Performance design optimization method, device, equipment and program product of vehicle component

Abstract

The invention provides a vehicle component performance design optimization method and device, equipment and a program product, and relates to the technical field of vehicles, and the method comprises the steps: obtaining a parameterized component finite element model established based on target model parameters related to static stiffness in a component, carrying out geometric kinematics and elastic kinematics performance analysis on the parameterized component finite element model to obtain geometric kinematics and elastic kinematics performance parameters of the component; performing static stiffness analysis on the parameterized component finite element model to obtain first static stiffness; according to geometric kinematics and elastic kinematics performance parameters, the first static stiffness is corrected, the corrected static stiffness is made to be more suitable for the actual working condition, and therefore component structure design is assisted; according to the method, the first static stiffness is corrected, screening of target model parameters related to the static stiffness, geometric kinematics and elastic kinematics performance analysis are carried out, automatic execution can be conveniently carried out by utilizing artificial intelligence, and the rigidity of the vehicle component is automatically corrected.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of vehicles, and particularly relates to a performance design optimization method, device, equipment and program product of a vehicle component. BACKGROUND

[0002] The structural design of a vehicle component is crucial to the comfort and maneuverability of the vehicle during driving, and correcting the stiffness of the vehicle component helps to assist the structural design of the component. Therefore, how to correct the stiffness of the vehicle component is a problem to be solved. SUMMARY

[0003] The present disclosure provides a performance design optimization method, device, equipment and program product of a vehicle component to solve the problems in the related art.

[0004] A first aspect embodiment of the present disclosure provides a performance design optimization method of a vehicle component, the method comprising:

[0005] obtaining a parameterized component finite element model of a component in a vehicle; the parameterized component finite element model is a component finite element model established based on a target model parameter related to static stiffness in the component;

[0006] performing geometric kinematics and elastic kinematics performance analysis on the parameterized component finite element model to obtain geometric kinematics and elastic kinematics performance parameters of the component;

[0007] performing static stiffness analysis on the parameterized component finite element model to obtain a first static stiffness of the component;

[0008] correcting the first static stiffness according to the geometric kinematics and elastic kinematics performance parameters to obtain a corrected first static stiffness.

[0009] The geometric kinematics and elastic kinematics performance parameters are closely related to the comfort and maneuverability of the vehicle during driving; the first static stiffness is corrected according to the geometric kinematics and elastic kinematics performance parameters, so that the corrected static stiffness is more suitable for actual working conditions, thereby assisting the component structural design; the selection of the target model parameter related to the static stiffness, the geometric kinematics and elastic kinematics performance analysis, and the correction of the first static stiffness are facilitated to be automatically executed by artificial intelligence, and automatic correction of the stiffness of the vehicle component is realized.

[0010] In some embodiments, the obtaining of the parameterized component finite element model of the component in the vehicle comprises:

[0011] obtaining a correlation coefficient between all model parameters in the component and the static stiffness; and obtaining an initial component finite element model of the component;

[0012] The model parameters corresponding to the correlation coefficients that are greater than a preset threshold among all correlation coefficients are determined as the target model parameters;

[0013] The target model parameters are added to the initial component finite element model to obtain the parameterized component finite element model.

[0014] Parametric component finite element models can easily change the values ​​of the target model parameters and quickly simulate the performance changes of components under different parameter combinations.

[0015] In some embodiments, the geometric kinematics and elastic kinematics performance analysis of the finite element model of the parameterized component to obtain the geometric kinematics and elastic kinematics performance parameters of the component includes:

[0016] The parameterized component finite element model is reduced to a super-element model.

[0017] Geometric kinematics and elastic kinematics performance models are constructed for the super-unit model to obtain the geometric kinematics and elastic kinematics performance models;

[0018] Geometric kinematics and elastic kinematics performance models are analyzed to obtain their performance parameters.

[0019] By accurately modeling and analyzing geometric and elastic deformations, complex behaviors that may be overlooked in traditional models can be effectively avoided, thus improving the accuracy of the design.

[0020] In some embodiments, performing static stiffness analysis on the finite element model of the parameterized component to obtain the first static stiffness of the component includes:

[0021] Obtain the constraint conditions of the component;

[0022] According to the aforementioned constraints, constraints are applied to the parameterized component finite element model to obtain the component constraint model;

[0023] A static stiffness analysis is performed on the component constraint model to obtain the first static stiffness.

[0024] Static stiffness analysis is based on numerical simulation and can be completed in a computer environment. It can quickly obtain the stiffness data of components without the need for actual physical experiments, thus saving time and resources.

[0025] In some embodiments, performing static stiffness analysis on the component constraint model to obtain the first static stiffness includes:

[0026] The stiffness of the flexible component is analyzed by performing a stiffness analysis on the component constraint model to obtain the second static stiffness;

[0027] The rigid component stiffness analysis of the component constraint model is performed to obtain the third static stiffness;

[0028] The first static stiffness is calculated based on the second static stiffness and the third static stiffness.

[0029] By combining the second and third static stiffness to calculate the first static stiffness, the mechanical performance of the component under actual working conditions can be more accurately reflected, avoiding the limitations of a single static stiffness model and obtaining a more accurate stiffness estimate.

[0030] In some embodiments, calculating the first static stiffness based on the second static stiffness and the third static stiffness includes:

[0031] Obtain the first reciprocal of the second static stiffness, and obtain the second reciprocal of the third static stiffness;

[0032] Calculate the difference between the first reciprocal and the second reciprocal to obtain the difference result;

[0033] The first static stiffness is obtained by reciprocal calculation of the difference result.

[0034] By using reciprocal calculations and difference calculations, the differences between stiffness analysis results can be captured more accurately, and the influence of both can be combined.

[0035] In some embodiments, the step of correcting the first static stiffness based on the geometric kinematics and elastic kinematics performance parameters to obtain the corrected first static stiffness includes:

[0036] Based on different target model parameters, generate different first static stiffnesses and corresponding geometric kinematics and elastic kinematics performance parameters;

[0037] Establish the mapping relationship between the different first static stiffnesses and the corresponding geometric kinematics and elastic kinematics performance parameters;

[0038] Using the mapping relationship, a proxy model is constructed for the first static stiffness and the geometric kinematics and elastic kinematics performance parameters;

[0039] Based on the surrogate model, the first static stiffness is corrected to obtain the corrected first static stiffness.

[0040] By using a surrogate model to correct the first static stiffness, more factors and more complex mapping relationships can be taken into account, thereby improving the accuracy of stiffness prediction.

[0041] In some embodiments, the step of correcting the first static stiffness based on the surrogate model to obtain the corrected first static stiffness includes:

[0042] Obtain the value range of the geometric kinematics and elastic kinematics performance parameters in the surrogate model;

[0043] Within the range of values, the first static stiffness is adjusted in a direction that makes the geometric kinematics and elastic kinematics performance parameters approach their minimum values ​​to obtain the corrected first static stiffness.

[0044] By minimizing the geometric and elastic kinematic properties, the deformation of the structure under load can be effectively reduced, thereby improving the rigidity and stability of the structure.

[0045] A second aspect of this disclosure provides a performance design optimization apparatus for vehicle components, the apparatus comprising:

[0046] The acquisition unit is used to acquire the parametric component finite element model of the components in the vehicle; the parametric component finite element model is a component finite element model established based on the target model parameters of the component that are related to the static stiffness.

[0047] The analysis unit is used to perform geometric kinematics and elastic kinematics performance analysis on the finite element model of the parameterized component, and obtain the geometric kinematics and elastic kinematics performance parameters of the component.

[0048] The analysis unit is also used to perform static stiffness analysis on the finite element model of the parameterized component to obtain the first static stiffness of the component.

[0049] The correction unit is used to correct the first static stiffness according to the geometric kinematics and elastic kinematics performance parameters to obtain the corrected first static stiffness.

[0050] In some embodiments, the acquisition unit is further configured to:

[0051] Obtain the correlation coefficients between all model parameters and static stiffness of the component; and obtain the initial finite element model of the component.

[0052] The model parameters corresponding to the correlation coefficients that are greater than a preset threshold among all correlation coefficients are determined as the target model parameters;

[0053] The target model parameters are added to the initial component finite element model to obtain the parameterized component finite element model.

[0054] In some embodiments, the analysis unit is further configured to:

[0055] The parameterized component finite element model is reduced to a super-element model.

[0056] Geometric kinematics and elastic kinematics performance models are constructed for the super-unit model to obtain the geometric kinematics and elastic kinematics performance models;

[0057] Geometric kinematics and elastic kinematics performance models are analyzed to obtain their performance parameters.

[0058] In some embodiments, the analysis unit includes:

[0059] An acquisition module is used to acquire the constraint conditions of the component;

[0060] The constraint module is used to apply constraints to the parameterized component finite element model according to the constraint conditions to obtain the component constraint model;

[0061] The analysis module is used to perform static stiffness analysis on the component constraint model to obtain the first static stiffness.

[0062] In some embodiments, the analysis module is further configured to:

[0063] The stiffness of the flexible component is analyzed by performing a stiffness analysis on the component constraint model to obtain the second static stiffness;

[0064] The rigid component stiffness analysis of the component constraint model is performed to obtain the third static stiffness;

[0065] The first static stiffness is calculated based on the second static stiffness and the third static stiffness.

