity comprises the following components: the auxiliary frame constraint model is used for defining auxiliary frame static stiffness analysis working conditions and boundary information, determining a first data matrix between auxiliary frame static stiffness and KC performance according to the auxiliary frame constraint model, and generating a first approximate model set according to the first data matrix, wherein the first approximate model set comprises first approximate models corresponding to each KC performance, and the first approximate models are used for carrying out equivalent modeling on association rules between the auxiliary frame static stiffness and the KC performance, so that the effect of carrying out equivalent modeling on the association rules between the auxiliary frame static stiffness and the KC performance can be effectively improved. When the first association rule information between the KC performance and the static stiffness of the auxiliary frame is guided and analyzed based on the optimal first approximate model corresponding to each KC performance, the accuracy of the analysis of the first association rule information can be effectively improved, and the accuracy of determining the static stiffness of the auxiliary frame is further improved.

Fig. 8 is a flow chart illustrating a method for determining static stiffness of a subframe according to another embodiment of the present disclosure.

As shown in fig. 8, the method for determining the static stiffness of the auxiliary frame comprises the following steps:

s801: and generating a subframe-related visual superunit model.

S802: and generating a KC characteristic analysis model according to the subframe related visual superunit model, wherein the KC characteristic analysis model is used for analyzing KC characteristics.

S803: equivalent modeling is carried out on connection rigidity of the auxiliary frame and the vehicle body in the auxiliary frame reference finite element model so as to obtain an auxiliary frame constraint model, wherein the connection rigidity comprises the following components: the suspension rigidity and/or the auxiliary frame attachment point rigidity are in series connection, and the auxiliary frame constraint model is used for defining auxiliary frame static rigidity analysis working conditions and boundary information.

S804: and determining a first data matrix between the static stiffness and the KC performance of the auxiliary frame according to the auxiliary frame constraint model.

S805: and generating a first approximate model set according to the first data matrix, wherein the first approximate model set comprises a first approximate model corresponding to each KC performance, and the first approximate model is used for carrying out equivalent modeling on the association rule between the static stiffness of the auxiliary frame and the KC performance.

The descriptions of S801 to S805 may be specifically referred to the above embodiments, and are not repeated herein.

S806: and determining a second data matrix between the material thickness parameter and the KC performance according to the auxiliary frame constraint model.

The second data matrix may include some optional value data of the material thickness parameter and optional value data of KC performance, which is not limited.

In some embodiments of the present disclosure, in performing the determining of the second data matrix between the material thickness parameter and the KC performance according to the subframe constraint model, it may be determining an experimental design data matrix and extracting the second data matrix from the experimental design data matrix, without limitation.

The construction manner of the experimental design data matrix can be referred to the above embodiments, and will not be described herein.

In some embodiments of the present disclosure, determining the performance associated with the KC analysis conditions according to the subframe constraint model may further include: the method comprises the steps of determining a material thickness parameter and KC performance, determining constraint range information of the associated performance, and determining a second data matrix between the material thickness parameter and the KC performance according to the constraint range information, so that a separate experimental design data matrix between the material thickness parameter and the KC performance is accurately determined, wherein the separate experimental design data matrix can be an optional example of the second data matrix.

The constraint range information represents optional range information of the corresponding performance under the constraint of the subframe constraint model, for example, the optional range information of the KC performance and the optional range information of the material thickness parameter can be contained, and the constraint range information is not limited.

In some embodiments, the second data matrix between the gauge parameter and the KC performance may be used to guide the design of optimizing the static stiffness of the subframe, without limitation.

S807: and generating a second approximate model set according to the second data matrix, wherein the second approximate model set comprises a second approximate model corresponding to each KC performance, and the second approximate model is used for carrying out equivalent modeling on the association rule between the material thickness parameter and the KC performance.

After the second data matrix between the material thickness parameters and the KC performances is formed, a second approximation model set may be generated according to the second data matrix, where the second approximation model set includes a second approximation model corresponding to each KC performance, and the second approximation model is used for performing equivalent modeling on the correlation rule between the material thickness parameters and the KC performances, which is not limited.

