Vehicle performance analysis method and device, electronic equipment and storage medium

Abstract

This application relates to a method, apparatus, electronic device, and storage medium for analyzing the overall performance of a vehicle. The method includes: determining the vehicle's body module division and assembly form to determine the error variables and error ranges of assembly errors; determining the vehicle's response indicators and pass / fail thresholds based on the vehicle's model design objectives, and generating a test matrix based on the error variables and error ranges; generating a sample set using the test matrix and a preset basic finite element model; constructing a response surface model using the sample set, and solving for the optimal error combination based on a multi-objective optimization function and at least one constraint condition to generate the optimal error range and error control scheme, thereby determining the overall vehicle performance analysis results. This solves the problem in related technologies where the analysis of structural stiffness, strength, and other performance characteristics is based on the design state, leading to a failure to effectively consider assembly errors between modules, thus affecting the local stiffness, strength, and collision safety performance of the structure.

Description

Technical Field

[0001] This application relates to the field of vehicle performance analysis technology, and in particular to a method, apparatus, electronic device and storage medium for analyzing the overall performance of a vehicle. Background Technology

[0002] Currently, the automotive industry is highly competitive, and rapid development has become a key goal for major automakers. Modular and integrated structural design has become a consensus. By developing and assembling individual modules in parallel and then combining them into a complete vehicle, assembly efficiency can be improved. Modular vehicle structures are typically assembled using methods such as screws and adhesives. Different modules will have varying degrees of manufacturing tolerances during the production process, resulting in cumulative assembly tolerances. Manufacturing errors between two modules need to be compensated for through inter-module connection structures, leading to a certain deviation between the actual structure and the designed structure. This causes assembly deviations between modules, affecting structural performance and appearance quality.

[0003] In related technologies, automobile bodies are connected by welding and threading with gaps to absorb assembly interference and structural adhesive to strengthen the connection. Modular vehicles are assembled by combining, welding and bonding multiple modules. In addition, the assembly tolerance of the whole vehicle can be optimized by three-dimensional deviation modeling, neural networks and genetic algorithms to balance the dimensional deviation rate and manufacturing cost, or the front-end module of the automobile can be optimized based on multi-working-condition topology to improve structural rigidity and achieve lightweighting.

[0004] However, in related technologies, the analysis of structural stiffness, strength and other properties is based on the design state and only addresses a single processing deviation. This results in the failure to effectively consider the impact of assembly errors between modules on performance, which cannot guarantee the structural stability and reliability of modular vehicle assembly. Consequently, it affects the local stiffness, strength and collision safety performance of the structure, leading to safety hazards and increased manufacturing costs, which urgently need to be improved. Summary of the Invention

[0005] This application provides a vehicle performance analysis method, device, electronic device, and storage medium to solve the problem in related technologies where the analysis of structural stiffness, strength, and other performance is based on the design state, which fails to effectively consider the impact of assembly errors between modules on performance, thus failing to guarantee the structural stability and reliability of modular vehicle assembly, and consequently affecting the local stiffness, strength, and collision safety performance of the structure.

[0006] The first aspect of this application provides a method for analyzing the overall performance of a vehicle, comprising the following steps: determining the vehicle's body module division and assembly form to determine the error variables and error range of assembly errors; determining the vehicle's response indicators and pass thresholds based on the vehicle's model design objectives, and generating a test matrix based on the error variables and error ranges, thereby generating a sample set using the test matrix and a preset basic finite element model; constructing a response surface model using the sample set, and solving for the optimal error combination based on a multi-objective optimization function and at least one constraint condition to generate the optimal error range and error control scheme, and determining the overall vehicle performance analysis results.

[0007] Through the above-mentioned technical means, the embodiments of this application can determine the body module division, assembly form, error variables and value range, determine the performance response index and qualified threshold in combination with the vehicle design goals, generate an experimental matrix to construct a sample set and train the response surface model, and solve the optimal error combination through a multi-objective optimization function to generate the optimal error range and error control scheme. In this way, the modular assembly characteristics, assembly error analysis and whole vehicle performance optimization are deeply coupled, and the performance fluctuations caused by the connection error between modules are predicted and optimized during the whole vehicle development stage, so as to ensure the assembly accuracy of modular vehicles and the performance of whole vehicles meet the standards from the source.

[0008] Optionally, in one embodiment of this application, determining the vehicle body module division and assembly form includes: determining at least one body module according to the modular design scheme of the vehicle; establishing a corresponding finite element simulation analysis module based on at least one body module to perform finite element modeling, and determining the assembly form by combining the connection type of the connection parts between modules.

[0009] Through the above-mentioned technical means, the embodiments of this application can establish corresponding finite element simulation analysis modules according to the modular design scheme, and determine the assembly form according to the actual type of the connection parts between modules, so that the finite element modeling is more in line with the real assembly structure, providing a model basis and assembly boundary conditions for subsequent error analysis, thereby improving the engineering credibility of the vehicle performance simulation and optimization results.

[0010] Optionally, in one embodiment of this application, determining the error variable and error range of the assembly error includes: determining the main assembly error types of the connection parts between the body modules based on the assembly process of the vehicle, so as to determine the error source; determining the error range corresponding to multiple error components according to the error source, and selecting at least one error component that meets the preset analysis conditions from the multiple error components, so as to determine the error variable and the corresponding error range.

[0011] Through the above-mentioned technical means, the embodiments of this application can identify error sources based on the vehicle assembly process, determine the range of error component values ​​and screen key error components, clarify error variables and corresponding value intervals, sort out the core error sources from the assembly process, screen key error components to simplify the analysis dimensions, achieve a balance between analysis accuracy and calculation efficiency, and provide accurate and reasonable error input conditions for subsequent experimental design and performance simulation.

[0012] Optionally, in one embodiment of this application, determining the vehicle's response index and pass threshold based on the vehicle's model design goals includes: determining the vehicle's core performance indicators; determining the vehicle's body torsional stiffness and bending stiffness based on the core performance indicators and the model design goals to determine the response index; and determining the minimum requirement or allowable range of the response index based on the model design specifications corresponding to the model design goals to determine the pass threshold.

