Vehicle lateral stiffness determination method, device, electronic equipment and vehicle

Abstract

The present invention relates to a method, device, electronic device, and vehicle for determining vehicle lateral stiffness, comprising: obtaining a first proxy model, the first proxy model being a proxy model for predicting the vehicle's lateral stiffness and weight; obtaining a second proxy model, the second proxy model being a proxy model for predicting the vehicle's understeer and lateral compliance; determining a vehicle optimization target; obtaining at least one set of first vehicle parameters, some of which have corresponding value ranges; inputting the first vehicle parameters into the first proxy model to obtain candidate lateral stiffness and candidate weight; inputting the first vehicle parameters into the second proxy model to obtain candidate understeer and candidate lateral compliance; and using the candidate lateral stiffness as a target lateral stiffness when the candidate weight and candidate understeer meet the optimization target and the candidate lateral compliance is within preset constraints. Embodiments of the present invention shorten the vehicle R&D cycle.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and device for determining the lateral stiffness of a vehicle, an electronic device, and a vehicle. Background Art

[0002] In specific implementation, the lateral stiffness of a vehicle refers to the vehicle's ability to resist deformation under the action of lateral force. It is one of the important indicators for measuring vehicle handling stability and safety, and can affect the vehicle's steering response and roll performance.

[0003] However, the lateral stiffness of vehicles is currently mainly obtained through simulation, which takes a lot of time and has low computational efficiency, affecting the vehicle's R&D cycle. Summary of the Invention

[0004] One of the purposes of the present invention is to provide a method, device, electronic device and vehicle for determining the lateral stiffness of a vehicle, so as to solve the problem in the prior art that the lateral stiffness of a vehicle is currently obtained mainly through simulation, which requires a lot of time and has low computational efficiency, thus affecting the vehicle's R&D cycle; the second purpose is to provide a device; the third purpose is to provide an electronic device; and the fourth purpose is to provide a vehicle.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0006] A method for determining vehicle lateral stiffness, the method comprising:

[0007] Obtaining a first proxy model; the first proxy model is a proxy model for predicting the lateral stiffness and weight of the vehicle;

[0008] Acquire a second proxy model; the second proxy model is a proxy model for predicting understeer and corner compliance of the vehicle;

[0009] determining an optimization goal for the vehicle;

[0010] obtaining at least one set of first vehicle parameters of the vehicle;

[0011] Inputting the first vehicle parameter into the first proxy model to obtain a candidate lateral stiffness and a candidate weight;

[0012] Inputting the first vehicle parameter into the second proxy model to obtain a candidate understeer degree and a candidate lateral compliance degree;

[0013] When the candidate weight and the candidate understeer meet the optimization target and the candidate lateral compliance is within a preset constraint, the candidate lateral stiffness is used as the target lateral stiffness.

[0014] A vehicle lateral stiffness determination device, comprising:

[0015] A first acquisition module is configured to acquire a first proxy model; the first proxy model is a proxy model for predicting the lateral stiffness and weight of the vehicle;

[0016] a second acquisition module, configured to acquire a second proxy model; the second proxy model is a proxy model for predicting understeer and corner compliance of the vehicle;

[0017] An optimization target determination module, configured to determine an optimization target for the vehicle;

[0018] a third acquisition module, configured to acquire at least one set of first vehicle parameters of the vehicle;

[0019] a first candidate module, configured to input the first vehicle parameter into the first proxy model to obtain a candidate lateral stiffness and a candidate weight;

[0020] a second candidate module, configured to input the first vehicle parameter into the second proxy model to obtain a candidate understeer degree and a candidate lateral compliance degree;

[0021] A determination module is configured to use the candidate lateral stiffness as a target lateral stiffness when the candidate weight and the candidate understeer meet the optimization target and the candidate lateral compliance is within a preset constraint condition.

[0022] An electronic device comprising: a processor; a memory for storing instructions executable by the processor;

[0023] The processor is configured to execute the instructions to implement the above-mentioned method for determining the lateral stiffness of the vehicle.

[0024] A computer-readable storage medium, when instructions in the storage medium are executed by a processor of a mobile terminal, enables the mobile terminal to execute the above-mentioned vehicle lateral stiffness determination method.

[0025] A vehicle comprises the electronic device described above.

[0026] Beneficial effects of the present invention:

[0027] In an embodiment of the present invention, during the vehicle development process, a first proxy model and a second proxy model may be obtained. The first proxy model is a proxy model for predicting the vehicle's lateral stiffness and weight, and the second proxy model is a proxy model for predicting the vehicle's understeer and lateral compliance. After determining the vehicle's optimization objective, at least one set of first vehicle parameters may be obtained, wherein some of the first vehicle parameters have corresponding value ranges. The first vehicle parameters may then be input into the first proxy model to obtain candidate lateral stiffness and candidate weight, and the first vehicle parameters may be input into the second proxy model to obtain candidate understeer and candidate lateral compliance. When the candidate weight and candidate understeer meet the optimization objective and the candidate lateral compliance is within preset constraints, the candidate lateral stiffness may be used as the target lateral stiffness. In this embodiment of the present invention, during the vehicle development process, the vehicle's lateral stiffness may be determined based on the first and second proxy models, eliminating the need for simulation, which is time-consuming and computationally efficient, thereby shortening the vehicle development cycle.

[0028] In addition, the weight and understeer corresponding to the vehicle's stiffness meet the optimization objectives, and the candidate lateral compliance is within the preset constraints. In this way, by collaboratively optimizing the understeer and weight of the entire vehicle and using proxy model technology, the vehicle's optimization efficiency is greatly improved, the optimization cost is low, and the forward design of the vehicle's lateral stiffness target can be quickly achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 A flowchart of a method for determining vehicle lateral stiffness provided in an embodiment of the present invention;

[0030] Figure 2 A residual graph of a lateral stiffness proxy model provided in an embodiment of the present invention;

[0031] Figure 3 A contribution analysis diagram provided in an embodiment of the present invention;

[0032] Figure 4 A residual graph of an insufficient steering proxy model provided in an embodiment of the present invention;

[0033] Figure 5 This is an optimization effect diagram provided in an embodiment of the present invention;

[0034] Figure 6 A flow chart of a vehicle lateral stiffness target decomposition method provided in an embodiment of the present invention;

[0035] Figure 7 Schematic diagram of the structure of a vehicle lateral stiffness determination device provided in an embodiment of the present invention;

[0036] Figure 8A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0037] The following describes the embodiments of the present invention with reference to the accompanying drawings and preferred embodiments. Those skilled in the art will readily appreciate the other advantages and benefits of the present invention from the disclosure herein. The present invention may also be implemented or applied through various other specific embodiments, and the various details in this specification may be modified or altered based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are intended only to illustrate the present invention and are not intended to limit the scope of protection of the present invention.

[0038] Reference Figure 1 , shows a flowchart of a method for determining the lateral stiffness of a vehicle provided in an embodiment of the present invention, wherein the vehicle is equipped with at least one sensor and components, and specifically includes the following steps:

[0039] Step 101: Obtain a first proxy model; the first proxy model is a proxy model used to predict the lateral stiffness and weight of the vehicle.

[0040] Step 102 : Acquire a second proxy model; the second proxy model is a proxy model for predicting understeer and corner compliance of the vehicle.

[0041] In specific implementations, the proxy model is a mathematical model that can be used to replace complex and time-consuming simulation experiments or physical experiments. After training with a limited number of samples, the proxy model can simulate real vehicle data, such as the vehicle's lateral stiffness, understeer, weight (mass), and lateral compliance.