[0066] In some embodiments, the analysis module is further configured to:

[0067] Obtain the first reciprocal of the second static stiffness, and obtain the second reciprocal of the third static stiffness;

[0068] Calculate the difference between the first reciprocal and the second reciprocal to obtain the difference result;

[0069] The first static stiffness is obtained by reciprocal calculation of the difference result.

[0070] In some embodiments, the correction unit is further configured to:

[0071] Based on different target model parameters, generate different first static stiffnesses and corresponding geometric kinematics and elastic kinematics performance parameters;

[0072] Establish the mapping relationship between the different first static stiffnesses and the corresponding geometric kinematics and elastic kinematics performance parameters;

[0073] Using the mapping relationship, a proxy model is constructed for the first static stiffness and the geometric kinematics and elastic kinematics performance parameters;

[0074] Based on the surrogate model, the first static stiffness is corrected to obtain the corrected first static stiffness.

[0075] In some embodiments, the correction unit is further configured to:

[0076] Obtain the value range of the geometric kinematics and elastic kinematics performance parameters in the surrogate model;

[0077] Within the range of values, the first static stiffness is adjusted in a direction that makes the geometric kinematics and elastic kinematics performance parameters approach their minimum values ​​to obtain the corrected first static stiffness.

[0078] A third aspect of this disclosure provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the methods described in the first aspect of this disclosure.

[0079] A fourth aspect of this disclosure provides a non-transitory computer-readable storage medium that, when instructions in the storage medium are executed by a processor of a mobile terminal, enables the mobile terminal to perform the methods described in the first aspect of this disclosure.

[0080] According to a fifth aspect of this disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the methods described in the embodiments of the first aspect of this disclosure.

[0081] In summary, the performance design optimization method for vehicle components proposed in this disclosure includes obtaining a parametric finite element model of the component based on target model parameters that are correlated with static stiffness, and performing geometric kinematics and elastic kinematics performance analysis on the parametric finite element model to obtain the geometric kinematics and elastic kinematics performance parameters of the component. These geometric kinematics and elastic kinematics performance parameters are closely related to the comfort and handling of the vehicle. Next, static stiffness analysis is performed on the parametric finite element model to obtain a first static stiffness. Based on the geometric kinematics and elastic kinematics performance parameters, the first static stiffness is corrected to make the corrected static stiffness more consistent with actual working conditions, thereby assisting in the structural design of the component. The selection of target model parameters correlated with static stiffness, the geometric kinematics and elastic kinematics performance analysis, and the correction of the first static stiffness are easily automated using artificial intelligence, achieving automatic correction of the stiffness of vehicle components.

[0082] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0083] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure, and are not intended to unduly limit this disclosure.

[0084] Figure 1 A flowchart illustrating a performance design optimization method for vehicle components provided in this disclosure embodiment;

[0085] Figure 2 A flowchart illustrating a method for obtaining a parametric component finite element model provided in an embodiment of this disclosure;

[0086] Figure 3 A flowchart illustrating a method for analyzing geometric kinematics and elastic kinematics properties provided in an embodiment of this disclosure;

[0087] Figure 4 A flowchart illustrating a static stiffness analysis method provided in an embodiment of this disclosure;

[0088] Figure 5 A flowchart illustrating another method for analyzing static stiffness provided in an embodiment of this disclosure;

[0089] Figure 6 A flowchart illustrating a method for correcting static stiffness provided in an embodiment of this disclosure;

[0090] Figure 7 A flowchart illustrating a method for constructing a proxy model provided in an embodiment of this disclosure;

[0091] Figure 8 A schematic diagram of the structure of a performance design optimization device for vehicle components provided in this embodiment of the present disclosure;

[0092] Figure 9 A schematic diagram of the structure of another vehicle component performance design optimization device provided in an embodiment of this disclosure;

[0093] Figure 10 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure;

[0094] Figure 11 This is a schematic diagram of the structure of a chip provided in an embodiment of the present disclosure. Detailed Implementation

[0095] Some embodiments of this disclosure will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. Various changes, modifications, and equivalents of the methods, apparatus, and / or systems described herein will become apparent upon understanding this disclosure. For example, the order of operations described herein is merely illustrative and is not limited to those orders set forth herein, but can be changed as will become apparent upon understanding this disclosure, except for operations that must be performed in a particular order. Furthermore, for clarity and brevity, descriptions of features known in the art may be omitted.

[0096] The embodiments described in the following examples of this disclosure are not representative of all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0097] The structural design of vehicle components has a crucial impact on vehicle comfort and handling during operation, and modifying the stiffness of these components can aid in their structural design. Therefore, how to modify the stiffness of vehicle components is a problem that urgently needs to be solved.

[0098] Therefore, to address the problems existing in related technologies, this disclosure proposes a performance design optimization method for vehicle components. This method includes obtaining a parametric finite element model of the component based on target model parameters that are correlated with static stiffness; performing geometric kinematics and elastic kinematics performance analysis on the parametric finite element model to obtain the component's geometric kinematics and elastic kinematics performance parameters, which are closely related to the vehicle's driving comfort and handling; then performing static stiffness analysis on the parametric finite element model to obtain a first static stiffness; and correcting the first static stiffness based on the geometric kinematics and elastic kinematics performance parameters to make the corrected static stiffness more consistent with actual working conditions, thereby assisting in component structural design. The selection of target model parameters correlated with static stiffness, the geometric kinematics and elastic kinematics performance analysis, and the correction of the first static stiffness are easily automated using artificial intelligence, achieving automatic correction of vehicle component stiffness.

[0099] This disclosure is not exhaustive, but merely illustrative of some embodiments, and is not intended to limit the scope of protection of this disclosure. Unless otherwise specified, each step in a particular embodiment can be implemented as an independent embodiment, and the steps can be arbitrarily combined. For example, a solution after removing some steps in a particular embodiment can also be implemented as an independent embodiment, and the order of the steps in a particular embodiment can be arbitrarily interchanged. Furthermore, the optional implementation methods in a particular embodiment can be arbitrarily combined; moreover, the embodiments can be arbitrarily combined, for example, some or all steps of different embodiments can be arbitrarily combined, and a particular embodiment can be arbitrarily combined with the optional implementation methods of other embodiments.

[0100] In each of the disclosed embodiments, unless otherwise specified or in case of logical conflict, the terminology and / or descriptions of the embodiments are consistent and can be referenced by each other. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0101] The terminology used in the embodiments of this disclosure is for the purpose of describing particular embodiments only and is not intended to limit the scope of this disclosure.

[0102] In this embodiment of the disclosure, unless otherwise stated, elements expressed in the singular form, such as "a," "an," "the," "the," "the," "the," "the," "the," "this," etc., can mean "one and only one," or "one or more," "at least one," etc. For example, when using articles such as "a," "an," "the," etc. in translation, the noun following the article can be understood as either a singular expression or a plural expression.

[0103] In some embodiments, the terms “in response to…”, “in response to determining…”, “in the case of…”, “when…”, “if…”, “if…”, etc., can be used interchangeably.

[0104] In some embodiments, the terms “greater than,” “greater than or equal to,” “not less than,” “more than,” “more than or equal to,” “not less than,” “higher than,” “higher than or equal to,” “not lower than,” and “above” can be used interchangeably, as can the terms “less than,” “less than or equal to,” “not greater than,” “less than,” “less than or equal to,” “not more than,” “lower than,” “lower than or equal to,” “not higher than,” and “below”.

[0105] The prefixes such as "first" and "second" in the embodiments of this disclosure are only for distinguishing different descriptive objects and do not constitute restrictions on the position, order, priority, number or content of the descriptive objects. For the description of the descriptive objects, please refer to the description in the claims or the context of the embodiments. The use of prefixes should not constitute unnecessary restrictions.

[0106] In the embodiments of this disclosure, "multiple" refers to two or more.

[0107] In the embodiments disclosed herein, terms such as “import”, “input”, and “read in” can be used interchangeably.

[0108] In some embodiments, devices, etc., can be interpreted as physical or virtual, and their names are not limited to the names recorded in the embodiments. Terms such as “device”, “equipment”, “circuit”, “network element”, “node”, “function”, “unit”, “section”, “system”, “network”, “chip”, “chip system”, “entity”, and “subject” can be used interchangeably.

[0109] Figure 1 This is a flowchart illustrating a performance design optimization method for vehicle components provided in an embodiment of this disclosure. This method can be applied to application scenarios such as smart terminals, for example, executed by a terminal integrating vehicle component performance design optimization functions, or by other devices suitable for performing performance design optimization of vehicle components and outputting the performance design optimization results. This disclosure does not limit the scope of the method. Figure 1 As shown, the performance design optimization method for the vehicle component includes steps 101-104. The serial numbers appearing in this disclosure do not imply that they are in chronological or procedural order. The execution order of the steps can be adjusted according to actual needs.

[0110] Step 101: Obtain the parametric component finite element model of the components in the vehicle; the parametric component finite element model is a component finite element model established based on the target model parameters of the component that are related to the static stiffness.