In some embodiments, the approximation model may also be referred to as a proxy model, without limitation.

In some embodiments, the types of KC performance may be multiple, and then a corresponding approximation model may be established for each KC performance, and an approximation model corresponding to the KC performance may be established, and may be referred to as a second approximation model, where the second approximation model is different from the first approximation model in the foregoing embodiment in terms of selection, and the second approximation model is used for performing equivalent modeling on the association rule between the material thickness parameter and the KC performance, and the second approximation model corresponding to each of the multiple KC performances forms a second approximation model set, which is not limited thereto.

In some embodiments, the first approximation model may be an approximation model based on precision preference, and the second approximation model may be weighted by several approximation models based on precision preference, without limitation.

In some embodiments of the present disclosure, in performing the generating of the second approximation model set according to the second data matrix, it may be that a plurality of approximation models corresponding to each KC performance in the second data matrix are determined, where the approximation models have corresponding weight information, the plurality of approximation models are weighted and fused according to the weight information, the fused approximation models are used as the second approximation models corresponding to the corresponding KC performance, and the second approximation model set is generated according to the plurality of second approximation models, so that the generating accuracy of the second approximation model set is improved, and the effect of performing equivalent modeling on the correlation rule between the material thickness parameter and the KC performance is effectively improved.

For example, a combined model set II (an alternative example of a second set of approximation models) between the stock thickness design variable and KC performance may be constructed, error analysis performed using a K-time cross-check approach, with the approximation model accuracy measured by the R2press evaluation index. For each KC performance, comprehensively adopting a plurality of approximation model methods to perform joint construction, preferentially selecting a part of approximation models from the plurality of approximation models obtained by construction, carrying out weighted planning combination on the part of approximation models according to the weight information of each approximation model to obtain a weighted planning combination model (which is an optional example of a second approximation model) corresponding to each KC performance, thereby fully utilizing the characterization information of each approximation model, leading the advantages to complement each other, effectively improving the prediction accuracy, and establishing a combination model set of the KC performance, namely a combination model set II (which is an optional example of the second approximation model set) as a basis of the next optimization.

For example, the weighted programming is described as follows:

k approximate model construction methods are provided, and the expected value of the ith method at the t th KC performance is recorded as The combined model predictive value algorithm corresponding to the t-th performance is as follows: />Wherein w is _i For weight coefficient (an optional example of weight information), a weight coefficient (a weight coefficient) is set to be equal to the weight coefficient (a weight coefficient) of the weight information>S is the effectiveness of the combined prediction model, s=e (1- σ), the greater S, the higher the prediction model accuracy, the more stable the prediction error, and the more effective the model.

The mean value E and the mean square error sigma are respectively as follows:

A _t a precision sequence of the combined predictions is constructed,

s808: and determining a target value of the static rigidity of the auxiliary frame according to the first approximate model set, the second approximate model set, the KC characteristic analysis model and the first association rule information.

After the first approximation model set, the second approximation model set and the KC characteristic analysis model are determined, the target value of the static stiffness of the subframe may be determined according to the first approximation model set, the second approximation model set, the KC characteristic analysis model and the first association rule information, which is not limited.

In some embodiments of the present disclosure, an initial value of static stiffness of the subframe may be generated according to a first approximate model set, and third association rule information between second association rule information between KC performance and a material thickness parameter and different KC performance may be determined according to a second approximate model set, wherein the material thickness parameter has a plurality of corresponding candidate design variables, constraint range information corresponding to KC performance is determined according to the second association rule information and the third association rule information, and a target design variable is selected from the plurality of candidate design variables according to the constraint range information and the second approximate model set, and a KC analysis result is determined according to a KC characteristic analysis model if a preset condition is satisfied between the initial value of static stiffness of the subframe and the target design variable, and a target value of static stiffness of the subframe is determined according to the KC analysis result and the first association rule information, so as to effectively promote determination rationality of the target value of static stiffness of the subframe.