[0013] Through the above-mentioned technical means, the embodiments of this application can extract the core performance indicators of the whole vehicle according to the vehicle design goals, determine the torsional stiffness and bending stiffness of the body as key response indicators, and set their qualified thresholds according to the design specifications, so that the optimization goals are directly aligned with the engineering requirements, avoiding any disconnect, ensuring that the performance analysis results are in line with the vehicle design requirements, and improving the engineering practical value of the analysis conclusions.

[0014] Optionally, in one embodiment of this application, the multi-objective optimization function is: , in, The response metric, x For the error variable, The number of the response metrics, m The number of the error variables, T This is the transpose symbol.

[0015] Through the above-mentioned technical means, the embodiments of this application can construct a multi-objective optimization function containing multiple response indicators, thereby realizing the synchronous and coordinated optimization of multiple performance indicators, providing core mathematical model support for balancing various performances and solving the optimal error combination, and ensuring the scientificity and rigor of the optimization solution.

[0016] Optionally, in one embodiment of this application, the at least one constraint includes error variable constraints, performance index constraints, and geometric constraints.

[0017] Through the above-mentioned technical means, the embodiments of this application can ensure the engineering feasibility and security of the optimization results. By constraining error variables, it is ensured that each error variable is within its value range. By constraining performance indicators, it is ensured that each performance indicator meets the design requirements. By constraining geometrically, it is avoided that unreasonable or unrealizable structural forms occur during the optimization process, thereby improving the practicality and reliability of the optimization scheme.

[0018] A second aspect of this application provides a vehicle performance analysis device, comprising: a determination module for determining the vehicle's body module division and assembly form to determine the error variables and error value range of the assembly error; a generation module for determining the vehicle's response index and pass threshold according to the vehicle's model design objectives, and generating a test matrix according to the error variables and the error value range, so as to generate a sample set using the test matrix and a preset basic finite element model; and an analysis module for constructing a response surface model using the sample set, and solving for the optimal error combination according to a multi-objective optimization function and at least one constraint condition to generate the optimal error range and error control scheme, and determining the vehicle performance analysis results.

[0019] Through the above-mentioned technical means, the embodiments of this application can determine the body module division, assembly form, error variables and value range, determine the performance response index and qualified threshold in combination with the vehicle design goals, generate an experimental matrix to construct a sample set and train the response surface model, and solve the optimal error combination through a multi-objective optimization function to generate the optimal error range and error control scheme. In this way, the modular assembly characteristics, assembly error analysis and whole vehicle performance optimization are deeply coupled, and the performance fluctuations caused by the connection error between modules are predicted and optimized during the whole vehicle development stage, so as to ensure the assembly accuracy of modular vehicles and the performance of whole vehicles meet the standards from the source.

[0020] Optionally, in one embodiment of this application, the determining module includes: a first determining unit, configured to determine at least one of the body modules according to the modular design scheme of the vehicle; and a second determining unit, configured to establish a corresponding finite element simulation analysis module based on at least one of the body modules to perform finite element modeling, and determine the assembly form in combination with the connection type of the connection parts between modules.

[0021] Through the above-mentioned technical means, the embodiments of this application can establish corresponding finite element simulation analysis modules according to the modular design scheme, and determine the assembly form according to the actual type of the connection parts between modules, so that the finite element modeling is more in line with the real assembly structure, providing a model basis and assembly boundary conditions for subsequent error analysis, thereby improving the engineering credibility of the vehicle performance simulation and optimization results.

[0022] Optionally, in one embodiment of this application, the determining module includes: a third determining unit, configured to determine the main assembly error type of the connection part between the body modules based on the assembly process of the vehicle, so as to determine the error source; and a fourth determining unit, configured to determine the error value range corresponding to multiple error components according to the error source, and to select at least one error component that meets the preset analysis conditions from the multiple error components, so as to determine the error variable and the corresponding error value range.

[0023] Through the above-mentioned technical means, the embodiments of this application can identify error sources based on the vehicle assembly process, determine the range of error component values ​​and screen key error components, clarify error variables and corresponding value intervals, sort out the core error sources from the assembly process, screen key error components to simplify the analysis dimensions, achieve a balance between analysis accuracy and calculation efficiency, and provide accurate and reasonable error input conditions for subsequent experimental design and performance simulation.

[0024] Optionally, in one embodiment of this application, the generation module includes: a fifth determining unit, used to determine the core performance indicators of the vehicle; a sixth determining unit, used to determine the torsional stiffness and bending stiffness of the vehicle body based on the core performance indicators and the vehicle design target, so as to determine the response index; and a seventh determining unit, used to determine the minimum requirement or allowable range of the response index based on the vehicle design specification corresponding to the vehicle design target, so as to determine the qualified threshold.

[0025] Through the above-mentioned technical means, the embodiments of this application can extract the core performance indicators of the whole vehicle according to the vehicle design goals, determine the torsional stiffness and bending stiffness of the body as key response indicators, and set their qualified thresholds according to the design specifications, so that the optimization goals are directly aligned with the engineering requirements, avoiding any disconnect, ensuring that the performance analysis results are in line with the vehicle design requirements, and improving the engineering practical value of the analysis conclusions.

[0026] Optionally, in one embodiment of this application, the multi-objective optimization function is: , in, The response metric, x For the error variable, The number of the response metrics, m The number of the error variables, T This is the transpose symbol.

[0027] Through the above-mentioned technical means, the embodiments of this application can construct a multi-objective optimization function containing multiple response indicators, thereby realizing the synchronous and coordinated optimization of multiple performance indicators, providing core mathematical model support for balancing various performances and solving the optimal error combination, and ensuring the scientificity and rigor of the optimization solution.

[0028] Optionally, in one embodiment of this application, the at least one constraint includes error variable constraints, performance index constraints, and geometric constraints.

[0029] Through the above-described technical means, the embodiments of this application can ensure the engineering feasibility and safety of the optimization results. Error variable constraints ensure that each error variable remains within its value range; performance index constraints ensure that each performance index meets design requirements; and geometric constraints prevent unreasonable or unrealizable structural forms from occurring during the optimization process, thereby improving the practicality and reliability of the optimization scheme. A third aspect of this application provides an electronic device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor. The processor executes the program to implement the vehicle performance analysis method described in the above embodiments.