[0111] A component refers to various parts in a vehicle used to bear, support, or transmit forces, including but not limited to subframes, control arms, body frames, and solid steering knuckles. A component finite element model is a digital model built using the Finite Element Method (FEM) to simulate the mechanical behavior of vehicle components. FEM is a numerical analysis method that discretizes a component into a combination of a finite number of elements and nodes. Elements can be of various shapes, such as rod elements, beam elements, plate elements, shell elements, and solid elements, used to approximately represent the geometry and physical properties of the component. A parametric component finite element model refers to a component finite element model that introduces target model parameters affecting the static stiffness of the component, such as the component's dimensions, shape, and material properties, as variable parameters. By changing the target model parameters, component models of different shapes can be quickly generated.

[0112] Static stiffness refers to the rigidity of a structural member when resisting external forces in a static state. Generally, the greater the static stiffness, the stronger the member's resistance to external deformation. The value of static stiffness is usually determined by factors such as the member's geometry, material properties, and structural configuration. Target model parameters refer to design parameters related to the static stiffness characteristics of a member. For example, the member's geometric dimensions, material properties, or structural configuration; target model parameters have a direct correlation with the member's static stiffness. In the embodiments of this disclosure, target model parameters are key design parameters that can affect the static stiffness of a member.

[0113] To facilitate a detailed understanding of the target model parameters for different components, the target model parameters for different components provided in this disclosure are shown in the following table:

[0114] Table 1

[0115]

[0116] Parametric characterization of two-force bar control arms involves dynamically adjusting the stiffness value of the two-force bar control arm by changing its model parameters. This study investigates the impact of different stiffnesses on the geometric kinematics and elastic kinematics (KC) parameters of the two-force bar control arm. One feasible approach is to change the elastic modulus of the two-force bar control arm.

[0117] Parametric characterization of the steering knuckle and trailing arm: for sheet metal trailing arms, the thickness or elastic modulus can be directly adjusted; for solid trailing arm models, the elastic modulus can be adjusted. For the stiffness of the steering knuckle control arm mounting points, the mounting area needs to be grouped into separate models, and the elastic modulus of each local grouped area at each mounting point can be adjusted.

[0118] The parametric characterization of the subframe, and the adjustment of the Y-axis stiffness at the subframe-suspension attachment points, are achieved by adding additional bar units at each attachment point. This is a versatile modular characterization method that can accommodate sheet metal subframes, cast subframes, and other types of subframes. A change in the radius of each bar unit will cause a change in the Y-axis stiffness at the corresponding attachment point. For hard points located far from the subframe main beam, the bar unit extends to the subframe main beam.

[0119] Using finite element analysis software, an initial finite element model of the subframe is created based on its actual design drawings and geometric dimensions. This model includes the mesh generation, node definitions, and material property settings (such as elastic modulus and Poisson's ratio) for each part of the subframe. Through theoretical analysis and preliminary experimental or simulation data, parameters in the subframe that are correlated with static stiffness are identified. For example, parameters such as the thickness, cross-sectional area, and elastic modulus of the material typically affect static stiffness. Then, the correlation between these parameters and static stiffness is quantitatively analyzed using methods such as statistical regression analysis and sensitivity analysis to determine which parameters exhibit a positive correlation with static stiffness and a nonlinear relationship; these parameters are the target model parameters. The determined target model parameters are then introduced into the initial component finite element model. By writing parametric scripts or using the parametric design function of the software, the target parameters in the model can be easily adjusted and modified. For example, in finite element analysis software, parameters can be defined using a parametric design language and referenced in the model, thereby achieving model parametricization.

[0120] By establishing a parametric component finite element model, subframe models with different parameter combinations can be quickly generated without having to manually rebuild the model, greatly improving modeling efficiency.

[0121] Step 102: Perform geometric kinematics and elastic kinematics performance analysis on the finite element model of the parameterized component to obtain the geometric kinematics and elastic kinematics performance parameters of the component.

[0122] Geometric kinematics refers to the characteristics of a component's relative position and trajectory during motion, with no deformation or only considering geometric changes. For example, for a vehicle's control arm, geometric kinematics involves the trajectory and angle of the control arm's various mounting points relative to the vehicle body when the wheels bounce up and down. Elastic kinematics, on the other hand, refers to the kinematic characteristics exhibited by a component after elastic deformation, building upon geometric kinematics. Geometric and elastic kinematics analysis involves a detailed analysis of a component's geometric deformation and elastic response under external forces. Geometric kinematics analysis focuses on the component's geometric deformation characteristics, such as displacement and shape changes; while elastic kinematics analysis focuses on the material's deformation, stress, strain, and mechanical behavior.

[0123] Dynamic substructure analysis, based on the fixed-interface modal synthesis method, appears as super-elements in the Optistruct calculation. Static reduction methods (such as the Guyan method) are used to reduce finite element models of the subframe, control arms, and steering knuckles to super-elements. Further modular assembly of these super-elements can simultaneously satisfy computational efficiency and model compatibility. According to the definition rules of super-elements, the boundary points of the super-elements are taken from the hard points of the suspension. The commands for creating super-elements are shown in Table 2.

[0124] Table 2

[0125]

[0126] Boundary nodes are defined using the ASET1 command, and these nodes and structural features are connected using PLOT elements to achieve visualization of the super-element model, facilitating KC model construction. A batch processing command is compiled to call the Optistruct solver to solve the subframe baseline finite element model and generate MNF format super-element model files. Similarly, the suspension control arm finite element models are all processed into super-element model files.

[0127] Based on the Automatic Dynamic Analysis of Mechanical Systems (ADAMS) software, the stiffness and damping of each elastic component in each direction are input to establish connection information. The kinematic relationships are then established by connecting the MNF model, completing the suspension C-characteristic analysis model. This avoids the drawback of traditional simplified modeling methods that cannot accurately obtain the stiffness and inertia parameters of components such as the subframe. Batch commands are compiled to call ADAMS to automatically solve the KC characteristic analysis model, and the solution results are converted into a directly readable and editable text file format. While the control arm MNF model can automatically link symmetrical components on both sides during modeling, the left and right sides cannot be automatically linked during automatic updates; therefore, two separate MNF models for the left and right sides need to be generated.

[0128] By analyzing and optimizing the geometric and elastic kinematic properties of key components such as control arms, vehicle handling stability and ride comfort can be effectively improved. Precise kinematic characteristics ensure that the wheels maintain good grip and positioning performance under various driving conditions, reducing adverse phenomena such as body roll and bumps during steering, acceleration, and braking, thus enhancing the driving experience.

[0129] Step 103: Perform static stiffness analysis on the finite element model of the parameterized component to obtain the first static stiffness of the component.

[0130] Static stiffness analysis refers to simulating the stress and deformation of a component under static load using the finite element method, calculating the force required for a unit deformation of the component, and thus obtaining the static stiffness of the component.

[0131] For the static stiffness of the subframe component, two stiffness analyses are performed on the constrained subframe. The first analysis is for the stiffness of the flexible subframe, denoted as K. total The second type is a rigid subframe (based on a flexible subframe, with the material's elastic modulus magnified 1000 times for an equivalent effect), with stiffness denoted as K. rigid Based on the combined results of the two stiffness analyses, the static stiffness K of the subframe is defined by eliminating the influence of the flexible connection between the subframe and the vehicle body.

[0132] The formula for calculating the static stiffness K of the subframe can be achieved using formula (1):

[0133]

[0134] Among them, K total =F / U flex K rigid =F / U rigid U flex U rigid These represent the displacements at the same loading point in the flexible subframe model and the rigid subframe model, respectively, while F represents the force applied to the loading point.

[0135] The connection model between the subframe and the body is as follows: two series-connected zero-length control arm bushing (CBUSH) elements are created, one to simulate the stiffness of the body mounting point and the other to simulate the stiffness of the subframe suspension, and the coupling points at the body end are constrained to degrees of freedom 1 to 6.

[0136] For the static stiffness of the component as a control arm, a local coordinate system is established (axial direction is Z). One end of the control arm is constrained for translational and rotational degrees of freedom along the axial direction, while the loading end is constrained for rotational degrees of freedom other than those along the axial direction. After static analysis, the Z-direction displacement d at the loading point is extracted. s .

[0137] The static stiffness of the control arm can be calculated using formula (2):

[0138] K s =F / d s (2)

[0139] Where F is the force applied to the loading point.

[0140] Static stiffness is an important indicator of structural safety. By optimizing the static stiffness of components, their load-bearing capacity can be improved, the deformation of the structure under stress can be reduced, and thus the overall safety can be enhanced.

[0141] Step 104: Based on the geometric kinematics and elastic kinematics performance parameters, the first static stiffness is corrected to obtain the corrected first static stiffness.

[0142] Static stiffness correction refers to adjusting and optimizing the initially calculated static stiffness based on the geometric and elastic kinematic performance parameters of a component, so that the stiffness of the component better matches the actual kinematic performance requirements.

[0143] Collect the geometrical and elastokinetic performance parameters of the component under different target model parameters, along with the corresponding first static stiffness data. Analyze the collected data to establish a mapping relationship between the first static stiffness and the geometrical and elastokinetic performance parameters. Using this mapping relationship, construct a surrogate model to simulate the relationship between the first static stiffness and kinematic performance. Through the surrogate model, correct the first static stiffness based on the geometrical and elastokinetic performance parameters.