[0042] In an embodiment of the present invention, at least one proxy model is constructed, and different proxy models can be used to predict different vehicle data. Specifically, a first proxy model and a second proxy model can be constructed. When it is necessary to predict the lateral stiffness, weight, understeer, and lateral compliance of the vehicle, the first proxy model and the second proxy model are obtained, so that the lateral stiffness and weight of the vehicle can be predicted by the first proxy model, and the understeer and lateral compliance of the vehicle can be predicted by the second proxy model.

[0043] Step 103: Determine the optimization target of the vehicle.

[0044] In an embodiment of the present invention, the optimization target of the vehicle can be set according to actual needs. For example, when predicting the lateral stiffness of the vehicle, if it is necessary to comprehensively consider the vehicle's handling performance and cost, the optimization target can be determined as understeer and weight.

[0045] In some embodiments, when understeer and weight are set as optimization targets, the optimization weight ratio can be set to 1:1, favoring greater understeer and less weight. Of course, for performance vehicles, the optimization weight ratio can be increased; for economy vehicles, the optimization weight ratio can be decreased. An adaptive optimization algorithm is employed to balance handling performance and cost, thereby determining the vehicle's lateral stiffness that achieves the optimal balance between handling performance and cost.

[0046] Step 104: Obtain at least one set of first vehicle parameters of the vehicle.

[0047] In an embodiment of the present invention, when predicting the lateral stiffness, understeer, weight and lateral compliance of a vehicle through an agent model, at least one set of first vehicle parameters about the vehicle can be obtained. For example, the vehicle parameters may include but are not limited to structural parameters of key areas of the vehicle's front and rear axles, as well as body stiffness, tire parameters, inertia parameters, four-wheel alignment parameters, bushing stiffness and shock absorber spring stiffness, etc.

[0048] In practical applications, considering the complexity of engineering implementation for adjusting inertia parameters, the significant impact of tire selection on NVH (Noise, Vibration, Harshness) performance, and the fact that excessive cornering compliance can slow the vehicle's steering response, corresponding constraints are set for parameters such as inertia parameters, tire parameters, and front and rear axle cornering compliance. For example, the value ranges of these parameters, such as inertia parameters, tire parameters, and front and rear axle cornering compliance, can be fixed within a preset range.

[0049] Step 105: Input the first vehicle parameters into the first proxy model to obtain candidate lateral stiffness and candidate weight.

[0050] Step 106: Input the first vehicle parameters into the second proxy model to obtain candidate understeer degrees and candidate lateral compliance degrees.

[0051] Step 107: When the candidate weight and the candidate understeer meet the optimization target, and the candidate lateral compliance is within a preset constraint, use the candidate lateral stiffness as the target lateral stiffness.

[0052] In an embodiment of the present invention, the structural parameters of the key areas of the front and rear axles of the vehicle body in the first vehicle parameters can be input into the first proxy model to obtain candidate lateral stiffness and candidate weight, and the vehicle body stiffness, tire parameters, inertia parameters, four-wheel alignment parameters, bushing stiffness and shock absorber spring stiffness in the first vehicle parameters can be input into the second proxy model to obtain candidate understeer and candidate lateral compliance. Then, when it is determined that the candidate weight and candidate understeer meet the optimization objective and the candidate lateral compliance is within the preset constraints, for example, when the candidate lateral compliance is within a preset value range, the candidate lateral stiffness at this time can be used as the target lateral stiffness of the vehicle.

[0053] In the above-described method for determining vehicle lateral stiffness, during the vehicle development process, a first proxy model and a second proxy model can be obtained. The first proxy model is a proxy model for predicting the vehicle's lateral stiffness and weight, and the second proxy model is a proxy model for predicting the vehicle's understeer and lateral compliance. After determining the vehicle's optimization objective, at least one set of first vehicle parameters can be obtained, wherein some of the first vehicle parameters have corresponding value ranges. The first vehicle parameters can then be input into the first proxy model to obtain candidate lateral stiffness and candidate weight, and the first vehicle parameters can be input into the second proxy model to obtain candidate understeer and candidate lateral compliance. When the candidate weight and candidate understeer meet the optimization objective and the candidate lateral compliance is within preset constraints, the candidate lateral stiffness can be used as the target lateral stiffness. In this embodiment of the present invention, during the vehicle development process, the vehicle's lateral stiffness can be determined based on the first and second proxy models, without the need for simulation, which is time-consuming and computationally efficient, thus shortening the vehicle development cycle.

[0054] In addition, the weight and understeer corresponding to the vehicle's stiffness meet the optimization objectives, and the candidate lateral compliance is within the preset constraints. In this way, by collaboratively optimizing the understeer and weight of the entire vehicle and using proxy model technology, the vehicle's optimization efficiency is greatly improved, the optimization cost is low, and the forward design of the vehicle's lateral stiffness target can be quickly achieved.

[0055] In one embodiment of the present invention, before step 101, obtaining the first proxy model, the method may further include:

[0056] Creating a finite element analysis model of the vehicle's lateral stiffness; the finite element analysis model of the vehicle's lateral stiffness is created based on the vehicle's body, interior and exterior trim, and rigidly connected chassis, and is used to simulate and obtain the vehicle's lateral stiffness and weight;

[0057] Obtaining second vehicle parameters corresponding to the preset first design variables; the second vehicle parameters at least include structural parameters of key areas of the front and rear axles of the vehicle body;

[0058] Inputting the second vehicle parameter into the whole vehicle lateral stiffness finite element analysis model to obtain simulated lateral stiffness and simulated weight obtained by simulating the whole vehicle lateral stiffness finite element analysis model;

[0059] Sampling the second vehicle parameter, the simulated lateral stiffness, and the simulated weight to obtain a first sampling space and a second sampling space; data in the first sampling space and the second sampling space do not overlap;

[0060] Training the first proxy model to be trained according to the first sampling space; the first proxy model is constructed according to the first design variables;

[0061] The trained first proxy model is verified using the second sampling space, and the trained first proxy model is obtained after the verification passes.

[0062] In an embodiment of the present invention, a finite element analysis model of the lateral stiffness of the entire vehicle may be constructed to perform lateral stiffness simulation analysis.

[0063] Specifically, a finite element analysis model for the vehicle's lateral stiffness is established. This can be based on the vehicle's body, interior and exterior trim, and rigidly connected chassis, applying appropriate constraints. After completing the finite element calculation, the lateral stiffness and weight of the front and rear axles of the vehicle can be simulated. It should be noted that both the finite element analysis model for the vehicle's lateral stiffness and weight can be predicted, but the finite element analysis model for the vehicle's lateral stiffness is a simulation model. While the simulation model is more accurate than the first proxy model, it takes a very long time to simulate, typically on the order of days, while the first proxy model's prediction time is very short, typically on the order of milliseconds, resulting in very high computational efficiency.

[0064] In an embodiment of the present invention, structural parameters of key areas of the front and rear axles of the vehicle body are selected as design variables of the first proxy model, ie, the first design variables, to drive changes in the lateral stiffness of the entire vehicle. In an embodiment of a whole vehicle lateral stiffness simulation workflow of the present invention, the whole vehicle lateral stiffness performance can be integrated based on the Optimus platform. Specifically, the design parameters / design variables (UCIInputsl) are determined; the design parameters are recorded in the file DVfile.text; the DVfile.text is parameterized by model through ansaVERSION11 (a CAE pre-processing software of version 11) to obtain a bdf file; the Nastran (finite element program) solver is called to calculate the bdf file to obtain f06 and C857EV_FV_SideSTI_para.pch; Action1 (automated process) is executed through Mass_py (script text) to obtain Output_mass.text from f06, and Output_mass.text includes mass (mass); Dis is extracted from the C857EV_FV_SideSTI_para.pch file, and Dis includes lateral stiffness (sideSTI).