[0030] A fourth aspect of this application provides a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the vehicle performance analysis method described above.

[0031] A fifth aspect of this application provides a computer program product that stores a computer program that, when executed by a processor, implements the vehicle performance analysis method described above.

[0032] This application embodiment can establish corresponding finite element simulation analysis modules based on modular design schemes, and determine the assembly form according to the actual type of connection parts between modules. This makes the finite element modeling more closely resemble the real assembly structure, providing a model basis and assembly boundary conditions for subsequent error analysis, thereby improving the engineering credibility of vehicle performance simulation and optimization results. Thus, it solves the problem in related technologies where the analysis of structural stiffness, strength, and other performance is based on the design state, resulting in the failure to effectively consider the impact of assembly errors between modules on performance. This fails to guarantee the structural stability and reliability of modular vehicle assembly, thereby affecting the local stiffness, strength, and collision safety performance of the structure.

[0033] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0034] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a vehicle performance analysis method according to an embodiment of this application; Figure 2 This is a flowchart of a vehicle performance analysis method according to an embodiment of this application; Figure 3 This is a schematic diagram of the structure of a vehicle performance analysis device according to an embodiment of this application; Figure 4 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of this application.

[0035] Figure label: 10- Vehicle performance analysis device; 100- Determine module, 200- Generate module, 300- Analysis module; 401- Memory, 402- Processor, 403- Communication interface. Detailed Implementation

[0036] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0037] The following description, with reference to the accompanying drawings, describes a vehicle performance analysis method, apparatus, electronic device, and storage medium according to embodiments of this application. Addressing the issues raised in the background art, where the analysis of structural stiffness, strength, and other properties is based on the design state, the impact of assembly errors between modules on performance is not effectively considered. This fails to guarantee the structural stability and reliability of modular vehicle assembly, thus affecting local structural stiffness, strength, and collision safety performance. This application provides a vehicle performance analysis method. In this method, a corresponding finite element simulation analysis module can be established based on the modular design scheme, and the assembly form can be determined according to the actual type of connection between modules. This makes the finite element modeling more closely resemble the actual assembly structure, providing a model basis and assembly boundary conditions for subsequent error analysis, thereby improving the engineering credibility of the vehicle performance simulation and optimization results. Thus, this solves the problem in the related art where the analysis of structural stiffness, strength, and other properties is based on the design state, failing to effectively consider the impact of assembly errors between modules on performance, thus failing to guarantee the structural stability and reliability of modular vehicle assembly, and consequently affecting local structural stiffness, strength, and collision safety performance.

[0038] Specifically, Figure 1 This is a flowchart illustrating a vehicle performance analysis method provided in an embodiment of this application.

[0039] like Figure 1 As shown, the vehicle performance analysis method includes the following steps: In step S101, the vehicle body module division and assembly form are determined in order to determine the error variables and error value range of the assembly error.

[0040] It is understood that the vehicle body module division in this application embodiment can be interpreted as splitting the entire vehicle body into independently manufacturable and assemblable units according to structure and assembly logic, such as the front engine compartment module, passenger compartment module, rear luggage compartment module, side panel module, roof module, etc. The assembly form can be understood as the connection method, positioning method, and assembly sequence between the modules.

[0041] In actual implementation, the embodiments of this application can clearly define the division and assembly form of the body-in-white modules; and define assembly error variables based on process documents, historical assembly data, etc. X= [ x 1 ,x 2 ,...,x m ] Error range a k ,b k ],in,( ) For the number of connection parts, The number of error variables after filtering ( , (Number of connection parts).

[0042] For example, embodiments of this application can, based on a modular design scheme, disassemble the body-in-white into several sub-modules, such as the front engine compartment module, passenger compartment module, rear luggage compartment module, side panel module, and roof module, and clarify the assembly connection forms between each sub-module. Furthermore, embodiments of this application can identify the main assembly error types at the connection points of each sub-module, including translational and rotational errors, and determine the value range of each error component through process research, historical assembly data statistics, or tolerance design requirements. Embodiments of this application can use correlation analysis or engineering experience to eliminate error components with minimal impact on assembly accuracy, ultimately determining the set of error variables that require focused analysis.

[0043] This application embodiment can determine the vehicle body module division and assembly form, and determine the error variables and error value range of assembly error, thereby providing standardized basic parameters for subsequent simulation analysis and optimization calculation, avoiding ambiguity of the analysis object and unreasonable error input.

[0044] In step S102, the vehicle's response indicators and qualification thresholds are determined according to the vehicle's model design objectives, and an experimental matrix is ​​generated based on the error variables and error value range, so as to generate a sample set using the experimental matrix and the preset basic finite element model.

[0045] It is understood that the response index in this application embodiment can be a key mechanical parameter used to evaluate the overall vehicle performance, such as the torsional stiffness and bending stiffness of the vehicle body. The pass threshold can be understood as the minimum requirement or allowable range that the response index must meet, which can be derived from vehicle design specifications or regulatory requirements. The test matrix can be understood as a combination of multiple error variable values ​​generated using experimental design methods based on the range of error variable values. The preset basic finite element model can be understood as a basic finite element model established based on the CAD model, including material properties, connection forms, etc. The preset basic finite element model can be set by those skilled in the art according to actual conditions, and no specific restrictions are imposed here.

[0046] In actual implementation, the embodiments of this application can define structural stiffness and strength performance indicators according to the vehicle's model design objectives. Y= [ y 1 ,y 2 ,...,y p ] Qualified threshold Y min The experimental matrix is ​​designed by combining the error variable X and its value range. A basic finite element model is built based on the submodule CAD model, material parameters, and connection type. Data coordinate transformation is performed on the basic finite element model according to the error combination to realize N sets of finite element models of the body-in-white containing errors. Load-boundary conditions are defined for the finite element models. Simulation results are extracted through calculation to form a sample set. S ={( X (s) , Y (s) )}.

[0047] For example, embodiments of this application can select response indicators in conjunction with vehicle model design, then determine performance indicator thresholds, and, based on vehicle model design specifications, clarify the minimum requirements or allowable ranges for each performance indicator. For the first The minimum pass value for each indicator ( ).