[0144] By making corrections, the static stiffness of the components is made to better match the actual kinematic performance requirements, thereby improving the accuracy and reliability of the design.

[0145] In summary, the performance design optimization method for vehicle components proposed in this disclosure includes obtaining a parametric finite element model of the component based on target model parameters that are correlated with static stiffness, and performing geometric kinematics and elastic kinematics performance analysis on the parametric finite element model to obtain the geometric kinematics and elastic kinematics performance parameters of the component. These geometric kinematics and elastic kinematics performance parameters are closely related to the comfort and handling of the vehicle. Next, static stiffness analysis is performed on the parametric finite element model to obtain a first static stiffness. Based on the geometric kinematics and elastic kinematics performance parameters, the first static stiffness is corrected to make the corrected static stiffness more consistent with actual working conditions, thereby assisting in the structural design of the component. The selection of target model parameters correlated with static stiffness, the geometric kinematics and elastic kinematics performance analysis, and the correction of the first static stiffness are easily automated using artificial intelligence, achieving automatic correction of the stiffness of vehicle components.

[0146] As a refinement of step 101, when performing the process of obtaining the parameterized component finite element model of the components in the vehicle, it can be implemented in, but is not limited to, the following ways: Figure 2 As shown, Figure 2 A flowchart of a method for obtaining a parametric component finite element model provided in this disclosure includes:

[0147] Step 201: Obtain the correlation coefficients between all model parameters and static stiffness of the component; and obtain the initial finite element model of the component.

[0148] The correlation coefficient refers to the degree of correlation between model parameters and static stiffness. A higher correlation coefficient indicates a stronger correlation between model parameters and static stiffness. The initial component finite element model refers to the initial finite element analysis model established based on the component's three-dimensional geometric model. It includes mesh generation, material property definition, and boundary condition settings, but does not embed parametric variables.

[0149] Based on the design requirements of the component, determine the various model parameters of the component. For example, for a suspension component, these parameters may include length, width, height, material elastic modulus, material density, and thickness. Construct an initial finite element model of the component using finite element analysis (FEM), and perform static stiffness analysis based on this model. Specifically, input the component's design parameters and material properties, and use finite element software to perform mechanical analysis of the component under static loads. Based on the static stiffness analysis, analyze the relationship between the various model parameters of the component and its static stiffness using statistical methods such as multiple regression analysis and correlation analysis. This can be done by: calculating the degree of influence of each model parameter on the static stiffness; using statistical analysis software to perform correlation analysis to identify the linear or nonlinear relationship between each parameter and the static stiffness; quantifying the correlation coefficient between the parameters and the static stiffness to obtain the correlation coefficient between each parameter and the static stiffness. Based on the analysis results, select the model parameters closely related to the static stiffness and establish the correlation relationship between the parameters and the static stiffness.

[0150] Based on the component's design drawings, construct a precise geometric model of the component using 3D modeling software. Ensure the dimensional accuracy of the geometric model meets design requirements; for example, for automotive seat components, the geometry and dimensions of the backrest, seat cushion, and other parts should match the actual design. Export the geometric model as a neutral format file for import into the finite element analysis software. Import the geometric model into the finite element analysis software. Based on the component's structural characteristics and analysis accuracy requirements, select an appropriate element type (such as tetrahedral or hexahedral elements) for mesh generation. For example, tetrahedral elements are used for the foam portion of the seat component; beam elements are used for the seat frame, with element lengths determined based on the actual dimensions of the frame. Check and optimize the mesh quality to ensure the mesh shape quality meets analysis requirements. Define material properties based on experimental data or material handbooks for the component materials. Set the boundary conditions for the component to obtain the initial finite element model of the component.

[0151] By eliminating irrelevant parameters through correlation analysis, the complexity of the parameterized model can be reduced, and computational efficiency can be improved.

[0152] Step 202: Determine the model parameters corresponding to the correlation coefficients that are greater than the preset threshold among all correlation coefficients as the target model parameters.

[0153] A preset threshold is a pre-defined criterion used to distinguish whether the correlation between model parameters and static stiffness reaches a significant level. For example, the preset threshold can be set to 0.5. Target model parameters refer to model parameters that have a strong correlation with static stiffness, i.e., model parameters whose correlation coefficient is greater than the preset threshold. Target model parameters have a crucial impact on the performance design optimization of vehicle components.

[0154] To facilitate understanding, an example is provided, assuming that the radius parameters of the four Bar elements of the subframe are positively correlated with the corresponding Y-direction stiffness as a whole, and there is a certain degree of nonlinearity; the elastic model parameters of the front upper and front lower two-force bar-shaped control arms are linearly correlated with the axial stiffness; each design parameter has a low correlation with other non-corresponding stiffnesses, that is, each design variable only affects the stiffness of the corresponding component.

[0155] Once the target model parameters are determined, they can be modified or optimized to further improve the static stiffness, comfort, and maneuverability of the components, thereby achieving a component design that better meets actual needs.

[0156] Step 203: Add the target model parameters to the initial component finite element model to obtain the parameterized component finite element model.

[0157] In the initial finite element model of the component, locate the positions or attributes corresponding to the parameters of the target model. For example, in the software, find the setting section for the component's cross-sectional dimensions, set the cross-sectional height and thickness as variables, and associate them with the previously determined target model parameters (such as the height and thickness of a car chassis beam). This operation can be achieved through the parametric design function provided by the software or by writing corresponding parametric scripts, allowing the model to dynamically adjust according to different input target model parameter values, thereby obtaining a parametric finite element model of the component.

[0158] Parametric component finite element models can easily change the values ​​of the target model parameters and quickly simulate the performance changes of components under different parameter combinations.

[0159] As a refinement of step 102, when performing the geometric kinematics and elastic kinematics performance analysis on the finite element model of the parameterized component to obtain the geometric kinematics and elastic kinematics performance parameters of the component, it can be implemented in, but is not limited to, the following ways: Figure 3 As shown, Figure 3 A flowchart of a method for analyzing geometric kinematics and elastic kinematics properties provided in this disclosure embodiment includes:

[0160] Step 301: Perform super-element reduction on the parameterized component finite element model to obtain a super-element model.

[0161] Superelement reduction refers to the simplification process of replacing some subsystems or components in a complex finite element model with superelements. A superelement is composed of information such as the degrees of freedom and stiffness matrix of the subsystem, preserving the key characteristics of the original system and its connections to other parts, while reducing the model's degrees of freedom and lowering the solution difficulty. A superelement model is the simplified model obtained after superelement reduction, typically used for more efficient simulation analysis. Superelement models are computationally more efficient than the original parametric component finite element model, while still retaining sufficient accuracy and model response characteristics, making them suitable for large-scale calculations or analyses requiring multiple iterations.

[0162] Using finite element analysis software, based on the component design drawings, target model parameters such as section height and thickness are input to construct the model, mesh it, and assign material properties, resulting in a finite element model with adjustable parameters. Key connection points and load application locations are identified to define the boundaries of superelements. For example, superelements are generated for the front longitudinal beam, rear longitudinal beam, and crossbeam of an automobile frame, with each superelement covering the connection area of ​​a longitudinal beam and its associated crossbeam. Using the superelement generation tool of the finite element software, a superelement reduction operation is performed. The software calculates the degrees of freedom, stiffness matrix, and other data for the selected region, generating superelements. Subsequently, the corresponding parts of the original model are replaced with superelements to obtain the superelement model.

[0163] By reducing certain regions in the finite element model of a component to super-elements, the computational complexity of the model is significantly reduced. When dealing with large-scale structures or performing multiple iterative calculations, the super-element model can greatly reduce computation time and memory consumption, thus improving the efficiency of simulation analysis.

[0164] Step 302: Construct geometric kinematics and elastic kinematics performance models for the super-unit model to obtain geometric kinematics and elastic kinematics performance models.

[0165] Geometric kinematics and elastic kinematics performance models refer to models that quantify geometric kinematics and elastic kinematics parameters through mathematical equations or simulation models.

[0166] Import the super-element model, obtained after super-element reduction, into professional multibody dynamics analysis software or other finite element analysis software with kinematic analysis capabilities. Ensure the interfaces and connections of the super-element model are correct for assembly and connection with other related components or subsystems. Define kinematic pairs (such as hinges, sliding pairs, gear pairs, etc.) and constraints between the super-element model and other components in the software, based on the actual motion of the components. For example, for an automotive steering system, it is necessary to define the revolute joint between the steering wheel and the steering gear, the prismatic joint between the steering gear and the steering tie rod, etc., to accurately simulate the motion transmission relationship of the steering system. Apply appropriate loads and boundary conditions to the super-element model according to the analysis purpose and actual working conditions. Loads can include forces, torques, displacements, velocities, etc., and boundary conditions can be fixed constraints, symmetric constraints, etc. For example, when analyzing the kinematic performance of an automotive suspension system, it is necessary to apply vertical loads, lateral forces, braking torques, etc., during vehicle operation, as well as contact constraints between the wheels and the ground. Use the software's kinematic analysis module to perform geometric kinematic analysis on the super-element model. By solving the kinematic equations, the distribution of geometric parameters such as displacement, velocity, and acceleration of the component during its motion is obtained. The analysis results can be displayed in the form of graphs, animations, etc., to help users intuitively understand the component's motion trajectory and relative motion relationships. Based on the geometric kinematic analysis, the elastic deformation of the material is further considered to perform elastic kinematic performance analysis. The software combines the material's elastic modulus, Poisson's ratio, and other properties, as well as the stiffness matrix of the super-element model, to calculate the elastic deformation and stress distribution of the component under load. At the same time, the elastic deformation is fed back into the kinematic analysis to obtain more accurate kinematic parameters, such as displacement, velocity, and acceleration after considering elastic deformation. Based on the results of the geometric kinematic and elastic kinematic analyses, geometric kinematic and elastic kinematic performance models are constructed using the software's modeling functions or custom mathematical models.