In some embodiments, the initial value of the static stiffness of the auxiliary frame can be understood as the value of the static stiffness of the auxiliary frame attachment point to be optimized under the initial condition, the target value of the static stiffness of the auxiliary frame can be understood as the optimal value of the static stiffness of the auxiliary frame attachment point, and the determined optimal value of the static stiffness of the auxiliary frame is not limited.

In some embodiments, the initial value of the static stiffness of the auxiliary frame can be generated according to the first approximate model set, and the initial value of the static stiffness of the auxiliary frame can be regarded as one auxiliary frame static stiffness which is originally formulated, and the initial value is not limited.

In some embodiments, optimization iterative updating of an initial value of the subframe static stiffness initially formulated may be supported to obtain an optimal value of the subframe static stiffness, which may be referred to as a target value of the subframe static stiffness, without limitation.

In some embodiments, the subframe static stiffness performance index (subframe static stiffness performance index, which is an optional example of an initial value of subframe static stiffness) may be formulated based on the optimal approximation model set I (an optional example of the first approximation model set), which is not limited.

In some embodiments, a composite multi-gradient path exploration optimization algorithm may be selected, with KC performance meeting a target value range (an optional example of constraint range information corresponding to KC performance) as an optimization target, optimization iteration is performed based on an optimal approximation model set I, and the whole optimization process is completed by self-programming Python (Python is a computer programming language), and schematic illustration of the KC performance and sub-frame static stiffness optimization iteration process may be shown in fig. 9, and fig. 9 is a schematic illustration of the KC performance and sub-frame static stiffness optimization iteration process in the embodiments of the disclosure, which is not limited thereto.

In some embodiments, an optimization scheme may be obtained through a composite multi-gradient path exploration optimization algorithm, if the static stiffness of the subframe to be optimized is determined to conform to first association rule information between KC performance and static stiffness of the subframe based on the optimization scheme, corresponding subframe attachment points may be selected, static stiffness of the corresponding subframe attachment points (such as points 302 and 304 shown in fig. 10) may be optimally adjusted, an initial value of the static stiffness of the corresponding subframe attachment points may be an optional example of the initial value of the static stiffness of the subframe, the initial value of the static stiffness of the corresponding subframe attachment points may also be an initial calibration, and based on the value of the static stiffness of the subframe attachment points determined by the optimization scheme, an optional example of a target value of the static stiffness of the subframe may be provided, as shown in fig. 10, fig. 10 is a schematic diagram illustrating initial determination of static stiffness performance indexes of the subframe in the embodiments of the disclosure, and the present disclosure is not limited thereto.

In some embodiments, the second association rule information between the KC performance and the thickness parameter and the third association rule information between the different KC performances may be determined according to the second approximation model set, which is not limited.

The second association rule information is information that can be used to describe an association rule between the KC performance and the material thickness parameter, for example, when the KC performance changes, the material thickness parameter changes along with the change of the KC performance, which is not limited.

The third association rule information refers to information that can be used to describe association rules between different KC performances, for example, association rules of association changes between different KC performances, for example, when one KC performance changes, another KC performance changes along with the change of the one KC performance, which is not limited.

In some embodiments, performance relevance analysis may be performed according to the combined model set II (an optional example of the second approximate model set), such as analyzing information of relevance rules between KC performance and thickness parameters, and analyzing information of relevance rules between different KC performance, which is not limited.

In some embodiments, a combined model set II may be invoked to construct a two-dimensional map (2D section) of each possible design variable (each possible design variable may be referred to as a candidate design variable) of the KC performance and the material thickness parameter, as shown in fig. 11, fig. 11 is a two-dimensional map of the KC performance and the material thickness parameter in the embodiment of the disclosure, the material thickness parameter is adjusted to be any possible design variable, all possible design variables of the material thickness parameter and each KC performance may still be automatically adjusted, and the condition that the material thickness parameter varies with the change of the KC performance satisfies the association relationship constructed by the approximate model set II, an upper and lower limit range (an optional example of constraint range information corresponding to the KC performance) of the KC performance may be set, and the correlation of each possible design variable of the KC performance and the material thickness parameter, and the conflict and the correlation law between different KC performances may be identified according to the two-dimensional map, which is not limited.