[0048] Furthermore, the embodiments of this application can design experimental schemes according to the number of variables, and can adopt experimental design methods such as orthogonal experiments, optimized Latin hypercubes, and full factorial experiments.

[0049] Taking orthogonal experimental design as an example, the selected error variables As experimental factors, each factor is determined according to its range of values. Division There are several levels (usually 3-5 levels, balancing accuracy and efficiency). The first factor Each level value is ( Based on the number of factors and level number Select the corresponding orthogonal array ,in For the number of trials, satisfying For example, an orthogonal array can be selected for three factors and three levels. Based on the orthogonal array, substitute the level values ​​of each factor into the table to generate... Groups of unique error combinations ( ),in For the first In the first group of trials The values ​​of each error variable. For each error combination... The rationality of error combinations is verified by considering the constraint types of the submodules (e.g., the error in a rigid connection must meet geometric compatibility). If a certain error combination violates the constraint conditions (e.g., interference between two modules), it is replaced with an adjacent reasonable error combination.

[0050] Furthermore, in this embodiment, a basic finite element model can be built based on the CAD model of the sub-modules, material parameters, and connection methods. It is essential to ensure that the mesh of each sub-module is independent to facilitate the construction of finite element models that introduce assembly errors. Assembly errors are introduced through coordinate transformation. A reference coordinate system is defined for each sub-module, and a global coordinate system is established with the body-in-white design reference point as the origin. Each submodule Establish a local coordinate system Through coordinate transformation matrix Implement the conversion between local and global coordinates: ,in For global coordinate vectors, For submodules The local coordinate vector, It is a 3×4 homogeneous coordinate transformation matrix (including translation and rotation).

[0051] The rotation transformation first corrects the attitude deviation of the submodule (rotation error around the X, Y, and Z axes) using a rotation matrix. The translation transformation then superimposes the position deviation of the submodule (translation error along the X, Y, and Z axes) using a translation vector. Through translation and rotation transformations, the assembly deviation of local modules is taken into account in the simulation model. The specific formula is: , in, These are the initial local coordinates (homogeneous coordinates, such as...) of the submodule node. ), It is a transformation matrix containing errors. These are the global coordinates of the transformed node.

[0052] Suppose that the initial local coordinates of a node in a submodule Mi are... =[10,20,30]T (unit: mm), the following errors exist during assembly: Translation error: along the X-axis + 2mm, Y-axis - 1mm, Z-axis + 3mm (i.e., δx=2, δy= 1, δz=3); Rotation error: Rotation only around the Z-axis θz=30° (simplified to single-axis rotation for ease of calculation; actual multi-axis rotation requires superimposed rotation matrices). Construct a simplified coordinate transformation matrix: Since it only rotates around the Z-axis, the rotation matrix is ​​simplified to a 2D rotation (the Z-axis is perpendicular to the paper, and rotation does not affect the Z-coordinate), and the 3×4 homogeneous transformation matrix is: , Substituting θz = 30° (cos30° ≈ 0.866, sin30° = 0.5) and the translation error, we get: , Perform the matrix transformation as follows: = = , In the absence of errors, the embodiment of this application can transform the matrix into an identity matrix, with the global and local coordinates being the same [10, 20, 30]. Under the influence of assembly errors, the global coordinates of this node change from the initial ideal position to [0.66, 21.32, 33] mm, accurately reflecting the assembly deviation of "rotating 30° around the Z-axis + translating along the X / Y / Z-axis".

[0053] This application embodiment can achieve coordinate transformation for the first... Group error combination Based on the error type (translation / rotation), the node coordinates of the corresponding submodule are modified through coordinate transformation. If a submodule has a translation error... and rotational error Then the global coordinate correction formula for its nodes is: .

[0054] In this application embodiment, the torsional stiffness and bending stiffness of the body-in-white can be selected as response indicators based on vehicle performance design requirements, denoted as a set. ,in The number of performance indicators is determined. Model boundary conditions are defined based on different performance characteristics. Boundary conditions for model analysis are defined according to the different performance analysis conditions. Then, simulation calculations and result extraction are performed: finite element analysis is conducted on each sample containing assembly errors, and the corresponding performance indicator values ​​are extracted. To form a simulation sample set .

[0055] The embodiments of this application can determine response indicators and qualified thresholds, and generate experimental matrices and sample sets to complete a systematic and quantitative analysis of the impact of assembly errors. Through scientific experimental design and efficient sample generation methods, it provides a high-quality data foundation for subsequent construction of response surface models and optimization analysis, significantly improving analysis efficiency and reliability.

[0056] In step S103, a response surface model is constructed using the sample set, and the optimal error combination is solved based on the multi-objective optimization function and at least one constraint condition to generate the optimal error range and error control scheme, thereby determining the vehicle performance analysis results.

[0057] It is understood that the response surface model in this application embodiment can be understood as an approximate model established through mathematical methods that describes the functional relationship between error variables and response indicators. The multi-objective optimization function can be understood as a mathematical expression used to comprehensively evaluate the performance of multiple response indicators. At least one constraint can be understood as a restriction that must be met during the optimization process, such as the error variable not exceeding its value range, the response indicator not falling below the qualified threshold, and no geometric interference between modules. The optimal error range can be understood as the range of error variable values ​​that optimizes the overall vehicle performance under all constraints. The error control scheme can be understood as specific process control measures formulated to achieve the optimal error range. The overall vehicle performance analysis results can be understood as a comprehensive analysis report including the optimal error range, control scheme, and expected performance level.

[0058] In practical implementation, embodiments of this application can construct a response surface model based on a sample set S using a quadratic polynomial model. The model is then fitted based on the output of the sample set S. j =f(X), coefficient vector By combining the fitted model and the sample set S, the model accuracy is assessed. If the accuracy is insufficient, it is improved by supplementing the sample set or changing the model. A multi-objective optimization function, max Y = ... (X) Constraints: Solve for the optimal error combination X*=[x1*,x2*,...,x] by optimizing the function and constraints. m *] The optimal solution is then used to verify the finite element model, determine the optimal error range and error control scheme.

