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

Abstract

The invention relates to a vehicle lateral stiffness determination method and device, electronic equipment and a vehicle. The method comprises the steps that a first agent model is acquired; the first agent model is an agent model used for predicting the lateral rigidity and weight of the vehicle; obtaining a second agent model; the second agent model is an agent model used for predicting the insufficient steering degree and the lateral deflection flexibility of the vehicle; determining an optimization target of the vehicle; acquiring first vehicle parameters of at least one group of vehicles; wherein part of the first vehicle parameters have corresponding value ranges; inputting the first vehicle parameter into a first agent model to obtain candidate lateral stiffness and candidate weight; inputting the first vehicle parameter into a second agent model to obtain a candidate insufficient steering degree and a candidate lateral deflection flexibility; and when the candidate weight and the candidate insufficient steering degree meet an optimization target and the candidate lateral deflection flexibility is within a preset constraint condition, taking the candidate lateral stiffness as the target lateral stiffness. The embodiment of the invention shortens the research and development cycle of the vehicle.

Description

Technical Field

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

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

[0003] However, at present, the lateral stiffness of a vehicle is mainly obtained through simulation, which requires a large amount of time, has low computational efficiency, and affects the R & D cycle of the vehicle. 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 mainly obtained through simulation, which requires a large amount of time, has low computational efficiency, and affects the R & D cycle of the vehicle; the second purpose is to provide a device; the third purpose is to provide an electronic device; the fourth purpose is to provide a vehicle.

[0005] In order to achieve the above purposes, the technical solutions adopted by the present invention are as follows: A method for determining the lateral stiffness of a vehicle, the method comprising: Obtaining a first surrogate model; the first surrogate model is a surrogate model for predicting the lateral stiffness and weight of the vehicle; Obtaining a second surrogate model; the second surrogate model is a surrogate model for predicting the understeer degree and cornering compliance of the vehicle; Determining an optimization target for the vehicle; Obtaining at least one set of first vehicle parameters of the vehicle; Inputting the first vehicle parameters into the first surrogate model to obtain a candidate lateral stiffness and a candidate weight; Inputting the first vehicle parameters into the second surrogate model to obtain a candidate understeer degree and a candidate cornering compliance; When the candidate weight and the candidate understeer degree meet the optimization target and the candidate cornering compliance is within a preset constraint condition, taking the candidate lateral stiffness as the target lateral stiffness.

[0006] A device for determining the lateral stiffness of a vehicle, the device comprising: A first acquisition module, configured to obtain a first surrogate model; the first surrogate model is a surrogate 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 the understeer degree and cornering compliance of the vehicle; An optimization objective determination module, configured to determine the optimization objective of 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 a candidate lateral stiffness and a candidate weight; A second candidate module, configured to input the first vehicle parameters into the second proxy model to obtain a candidate understeer degree and a candidate cornering compliance; A determination module, configured to use the candidate lateral stiffness as the target lateral stiffness when the candidate weight and the candidate understeer degree meet the optimization objective and the candidate cornering compliance is within a preset constraint condition.

[0007] An electronic device, comprising: a processor; and a memory for storing processor-executable instructions; Wherein, the processor is configured to execute the instructions to implement the above-mentioned vehicle lateral stiffness determination method.

[0008] A computer-readable storage medium, when the 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.

[0009] A vehicle, wherein the vehicle includes the above-mentioned electronic device.

[0010] Advantages of the present invention: In the embodiment of the present invention, during the vehicle R & D process, a first proxy model and a second proxy model can be acquired. Among them, the first proxy model is a proxy model for predicting the lateral stiffness and weight of the vehicle, and the second proxy model is a proxy model for predicting the understeer degree and cornering compliance of the vehicle. After determining the optimization objective of the vehicle, at least one set of first vehicle parameters of the vehicle can be acquired. Among them, some first vehicle parameters have corresponding value ranges. Then, the first vehicle parameters can be input into the first proxy model to obtain a candidate lateral stiffness and a candidate weight, and the first vehicle parameters can be input into the second proxy model to obtain a candidate understeer degree and a candidate cornering compliance; when the candidate weight and the candidate understeer degree meet the optimization objective and the candidate cornering compliance is within a preset constraint condition, the candidate lateral stiffness can be used as the target lateral stiffness. In the embodiment of the present invention, during the vehicle R & D process, the lateral stiffness of the vehicle can be determined according to the first proxy model and the second proxy model, without obtaining it through simulation, without consuming a large amount of time, with high calculation efficiency, and shortening the vehicle R & D cycle.