In some embodiments, referring to fig. 10, the material thickness parameter has a plurality of candidate design variables corresponding to each other, and as shown in the first left column in fig. 10, since one candidate design variable of the material thickness parameter may act on one attachment point, the target design variable determined from the plurality of candidate design variables may have an attachment point corresponding to the acting attachment point, and the points 302 and 304 shown in fig. 10 may be attachment points corresponding to the acting attachment point of the target design variable, which is not limited.

In some embodiments, constraint range information corresponding to KC performance may also be determined, and a target design variable may be selected from a plurality of candidate design variables based on the constraint range information and the second approximation model set, the selected target design variable may reference the subframe structural design, which is not limited.

In some embodiments, after the target design variable is selected from the plurality of candidate design variables, a KC analysis result is determined according to the KC characteristic analysis model under the condition that a preset condition is satisfied between an initial value of the static stiffness of the subframe and the target design variable, and a target value of the static stiffness of the subframe is determined according to the KC analysis result and the first association rule information, so that the integrity and the optimization effect of the static stiffness of the subframe are effectively improved.

That is, the combined model set II (an optional example of the second approximate model set) constructed in the embodiment of the present disclosure may be used not only to analyze information of the association rule between KC performance and the thickness parameter, but also to analyze information of the association rule between different KC performance, and may be used to perform optimization of the subframe thickness, i.e., a process of selecting a target design variable from a plurality of candidate design variables, which is not limited.

In some embodiments, a global criterion optimization algorithm may be selected, where KC performance meets a target value range (the target value range is an optional example of constraint range information corresponding to KC performance) is used as an optimization target, optimization iteration is performed based on a combined model set II, and the whole optimization process is completed by performing Python (Python is a computer programming language) self-programming, and a material thickness optimization scheme (i.e. selecting a preferred target design variable from a plurality of candidate design variables of a material thickness parameter) is obtained through the optimization iteration, as shown in fig. 12, and fig. 12 is a schematic diagram of a subframe material thickness optimization result in an embodiment of the disclosure, which is not limited thereto.

In some embodiments, the preset condition is satisfied between the initial value of the static stiffness of the subframe and the target design variable, for example, the area of the attachment point corresponding to the static stiffness of the subframe with the initial value determined is consistent with the area of the attachment point acted on by the target design variable, that is, the area indicating that the actual material thickness is increased is consistent with the area for optimizing and lifting the static stiffness of the subframe, which indicates that no conflict exists in optimizing the two aspects of the subframe, further, the KC analysis result can be determined according to the KC characteristic analysis model, and the target value of the static stiffness of the subframe can be determined according to the KC analysis result and the first association rule information, which is not limited.

In some embodiments of the present disclosure, a reference value of the static stiffness of the subframe may be determined according to a KC analysis result and first association rule information, if the reference value of the static stiffness of the subframe is the same as an initial value of the static stiffness of the subframe, the initial value of the static stiffness of the subframe or the reference value of the static stiffness of the subframe is used as a target value of the static stiffness of the subframe, if the reference value of the static stiffness of the subframe is different from the initial value of the static stiffness of the subframe, the reference value of the static stiffness of the subframe is optimized according to a target design variable, and the value obtained by the optimization is used as the target value of the static stiffness of the subframe, so that optimization of the static stiffness of the subframe based on a multi-dimensional manner is realized, the rationality of the optimization of the static stiffness of the subframe is further improved, and the determination accuracy of the static stiffness of the subframe is improved.

That is, the determination of the target value of the subframe static stiffness based on the physical model constructed as described above is supported in the embodiments of the present disclosure, and the physical model includes: finite element models and kinematic models, to which no limitation is imposed.

In some embodiments, according to the target design variable of the material thickness parameter selected and selected, based on the workflow shown in fig. 4, automatic simulation analysis is performed, a physical model of a corresponding working condition is selected, static stiffness and KC analysis results are automatically extracted, the extracted static stiffness can be an optional example of a reference value of the static stiffness of the subframe, and the extracted KC analysis results can be an optional example of the KC analysis results, which is not limited.