[0065] In some embodiments, the workflow may use an optimal Latin hypercube design of experiments method to perform DOE (Design Of Experiment) sampling calculations. For example, vehicle parameters may be obtained by performing DOE sampling calculations using the optimal Latin hypercube design of experiments method.

[0066] Specifically, a second vehicle parameter corresponding to a preset first design variable is obtained, wherein the second vehicle parameter may include at least structural parameters of key areas of the vehicle's front and rear axles. The second vehicle parameter may then be input into a finite element analysis model of the vehicle's lateral stiffness to obtain simulated lateral stiffness and simulated weight obtained by simulating the finite element analysis model of the vehicle's lateral stiffness. The second vehicle parameter, the simulated lateral stiffness, and the simulated weight may then be sampled to obtain a first sampling space and a second sampling space. The data of the first sampling space and the second sampling space do not overlap. The vehicle parameters in the first sampling space may be used to train the first proxy model, and the vehicle parameters in the second sampling space may be used to verify the first proxy model. After completing the training and passing the verification, the trained first proxy model may be obtained, and the lateral stiffness and weight may subsequently be quickly predicted based on the first proxy model.

[0067] In one embodiment of the present invention, verifying the trained first proxy model using the second sampling space, and obtaining the trained first proxy model after the verification passes, may include:

[0068] inputting the second vehicle parameter in the second sampling space into the trained first proxy model to obtain a calibration lateral stiffness and a calibration weight output by the trained first proxy model;

[0069] determining a relative error between the simulated lateral stiffness and the simulated weight and the verification lateral stiffness and the verification weight in the second sampling space;

[0070] When the relative error meets the preset accuracy requirement, determining that the first proxy model has been trained after passing the verification;

[0071] When the relative error does not meet the preset accuracy requirement, the model parameters of the first proxy model are adjusted, new second vehicle parameters corresponding to the first design variables are obtained, and the process returns to the step of inputting the second vehicle parameters into the whole vehicle lateral stiffness finite element analysis model to obtain the simulated lateral stiffness and simulated weight obtained by simulating the whole vehicle lateral stiffness finite element analysis model, and / or, the model type of the first proxy model is adjusted and the process returns to the step of training the first proxy model to be trained according to the first sampling space.

[0072] In the embodiment of the present invention, based on the DOE calculation results, i.e., the first sampling space and the second sampling space, a first proxy model of lateral stiffness and weight is constructed, and the accuracy of the first proxy model is verified to see whether it meets the preset accuracy requirements. The second sampling space can be 10-20 groups of samples outside the first sampling space, and residual analysis is used to verify whether the first proxy model meets the accuracy requirements. For details, please refer to Figure 2 The figure shows a residual graph of the lateral stiffness proxy model, where the vertical axis represents the Residual (normalized) of the lateral stiffness, and the horizontal axis represents multiple groups of data in the second sampling space. For example, if the accuracy of the first proxy model is greater than or equal to 95%, the first proxy model is determined to have passed the accuracy verification and can be considered as a trained first proxy model after passing the verification. If the accuracy of the first proxy model is less than 95%, the DOE sampling calculation can be repeated to generate new second vehicle parameters, and then simulation, DOE sampling calculation, model training, and verification can be repeated. Alternatively, the model type of the first proxy model can be adjusted, for example, from a convolutional model to a neural network model, and simulation, DOE sampling calculation, model training, and verification can be repeated using the second vehicle parameters until the first proxy model meets the preset accuracy requirements. In this way, the vehicle's lateral stiffness can be accurately determined using the higher-accuracy first proxy model.

[0073] In one embodiment of the present invention, before step 102, obtaining the second proxy model, the method may further include:

[0074] Creating a full-vehicle multi-body simulation analysis model and a suspension multi-body simulation analysis model; the full-vehicle multi-body simulation analysis model is created based on the vehicle's front suspension, rear suspension, steering system, powertrain, tires, body, and stabilizer bar, and is used to simulate and obtain the vehicle's understeer; the suspension multi-body simulation analysis model is created based on the vehicle's rear suspension, steering system, test bench, and stabilizer bar, and is used to simulate and obtain the vehicle's cornering compliance;

[0075] Obtaining third vehicle parameters corresponding to the preset second design variables; the third vehicle parameters at least include chassis elastic parameters of the vehicle; the chassis elastic parameters at least include body stiffness, tire parameters, inertia parameters, four-wheel alignment parameters, bushing stiffness, and shock absorber spring stiffness;

[0076] Inputting the third vehicle parameter into the multi-body simulation analysis model and the suspension multi-body simulation analysis model to obtain a simulated understeer and a simulated cornering compliance obtained by simulating the multi-body simulation analysis model and the suspension multi-body simulation analysis model;

[0077] Sampling the third vehicle parameter, the simulated understeer, and the simulated lateral compliance to obtain a third sampling space and a fourth sampling space; wherein data of the third sampling space and the fourth sampling space do not overlap;

[0078] Training the second proxy model to be trained according to the third sampling space; the second proxy model is constructed according to the second design variable;

[0079] The trained second proxy model is verified using the fourth sampling space, and the trained second proxy model is obtained after the verification passes.

[0080] In an embodiment of the present invention, a multi-body simulation analysis model of a vehicle with flexible body can be constructed and a simulation analysis of understeer of the vehicle can be performed, as well as a multi-body simulation analysis model of the suspension and a simulation analysis of suspension C can be performed, for example, a simulation analysis of lateral flexibility can be performed.

[0081] Specifically, subsystems such as the front suspension, rear suspension, steering system, powertrain, tires, body, and stabilizer bar are established, a rigid-flexible coupled multi-body simulation analysis model of the vehicle with flexible body is built, and Matlab scripts are written to automatically extract linear and nonlinear front and rear axle lateral compliance. Subsystems such as the rear suspension, steering system, test bench, and stabilizer bar are established, a suspension multi-body simulation analysis model is built, and Matlab scripts are written to automatically extract indicators such as rear suspension roll, lateral force, longitudinal force, and lateral compliance. Among them, indicators such as lateral force (Side_F), initial value of rear wheel toe angle (R_TOE_I_x), and Y-position of front suspension roll axis (F_LSA_y) can be used to determine understeer. In an embodiment of the present invention, through contribution analysis, design variables that contribute more to understeer are screened out, and such design variables are optimized. The contribution analysis results are as follows: Figure 3 As shown in the contribution analysis diagram, the vertical axis represents the design variables that affect understeer, and the horizontal axis represents the influence value on understeer. It can be seen that Side_F (lateral force) has a greater influence on understeer, while R_TOE_I_x (initial value of rear wheel toe angle) and F_LSA_y (Y-direction position of front suspension roll axis) have less influence on understeer. Therefore, we can focus on optimizing the lateral force to optimize parameters such as understeer.

[0082] It should be added that the whole vehicle multi-body simulation analysis model, the suspension multi-body simulation analysis model and the second agent model can all predict the vehicle's understeer and lateral compliance. However, the whole vehicle multi-body simulation analysis model and the suspension multi-body simulation analysis model are simulation models. The accuracy of the simulation model is higher than that of the second agent model, but the simulation time is very long, usually at the day level, while the prediction time of the second agent model is very short, usually at the millisecond level, and the computational efficiency is very high.