[0011] In addition, the weight corresponding to the vehicle stiffness and the understeer degree meet the optimization objectives, and the candidate cornering compliance is within the preset constraint conditions. Thus, by co-optimizing the understeer degree and weight of the whole vehicle, as well as the surrogate model technology, the optimization efficiency of the vehicle is greatly improved, the optimization cost is low, and the forward design of the vehicle's lateral stiffness target can be quickly achieved. Description of the Drawings

[0012] Figure 1 It is a flowchart of the steps of a method for determining the lateral stiffness of a vehicle provided in an embodiment of the present invention; Figure 2 It is a residual plot of a lateral stiffness surrogate model provided in an embodiment of the present invention; Figure 3 It is a contribution analysis diagram provided in an embodiment of the present invention; Figure 4 It is a residual plot of an understeer degree surrogate model provided in an embodiment of the present invention; Figure 5 It is an optimization effect diagram provided in an embodiment of the present invention; Figure 6 It is a flowchart of a method for decomposing the lateral stiffness target of a vehicle provided in an embodiment of the present invention; Figure 7 It is a schematic structural diagram of a device for determining the lateral stiffness of a vehicle provided in an embodiment of the present invention; Figure 8 It is a schematic structural diagram of an electronic device provided in an embodiment of the present invention. Detailed Embodiments

[0013] The following will describe the embodiments of the present invention with reference to the accompanying drawings and preferred embodiments. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are only for explaining the present invention and not for limiting the protection scope of the present invention.

[0014] Referring to Figure 1 , it shows a flowchart of the steps of a method for determining the lateral stiffness of a vehicle provided in an embodiment of the present invention. The vehicle is equipped with at least one sensor and components, and specifically includes the following steps: Step 101, obtain a first surrogate model; the first surrogate model is a surrogate model for predicting the lateral stiffness and weight of the vehicle.

[0015] Step 102: Obtain a second surrogate model; the second surrogate model is a surrogate model used to predict the understeer degree and cornering compliance of the vehicle.

[0016] In a specific implementation, a surrogate 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 surrogate model can simulate the data of a real vehicle, such as the lateral stiffness, understeer degree, weight (mass), and cornering compliance of the vehicle.

[0017] In the embodiment of the present invention, at least one surrogate model is constructed. Different surrogate models can be used to predict different vehicle data. Specifically, a first surrogate model and a second surrogate model can be constructed. When it is necessary to predict the lateral stiffness, weight, understeer degree, and cornering compliance of the vehicle, the first surrogate model and the second surrogate model are obtained, so that the lateral stiffness and weight of the vehicle can be predicted through the first surrogate model, and the understeer degree and cornering compliance of the vehicle can be predicted through the second surrogate model.

[0018] Step 103: Determine the optimization objective of the vehicle.

[0019] In the embodiment of the present invention, the optimization objective 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 handling and stability performance and cost of the vehicle, the optimization objective can be determined as the understeer degree and weight.

[0020] In some embodiments, when the understeer degree and weight are set as the optimization objectives, the optimization weight ratio can be set to 1:1. The understeer degree is expected to be large, and the weight is expected to be small. Of course, if the vehicle model is a performance car, the optimization weight ratio can be appropriately increased; if the vehicle model is an economy car, the optimization weight ratio can be decreased. An adaptive optimization algorithm is used to balance and optimize the handling and stability performance and cost, so that the lateral stiffness of the vehicle under the best balance condition of the handling and stability performance and cost can be obtained.

[0021] Step 104: Obtain at least one set of first vehicle parameters of the vehicle.

[0022] In the embodiment of the present invention, when predicting the lateral stiffness, understeer degree, weight, and cornering compliance of the vehicle through the surrogate model, at least one set of first vehicle parameters of the vehicle can be obtained. Exemplarily, the vehicle parameters can include but are not limited to the structural parameters of the key areas of the front and rear axles of the vehicle body, as well as the body stiffness, tire parameters, inertia parameters, four-wheel alignment parameters, bushing stiffness, and shock spring stiffness.

[0023] In practical applications, considering that the engineering implementation of adjusting the inertia parameters is relatively complex, the tire selection has a great impact on the NVH (Noise, Vibration, Harshness) performance, and an excessive cornering compliance will cause the vehicle's steering response to slow down, corresponding constraint conditions are set for parameters such as inertia parameters, tire parameters, and the cornering compliance of the front and rear axles. For example, the value ranges of parameters such as inertia parameters, tire parameters, and the cornering compliance of the front and rear axles can be fixed within a preset value range.