In some embodiments, it may also be determined whether the extracted static stiffness is consistent with the initial value of the static stiffness of the subframe that is preliminarily formulated, and if so, the initial value of the static stiffness of the subframe may be directly used as the target value of the static stiffness of the subframe, or the reference value of the static stiffness of the subframe may be also used as the target value of the static stiffness of the subframe, which is not limited.

In other embodiments, if it is determined that the extracted static stiffness is inconsistent with the initial value of the static stiffness of the subframe, the reference value of the static stiffness of the subframe may be optimized according to a material thickness optimization scheme (i.e., the target design variable of the preferred material thickness parameter), and the value obtained by the optimization is used as the target value of the static stiffness of the subframe.

According to the subframe static stiffness determination method, the subframe reference finite element model is established, the subframe is equivalent to the connection of a vehicle body, the subframe constraint model is established, the static stiffness of attachment points is analyzed, then the subframe reference finite element model is processed into the superunit model, meanwhile, the finite element models such as the suspension control arm, the swing arm and the fork arm are processed into the superunit model, a high-precision KC characteristic analysis model is established by combining the superunit model, and kinematic analysis is performed to obtain KC analysis results. The method comprises the steps of establishing an integrated workflow, integrating a static stiffness analysis working condition, a superunit analysis working condition and a KC analysis working condition, driving the three working conditions to automatically operate, establishing a first approximate model set between KC performance and static stiffness of the auxiliary frame, analyzing association rules of KC performance and static stiffness of the auxiliary frame, accurately formulating a target value of the static stiffness of the auxiliary frame according to KC analysis results, establishing a combined model set (an optional example of a second approximate model set) of KC performance and auxiliary frame structural parameters (material thickness parameters) so as to analyze and optimize the association rules of KC performance and auxiliary frame structural parameters (material thickness parameters), and re-analyzing rationality of the target value of the static stiffness of the auxiliary frame based on a physical model.

The technical scheme of the mapping relation between the static stiffness of the auxiliary frame and the KC performance is accurately established, the static stiffness of the auxiliary frame can be developed forward through the KC performance, the accurate design of the static stiffness target value of the auxiliary frame is facilitated, the development period of an automobile is effectively shortened, and the quality of an automobile product is improved.

Fig. 13 is a schematic structural view of a subframe static stiffness determining apparatus according to an embodiment of the present disclosure.

As shown in fig. 13, the sub-frame static stiffness determining device 130 includes:

a first generating unit 1301 is configured to generate a subframe-related visual superunit model.

A second generating unit 1302, configured to generate a KC characteristic analysis model according to the subframe-related visual superunit model, where the KC characteristic analysis model is used to analyze KC characteristics.

The first determining unit 1303 is configured to determine first association rule information between KC performance and subframe static stiffness.

A second determining unit 1304, configured to determine a target value of the static stiffness of the subframe according to the KC characteristic analysis model and the first association rule information.

The specific manner in which the individual units perform the operations in relation to the apparatus of the above embodiments has been described in detail in relation to the embodiments of the method and will not be described in detail here.

FIG. 14 is a functional block diagram of a vehicle shown in an exemplary embodiment. For example, the vehicle 1400 may be a hybrid vehicle, or may be a non-hybrid vehicle, an electric vehicle, a fuel cell vehicle, or other type of vehicle. The vehicle 1400 may be an autonomous vehicle, a semi-autonomous vehicle, or a non-autonomous vehicle.

Referring to fig. 14, a vehicle 1400 may include various subsystems, such as an infotainment system 1410, a perception system 1420, a decision control system 1430, a drive system 1440, and a computing platform 1450. Vehicle 1400 may also include more or fewer subsystems, and each subsystem may include multiple components. In addition, interconnections between each subsystem and between each component of the vehicle 1400 may be achieved by wired or wireless means.

In some embodiments, the infotainment system 1410 may include a communication system, an entertainment system, a navigation system, and the like.

The sensing system 1420 may include several sensors for sensing information of the environment surrounding the vehicle 1400. For example, sensing system 1420 may include a global positioning system (which may be a GPS system, or may be a beidou system or other positioning system), an inertial measurement unit (inertial measurement unit, IMU), a lidar, millimeter wave radar, an ultrasonic radar, and a camera device.