[0083] In an embodiment of the present invention, the chassis elastic parameters of the vehicle are selected as the design variables of the second agent model, i.e., the second design variables. The chassis elastic parameters may at least include body stiffness, tire parameters, inertia parameters, four-wheel alignment parameters, bushing stiffness, and shock absorber spring stiffness. In an embodiment of the integrated development workflow for vehicle understeer and suspension C-characteristics of the present invention, parametric modeling is implemented for design variables based on the Optimus platform. Design variables may include, but are not limited to, F_LSA (front suspension lateral alignment parameter), R_TOE (rear wheel toe angle), R_LSA (rear suspension lateral alignment parameter), R_Suspension (the complete set of rear suspension system parameters), STI (lateral torque input), Elastic_parameter (elasticity parameter), inertial_parameter (inertial parameter), HardPoint (hard point), and tire_parameter (tire parameter). Based on the characteristics of the design variables, cost models / parametric models (i.e., second proxy models) are constructed. The parameterized models are then used to perform circle analysis and C-characteristic analysis, yielding nonlinear understeer and cornering compliance, lateral stiffness cost, linear understeer and cornering compliance, inertia parameter cost, and tire cost. In practical applications, design variables can be divided into cost-related and non-cost-related design variables. Cost-related design variables include vehicle body stiffness, inertial parameters, and tire parameters. Specifically, by establishing a specific relationship between weight and body stiffness, the cost of body stiffness is expressed in terms of weight; the cost of inertia parameter adjustment is expressed in terms of the percentage change in center of mass position; and the cost of tire parameter adjustment is expressed in terms of the percentage change in tire cornering stiffness factor and friction coefficient factor. Non-cost design variables can include bushing stiffness, shock absorber spring stiffness, four-wheel alignment parameters, and hard points.

[0084] In some embodiments, the workflow can use an optimal Latin hypersquare experimental design method to perform DOE sampling calculations. Through correlation analysis, design variables that are strongly correlated with the above response are screened out, insensitive design variables are eliminated, and then DOE sampling calculations are performed on the remaining design variables.

[0085] Specifically, a third vehicle parameter corresponding to a preset second design variable is obtained, wherein the third vehicle parameter may include at least body stiffness, tire parameters, inertia parameters, four-wheel alignment parameters, bushing stiffness, and shock absorber spring stiffness, etc., and then the third vehicle parameter may be input into the whole vehicle multi-body simulation analysis model and the suspension multi-body simulation analysis model to obtain a simulated understeer and a simulated lateral compliance obtained by simulating the whole vehicle multi-body simulation analysis model and the suspension multi-body simulation analysis model, and then the third vehicle parameter, the simulated understeer, and the simulated lateral compliance may be sampled to obtain a third sampling space and a fourth sampling space, and the data of the third sampling space and the fourth sampling space do not overlap, the vehicle parameters in the third sampling space may be used to train the second proxy model, and the vehicle parameters in the fourth sampling space may be used to verify the second proxy model, and after completing the training and passing the verification, the trained second proxy model may be obtained, and subsequently the understeer and lateral compliance may be quickly predicted based on the second proxy model.

[0086] In an optional embodiment of the present invention, the understeer is affected by the lateral stiffness, and the lateral stiffness is equivalently replaced by the torsional stiffness of the bushing in the Z direction according to a preset equivalent formula. The preset equivalent formula is a formula for converting the lateral stiffness and the torsional stiffness of the bushing in the Z direction.

[0087] In an embodiment of the present invention, in order to decouple the influence of the lateral stiffness of the vehicle body and the torsional stiffness of the bushing in the Z direction on the handling performance, the lateral stiffness of the vehicle body can be equivalent to the torsional stiffness of the bushing in the Z direction according to the constraint conditions of the simulation analysis of the lateral stiffness of the whole vehicle.

[0088] In some embodiments, the preset equivalent formula is as follows:

[0089]

[0090]

[0091] Wherein, M represents torque; Indicates the torsional stiffness of the bushing in the Z direction; represents the torsion angle; represents the lateral stiffness of the vehicle body; F represents the loading force of the vehicle lateral stiffness simulation analysis; L represents the distance between the bushing and the loading point of the lateral stiffness loading force F in the x-direction.

[0092] In one embodiment of the present invention, verifying the trained second proxy model using the fourth sampling space and obtaining the trained second proxy model after the verification passes may include:

[0093] inputting the third vehicle parameter in the fourth sampling space into the trained second proxy model to obtain a verified understeer degree and a verified lateral compliance output by the trained second proxy model;

[0094] Determining relative errors between the simulated lateral stiffness and the simulated weight of the fourth sampling space and the verified understeer and the verified lateral compliance;

[0095] When the relative error meets the preset accuracy requirement, determining that the second proxy model has been trained after passing the verification;

[0096] When the relative error does not meet the preset accuracy requirement, the model parameters of the second proxy model are adjusted, new third vehicle parameters corresponding to the second design variables are obtained, and the process returns to the step of inputting the third vehicle parameters into the multi-body simulation analysis model and the suspension multi-body simulation analysis model to obtain the simulated understeer and simulated lateral compliance simulated by the multi-body simulation analysis model and the suspension multi-body simulation analysis model, and / or, the model type of the second proxy model is adjusted and the process returns to the step of training the second proxy model to be trained according to the third sampling space.

[0097] In the embodiment of the present invention, based on the DOE calculation results, i.e., the third sampling space and the fourth sampling space, a second proxy model of understeer and lateral compliance is constructed, and the accuracy of the second proxy model is verified to see whether it meets the preset accuracy requirements. The fourth sampling space can be 10-20 groups of samples outside the third sampling space, and residual analysis is used to verify whether the second proxy model meets the accuracy requirements. For details, please refer to Figure 4 The figure shows the residual error graph of the understeer proxy model, where the vertical axis represents the Residual (normalized) of understeer, and the horizontal axis represents multiple groups of data in the fourth sampling space. For example, if the accuracy of the second proxy model is greater than or equal to 95%, the second proxy model is determined to have passed the accuracy verification and can be considered trained after passing the verification. If the accuracy of the first proxy model is less than 95%, the DOE sampling calculation can be re-performed to generate new third vehicle parameters, and simulation, DOE sampling calculation, model training, and verification can be repeated. Alternatively, the model type of the second proxy model can be adjusted, for example, from a convolutional model to a neural network model, and simulation, DOE sampling calculation, model training, and verification can be repeated using the third vehicle parameters until the second proxy model meets the preset accuracy requirements. In this way, the more accurate first proxy model can be used to accurately determine the vehicle's understeer and corner compliance.

[0098] In one embodiment of the present invention, in step 107, when the candidate weight and the candidate understeer meet the optimization objective and the candidate lateral compliance is within a preset constraint, after taking the candidate lateral stiffness as the target lateral stiffness, the method may further include:

[0099] Inputting a first vehicle parameter corresponding to the target lateral stiffness into a finite element analysis model of the vehicle lateral stiffness to obtain a target simulated lateral stiffness and a target simulated weight obtained by simulating the finite element analysis model of the vehicle lateral stiffness;

[0100] Inputting a first vehicle parameter corresponding to the target lateral stiffness into a multi-body simulation analysis model and a suspension multi-body simulation analysis model to obtain a target simulated understeer and a target simulated lateral compliance obtained by simulating the multi-body simulation analysis model and the suspension multi-body simulation analysis model;

[0101] When the target simulated weight and the target simulated understeer meet the optimization target, and the target simulated lateral compliance is within preset constraints, it is determined that the target lateral stiffness passes the simulation verification.