[0024] Step 105: Input the first vehicle parameters into the first surrogate model to obtain a candidate lateral stiffness and a candidate weight.

[0025] Step 106: Input the first vehicle parameters into the second surrogate model to obtain a candidate understeer degree and a candidate cornering compliance.

[0026] Step 107: When the candidate weight and the candidate understeer degree meet the optimization objective and the candidate cornering compliance is within the preset constraint conditions, use the candidate lateral stiffness as the target lateral stiffness.

[0027] In the 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 surrogate model to obtain a candidate lateral stiffness and a candidate weight, and the 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 surrogate model to obtain a candidate understeer degree and a candidate cornering compliance. Then, when it is determined that the candidate weight and the candidate understeer degree meet the optimization objective and, at the same time, the candidate cornering compliance is within the preset constraint conditions, for example, when the candidate cornering compliance is within the preset value range, the candidate lateral stiffness at this time can be used as the target lateral stiffness of the vehicle.

[0028] In the above method for determining the lateral stiffness of a vehicle, during the vehicle R & D process, a first surrogate model and a second surrogate model can be obtained. Among them, the first surrogate model is a surrogate model for predicting the lateral stiffness and weight of the vehicle, and the second surrogate model is a surrogate model for predicting the understeer degree and cornering compliance of the vehicle. After determining the optimization objective of the vehicle, at least one set of first vehicle parameters of the vehicle can be obtained. Among them, some of the first vehicle parameters have corresponding value ranges. Then, the first vehicle parameters can be input into the first surrogate model to obtain a candidate lateral stiffness and a candidate weight, and the first vehicle parameters can be input into the second surrogate model to obtain a candidate understeer degree and a candidate cornering compliance. When the candidate weight and the candidate understeer degree meet the optimization objective and the candidate cornering compliance is within the preset constraint conditions, the candidate lateral stiffness can be used as the target lateral stiffness. In the embodiment of the present invention, during the vehicle R & D process, the lateral stiffness of the vehicle can be determined according to the first surrogate model and the second surrogate model, without obtaining it through simulation, without consuming a large amount of time, with high calculation efficiency, and shortening the vehicle R & D cycle.

[0029] In addition, the weight and understeer degree corresponding to the vehicle stiffness meet the optimization objective, and the candidate cornering compliance is within the preset constraint conditions. Thus, by jointly optimizing the understeer degree and weight of the whole vehicle and the surrogate model technology, the optimization efficiency of the vehicle is greatly improved, the optimization cost is low, and the forward design of the vehicle lateral stiffness target can be quickly realized.

[0030] In an embodiment of the present invention, before step 101, obtaining the first surrogate model, the method may further include: Create a finite element analysis model for the lateral stiffness of the whole vehicle; the finite element analysis model for the lateral stiffness of the whole vehicle is created according to the vehicle body, interior and exterior trims, and the rigidly connected chassis, and is used to simulate and obtain the lateral stiffness and weight of the vehicle; Obtain the second vehicle parameters corresponding to the preset first design variables; the second vehicle parameters at least include the structural parameters of the key areas of the front axle and the rear axle of the vehicle body; Input the second vehicle parameters into the finite element analysis model for the lateral stiffness of the whole vehicle to obtain the simulated lateral stiffness and the simulated weight obtained by simulating the finite element analysis model for the lateral stiffness of the whole vehicle; Sample the second vehicle parameters, the simulated lateral stiffness, and the simulated weight to obtain a first sampling space and a second sampling space; the data in the first sampling space and the second sampling space do not overlap; Train the first surrogate model to be trained according to the first sampling space; the first surrogate model is constructed according to the first design variables; Use the second sampling space to verify the trained first surrogate model, and obtain the trained first surrogate model after passing the verification.

[0031] In the embodiments of the present invention, a finite element analysis model for the lateral stiffness of the whole vehicle can be established to perform lateral stiffness simulation analysis.

[0032] Specifically, a finite element analysis model for the lateral stiffness of the whole vehicle is established. Specifically, it can be created based on the vehicle body, interior and exterior trim, and the rigidly connected chassis, and corresponding constraint conditions are applied. After completing the finite element calculation, the lateral stiffness and weight of the front axle and rear axle of the vehicle body can be obtained through simulation. It should be added that both the finite element analysis model for the lateral stiffness of the whole vehicle and the first surrogate model can predict the lateral stiffness and weight of the vehicle. However, the finite element analysis model for the lateral stiffness of the whole vehicle is a simulation model, and the accuracy of the simulation model is higher than that of the first surrogate model. However, the simulation time is very long, usually at the day level, while the prediction time of the first surrogate model is very short, usually at the millisecond level, and the calculation efficiency is very high.