[0167] By constructing geometric kinematics and elastic kinematics performance models, we can simultaneously consider the geometric motion characteristics and elastic deformation characteristics of components, and more comprehensively and accurately evaluate the performance of components in actual work.

[0168] Step 303: Perform geometric kinematics and elastic kinematics performance analysis on the geometric kinematics and elastic kinematics performance model to obtain the geometric kinematics and elastic kinematics performance parameters.

[0169] Geometric kinematics analysis refers to the deformation and motion analysis of a structure or component under external loads, based on its geometric properties and constraints. Geometric kinematics analysis primarily focuses on the geometric deformation patterns, displacements, and angular changes of the structure under different working conditions. Elastic kinematics analysis, on the other hand, analyzes the deformation and stress state of a material or structure under load within its elastic range. Elastic kinematics analysis considers the elastic properties of the material and its geometric deformation, evaluating the elastic response of the structure, including stress distribution and strain distribution.

[0170] Ensure that the geometric kinematics and elastic kinematics performance models have been constructed. These models include information such as the component's geometric parameters (e.g., length, angles), material properties (e.g., elastic modulus, Poisson's ratio), kinematic pairs (e.g., revolute pairs, prismatic pairs), and constraints (e.g., fixed constraints, symmetric constraints). This information must be correctly imported and assembled into professional multibody dynamics analysis software (e.g., Adams) or other finite element analysis software with corresponding analysis capabilities. Based on the actual application scenario and analysis objectives, set the working conditions and parameters for the geometric kinematics and elastic kinematics performance analysis in the software. This includes defining load conditions (e.g., force, torque, displacement, velocity), boundary conditions (e.g., fixed points, contact surfaces), motion inputs (e.g., rotation angle, linear displacement), and parameters such as the analysis time step and solution accuracy. For example, when analyzing the performance of a car steering system, different steering angle inputs and wheel-to-ground contact load parameters can be set. Use the software's geometric kinematics analysis module to solve and calculate the performance model. The software calculates the geometric parameters of a component during motion, such as displacement, velocity, and acceleration, based on the set kinematic pairs and constraints, and according to the principles of geometric kinematics. During the analysis, the component's trajectory and relative motion relationships can be observed in real time, allowing for the detection of any abnormalities such as motion interference or jamming. Analysis results are typically displayed in the form of graphs, vector diagrams, or animations, providing users with a clear understanding of the component's geometric kinematic performance. After the geometric kinematic performance analysis is completed and confirmed to be correct, the software further considers the elastic deformation of the material and performs an elastic kinematic performance analysis. The software combines the material's elastic properties and the stiffness matrix of the super-element model to calculate the stress and strain distribution of the component under load, as well as the additional displacement and changes in kinematic parameters caused by elastic deformation.

[0171] By accurately modeling and analyzing geometric and elastic deformations, complex behaviors that may be overlooked in traditional models can be effectively avoided, thus improving the accuracy of the design.

[0172] As a refinement of step 103, when performing static stiffness analysis on the finite element model of the parameterized component to obtain the first static stiffness of the component, it can be achieved in the following ways, but is not limited to: Figure 4As shown, Figure 4 A flowchart of a static stiffness analysis method provided for embodiments of this disclosure includes:

[0173] Step 401: Obtain the constraint conditions of the component.

[0174] Constraints refer to the boundary conditions that restrict the degrees of freedom of a component in finite element analysis. They are used to simulate the installation method and connection characteristics of the component under actual working conditions. Constraints include, but are not limited to, displacement constraints, load constraints, and connection constraints.

[0175] By obtaining and defining the constraints of components, the accuracy of mechanical analysis and structural design can be greatly improved.

[0176] Step 402: Apply constraints to the parameterized component finite element model according to the constraints to obtain the component constraint model.

[0177] A component constraint model is a model obtained by applying constraints according to actual constraints to a parametric component finite element model. This model is used for subsequent mechanical performance evaluations such as static stiffness analysis. By applying constraints to the parametric component finite element model, the stress and deformation state of the component during actual use can be simulated, providing a basis for accurately calculating its static stiffness and other performance indicators.

[0178] In finite element analysis software, appropriate constraint tools and commands are used to apply constraints to the parametric component finite element model according to the defined constraint conditions. For fixed constraints, this can be achieved by constraining all displacement degrees of freedom of the corresponding nodes (such as displacement in the x, y, and z directions and rotation about the x, y, and z axes). For hinged constraints, the "hinged" function in the software can be used, or specific constraint equations can be defined to restrict the displacement of nodes in certain directions while allowing rotation in other directions. After applying constraints, the degrees of freedom of the model will be restricted, conforming to the state under actual working conditions. After applying constraints, the parametric component finite element model is transformed into a component constraint model. The component constraint model contains information such as the component's geometry, material properties, finite element mesh generation, and applied constraint conditions, reflecting the stress and deformation of the component during actual use, and providing a foundation for subsequent calculations such as static stiffness analysis.

[0179] By applying realistic constraints to the finite element model of a parametric component, the mechanical state of the component in its actual working environment can be simulated more accurately. The application of constraints takes into account the connection relationships and boundary conditions between the component and other parts, making the finite element analysis results closer to reality, thereby improving the accuracy of mechanical performance evaluations such as static stiffness analysis.

[0180] Step 403: Perform static stiffness analysis on the component constraint model to obtain the first static stiffness.

[0181] Start the static stiffness analysis function in the analysis software. The software solves the deformation of the component through numerical calculation (such as the finite element method) to obtain information such as displacement and strain distribution of the component under static load. After the static stiffness analysis is completed, the first static stiffness value of the component is extracted from the analysis results. This value is usually the preliminary static stiffness of the component under the set loading conditions, which represents the component's resistance to deformation under external forces. The first static stiffness is calculated using formula (1) based on the deformation data of the component and the external load. During the solution process, the software will calculate the displacement and deformation of each node of the component according to the constraint conditions and load conditions, and then deduce the static stiffness. The static stiffness can be defined by the ratio between the load and the corresponding displacement of the component.

[0182] Static stiffness analysis is based on numerical simulation and can be performed in a computer environment. This allows engineers to quickly obtain stiffness data of components without conducting actual physical experiments, thus saving time and resources.

[0183] As a refinement of step 403, when performing the static stiffness analysis on the component constraint model to obtain the first static stiffness, it can be achieved in, but is not limited to, the following ways: Figure 5 As shown, Figure 5 A flowchart of a static stiffness analysis method provided for embodiments of this disclosure includes:

[0184] Step 501: Perform flexible component stiffness analysis on the component constraint model to obtain the second static stiffness.

[0185] Stiffness analysis of flexible components refers to finite element analysis of the local stiffness characteristics of flexible regions within a component, used to quantify the region's ability to resist deformation. Secondary static stiffness refers to the local stiffness value obtained through stiffness analysis of flexible components, characterizing the deformation response characteristics of the flexible region under static loads.

[0186] For ease of understanding, an example is provided: the second static stiffness can be K in formula (1). total K total =F / U flex .

[0187] Stiffness analysis of flexible components allows for more accurate calculation and evaluation of the deformation and stiffness performance of components under static loads.

[0188] Step 502: Perform rigid component stiffness analysis on the component constraint model to obtain the third static stiffness.

[0189] Rigid member stiffness analysis refers to finite element analysis of the constraint contribution of rigid regions within a member, quantifying their inhibitory effect on overall deformation and reflecting the supporting capacity of rigid regions for structural stability. The third static stiffness refers to the equivalent constraint stiffness obtained through rigid member stiffness analysis.

[0190] For ease of understanding, an example is provided: the third static stiffness can be K in formula (1). rigid K rigid =F / U rigid .

[0191] By analyzing the stiffness of rigid components, we can accurately assess the static stiffness of the components under external loads, ensuring that the structural design has sufficient resistance to deformation.

[0192] Step 503: Calculate the first static stiffness based on the second static stiffness and the third static stiffness.

[0193] Take the reciprocal of the obtained second and third static stiffnesses respectively, subtract the reciprocal of the second and third static stiffnesses to obtain their difference, and take the reciprocal of the difference to obtain the first static stiffness.

[0194] By combining the second and third static stiffness to calculate the first static stiffness, the mechanical performance of the component under actual working conditions can be more accurately reflected, avoiding the limitations of a single static stiffness model and obtaining a more accurate stiffness estimate.