[0102] In the embodiment of the present invention, the simulation model has higher accuracy than the proxy model. After the target lateral stiffness is obtained, the simulation model can be further used to verify the target lateral stiffness.

[0103] Specifically, the first vehicle parameter corresponding to the target lateral stiffness can be input into the finite element analysis model of the lateral stiffness of the whole vehicle to obtain the target simulated lateral stiffness and the target simulated weight obtained by simulating the finite element analysis model of the lateral stiffness of the whole vehicle. At the same time, the first vehicle parameter corresponding to the target lateral stiffness can be input into the multi-body simulation analysis model and the suspension multi-body simulation analysis model to obtain the target simulated understeer and the target simulated lateral flexibility obtained by simulating the multi-body simulation analysis model and the suspension multi-body simulation analysis model. Then, when it is determined that the target simulated weight and the target simulated understeer meet the optimization target, and at the same time, the target simulated lateral flexibility is within the preset constraints, for example, when the target simulated lateral flexibility is within the preset value range, it can be determined that the target lateral stiffness passes the simulation verification of the simulation model, that is, it can be used as the final predicted lateral stiffness.

[0104] In one embodiment of the present invention, the method may further include:

[0105] When the target simulated weight and the target simulated understeer do not meet the optimization target, or when the target simulated lateral compliance is within preset constraints, model parameters of the first proxy model and the second proxy model are adjusted, and the first proxy model and the second proxy model are retrained, or model types of the first proxy model and the second proxy model are adjusted and then the first proxy model and the second proxy model are retrained.

[0106] In an embodiment of the present invention, the optimization result, i.e., the target lateral stiffness, is verified through a simulation model. If the handling performance and cost meet the requirements, the optimization is completed, that is, if the understeer and weight meet the optimization targets, the optimization is completed, and the target lateral stiffness is the vehicle lateral stiffness target. If the understeer and weight do not meet the optimization targets, the optimization fails, and the optimization algorithm parameters and / or model type are adjusted, or sampling points are added near the current optimal solution, a new proxy model is constructed, and the balance optimization is carried out again until the handling performance and cost meet the optimization targets.

[0107] Reference Figure 5 , is an optimization effect diagram provided in an embodiment of the present invention. The dashed line represents the pre-optimization data (Base), and the solid line represents the post-optimization data (Opt). After optimizing lateral stiffness and chassis parameters, understeer increased by 19%, front axle lateral compliance increased by 1%, and rear axle lateral compliance decreased by 2.9%, meeting the required handling stability. At the same time, the lateral stiffness increased by 10% relative to the baseline value, resulting in a 3kg weight increase. However, as the cost increase is acceptable, the target lateral stiffness for this vehicle model should be increased by another 10% relative to the baseline value.

[0108] In order to help those skilled in the art better understand the embodiments of the present invention, an example is used below for illustration. Specifically, the present invention provides a vehicle body lateral stiffness target design method under the condition of comprehensive consideration of handling stability and cost, which can fully ensure the rear axle tail-swing performance of the vehicle and has the advantages of low cost and high efficiency. Figure 6 , is a flow chart of a vehicle lateral stiffness target decomposition method provided by an embodiment of the present invention, which may specifically include the following steps:

[0109] Step 1: Build a finite element analysis model of the vehicle's lateral stiffness and perform lateral stiffness simulation analysis.

[0110] Specifically, a finite element analysis model of the vehicle's lateral stiffness, that is, a simulation analysis model of the vehicle's lateral stiffness, is constructed. After completing the finite element analysis, the lateral stiffness of the front and rear axles of the vehicle body are extracted.

[0111] Step 2: Build a vehicle lateral stiffness simulation workflow, select the corresponding design variables, and perform DOE sampling calculations.

[0112] Specifically, based on the Optimus platform, the entire vehicle's lateral stiffness performance is integrated to establish a full-vehicle lateral stiffness simulation workflow. Key structural parameters in the front and rear axle regions are selected as design variables to drive changes in the vehicle's lateral stiffness. The workflow utilizes the optimal Latin hypersquare design of experiments (DOE) method to perform design-of-experiments (DOE) calculations.

[0113] Step 3: Construct a proxy model of lateral stiffness and weight based on the DOE data and check the accuracy of the proxy model.

[0114] Specifically, based on the DOE calculation results, a proxy model of lateral stiffness and weight (i.e., the first proxy model) is constructed and its accuracy is verified. If the proxy model accuracy is greater than or equal to 95%, the accuracy verification passes. If the proxy model accuracy is less than 95%, return to step 2 to add DOE sampling points or change the proxy model type until the proxy model meets the accuracy requirements.

[0115] Step 4: Construct a multi-body simulation analysis model of the vehicle with body flexibility and perform simulation analysis of the vehicle understeer, as well as a multi-body simulation analysis model of the suspension and perform suspension C simulation analysis.

[0116] Specifically, a rigid-flexible coupled multi-body dynamics model of the vehicle body was established to extract linear and nonlinear front and rear axle lateral compliance. Based on a suspension system simulation analysis model, indicators such as suspension roll, lateral force, and longitudinal force were extracted.

[0117] Step 5: Build the vehicle understeer and suspension C simulation workflow, select the corresponding design variables, construct the cost model, and perform DOE sampling calculations.

[0118] Specifically, based on the Optimus platform, this workflow integrates vehicle understeer and suspension C performance, creating a simulation workflow for these parameters. Chassis elasticity parameters, such as body stiffness, tire parameters, inertia parameters, four-wheel alignment parameters, hard points, bushing stiffness, and shock absorber spring stiffness, are selected as design variables, and cost models are constructed based on the characteristics of these design variables. The workflow utilizes the optimal Latin hypersquare design of experiments (DOE) method to perform DOE calculations.

[0119] Step 6: Construct proxy models of understeer and suspension C based on the DOE data, and verify the accuracy of the proxy models.

[0120] Specifically, based on the DOE calculation results, proxy models for understeer and suspension C (i.e., the second proxy model) are constructed, and their accuracy is verified. If the proxy model accuracy is greater than or equal to 95%, the accuracy verification passes. If the proxy model accuracy is less than 95%, return to step 4 to add DOE sampling points or change the proxy model type until the proxy model meets the accuracy requirements.

[0121] Step 7: Based on the proxy models in steps 3 and 6, perform a balanced optimization based on the vehicle's understeer performance and cost, and verify the optimization results through a simulation model.

[0122] Specifically, using inertia parameters, tire parameter variations, and front and rear axle lateral compliance as pre-set constraints, and understeer and cost as optimization targets, the optimization weighting can be 1:1, with a preference for high understeer and low cost. An adaptive optimization algorithm is employed to balance performance and cost, obtaining the vehicle's lateral stiffness that optimally balances handling and cost. This is then verified within a simulation model. If the simulation passes, the optimization is complete, and this lateral stiffness becomes the target vehicle lateral stiffness. If the handling and cost requirements are not met, the optimization algorithm parameters or the proxy model type are adjusted, and the optimization is repeated.