[0059] For example, embodiments of this application can select a suitable response surface model form based on the relationship characteristics between error variables and performance indicators. A commonly used quadratic polynomial response surface model has the following general expression: , in, For the first One performance metric, For constant terms, The coefficient of the linear term, The coefficient of the quadratic term, The coefficient of the interaction term. For random error (satisfying) ).

[0060] This application embodiment can use sample sets Substitute the data from the model into the equations to construct a system of regression equations. For the first... The performance metrics are in matrix form as follows: ,in For the sample response vector, The regression matrix consists of the first-order, second-order, and interaction terms of the error variable. This is the coefficient vector.

[0061] The regression coefficients are solved using the least squares method, which yields the sum of squared residuals. Minimum, of which These are the model's predicted values. The solution to the coefficient vector is: , The solution obtained Substituting into the response surface model, we obtain the first... Fitting models of performance indicators with respect to error variables: .

[0062] The embodiments of this application can be used to verify model accuracy and perform variance analysis. The formula for calculating the total sum of squares is as follows: , in Let be the sample mean. The formula for calculating the regression sum of squares is as follows: , The sum of squared residuals is .

[0063] Calculate the degrees of freedom: total degrees of freedom Return to freedom ( (Number of regression coefficients), residual degrees of freedom .

[0064] Calculating the mean square: Regression mean square residual mean square .

[0065] Construct the F-statistic: Test the significance of the model. If ( significance level The critical value of the F-distribution under the given conditions is usually ), then the model is Significant at the lower level, meaning the linear relationship between the error variable and the performance index is significant.

[0066] Furthermore, embodiments of this application perform a goodness-of-fit test and calculate the coefficient of determination. , The closer to 1, the better the model fit (usually required). Calculate the adjusted coefficient of determination. Correcting the increase in the number of dependent variables Inflated, requirements The embodiments of this application can also perform residual analysis, plotting residual histograms and residual-predicted value scatter plots to test whether the residuals follow a normal distribution and whether they are independent. If the residuals do not show a clear pattern, the model fit is effective; otherwise, the model form needs to be reselected or the samples need to be corrected.

[0067] If the model accuracy does not meet the requirements (e.g.) or If the model fails to meet the required accuracy, the experimental level should be increased, additional sample points should be added, or a higher-order response surface model (such as a cubic polynomial) should be used to refit and validate the model until the model accuracy meets the requirements.

[0068] Furthermore, the embodiments of this application can set optimization objectives and constraints. The embodiments of this application can take maximizing performance indicators as the optimization objective. When multiple performance indicators are selected for optimization, a multi-objective optimization function is constructed. If it is necessary to transform it into a single-objective optimization, a weighted summation method can be used.

[0069] The embodiments of this application may select an optimization algorithm suitable for multiple constraints, such as particle swarm optimization, including: initializing the particle swarm, calculating fitness, updating particle extrema and global extrema, updating particle velocity and position, and terminating the iteration.

[0070] Among them, initializing the particle swarm: setting the number of particles Maximum number of iterations Inertia weight Learning factors Randomly generated There are 10 particles, and the position of each particle corresponds to a set of error variables. ( ), which satisfies the constraints.

[0071] Fitness calculation: Substitute the position of each particle into the response surface model and calculate its fitness value (i.e., the optimization objective function value). If a particle does not meet the constraints, set the fitness value to a minimum (penalty mechanism).

[0072] Update particle extrema and global extrema: Record the optimal position of each particle. (The position corresponding to the historical best fitness) and the best position of the entire particle swarm. (The position corresponding to the global optimal fitness).

[0073] Update particle velocity and position: , , in, For the first Sub-particles No. Speed ​​in each dimension The value is a random number between 0 and 1. After the update, it is necessary to check whether the particle position meets the constraints. If it does not meet the constraints, boundary correction is performed (such as taking the endpoint values ​​of the constraint interval).

[0074] Iteration Termination: Repeat the above steps until the maximum number of iterations is reached. or global extremum No longer changing (the amount of change is less than the threshold) ),at this time The corresponding combination of error variables is the optimal error combination. .

[0075] The embodiments of this application can combine the optimal error. Substitute the finite element model of the body-in-white into the simulation calculation to obtain the actual performance index values. .like ( If the optimal solution is close to the predicted value of the response surface model (error less than 5%), then the optimal solution is valid; otherwise, the optimization parameters (such as the number of iterations and weight coefficients) need to be readjusted and the solution needs to be solved again.

[0076] The embodiments of this application can be based on the optimal error combination. Determine the control range for each error variable. For critical error variables (variables that significantly affect performance, which can be determined by the absolute value of the regression coefficient), narrow their value range; for minor error variables, maintain the original value range or appropriately widen it to balance assembly accuracy requirements and manufacturing costs. For example, if It has the greatest impact on torsional stiffness, and its control range can be extended from... Adjusted to ( (The allowable fluctuation range is determined based on simulation verification results). According to the established error control scheme, 3-5 error combinations (including boundary values) are selected for finite element simulation to verify that the performance indicators can meet the design requirements, and finally a feasible body-in-white assembly error control scheme is formed.

[0077] This application embodiment can significantly improve the efficiency of optimization calculation by constructing a response surface model. It relies on multi-objective optimization to achieve the synergistic optimization of assembly error and vehicle performance, thereby generating the optimal error range and error control scheme, determining the vehicle performance analysis results, and outputting an error control scheme that meets both performance indicators and process feasibility. This avoids performance degradation caused by assembly errors from the design source, effectively reducing the risk of performance degradation caused by assembly deviations, and achieving robust design of vehicle performance.

[0078] Optionally, in one embodiment of this application, determining the vehicle body module division and assembly form includes: determining at least one body module based on the vehicle's modular design scheme; establishing a corresponding finite element simulation analysis module based on at least one body module to perform finite element modeling, and determining the assembly form by combining the connection type of the connection parts between modules.

[0079] It is understood that the finite element simulation analysis module in this application embodiment can be understood as a finite element sub-model separately established for each body module, which can be reused in subsequent assembly analysis. The connection type may include bolted connection, adhesive bonding, welded connection, welded seam connection, or a combination thereof.