Decision control system 1430 may include a computing system, a vehicle controller, a steering system, a throttle, and a braking system.

The drive system 1440 may include components that provide powered movement of the vehicle 1400. In one embodiment, the drive system 1440 may include an engine, an energy source, a transmission, and wheels. The engine may be one or a combination of an internal combustion engine, an electric motor, an air compression engine. The engine is capable of converting energy provided by the energy source into mechanical energy.

Some or all of the functions of the vehicle 1400 are controlled by a computing platform 1450. The computing platform 1450 may include at least one processor 1451 and a memory 1452, the processor 1451 may execute instructions 1453 stored in the memory 1452.

The processor 1451 may be any conventional processor, such as a commercially available CPU. The processor may also include, for example, an image processor (Graphic Process Unit, GPU), a field programmable gate array (Field Programmable Gate Array, FPGA), a System On Chip (SOC), an application specific integrated Chip (Application Specific Integrated Circuit, ASIC), or a combination thereof.

The memory 1452 may be implemented by any type or combination of volatile or nonvolatile memory devices such as Static Random Access Memory (SRAM), electrically Erasable Programmable Read Only Memory (EEPROM), erasable Programmable Read Only Memory (EPROM), programmable Read Only Memory (PROM), read Only Memory (ROM), magnetic memory, flash memory, magnetic or optical disk.

In addition to instructions 1453, the memory 1452 may also store data such as road maps, route information, vehicle position, direction, speed, etc. The data stored by memory 1452 may be used by computing platform 1450.

In an embodiment of the present disclosure, the processor 1451 may execute instructions 1453 to perform all or part of the steps of the subframe static stiffness determination method described above.

The present disclosure also provides a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the steps of the subframe static stiffness determination method provided by the present disclosure.

Furthermore, the word "exemplary" is used herein to mean serving as an example, instance, illustration. Any aspect or design described herein as "exemplary" is not necessarily to be construed as advantageous over other aspects or designs. Rather, the use of the word exemplary is intended to present concepts in a concrete fashion. As used herein, the term "or" is intended to mean an inclusive "or" rather than an exclusive "or". That is, unless specified otherwise, or clear from context, "X application a or B" is intended to mean any one of the natural inclusive permutations. I.e. if X applies a; x is applied with B; or both X applications a and B, "X application a or B" is satisfied under any of the foregoing examples. In addition, the articles "a" and "an" as used in this application and the appended claims are generally understood to mean "one or more" unless specified otherwise or clear from context to be directed to a singular form.

Also, although the disclosure has been shown and described with respect to one or more implementations, equivalent alterations and modifications will occur to others skilled in the art upon the reading and understanding of this specification and the annexed drawings. The present disclosure includes all such modifications and alterations and is limited only by the scope of the claims. In particular regard to the various functions performed by the above described components (e.g., elements, resources, etc.), the terms used to describe such components are intended to correspond, unless otherwise indicated, to any component which performs the specified function of the described component (which is functionally equivalent), even though not structurally equivalent to the disclosed structure. In addition, while a particular feature of the disclosure may have been disclosed with respect to only one of several implementations, such feature may be combined with one or more other features of the other implementations as may be desired and advantageous for any given or particular application. Furthermore, to the extent that the terms "includes," including, "" has, "" having, "or variants thereof are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term" comprising.

Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This disclosure is intended to cover any adaptations, uses, or adaptations of the disclosure following the general principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.

It is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

It should be understood that features of some embodiments of the various disclosure described herein may be combined with one another, unless specifically indicated otherwise. As used herein, the term "and/or" includes any one of the items listed in relation and any combination of any two or more; similarly, ".a.at least one of the" includes any of the relevant listed items and any combination of any two or more. In addition, the terms "first," "second," are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description herein, the meaning of "plurality" means at least two, e.g., two, three, etc., unless specifically defined otherwise.