[0123] As can be seen, the present invention proposes a method for decomposing vehicle lateral stiffness targets. By analyzing data to determine the relationship between vehicle understeer and lateral stiffness, the method minimizes cost while maintaining vehicle handling performance, thereby achieving the target decomposition of vehicle lateral stiffness. By collaboratively optimizing vehicle understeer and cost, and using proxy model technology, the present invention significantly improves optimization efficiency, reduces optimization costs, and enables rapid forward design of vehicle lateral stiffness targets.

[0124] It should be noted that for the sake of simplicity, the method embodiments are described as a series of actions. However, those skilled in the art should be aware that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all preferred embodiments, and the actions involved are not necessarily required for the embodiments of the present invention.

[0125] like Figure 7 As shown, the present invention discloses a device for determining the lateral stiffness of a vehicle, wherein the vehicle is equipped with at least one sensor and components, and the device includes:

[0126] A first acquisition module 901 is configured to acquire a first proxy model; the first proxy model is a proxy model for predicting the lateral stiffness and weight of the vehicle;

[0127] A second acquisition module 902 is configured to acquire a second proxy model; the second proxy model is a proxy model for predicting understeer and corner compliance of the vehicle;

[0128] An optimization target determination module 903 is used to determine the optimization target of the vehicle;

[0129] A third acquisition module 904 is configured to acquire at least one set of first vehicle parameters of the vehicle;

[0130] A first candidate module 905 is configured to input the first vehicle parameter into the first proxy model to obtain a candidate lateral stiffness and a candidate weight;

[0131] A second candidate module 906 is configured to input the first vehicle parameter into the second proxy model to obtain a candidate understeer and a candidate lateral compliance;

[0132] The determination module 907 is configured to use the candidate lateral stiffness as the target lateral stiffness when the candidate weight and the candidate understeer meet the optimization target and the candidate lateral compliance is within a preset constraint.

[0133] In one embodiment of the present invention, the apparatus further includes: a first building module, configured to:

[0134] Creating a finite element analysis model of the vehicle's lateral stiffness; the finite element analysis model of the vehicle's lateral stiffness is created based on the vehicle's body, interior and exterior trim, and rigidly connected chassis, and is used to simulate and obtain the vehicle's lateral stiffness and weight;

[0135] Obtaining second vehicle parameters corresponding to the preset first design variables; the second vehicle parameters at least include structural parameters of key areas of the front and rear axles of the vehicle body;

[0136] Inputting the second vehicle parameter into the whole vehicle lateral stiffness finite element analysis model to obtain simulated lateral stiffness and simulated weight obtained by simulating the whole vehicle lateral stiffness finite element analysis model;

[0137] Sampling the second vehicle parameter, the simulated lateral stiffness, and the simulated weight to obtain a first sampling space and a second sampling space; data in the first sampling space and the second sampling space do not overlap;

[0138] Training the first proxy model to be trained according to the first sampling space; the first proxy model is constructed according to the first design variables;

[0139] The trained first proxy model is verified using the second sampling space, and the trained first proxy model is obtained after the verification passes.

[0140] In one embodiment of the present invention, the first building block is further configured to:

[0141] inputting the second vehicle parameter in the second sampling space into the trained first proxy model to obtain a calibration lateral stiffness and a calibration weight output by the trained first proxy model;

[0142] determining a relative error between the simulated lateral stiffness and the simulated weight and the verification lateral stiffness and the verification weight in the second sampling space;

[0143] When the relative error meets the preset accuracy requirement, determining that the first proxy model has been trained after passing the verification;

[0144] When the relative error does not meet the preset accuracy requirement, the model parameters of the first proxy model are adjusted, new second vehicle parameters corresponding to the first design variables are obtained, and the process returns to the step of inputting the second vehicle parameters into the whole vehicle lateral stiffness finite element analysis model to obtain the simulated lateral stiffness and simulated weight obtained by simulating the whole vehicle lateral stiffness finite element analysis model, and / or, the model type of the first proxy model is adjusted and the process returns to the step of training the first proxy model to be trained according to the first sampling space.

[0145] In one embodiment of the present invention, the apparatus further includes: a second building module, configured to:

[0146] Creating a full-vehicle multi-body simulation analysis model and a suspension multi-body simulation analysis model; the full-vehicle multi-body simulation analysis model is created based on the vehicle's front suspension, rear suspension, steering system, powertrain, tires, body, and stabilizer bar, and is used to simulate and obtain the vehicle's understeer; the suspension multi-body simulation analysis model is created based on the vehicle's rear suspension, steering system, test bench, and stabilizer bar, and is used to simulate and obtain the vehicle's cornering compliance;

[0147] Obtaining third vehicle parameters corresponding to the preset second design variables; the third vehicle parameters at least include chassis elastic parameters of the vehicle; the chassis elastic parameters at least include body stiffness, tire parameters, inertia parameters, four-wheel alignment parameters, bushing stiffness, and shock absorber spring stiffness;

[0148] Inputting the third vehicle parameter into the multi-body simulation analysis model and the suspension multi-body simulation analysis model to obtain a simulated understeer and a simulated cornering compliance obtained by simulating the multi-body simulation analysis model and the suspension multi-body simulation analysis model;

[0149] Sampling the third vehicle parameter, the simulated understeer, and the simulated lateral compliance to obtain a third sampling space and a fourth sampling space; wherein data of the third sampling space and the fourth sampling space do not overlap;

[0150] Training the second proxy model to be trained according to the third sampling space; the second proxy model is constructed according to the second design variable;

[0151] The trained second proxy model is verified using the fourth sampling space, and the trained second proxy model is obtained after the verification passes.

[0152] In one embodiment of the present invention, the second building block is further configured to:

[0153] inputting the third vehicle parameter in the fourth sampling space into the trained second proxy model to obtain a verified understeer degree and a verified lateral compliance output by the trained second proxy model;

[0154] Determining relative errors between the simulated lateral stiffness and the simulated weight of the fourth sampling space and the verified understeer and the verified lateral compliance;

[0155] When the relative error meets the preset accuracy requirement, determining that the second proxy model has been trained after passing the verification;

[0156] When the relative error does not meet the preset accuracy requirement, the model parameters of the second proxy model are adjusted, new third vehicle parameters corresponding to the second design variables are obtained, and the process returns to the step of inputting the third vehicle parameters into the multi-body simulation analysis model and the suspension multi-body simulation analysis model to obtain the simulated understeer and simulated lateral compliance simulated by the multi-body simulation analysis model and the suspension multi-body simulation analysis model, and / or, the model type of the second proxy model is adjusted and the process returns to the step of training the second proxy model to be trained according to the third sampling space.

[0157] In one embodiment of the present invention, the understeer is affected by the lateral stiffness, and the lateral stiffness is equivalently replaced by the torsional stiffness of the bushing in the Z direction according to a preset equivalent formula. The preset equivalent formula is a formula for converting the lateral stiffness and the torsional stiffness of the bushing in the Z direction.

[0158] In an embodiment of the present invention, some of the first vehicle parameters are within preset constraints, and the some of the first vehicle parameters at least include the inertia parameters and the tire parameters.

[0159] In one embodiment of the present invention, the apparatus further comprises: a simulation verification module, configured to:

[0160] Inputting a first vehicle parameter corresponding to the target lateral stiffness into a finite element analysis model of the vehicle lateral stiffness to obtain a target simulated lateral stiffness and a target simulated weight obtained by simulating the finite element analysis model of the vehicle lateral stiffness;

[0161] Inputting a first vehicle parameter corresponding to the target lateral stiffness into a multi-body simulation analysis model and a suspension multi-body simulation analysis model to obtain a target simulated understeer and a target simulated lateral compliance obtained by simulating the multi-body simulation analysis model and the suspension multi-body simulation analysis model;

[0162] When the target simulated weight and the target simulated understeer meet the optimization target, and the target simulated lateral compliance is within preset constraints, it is determined that the target lateral stiffness passes the simulation verification.