[0080] In actual implementation, the embodiments of this application can, according to a modular design scheme, disassemble the body-in-white into several sub-modules, such as the front engine compartment module, passenger compartment module, rear luggage compartment module, side panel module, roof module, etc., and the set of sub-modules is denoted as […]. ,in The number of sub-modules. Finite element simulation analysis models are created for each module, with each module forming a separate file and labeled.

[0081] Furthermore, in this embodiment, a modular CAD model can be prepared by dividing the model into sub-modules, establishing finite element models for each sub-module, and identifying the assembly and connection forms between each sub-module. Finite element modeling is then performed on the sub-modules. Simultaneously, considering the connection types (bolted connections, adhesive bonding, welded joints, weld seams, or combinations) of the connection points between modules, a correspondence table between the connection points and the sub-modules is established, as shown in the following example: Sub-module combination. The connection type and constraint type of the connection parts. This indicates the front end of the forward engine compartment / crew compartment: bolted connection + welded connection + adhesive connection.

[0082] The embodiments of this application can establish corresponding finite element simulation analysis modules according to the modular design scheme, and determine the assembly form according to the actual type of the connection parts between modules, so that the finite element modeling is more in line with the real assembly structure, providing a model basis and assembly boundary conditions for subsequent error analysis, thereby improving the engineering credibility of the vehicle performance simulation and optimization results.

[0083] Optionally, in one embodiment of this application, determining the error variable and error range of assembly error includes: determining the main assembly error types of the connection parts between body modules based on the vehicle assembly process to determine the error source; determining the error range corresponding to multiple error components according to the error source, and selecting at least one error component that meets the preset analysis conditions from the multiple error components to determine the error variable and the corresponding error range.

[0084] It is understood that, in the embodiments of this application, the error source can be understood as the root cause of assembly errors; the assembly error type can be understood as the deviation form corresponding to the error source. The error component can be understood as the specific quantitative parameter of the error source. The preset analysis conditions can be understood as the criteria for screening key error components, such as the absolute value of the sensitivity of the error component to the response index being greater than 5%, or the error component fluctuating significantly but controllable in production. The preset analysis conditions can be set by those skilled in the art according to the actual situation, and no specific restrictions are imposed here.

[0085] For example, embodiments of this application can, based on the assembly process, identify the main assembly error types at the connection points of each sub-module, including translational errors (along the X, Y, and Z axes) and rotational errors (around the X, Y, and Z axes), and denote the error vector of a certain connection point as... ,in The translation error component (unit: mm) This represents the rotational error component (unit: rad).

[0086] This application embodiment can determine the value range of each error component through process research, historical assembly data statistics, or tolerance design requirements. Let the first... The lower limit of each error component is The upper limit is Then the range of values ​​for the error component is: ( ) This refers to the number of connection points.

[0087] In this application, the embodiments can employ correlation analysis or engineering experience to eliminate error components with minimal impact on assembly accuracy, ultimately determining the set of error variables that require focused analysis. ,in The number of error variables after filtering ( , (Number of connection parts).

[0088] This application embodiment can identify error sources based on the vehicle assembly process, determine the range of error component values ​​and screen key error components, clarify error variables and their corresponding value intervals, sort out the core error sources from the assembly process, screen key error components to simplify the analysis dimensions, achieve a balance between analysis accuracy and computational efficiency, and provide accurate and reasonable error input conditions for subsequent experimental design and performance simulation.

[0089] Optionally, in one embodiment of this application, determining the vehicle's response indicators and pass thresholds based on the vehicle's model design objectives includes: determining the vehicle's core performance indicators; determining the vehicle's body torsional stiffness and bending stiffness based on the core performance indicators and model design objectives to determine the response indicators; and determining the minimum requirements or allowable range of the response indicators based on the model design specifications corresponding to the model design objectives to determine the pass thresholds.

[0090] It is understood that the core performance indicators in this application embodiment can be understood as the key performance requirements of a vehicle, such as safety, comfort, durability, and handling. Vehicle design goals can be understood as performance targets set for a specific vehicle model positioning, such as sports sedans emphasizing handling and power. Body torsional stiffness can be understood as the body's ability to resist torsional deformation; bending stiffness refers to the body's ability to resist bending deformation; vehicle design specifications refer to the design standards and requirements set by enterprises or industries for different vehicle models.

[0091] In actual implementation, the embodiments of this application can clearly define core performance indicators, and in conjunction with vehicle design requirements, select indicators such as torsional stiffness and bending stiffness of the body-in-white as response indicators, denoted as a set. ,in This involves determining the number of performance indicators. Then, the threshold values ​​for each performance indicator are determined. Based on the vehicle design specifications, the minimum requirements (lower limits) or allowable ranges for each performance indicator are clearly defined. For the first The minimum pass value for each indicator ( ).

[0092] This application embodiment can extract the core performance indicators of the whole vehicle according to the vehicle design goals, determine the torsional stiffness and bending stiffness of the body as key response indicators, and set their qualified thresholds according to the design specifications, so that the optimization goals are directly aligned with the engineering requirements, avoiding any disconnect, ensuring that the performance analysis results are in line with the vehicle design requirements, and improving the engineering practical value of the analysis conclusions.

[0093] Optionally, in one embodiment of this application, the multi-objective optimization function is: , in, In response to the indicators, x For error variables, In response to the number of indicators, m The number of error variables, T This is the transpose symbol.

[0094] For example, embodiments of this application can set optimization objectives and constraints. The optimization objective of the response surface model can be to maximize performance indicators. When multiple performance indicators are selected for optimization, a multi-objective optimization function is constructed: , If it needs to be transformed into a single-objective optimization, a weighted summation method can be used, with the objective function being: ,in For the first The weights of each indicator satisfy... The weight is determined by design requirements (e.g., if stiffness has a higher priority, then...). (relatively large)

[0095] The embodiments of this application can construct a multi-objective optimization function that includes multiple response indicators, thereby realizing the synchronous and coordinated optimization of multiple performance indicators, providing core mathematical model support for balancing various performances and solving the optimal error combination, and ensuring the scientific nature and rigor of the optimization solution.