Claims

1. A method of determining the static stiffness of a subframe, the method comprising:

generating a subframe-related visual superunit model;

generating a KC characteristic analysis model according to the subframe related visual superunit model, wherein the KC characteristic analysis model is used for analyzing KC characteristics;

determining first association rule information between KC performance and static rigidity of the auxiliary frame;

and determining a target value of the static rigidity of the auxiliary frame according to the KC characteristic analysis model and the first association rule information.

2. The method of claim 1, wherein the generating the subframe-related visual superunit model comprises:

establishing a subframe reference finite element model and a subframe related component finite element model;

generating a sub-frame visual superunit model according to the sub-frame reference finite element model;

generating a subframe-related component visual superunit model according to the subframe-related component finite element model, wherein the subframe-related component visual superunit model and the subframe-related component visual superunit model are jointly used as the subframe-related visual superunit model;

wherein the generating a KC characteristic analysis model according to the subframe-related visual superunit model includes:

And generating the KC characteristic analysis model according to the auxiliary frame visual superunit model and the auxiliary frame related component visual superunit model.

3. The method of claim 2, wherein the generating the KC characteristics analysis model from the subframe visualization superunit model and the subframe related component visualization superunit model comprises:

determining the performance parameters of related parts of the auxiliary frame;

establishing a C characteristic analysis model according to the auxiliary frame related component performance parameters, the auxiliary frame visual superunit model and the auxiliary frame related component visual superunit model, wherein the C characteristic analysis model comprises the following components: a plurality of elastic elements, the C-characteristic analysis model for analyzing C-characteristics;

and establishing a constraint relation for each elastic element in the C characteristic analysis model, and taking the established C characteristic analysis model as the K characteristic analysis model, wherein the K characteristic analysis model is used for analyzing K characteristics, and the C characteristic analysis model and the K characteristic analysis model are jointly taken as the KC characteristic analysis model.

4. A method according to any one of claims 1-3, wherein the method further comprises:

Equivalent modeling is carried out on connection rigidity of the auxiliary frame and the vehicle body in the auxiliary frame reference finite element model so as to obtain an auxiliary frame constraint model, wherein the connection rigidity comprises the following components: the auxiliary frame constraint model is used for defining auxiliary frame static stiffness analysis working conditions and boundary information;

determining a first data matrix between static stiffness of the auxiliary frame and KC performance according to the auxiliary frame constraint model;

generating a first approximate model set according to the first data matrix, wherein the first approximate model set comprises a first approximate model corresponding to each KC performance, and the first approximate model is used for carrying out equivalent modeling on the association rule between the static stiffness of the auxiliary frame and the KC performance;

wherein, determining the first association rule information between KC performance and subframe static rigidity includes:

and determining first association rule information between the KC performance and the static rigidity of the auxiliary frame according to the first approximate model set.

5. The method of claim 4, wherein said determining a first data matrix between subframe static stiffness and KC performance based on said subframe constraint model comprises:

Determining the performance associated with KC analysis working conditions according to the auxiliary frame constraint model, wherein the associated performance at least comprises: static rigidity and KC performance of the auxiliary frame;

determining constraint range information of the associated performance;

and determining a first data matrix between the static stiffness of the auxiliary frame and the KC performance according to the constraint range information.

6. The method of claim 4, wherein generating a first set of approximation models from the first data matrix comprises:

determining a plurality of approximation models corresponding to each of the KC performances in the first data matrix, wherein the approximation models have corresponding model accuracy values;

selecting a value maximum model precision value from a plurality of model precision values, and determining a first approximate model corresponding to the value maximum model precision value, wherein the first approximate model belongs to the plurality of approximate models;

generating the first approximation model set according to a plurality of the first approximation models.

7. The method of claim 4, wherein the determining first association rule information between the KC performance and the subframe static stiffness from the first set of approximation models comprises:

Determining an adjustment amount corresponding to the KC performance;

adjusting the KC performance according to the adjustment quantity, and determining index change information corresponding to the static rigidity of the auxiliary frame in the process of adjusting the KC performance, wherein the index change information meets the association relation constrained by the first approximate model set;

and generating the first association rule information according to the adjustment quantity and the index change information.