[0195] As a refinement of the above embodiments, when performing the calculation of the first static stiffness based on the second static stiffness and the third static stiffness, it can be implemented in the following manner, but is not limited to: obtaining a first reciprocal of the second static stiffness and obtaining a second reciprocal of the third static stiffness; performing a difference calculation on the first reciprocal and the second reciprocal to obtain a difference result; performing a reciprocal calculation on the difference result to obtain the first static stiffness.

[0196] Specifically, the implementation process of this embodiment is a textual description of formula (1).

[0197] As a refinement of step 104, when performing the step of correcting the first static stiffness based on the geometric kinematics and elastic kinematics performance parameters to obtain the corrected first static stiffness, it can be implemented in, but is not limited to, the following ways: Figure 6 As shown, Figure 6 A flowchart of a static stiffness correction method provided for embodiments of this disclosure includes:

[0198] Step 601: Generate different first static stiffnesses and corresponding geometric kinematics and elastic kinematics performance parameters according to different target model parameters.

[0199] Based on design requirements and experience, different combinations of target model parameters are determined. For example, for the target model parameters of an automobile chassis crossbeam, various combinations can be set, such as section heights of h1, h2, and h3, widths of b1, b2, and b3, wall thicknesses of t1, t2, and t3, and material elastic moduli of E1 and E2. For each set of target model parameter values, a corresponding parametric component finite element model is generated using finite element modeling software. Geometric kinematics and elastic kinematics performance analyses, as well as static stiffness analyses, are performed on each generated finite element model to obtain the corresponding first static stiffness. The specific steps are as follows: The model is reduced to a super-element model. Geometric kinematics and elastic kinematics performance models of the super-element model are constructed. The performance models are analyzed to obtain geometric kinematics and elastic kinematics performance indices, such as displacement, deformation, velocity, and acceleration. The constraint conditions of the component are obtained. Constraints are applied to the model according to the constraint conditions to obtain the component constraint model. Static stiffness analysis is performed on the constraint model to obtain the initial static stiffness. The initial static stiffness is corrected based on the geometric kinematics and elastic kinematics properties to obtain the first static stiffness. The parameters of each target model and its corresponding first static stiffness, geometric kinematics, and elastic kinematics performance indices are recorded.

[0200] Based on the performance results obtained under different parameter combinations, the parameter combination that optimizes the component performance can be identified.

[0201] Step 602: Establish the mapping relationship between the different first static stiffnesses and the corresponding geometric kinematics and elastic kinematics performance parameters.

[0202] The mapping relationship refers to the correspondence between the first static stiffness and the geometric kinematics and elastic kinematics performance parameters established through mathematical models or data tables.

[0203] By combining the calculated first static stiffness with the corresponding geometric kinematics and elastic kinematics performance parameters, a mathematical model or mapping relationship is established using regression analysis, machine learning algorithms (such as support vector machines, neural networks, etc.), or interpolation methods. This mapping relationship takes the first static stiffness as input and outputs the corresponding geometric kinematics and elastic kinematics performance parameters.

[0204] To facilitate a better understanding of the mapping relationship between different first static stiffnesses and their corresponding geometric kinematics and elastic kinematics performance parameters, embodiments of this disclosure construct the mapping relationship between different first static stiffnesses and their corresponding geometric kinematics and elastic kinematics performance parameters through self-organizing map (SOM) analysis.

[0205] Self-organizing map analysis is a neural network unsupervised learning model used for clustering and feature extraction. The SOM diagram consists of hexagonal cells in a two-dimensional grid, with each cell corresponding to a weight vector. The weights range from [0,1] and are mapped to the weights of the corresponding variables through color levels in the color scheme.

[0206] The preliminary analysis of the above data is insufficient to determine whether there are interactions between variables. To further identify their correlations, a sensitivity (Sobol) analysis is conducted. The Sobol index plot is a useful tool for sensitivity analysis. It quantitatively identifies the input variables X that have the greatest impact on the variance of the output Y. These indices rely on decomposing the variance of the output quantity into terms that add dimension. These terms are the variance of the conditional expectation of Y (conditionally with respect to X), providing an appropriate measure of the importance of the input variable X. The amount of variance that cannot be explained by the variance of the input variable Xi is handled in the residuals.

[0207] Therefore, the sum of all indices of an output is always equal to 1. To estimate the Sobol exponent, Monte Carlo integration is performed by evaluating random points on the response surface. The top-level Sobol matrix contains the first-order Sobol exponent, with each cell having two values: the first-order Sobol exponent and the total first-order Sobol exponent. The total first-order Sobol exponent is the sum of the first-order Sobol exponent and the second-order Sobol exponent (interaction effect). When the checkbox in a cell of the first-order Sobol matrix is ​​enabled, a second table will be displayed below, explaining the decomposition of the total first-order Sobol exponent in the first-order and second-order Sobol exponents. Meanwhile, the second-order Sobol exponent is negligible relative to the first-order Sobol exponent, indicating a weak interaction between stiffness variables. The dynamic characterization method of the subframe's Y-direction static stiffness can achieve decoupling between stiffness performance.

[0208] By establishing a mapping relationship, designers can quickly predict the first static stiffness based on geometric kinematics and elastic kinematics performance parameters, thereby reducing the time spent on repeated trial and error and optimization, and improving design efficiency.

[0209] Step 603: Using the mapping relationship, construct a proxy model of the first static stiffness and the geometric kinematics and elastic kinematics performance parameters.

[0210] A surrogate model is an approximate model constructed through mathematical expressions or algorithms to replace complex finite element analysis or physical experiments, quickly predicting the relationship between target outputs (such as first static stiffness, geometric kinematics, and elastic kinematics performance parameters) and input variables (target model parameters). Based on established mapping relationships, it achieves high-accuracy performance prediction with low computational cost.

[0211] Based on the characteristics of the data and the purpose of the analysis, a suitable modeling method is selected to construct a surrogate model. Common methods include, but are not limited to, multinomial regression, neural networks, and radial basis functions. For example, if the data exhibits a nonlinear relationship, a neural network method can be chosen to construct the surrogate model because it has strong nonlinear fitting capabilities. The preprocessed data is then input into the selected modeling method for model training. The purpose of this step is to allow the model to learn the intrinsic relationship between the first static stiffness and the geometric and elastic kinematic performance parameters. For example, for a neural network model, it is necessary to determine the network structure (such as the number of neurons in the input, hidden, and output layers), activation functions, and other parameters, and adjust the network weights and biases through iterative training to minimize the error between the predicted and actual values. A portion of the retained data is used to verify the accuracy of the surrogate model. If the error between the model's predictions and the actual values ​​is large, the model needs to be optimized, which may require adjusting model parameters or changing the modeling method. For example, cross-validation can be used to divide the dataset into training and test sets. The model is trained on the training set and validated on the test set. Based on the validation results, the model structure or parameters are adjusted, such as increasing the number of neurons in the hidden layers of the neural network or changing the learning rate, until the model's prediction accuracy meets the requirements.

[0212] After constructing a surrogate model, the first static stiffness can be quickly predicted based on geometric kinematics and elastic kinematics performance parameters, or vice versa, without performing complex primitive analyses (such as finite element analysis). This saves analysis time and improves the efficiency of design and optimization.

[0213] Step 604: Based on the surrogate model, the first static stiffness is corrected to obtain the corrected first static stiffness.

[0214] Ensure an accurate surrogate model has been constructed and initial static stiffness data is available. This initial static stiffness data can come from preliminary finite element analysis, experimental estimation, or other simplified calculation methods. Based on actual design requirements and performance objectives, determine the conditions that the corrected static stiffness must meet. For example, the corrected static stiffness may require that the deformation of the component under a specific load does not exceed a certain threshold, or that it meets certain fatigue life requirements. Simultaneously, practical engineering constraints, such as material costs and manufacturing processes, must be considered. Input the initial static stiffness data and related geometric kinematics and elastic kinematics performance data into the surrogate model. The surrogate model will calculate the corrected static stiffness based on the previously established mapping relationship. For example, the surrogate model might be a neural network model; by inputting the initial static stiffness and the displacement, strain, and other performance data of the link, the neural network calculates and outputs the corrected static stiffness.

[0215] To facilitate understanding, an example is provided, which uses a proxy model to perform multi-objective optimization of the subframe static stiffness based on K&C performance. The optimization model is defined as follows:

[0216]

[0217] In the formula, p i For the i-th performance in KC characteristics, specifically KC performance 1 and KC performance 2, p iL p iH ky_UCA, ky_LCA, and ky_SCA are the upper and lower limits of the i-th performance requirement, respectively, and static stiffness 1, static stiffness 2, and static stiffness 3 are respectively.

[0218] The standard boundary cross method was used for optimization to obtain a Pareto optimal solution set that satisfies the requirements of longitudinal force camber compliance and wheel hop steering gradient performance. The empirical design values ​​of the subframe and the initial design values ​​were then combined to adjust the empirical static stiffness values. Based on the subframe characteristics and the performance of the initial design, it was found that the initial design value of the Y-direction static stiffness of the lower rear control arm was relatively high. Therefore, while maintaining a high level of corresponding stiffness target values, the target static stiffness values ​​for the toe arm and upper control arm were determined.