[0163] In one embodiment of the present invention, the simulation verification module is further configured to:

[0164] When the target simulated weight and the target simulated understeer do not meet the optimization target, or when the target simulated lateral compliance is within preset constraints, model parameters of the first proxy model and the second proxy model are adjusted, and the first proxy model and the second proxy model are retrained, or model types of the first proxy model and the second proxy model are adjusted and then the first proxy model and the second proxy model are retrained.

[0165] In an embodiment of the present invention, during the vehicle development process, a first proxy model and a second proxy model may be obtained. The first proxy model is a proxy model for predicting the vehicle's lateral stiffness and weight, and the second proxy model is a proxy model for predicting the vehicle's understeer and lateral compliance. After determining the vehicle's optimization objective, at least one set of first vehicle parameters may be obtained, wherein some of the first vehicle parameters have corresponding value ranges. The first vehicle parameters may then be input into the first proxy model to obtain candidate lateral stiffness and candidate weight, and the first vehicle parameters may be input into the second proxy model to obtain candidate understeer and candidate lateral compliance. When the candidate weight and candidate understeer meet the optimization objective and the candidate lateral compliance is within preset constraints, the candidate lateral stiffness may be used as the target lateral stiffness. In this embodiment of the present invention, during the vehicle development process, the vehicle's lateral stiffness may be determined based on the first and second proxy models, eliminating the need for simulation, which is time-consuming and computationally efficient, thereby shortening the vehicle development cycle.

[0166] In addition, the weight and understeer corresponding to the vehicle's stiffness meet the optimization objectives, and the candidate lateral compliance is within the preset constraints. In this way, by collaboratively optimizing the understeer and weight of the entire vehicle and using proxy model technology, the vehicle's optimization efficiency is greatly improved, the optimization cost is low, and the forward design of the vehicle's lateral stiffness target can be quickly achieved.

[0167] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0168] The embodiment of the present invention further provides an electronic device, such as Figure 8 As shown, it includes a processor 1001, a device interface 1002, a memory 1003 and a bus 1004;

[0169] Memory 1003, used for storing computer programs;

[0170] The processor 1001 is configured to implement the above steps when executing the program stored in the memory 1003 .

[0171] The bus mentioned in the terminal above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. This bus can be divided into address buses, data buses, control buses, etc. For ease of illustration, the figure shows only one thick line, but this does not mean that there is only one bus or only one type of bus.

[0172] The memory may include random access memory (RAM) or non-volatile memory, such as at least one disk storage. Alternatively, the memory may be at least one storage device located away from the processor.

[0173] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0174] The present invention also provides a storage medium. When instructions in the storage medium are executed by a processor of an electronic device, the electronic device can execute the vehicle lateral stiffness determination method of the aforementioned embodiment.

[0175] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0176] The algorithm and display provided herein are not inherently related to any particular computer, virtual device or other equipment. According to the above description, it is obvious that the structure required for constructing this type of device is suitable. In addition, the present invention is not directed to any specific programming language. It should be understood that various programming languages can be utilized to realize the content of the present invention described herein, and the above description of specific languages is for the purpose of disclosing the best mode of the present invention.

[0177] In the description provided herein, numerous specific details are described. However, it is understood that embodiments of the present invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques are not shown in detail so as not to obscure the understanding of this description.

[0178] Similarly, it should be understood that in order to streamline the present invention and aid in understanding one or more of the various inventive aspects, in the above description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, this disclosed method should not be interpreted as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as reflected in the claims below, inventive aspects lie in less than all the features of the individual embodiments disclosed above. Accordingly, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of the invention.

[0179] Those skilled in the art will appreciate that the modules in the devices of the embodiments can be adaptively modified and installed in one or more devices different from the embodiments. The modules, units, or components in the embodiments can be combined into a single module, unit, or component, and furthermore, they can be divided into multiple sub-modules, sub-units, or sub-components. All features disclosed in this specification (including the accompanying claims, abstract, and drawings), and all processes or units of any method or device disclosed therein, can be combined in any combination, unless at least some of such features and / or processes or units are mutually exclusive. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0180] The various component embodiments of the present invention may be implemented in hardware, or as software modules running on one or more processors, or a combination thereof. Those skilled in the art will appreciate that in practice, a microprocessor or digital signal processor (DSP) may be used to implement some or all of the functions of some or all of the components of the sorting device according to the present invention. The present invention may also be implemented as an apparatus or device program for performing part or all of the methods described herein. Such a program implementing the present invention may be stored on a computer-readable medium or in the form of one or more signals. Such signals may be downloaded from an Internet website, provided on a carrier signal, or provided in any other form.

[0181] It should be noted that the above embodiments illustrate rather than limit the invention, and that those skilled in the art may devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between brackets should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present invention may be implemented by means of hardware comprising several different elements and by means of appropriately programmed computers. In a unit claim enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third etc. does not indicate any order. These words may be interpreted as names.

[0182] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices, apparatuses and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0183] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

[0184] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

[0185] It should be noted that the various data-related processes in the embodiments of the present application are all carried out in compliance with the corresponding data protection laws and policies of the country where they are located, and with the authorization given by the owner of the corresponding device.

Claims

1. A method for determining vehicle lateral stiffness, characterized in that: The method comprises: Obtaining a first proxy model; the first proxy model is a proxy model for predicting the lateral stiffness and weight of the vehicle; Acquire a second proxy model; the second proxy model is a proxy model for predicting understeer and corner compliance of the vehicle; determining an optimization goal for the vehicle; obtaining at least one set of first vehicle parameters of the vehicle; Inputting the first vehicle parameters into the first proxy model to obtain candidate lateral stiffness and candidate weight, specifically, inputting key structural parameters of the front and rear axle regions of the vehicle body in the first vehicle parameters into the first proxy model to obtain candidate lateral stiffness and candidate weight; Inputting the first vehicle parameters into the second proxy model to obtain candidate understeer and candidate lateral compliance, specifically, inputting vehicle body stiffness, tire parameters, inertia parameters, four-wheel alignment parameters, bushing stiffness, and shock absorber spring stiffness among the first vehicle parameters into the second proxy model to obtain candidate understeer and candidate lateral compliance; When the candidate weight and the candidate understeer meet the optimization target and the candidate lateral compliance is within a preset constraint, the candidate lateral stiffness is used as the target lateral stiffness.

2. The method according to claim 1, characterized in that Before obtaining the first proxy model, the method further includes: Creating a finite element analysis model of the vehicle's lateral stiffness; the finite element analysis model of the vehicle's lateral stiffness is created based on the vehicle's body, interior and exterior trim, and rigidly connected chassis, and is used to simulate and obtain the vehicle's lateral stiffness and weight; Obtaining second vehicle parameters corresponding to the preset first design variables; the second vehicle parameters at least include structural parameters of key areas of the front and rear axles of the vehicle body; Inputting the second vehicle parameter into the whole vehicle lateral stiffness finite element analysis model to obtain simulated lateral stiffness and simulated weight obtained by simulating the whole vehicle lateral stiffness finite element analysis model; Sampling the second vehicle parameter, the simulated lateral stiffness, and the simulated weight to obtain a first sampling space and a second sampling space; data in the first sampling space and the second sampling space do not overlap; Training the first proxy model to be trained according to the first sampling space; the first proxy model is constructed according to the first design variables; The trained first proxy model is verified using the second sampling space, and the trained first proxy model is obtained after the verification passes.