8. The method of claim 4, wherein determining the target value of subframe static stiffness based on the KC characteristic analysis model and the first association rule information comprises:

determining a second data matrix between the material thickness parameter and the KC performance according to the auxiliary frame constraint model;

generating a second approximation model set according to the second data matrix, wherein the second approximation model set comprises a second approximation model corresponding to each KC performance, and the second approximation model is used for carrying out equivalent modeling on the association rule between the material thickness parameter and the KC performance;

and determining a target value of the static rigidity of the auxiliary frame according to the first approximate model set, the second approximate model set, the KC characteristic analysis model and the first association rule information.

9. The method of claim 8, wherein the determining a second data matrix between the thickness parameter and the KC performance according to the subframe constraint model comprises:

determining the performance associated with KC analysis working conditions according to the auxiliary frame constraint model, wherein the associated performance at least comprises: the gauge parameter and the KC performance;

determining constraint range information of the associated performance;

and determining a second data matrix between the material thickness parameter and the KC performance according to the constraint range information.

10. The method of claim 8, wherein the generating a second set of approximation models from the second data matrix comprises:

determining a plurality of approximation models corresponding to each of the KC performances in the second data matrix, wherein the approximation models have corresponding weight information;

weighting and fusing the plurality of approximate models according to the weight information, and taking the approximate model obtained by fusion as a second approximate model corresponding to the corresponding KC performance;

generating the second approximation model set according to a plurality of the second approximation models.

11. The method of claim 8, wherein the determining the target value of the subframe static stiffness from the first set of approximation models, the second set of approximation models, the KC characteristic analysis model, and the first association rule information comprises:

Generating an initial value of the static rigidity of the auxiliary frame according to the first approximate model set;

determining second association rule information between the KC performance and the material thickness parameters and third association rule information between different KC performances according to the second approximation model set, wherein the material thickness parameters have a plurality of corresponding candidate design variables;

determining constraint range information corresponding to the KC performance according to the second association rule information and the third association rule information, and selecting a target design variable from the plurality of candidate design variables according to the constraint range information and the second approximation model set;

and under the condition that a preset condition is met between the initial value of the static stiffness of the auxiliary frame and the target design variable, determining a KC analysis result according to the KC characteristic analysis model, and determining the target value of the static stiffness of the auxiliary frame according to the KC analysis result and the first association rule information.

12. The method of claim 11, wherein the determining the target value of the subframe static stiffness based on the KC analysis and the first association rule information comprises:

determining a reference value of the static rigidity of the auxiliary frame according to the KC analysis result and the first association rule information;

If the reference value of the static stiffness of the auxiliary frame is the same as the initial value of the static stiffness of the auxiliary frame, taking the initial value of the static stiffness of the auxiliary frame or the reference value of the static stiffness of the auxiliary frame as the target value of the static stiffness of the auxiliary frame;

if the reference value of the static stiffness of the auxiliary frame is different from the initial value of the static stiffness of the auxiliary frame, optimizing the reference value of the static stiffness of the auxiliary frame according to the target design variable, and taking the value obtained by optimizing as the target value of the static stiffness of the auxiliary frame.

13. A subframe static stiffness determination apparatus, the apparatus comprising:

the first generation unit is used for generating a subframe-related visual superunit model;

the second generation unit is used for generating a KC characteristic analysis model according to the subframe related visual superunit model, wherein the KC characteristic analysis model is used for analyzing KC characteristics;

the first determining unit is used for determining first association rule information between KC performance and static rigidity of the auxiliary frame;

and the second determining unit is used for determining the target value of the static rigidity of the auxiliary frame according to the KC characteristic analysis model and the first association rule information.

14. A vehicle, characterized by comprising:

a processor;

a memory for storing processor-executable instructions;

wherein the processor is configured to:

the steps of carrying out the method of any one of claims 1 to 12.

15. A non-transitory computer readable storage medium, which when executed by a processor of a mobile terminal, causes the mobile terminal to perform a subframe static stiffness determination method, the method comprising:

generating a subframe-related visual superunit model;