[0219] By using a surrogate model to correct the first static stiffness, more factors and more complex mapping relationships can be taken into account, thereby improving the accuracy of stiffness prediction.

[0220] As a refinement of the above embodiments, when performing the modification of the first static stiffness based on the surrogate model to obtain the modified first static stiffness, it can be implemented in the following ways, but is not limited to: obtaining the value range of the geometric kinematics and elastic kinematics performance parameters in the surrogate model; adjusting the first static stiffness within the value range in a direction that makes the geometric kinematics and elastic kinematics performance parameters approach their minimum values ​​to obtain the modified first static stiffness.

[0221] The range of values ​​refers to the variation range of geometric kinematics and elastic kinematics properties. By establishing a surrogate model, the maximum and minimum value ranges of the geometric kinematics and elastic kinematics property parameters can be obtained, and then the static stiffness can be optimized and adjusted within the maximum and minimum value ranges.

[0222] An initial static stiffness value is set as the starting point for adjustment. This can be the original static stiffness value or other reasonable initial estimates. In each iteration, the geometric and elastic kinematic performance values ​​corresponding to the current static stiffness are calculated according to the optimization algorithm. The adjustment direction is determined based on the relationship between the performance index and its range. Specifically, the gradient of the performance index relative to the first static stiffness or other sensitivity indicators are calculated to determine how to adjust the first static stiffness to move the performance index towards its minimum. The first static stiffness value is updated according to the determined adjustment direction. Termination conditions are set, such as reaching the maximum number of iterations or the change in the performance index being less than a certain threshold. When the termination conditions are met, the optimization process stops.

[0223] By minimizing the geometric and elastic kinematic properties, the deformation of the structure under load can be effectively reduced, thereby improving the rigidity and stability of the structure.

[0224] To facilitate a better understanding of the entire process of performance design optimization for vehicle components, an example is provided. After starting the parametric subframe finite element model, two operations are performed: First, creating a subframe Mns format superelement model: First, parameters such as stiffness, damping, and coordinate system of elastic components are defined. Then, Mns format superelement models such as control arms are created, forming a KC characteristic rigid-flexible hybrid model. Finally, the KC characteristic analysis results are extracted. Second, creating a subframe constraint model: After defining the subframe static stiffness boundary, flexible and rigid subframe analyses are performed, and the static stiffness analysis results are extracted. Then, based on the two extracted results (KC characteristic analysis results and static stiffness analysis results), a surrogate model for static stiffness and KC performance is constructed. Finally, the static stiffness index is obtained based on the KC performance of interest. The subframe is used as a component. The subframe, for which flexible connections to the vehicle body have not yet been established, is used as the baseline finite element model. The connection between the subframe and the vehicle body is equivalently represented, and a subframe constraint model is established for static stiffness analysis (referencing subframe static stiffness analysis specifications). Furthermore, the baseline finite element model of the subframe and the finite element models of each control arm are transformed into super-element models. A high-precision model is established for KC analysis. A joint simulation process integrating subframe static stiffness analysis, model reduction analysis, and KC performance analysis is established, capable of self-driven multi-sample iterative calculation.

[0225] To facilitate a better understanding of the construction of the proxy model, such as Figure 7 As shown, Figure 7 The flowchart illustrates a method for constructing a proxy model according to an embodiment of this disclosure. Figure 7 The subframe is the component, and the design variables are the target model parameters. The subframe is experimentally designed based on the co-simulation process. Through experimental design matrix transformation, a combined proxy model between KC performance and static stiffness performance is established, and the static stiffness performance target is accurately formulated based on the KC performance index.

[0226] In summary, the embodiments disclosed herein can achieve the following beneficial effects:

[0227] This disclosure establishes a parametric finite element model of a component based on target model parameters that are correlated with static stiffness. Geometric kinematics and elastic kinematics performance analyses are then performed on the parametric finite element model to obtain the component's geometric kinematics and elastic kinematics performance parameters, which are closely related to vehicle driving comfort and handling. Next, static stiffness analysis is performed on the parametric finite element model to obtain a first static stiffness. Based on the geometric kinematics and elastic kinematics performance parameters, the first static stiffness is corrected to better reflect actual working conditions, thereby assisting in component structural design. The selection of target model parameters correlated with static stiffness, the geometric kinematics and elastic kinematics performance analysis, and the correction of the first static stiffness can be automated using artificial intelligence, achieving automatic correction of vehicle component stiffness.

[0228] Corresponding to the above-described method for optimizing the performance design of vehicle components, this invention also proposes a device for optimizing the performance design of vehicle components. Since the device embodiments of this invention correspond to the method embodiments described above, details not disclosed in the device embodiments can be referred to in the method embodiments described above, and will not be repeated here.

[0229] Figure 8 This is a schematic diagram of the structure of a performance design optimization device 700 for a vehicle component provided in an embodiment of the present disclosure. The control device for the motor includes:

[0230] The acquisition unit 71 is used to acquire the parameterized component finite element model of the components in the vehicle; the parameterized component finite element model is a component finite element model established based on the target model parameters of the component that are related to the static stiffness.

[0231] Analysis unit 72 is used to perform geometric kinematics and elastic kinematics performance analysis on the finite element model of the parameterized component, and obtain the geometric kinematics and elastic kinematics performance parameters of the component;

[0232] The analysis unit 72 is also used to perform static stiffness analysis on the parameterized component finite element model to obtain the first static stiffness of the component.

[0233] The correction unit 73 is used to correct the first static stiffness according to the geometric kinematics and elastic kinematics performance parameters to obtain the corrected first static stiffness.

[0234] In summary, the vehicle component performance design optimization device proposed in this disclosure includes: acquiring a parametric finite element model of the component based on target model parameters that are correlated with static stiffness; performing geometric kinematics and elastic kinematics performance analysis on the parametric finite element model to obtain the geometric kinematics and elastic kinematics performance parameters of the component, which are closely related to the comfort and handling of the vehicle; then performing static stiffness analysis on the parametric finite element model to obtain a first static stiffness; and correcting the first static stiffness based on the geometric kinematics and elastic kinematics performance parameters to make the corrected static stiffness more consistent with actual working conditions, thereby assisting in the structural design of the component. The selection of target model parameters correlated with static stiffness, the geometric kinematics and elastic kinematics performance analysis, and the correction of the first static stiffness are easily automated using artificial intelligence, realizing automatic correction of the stiffness of vehicle components.

[0235] Furthermore, in one possible implementation of this disclosure, the acquisition unit 71 is further configured to:

[0236] Obtain the correlation coefficients between all model parameters and static stiffness of the component; and obtain the initial finite element model of the component.

[0237] The model parameters corresponding to the correlation coefficients that are greater than a preset threshold among all correlation coefficients are determined as the target model parameters;

[0238] The target model parameters are added to the initial component finite element model to obtain the parameterized component finite element model.

[0239] Furthermore, in one possible implementation of this disclosure, the analysis unit 72 is further configured to:

[0240] The parameterized component finite element model is reduced to a super-element model.

[0241] Geometric kinematics and elastic kinematics performance models are constructed for the super-unit model to obtain the geometric kinematics and elastic kinematics performance models;

[0242] Geometric kinematics and elastic kinematics performance models are analyzed to obtain their performance parameters.

[0243] Furthermore, in one possible implementation of the embodiments of this disclosure, such as Figure 9 As shown, the analysis unit 72 includes:

[0244] The acquisition module 721 is used to acquire the constraint conditions of the component;

[0245] The constraint module 722 is used to apply constraints to the parameterized component finite element model according to the constraint conditions to obtain the component constraint model;

[0246] Analysis module 723 is used to perform static stiffness analysis on the component constraint model to obtain the first static stiffness.

[0247] Furthermore, in one possible implementation of this disclosure, the analysis module 723 is further configured to:

[0248] The stiffness of the flexible component is analyzed by performing a stiffness analysis on the component constraint model to obtain the second static stiffness;

[0249] The rigid component stiffness analysis of the component constraint model is performed to obtain the third static stiffness;

[0250] The first static stiffness is calculated based on the second static stiffness and the third static stiffness.

[0251] Furthermore, in one possible implementation of the embodiments of this disclosure, such as Figure 9 As shown, the analysis module 723 is also used for:

[0252] Obtain the first reciprocal of the second static stiffness, and obtain the second reciprocal of the third static stiffness;

[0253] Calculate the difference between the first reciprocal and the second reciprocal to obtain the difference result;

[0254] The first static stiffness is obtained by reciprocal calculation of the difference result.

[0255] Furthermore, in one possible implementation of this disclosure, the correction unit 73 is further configured to:

[0256] Based on different target model parameters, generate different first static stiffnesses and corresponding geometric kinematics and elastic kinematics performance parameters;

[0257] Establish the mapping relationship between the different first static stiffnesses and the corresponding geometric kinematics and elastic kinematics performance parameters;

[0258] Using the mapping relationship, a proxy model is constructed for the first static stiffness and the geometric kinematics and elastic kinematics performance parameters;

[0259] Based on the surrogate model, the first static stiffness is corrected to obtain the corrected first static stiffness.