3. The method according to claim 2, characterized in that Verifying the trained first proxy model using the second sampling space, and obtaining the trained first proxy model after the verification passes, including: inputting the second vehicle parameter in the second sampling space into the trained first proxy model to obtain a calibration lateral stiffness and a calibration weight output by the trained first proxy model; determining a relative error between the simulated lateral stiffness and the simulated weight and the verification lateral stiffness and the verification weight in the second sampling space; When the relative error meets the preset accuracy requirement, determining that the first proxy model has been trained after passing the verification; When the relative error does not meet the preset accuracy requirement, the model parameters of the first proxy model are adjusted, new second vehicle parameters corresponding to the first design variables are obtained, and the process returns to the step of inputting the second vehicle parameters into the whole vehicle lateral stiffness finite element analysis model to obtain the simulated lateral stiffness and simulated weight obtained by simulating the whole vehicle lateral stiffness finite element analysis model, and / or, the model type of the first proxy model is adjusted and the process returns to the step of training the first proxy model to be trained according to the first sampling space.

4. The method according to claim 1, wherein Before obtaining the second proxy model, the method further includes: Creating a full-vehicle multi-body simulation analysis model and a suspension multi-body simulation analysis model; the full-vehicle multi-body simulation analysis model is created based on the vehicle's front suspension, rear suspension, steering system, powertrain, tires, body, and stabilizer bar, and is used to simulate and obtain the vehicle's understeer; the suspension multi-body simulation analysis model is created based on the vehicle's rear suspension, steering system, test bench, and stabilizer bar, and is used to simulate and obtain the vehicle's cornering compliance; Obtaining third vehicle parameters corresponding to the preset second design variables; the third vehicle parameters at least include chassis elastic parameters of the vehicle; the chassis elastic parameters at least include body stiffness, tire parameters, inertia parameters, four-wheel alignment parameters, bushing stiffness, and shock absorber spring stiffness; Inputting the third vehicle parameter into the multi-body simulation analysis model and the suspension multi-body simulation analysis model to obtain a simulated understeer and a simulated cornering compliance obtained by simulating the multi-body simulation analysis model and the suspension multi-body simulation analysis model; Sampling the third vehicle parameter, the simulated understeer, and the simulated lateral compliance to obtain a third sampling space and a fourth sampling space; wherein data of the third sampling space and the fourth sampling space do not overlap; Training the second proxy model to be trained according to the third sampling space; the second proxy model is constructed according to the second design variable; The trained second proxy model is verified using the fourth sampling space, and the trained second proxy model is obtained after the verification passes.

5. The method according to claim 4, characterized in that Verifying the trained second proxy model using the fourth sampling space, and obtaining the trained second proxy model after the verification passes, including: inputting the third vehicle parameter in the fourth sampling space into the trained second proxy model to obtain a verified understeer degree and a verified lateral compliance output by the trained second proxy model; Determining relative errors between the simulated lateral stiffness and the simulated weight of the fourth sampling space and the verified understeer and the verified lateral compliance; When the relative error meets the preset accuracy requirement, determining that the second proxy model has been trained after passing the verification; When the relative error does not meet the preset accuracy requirement, the model parameters of the second proxy model are adjusted, new third vehicle parameters corresponding to the second design variables are obtained, and the process returns to the step of inputting the third vehicle parameters into the multi-body simulation analysis model and the suspension multi-body simulation analysis model to obtain the simulated understeer and simulated lateral compliance simulated by the multi-body simulation analysis model and the suspension multi-body simulation analysis model, and / or, the model type of the second proxy model is adjusted and the process returns to the step of training the second proxy model to be trained according to the third sampling space.

6. The method according to claim 4, characterized in that The understeer degree is affected by the lateral stiffness, and the lateral stiffness is equivalently replaced by the torsional stiffness of the bushing in the Z direction according to a preset equivalent formula. The preset equivalent formula is a formula for converting the lateral stiffness and the torsional stiffness of the bushing in the Z direction.

7. The method according to claim 4, characterized in that Some of the first vehicle parameters are within preset constraints, and the some of the first vehicle parameters at least include the inertia parameter and the tire parameter.

8. The method according to claim 1, characterized in that When the candidate weight and the candidate understeer meet the optimization target and the candidate lateral compliance is within a preset constraint, after using the candidate lateral stiffness as the target lateral stiffness, the method further includes: Inputting a first vehicle parameter corresponding to the target lateral stiffness into a finite element analysis model of the vehicle lateral stiffness to obtain a target simulated lateral stiffness and a target simulated weight obtained by simulating the finite element analysis model of the vehicle lateral stiffness; Inputting a first vehicle parameter corresponding to the target lateral stiffness into a multi-body simulation analysis model and a suspension multi-body simulation analysis model to obtain a target simulated understeer and a target simulated lateral compliance obtained by simulating the multi-body simulation analysis model and the suspension multi-body simulation analysis model; When the target simulated weight and the target simulated understeer meet the optimization target, and the target simulated lateral compliance is within preset constraints, it is determined that the target lateral stiffness passes the simulation verification.

9. The method according to claim 8, characterized in that The method further comprises: When the target simulated weight and the target simulated understeer do not meet the optimization target, or when the target simulated lateral compliance is within preset constraints, model parameters of the first proxy model and the second proxy model are adjusted, and the first proxy model and the second proxy model are retrained, or model types of the first proxy model and the second proxy model are adjusted and then the first proxy model and the second proxy model are retrained.

10. A vehicle lateral stiffness determination device, characterized in that: The device comprises: A first acquisition module is configured to acquire a first proxy model; the first proxy model is a proxy model for predicting the lateral stiffness and weight of the vehicle; a second acquisition module, configured to acquire a second proxy model; the second proxy model is a proxy model for predicting understeer and corner compliance of the vehicle; An optimization target determination module, configured to determine an optimization target for the vehicle; a third acquisition module, configured to acquire at least one set of first vehicle parameters of the vehicle; a first candidate module, configured to input the first vehicle parameters into the first proxy model to obtain candidate lateral stiffness and candidate weight, specifically, inputting structural parameters of key areas of the front and rear axles of the vehicle body in the first vehicle parameters into the first proxy model to obtain candidate lateral stiffness and candidate weight; a second candidate module, configured to input the first vehicle parameters into the second proxy model to obtain candidate understeer and candidate lateral compliance, specifically, inputting body stiffness, tire parameters, inertia parameters, four-wheel alignment parameters, bushing stiffness, and shock absorber spring stiffness among the first vehicle parameters into the second proxy model to obtain candidate understeer and candidate lateral compliance; A determination module is configured to use the candidate lateral stiffness as a target lateral stiffness when the candidate weight and the candidate understeer meet the optimization target and the candidate lateral compliance is within a preset constraint condition.

11. An electronic device, characterized in that: include: processor; a memory for storing processor-executable instructions; The processor is configured to execute the instructions to implement the vehicle lateral stiffness determination method according to any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that When the instructions in the storage medium are executed by a processor of a mobile terminal, the mobile terminal is enabled to execute the vehicle lateral stiffness determination method according to any one of claims 1 to 9.

13. A vehicle, characterized in that: The vehicle includes the electronic device according to claim 11.

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