[0096] Optionally, in one embodiment of this application, at least one constraint includes error variable constraints, performance index constraints, and geometric constraints.

[0097] For example, in the embodiments of this application, the constraint on the error variables can be that each error variable must be within its value range, that is... ( Performance constraints can be defined as the requirements that each performance indicator must meet, i.e. ( Geometric constraints can be defined as combinations of error variables that must satisfy the geometric compatibility of the submodule assembly, i.e., no interference and tight fit at connection points. . .

[0098] The embodiments of this application can ensure the engineering feasibility and safety of the optimization results. By constraining error variables, it ensures that each error variable is within its value range. By constraining performance indicators, it ensures that each performance indicator meets the design requirements. By constraining geometrically, it avoids unreasonable or unrealizable structural forms during the optimization process, thereby improving the practicality and reliability of the optimization scheme.

[0099] Specifically, it can be combined with Figure 2 As shown, the working principle of the vehicle performance analysis method in this application embodiment is explained in detail with a specific example.

[0100] like Figure 2 As shown, embodiments of this application may include the following steps: Step S201: Preparation stage: Clarify the division and assembly form of the body-in-white modules, define and quantify the assembly error, and define the structural stiffness and strength performance indicators.

[0101] Specifically, the division and assembly form of the body-in-white modules are clearly defined: modular vehicle CAD model; sub-module set, connection form - constraint correspondence table.

[0102] Assembly error definition and quantification: Based on process documents and historical assembly data, define the error variable X=[x1,x2,...,x] m ] Error range [a] k ,b k ].

[0103] Structural stiffness and strength performance indicators are defined as follows: The response index Y = [y1, y2, ..., y] is defined based on the vehicle design objectives. p ] Qualified threshold Y min .

[0104] Step S202: Experimental Design and Simulation Sample Generation Stage: Orthogonal experimental scheme design, finite element simulation model construction, error combination substitution and model assembly, simulation calculation and sample extraction.

[0105] Among them, the orthogonal experimental design involves designing an experimental matrix by combining the error variable X and its range.

[0106] Finite element simulation model construction: Based on the sub-module CAD model, material parameters, and connection methods, a basic finite element model is constructed.

[0107] Error combination substitution and model assembly: Based on the error combination, the data coordinate transformation of the basic finite element model is performed to realize N sets of finite element models of the white body containing errors.

[0108] Simulation Calculation and Sample Extraction: Define the load-boundary conditions of the finite element model, extract the simulation results through calculation, and form a sample set S={(X (s) ,Y (s) )}.

[0109] Step S203: Response surface model construction and accuracy verification stage: response surface model form selection, regression coefficient solution and model fitting, model accuracy verification, and model correction.

[0110] Among them, the response surface model form is selected as follows: based on the sample set S, the response surface model is constructed in the form of a quadratic polynomial model.

[0111] Regression coefficient calculation and model fitting: Based on the sample set S, the model is fitted. j =f(X), coefficient vector .

[0112] Model accuracy verification: Combine the fitted model and the sample set S to determine the model accuracy. Model correction: If the accuracy is insufficient, it can be improved by supplementing samples or changing the model.

[0113] Step S204: Setting optimization objectives and constraints: Clarify the optimization design objectives and constraints, select the optimization algorithm and find the optimal solution, verify the optimal solution and formulate an error control scheme.

[0114] Specifically, the optimization design objectives and constraints are clearly defined: a multi-objective optimization function maxY = (X), constraints.

[0115] Optimization algorithm selection and optimal solution finding: By optimizing the function and constraints, the optimal error combination X*=[x1*,x2*,...,x m *] .

[0116] Optimal solution verification and error control scheme formulation: The optimal solution is brought into the finite element model for verification, and the optimal error range and error control scheme are determined.

[0117] According to the vehicle performance analysis method proposed in this application, the embodiments of this application can establish corresponding finite element simulation analysis modules based on modular design schemes, and determine the assembly form according to the actual type of connection parts between modules. This makes the finite element modeling more closely resemble the real assembly structure, providing a model basis and assembly boundary conditions for subsequent error analysis, thereby improving the engineering credibility of vehicle performance simulation and optimization results. This solves the problem in related technologies where the analysis of structural stiffness, strength, and other performance is based on the design state, resulting in insufficient consideration of the impact of assembly errors between modules on performance. This fails to guarantee the structural stability and reliability of modular vehicle assembly, thus affecting the local stiffness, strength, and collision safety performance of the structure.

[0118] Next, the vehicle performance analysis device according to an embodiment of this application is described with reference to the accompanying drawings.

[0119] Figure 3 This is a schematic diagram of the vehicle performance analysis device according to an embodiment of this application.

[0120] like Figure 3As shown, the vehicle performance analysis device 10 includes: a determination module 100, a generation module 200, and an analysis module 300.

[0121] Among them, the determining module 100 is used to determine the vehicle body module division and assembly form, so as to determine the error variables and error value range of the assembly error.

[0122] The generation module 200 is used to determine the vehicle's response indicators and pass thresholds based on the vehicle's model design objectives, and to generate a test matrix based on error variables and error value ranges, so as to generate a sample set using the test matrix and a preset basic finite element model.

[0123] The analysis module 300 is used to construct a response surface model using a sample set, and solve for the optimal error combination based on a multi-objective optimization function and at least one constraint condition to generate the optimal error range and error control scheme, and determine the vehicle performance analysis results.

[0124] Optionally, in one embodiment of this application, the determining module 100 includes: a first determining unit and a second determining unit.

[0125] The first determining unit is used to determine at least one body module based on the vehicle's modular design scheme.

[0126] The second determining unit is used to establish a corresponding finite element simulation analysis module based on at least one body module to perform finite element modeling and determine the assembly form by combining the connection type of the connection parts between modules.

[0127] Optionally, in one embodiment of this application, the determining module 100 includes a third determining unit and a fourth determining unit.

[0128] The third determining unit is used to determine the main assembly error types of the connection parts between body modules based on the vehicle's assembly process, so as to identify the error source.

[0129] The fourth determining unit is used to determine the error value range corresponding to multiple error components based on the error source, and to select at least one error component that meets the preset analysis conditions from the multiple error components, so as to determine the error variable and the corresponding error value range.