[0260] Furthermore, in one possible implementation of this disclosure, the correction unit 73 is further configured to:

[0261] Obtain the value range of the geometric kinematics and elastic kinematics performance parameters in the surrogate model;

[0262] Within the range of values, the first static stiffness is adjusted in a direction that makes the geometric kinematics and elastic kinematics performance parameters approach their minimum values ​​to obtain the corrected first static stiffness.

[0263] Since the apparatus provided in this embodiment corresponds to the methods provided in the above embodiments, the implementation of the methods is also applicable to the apparatus provided in this embodiment, and will not be described in detail in this embodiment.

[0264] The methods and apparatus provided in the embodiments of this application have been described above. To implement the functions of the methods provided in the embodiments of this application, the electronic device may include a hardware structure and software modules, and may implement the above functions in the form of a hardware structure, software modules, or a hardware structure plus software modules. One of the above functions may be executed in the form of a hardware structure, software modules, or a hardware structure plus software modules.

[0265] Figure 10 This is a block diagram illustrating an electronic device 800 for implementing the performance design optimization method for the aforementioned vehicle components, according to an exemplary embodiment. For example, the electronic device 800 may be a mobile phone, computer, digital broadcasting terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, etc.

[0266] Reference Figure 10 The electronic device 800 may include one or more of the following components: a processing component 802, a memory 804, a power supply component 806, a multimedia component 808, an audio component 810, an input / output (I / O) interface 812, a sensor component 814, and a communication component 816.

[0267] Processing component 802 typically controls the overall operation of electronic device 800, such as operations associated with display, telephone calls, data communication, camera operation, and recording operations. Processing component 802 may include one or more processors 820 to execute instructions to complete all or part of the steps of the methods described above. Furthermore, processing component 802 may include one or more modules to facilitate interaction between processing component 802 and other components. For example, processing component 802 may include a multimedia module to facilitate interaction between multimedia component 808 and processing component 802.

[0268] Memory 804 is configured to store various types of data to support the operation of electronic device 800. Examples of this data include instructions for any application or method operating on electronic device 800, contact data, phonebook data, messages, pictures, videos, etc. Memory 804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, 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 storage, flash memory, magnetic disk, or optical disk.

[0269] Power supply component 806 provides power to various components of electronic device 800. Power supply component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 800.

[0270] Multimedia component 808 includes a screen that provides an output interface between electronic device 800 and user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of touch or swipe actions but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 808 includes a front-facing camera and / or a rear-facing camera. When electronic device 800 is in an operating mode, such as a shooting mode or video mode, the front-facing camera and / or rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0271] Audio component 810 is configured to output and / or input audio signals. For example, audio component 810 includes a microphone (MIC) configured to receive external audio signals when electronic device 800 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 804 or transmitted via communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

[0272] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0273] Sensor assembly 814 includes one or more sensors for providing state assessments of various aspects of electronic device 800. For example, sensor assembly 814 can detect the on / off state of electronic device 800, the relative positioning of components such as the display and keypad of electronic device 800, changes in position of electronic device 800 or a component of electronic device 800, the presence or absence of user contact with electronic device 800, orientation or acceleration / deceleration of electronic device 800, and temperature changes of electronic device 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 814 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.

[0274] Communication component 816 is configured to facilitate wired or wireless communication between electronic device 800 and other devices. Electronic device 800 can access wireless networks based on communication standards, such as WiFi, 2G or 3G, 4G LTE, 8G NR (NewRadio), or combinations thereof. In one exemplary embodiment, communication component 816 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 816 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0275] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0276] Embodiments of this disclosure also provide a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to perform the methods described in the above embodiments of this disclosure.

[0277] Embodiments of this disclosure also provide a computer program product comprising a computer program executable by a programmable device, the computer program having, when executed by the programmable device, the method described in the above embodiments of this disclosure.

[0278] For cases where electronic devices can be chips or chip systems, see [link to relevant documentation]. Figure 11 The diagram shows the structure of the chip. Figure 11 The chip shown includes a processor 901 and an interface 902. There can be one or more processors 901, and multiple interfaces 902.

[0279] Optionally, the chip also includes a memory 903, which is used to store necessary computer programs and data.

[0280] Those skilled in the art will also understand that the various illustrative logical blocks and steps listed in the embodiments of this application can be implemented by electronic hardware, computer software, or a combination of both. Whether such functionality is implemented through hardware or software depends on the specific application and the overall system design requirements. Those skilled in the art can implement the functionality using various methods for each specific application, but such implementation should not be construed as exceeding the scope of protection of the embodiments of this application.

[0281] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this disclosure are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this disclosure described herein can be implemented in orders other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0282] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "illustrative embodiment," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with an embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0283] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process, and the scope of the preferred embodiments of the invention includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as will be understood by those skilled in the art to which embodiments of the invention pertain.

[0284] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processing module, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (control method), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic device, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which programs can be printed, because programs can be obtained electronically, for example, by optically scanning the paper or other media, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0285] It should be understood that various parts of the embodiments of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0286] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware, and the program can be stored in a computer-readable storage medium. When executed, the program includes one or a combination of the steps of the method embodiments.

[0287] Furthermore, the functional units in the various embodiments of the present invention can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc.

[0288] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for performance design optimization of vehicle components, characterized in that, The method includes: Obtain a parametric finite element model of a component in a vehicle; the parametric finite element model is a component finite element model established based on target model parameters of the component that are related to static stiffness. Geometric kinematics and elastic kinematics performance analysis were performed on the finite element model of the parametric component to obtain the geometric kinematics and elastic kinematics performance parameters of the component; Static stiffness analysis was performed on the finite element model of the parametric component to obtain the first static stiffness of the component; Based on the geometric kinematics and elastic kinematics performance parameters, the first static stiffness is corrected to obtain the corrected first static stiffness.

2. The method according to claim 1, characterized in that, The process of obtaining the parametric finite element model of the components in the vehicle includes: Obtain the correlation coefficients between all model parameters and static stiffness of the component; and obtain the initial finite element model of the component. The model parameters corresponding to the correlation coefficients that are greater than a preset threshold among all correlation coefficients are determined as the target model parameters; The target model parameters are added to the initial component finite element model to obtain the parameterized component finite element model.

3. The method according to claim 1, characterized in that, The geometric kinematics and elastic kinematics performance analysis of the finite element model of the parameterized component yields the following geometric kinematics and elastic kinematics performance parameters of the component: The parameterized component finite element model is reduced to a super-element model. Geometric kinematics and elastic kinematics performance models are constructed for the super-unit model to obtain the geometric kinematics and elastic kinematics performance models; Geometric kinematics and elastic kinematics performance models are analyzed to obtain their performance parameters.

4. The method according to claim 1, characterized in that, The static stiffness analysis of the finite element model of the parameterized component to obtain the first static stiffness of the component includes: Obtain the constraint conditions of the component; According to the aforementioned constraints, constraints are applied to the parameterized component finite element model to obtain the component constraint model; A static stiffness analysis is performed on the component constraint model to obtain the first static stiffness.

5. The method according to claim 4, characterized in that, The static stiffness analysis performed on the component constraint model yields the first static stiffness, which includes: The stiffness of the flexible component is analyzed by performing a stiffness analysis on the component constraint model to obtain the second static stiffness; The rigid component stiffness analysis of the component constraint model is performed to obtain the third static stiffness; The first static stiffness is calculated based on the second static stiffness and the third static stiffness.

6. The method according to claim 1, characterized in that, The step of correcting the first static stiffness based on the geometric kinematics and elastic kinematics performance parameters to obtain the corrected first static stiffness includes: Based on different target model parameters, generate different first static stiffnesses and corresponding geometric kinematics and elastic kinematics performance parameters; Establish the mapping relationship between the different first static stiffnesses and the corresponding geometric kinematics and elastic kinematics performance parameters; Using the mapping relationship, a proxy model is constructed for the first static stiffness and the geometric kinematics and elastic kinematics performance parameters; Based on the surrogate model, the first static stiffness is corrected to obtain the corrected first static stiffness.

7. The method according to claim 6, characterized in that, The step of correcting the first static stiffness based on the surrogate model to obtain the corrected first static stiffness includes: Obtain the value range of the geometric kinematics and elastic kinematics performance parameters in the surrogate model; Within the range of values, the first static stiffness is adjusted in a direction that makes the geometric kinematics and elastic kinematics performance parameters approach their minimum values ​​to obtain the corrected first static stiffness.

8. A performance design optimization apparatus for a vehicle component performance design optimization method as described in any one of claims 1-7, characterized in that, The device includes: The acquisition unit is used to acquire the parametric component finite element model of the components in the vehicle; the parametric component finite element model is a component finite element model established based on the target model parameters of the component that are related to the static stiffness. The analysis unit is used to perform geometric kinematics and elastic kinematics performance analysis on the finite element model of the parameterized component, and obtain the geometric kinematics and elastic kinematics performance parameters of the component. The analysis unit is also used to perform static stiffness analysis on the finite element model of the parameterized component to obtain the first static stiffness of the component. The correction unit is used to correct the first static stiffness according to the geometric kinematics and elastic kinematics performance parameters to obtain the corrected first static stiffness.

9. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method according to any one of claims 1-7.

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