[0130] Optionally, in one embodiment of this application, the generation module 200 includes: a fifth determining unit, a sixth determining unit, and a seventh determining unit.

[0131] The fifth determining unit is used to determine the core performance indicators of the vehicle.

[0132] The sixth determining unit is used to determine the vehicle's body torsional stiffness and bending stiffness based on core performance indicators and vehicle design goals, in order to determine the response indicators.

[0133] The seventh determining unit is used to determine the minimum requirements or allowable range of response indicators based on the vehicle design specifications corresponding to the vehicle design objectives, so as to determine the qualification threshold.

[0134] Optionally, in one embodiment of this application, the multi-objective optimization function is: , in, In response to the indicators, x For error variables, In response to the number of indicators, m The number of error variables, T This is the transpose symbol.

[0135] Optionally, in one embodiment of this application, at least one constraint includes error variable constraints, performance index constraints, and geometric constraints.

[0136] It should be noted that the foregoing explanation of the vehicle performance analysis method embodiment also applies to the vehicle performance analysis device of this embodiment, and will not be repeated here.

[0137] The vehicle performance analysis device proposed in this application can establish corresponding finite element simulation analysis modules based on modular design schemes, and determine the assembly form according to the actual type of connection parts between modules. This makes the finite element modeling more closely resemble the real assembly structure, providing a model basis and assembly boundary conditions for subsequent error analysis, thereby improving the engineering credibility of vehicle performance simulation and optimization results. This solves the problem in related technologies where the analysis of structural stiffness, strength, and other performance is based on the design state, resulting in insufficient consideration of the impact of assembly errors between modules on performance. This fails to guarantee the structural stability and reliability of modular vehicle assembly, thus affecting the local stiffness, strength, and collision safety performance of the structure.

[0138] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device may include: The memory 401, the processor 402, and the computer program stored on the memory 401 and capable of running on the processor 402.

[0139] When the processor 402 executes the program, it implements the vehicle performance analysis method provided in the above embodiments.

[0140] Furthermore, electronic devices also include: Communication interface 403 is used for communication between memory 401 and processor 402.

[0141] The memory 401 is used to store computer programs that can run on the processor 402.

[0142] Memory 401 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.

[0143] If the memory 401, processor 402, and communication interface 403 are implemented independently, then the communication interface 403, memory 401, and processor 402 can be interconnected via a bus to complete communication between them. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized into address buses, data buses, control buses, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0144] Optionally, in a specific implementation, if the memory 401, processor 402, and communication interface 403 are integrated on a single chip, then the memory 401, processor 402, and communication interface 403 can communicate with each other through an internal interface.

[0145] Processor 402 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application.

[0146] This application also provides a non-volatile computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described vehicle performance analysis method.

[0147] This application also provides a computer program product storing a computer program that, when executed by a processor, implements the above-described vehicle performance analysis method.

[0148] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. 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. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0149] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0150] Any process or method described in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or N executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application 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 should be understood by those skilled in the art to which embodiments of this application pertain.

[0151] 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 processor-included system, 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 (electronic device), 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 devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0152] It should be understood that the various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. If implemented in hardware, as in another embodiment, it can be implemented using any one or more 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.

[0153] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0154] Furthermore, the functional units in the various embodiments of this application 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.

[0155] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for analyzing the overall performance of a vehicle, characterized in that, Includes the following steps: Determine the vehicle body module division and assembly method in order to determine the error variables and error range of assembly error; The response indicators and qualification thresholds of the vehicle are determined according to the vehicle model design objectives, and a test matrix is ​​generated according to the error variables and the error value range, so as to generate a sample set using the test matrix and the preset basic finite element model; A response surface model is constructed using the sample set, and the optimal error combination is solved based on a multi-objective optimization function and at least one constraint condition to generate the optimal error range and error control scheme, thereby determining the overall vehicle performance analysis results.

2. The method according to claim 1, characterized in that, The determination of the vehicle's body module division and assembly form includes: At least one of the body modules is determined according to the modular design scheme of the vehicle; A corresponding finite element simulation analysis module is established based on at least one of the vehicle body modules to perform finite element modeling, and the assembly form is determined by combining the connection type of the connection parts between modules.

3. The method according to claim 1 or 2, characterized in that, The determination of the error variables and error range for assembly errors includes: Based on the vehicle's assembly process, the main assembly error types of the connection points between the body modules are determined to identify the error sources. Based on the error source, determine the error value range corresponding to multiple error components, and select at least one error component that meets the preset analysis conditions from the multiple error components to determine the error variable and the corresponding error value range.

4. The method according to claim 1, characterized in that, The step of determining the vehicle's response indicators and qualification thresholds based on the vehicle's model design objectives includes: Determine the core performance indicators of the vehicle; The torsional stiffness and bending stiffness of the vehicle body are determined based on the core performance indicators and the vehicle design objectives, in order to determine the response indicators; The minimum requirements or allowable range of the response indicators are determined based on the vehicle design specifications corresponding to the vehicle design objectives, in order to determine the qualified threshold.

5. The method according to claim 1, characterized in that, The multi-objective optimization function is: ， in, The response metric, x For the error variable, The number of the response metrics, m The number of the error variables, T This is the transpose symbol.

6. The method according to claim 1, characterized in that, The at least one constraint includes error variable constraints, performance index constraints, and geometric constraints.

7. A vehicle performance analysis device, characterized in that, include: The determination module is used to determine the vehicle body module division and assembly form, so as to determine the error variables and error value range of assembly error; The generation module is used to determine the response index and qualification threshold of the vehicle according to the vehicle model design target, and generate a test matrix according to the error variable and the error value range, so as to generate a sample set using the test matrix and the preset basic finite element model; The analysis module is used to construct a response surface model using the sample set, and solve for the optimal error combination based on a multi-objective optimization function and at least one constraint condition to generate the optimal error range and error control scheme, and determine the vehicle performance analysis results.

8. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to implement the vehicle performance analysis method as described in any one of claims 1-6.

9. A non-volatile computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the vehicle performance analysis method as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, The computer program is executed to implement the vehicle performance analysis method as described in any one of claims 1-6.

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