[0033] In the embodiments of the present invention, the structural parameters of the key areas of the front and rear axles of the vehicle body are selected as the design variables of the first surrogate model, that is, the first design variables, to drive the change of the lateral stiffness of the whole vehicle. In an embodiment of the simulation workflow for the lateral stiffness of the whole vehicle of the present invention, based on the Optimus platform, the lateral stiffness performance of the whole vehicle can be integrated. Specifically, the design parameters / design variables (UCIInputsl) are determined; the design parameters are recorded in the file DVfile.text; the DVfile.text is parameterized into a bdf file through ansa VERSION11 (a CAE preprocessing software of version 11); the bdf file is calculated by calling the Nastran (finite element program) solver to obtain f06 and C857EV_FV_SideSTI_para.pch; Action1 (automation process) is executed through Mass_py (script text) to obtain Output_mass.text from f06, and the mass (mass) is included in Output_mass.text; Dis is extracted from the C857EV_FV_SideSTI_para.pch file, and the lateral stiffness (sideSTI) is included in Dis.

[0034] In some embodiments, the workflow can use the optimal Latin hypercube experimental design method for DOE (Design Of Experiment) sampling calculation. For example, the vehicle parameters can be obtained through DOE sampling calculation using the optimal Latin hypercube experimental design method.

[0035] Specifically, obtain the second vehicle parameters corresponding to the preset first design variables. The second vehicle parameters may at least include the structural parameters of the key areas of the front axle and rear axle of the vehicle body. Then, the second vehicle parameters can be input into the finite element analysis model of the vehicle's lateral stiffness to obtain the simulated lateral stiffness and simulated weight obtained by the simulation of the finite element analysis model of the vehicle's lateral stiffness. Then, the second vehicle parameters, simulated lateral stiffness, and simulated weight can be sampled to obtain the first sampling space and the second sampling space. The data in the first sampling space and the second sampling space do not overlap. The vehicle parameters in the first sampling space can be used to train the first surrogate model, and the vehicle parameters in the second sampling space can be used to verify the first surrogate model. After completion of training and passing the verification, the trained first surrogate model can be obtained. Subsequently, the lateral stiffness and weight can be quickly predicted based on the first surrogate model.

[0036] In an embodiment of the present invention, using the second sampling space to verify the trained first surrogate model, and obtaining the trained first surrogate model after passing the verification may include: Input the second vehicle parameters of the second sampling space into the trained first surrogate model to obtain the verified lateral stiffness and verified weight output by the trained first surrogate model; Determine the relative error between the simulated lateral stiffness and simulated weight in the second sampling space and the verified lateral stiffness and verified weight; When the relative error meets the preset accuracy requirement, it is determined that the verification is passed and the trained first surrogate model is obtained; When the relative error does not meet the preset accuracy requirement, adjust the model parameters of the first surrogate model, obtain new second vehicle parameters corresponding to the first design variables, and return to execute the step of inputting the second vehicle parameters into the finite element analysis model of the vehicle's lateral stiffness to obtain the simulated lateral stiffness and simulated weight obtained by the simulation of the finite element analysis model of the vehicle's lateral stiffness, and / or, adjust the model type of the first surrogate model and return to execute the step of training the first surrogate model to be trained according to the first sampling space.

[0037] In an embodiment of the present invention, based on the DOE calculation results, that is, the first sampling space and the second sampling space, construct a first surrogate model of the lateral stiffness and weight, and verify whether the accuracy of the first surrogate model meets the preset accuracy requirement. The second sampling space can be sampled 10 - 20 groups outside the first sampling space, and verify whether the first surrogate model meets the accuracy requirement through residual analysis. Specifically, refer to Figure 2The residual plot of the lateral stiffness surrogate model is shown, where the vertical axis represents the Residual (normalized) of the lateral stiffness, i.e., the residual of the lateral stiffness (normalized), and the horizontal axis represents multiple sets of data in the second sampling space. Exemplarily, if the accuracy of the first surrogate model is greater than or equal to 95%, it is determined that the first surrogate model passes the accuracy verification, and it can be determined that the first surrogate model after training is obtained after verification passes; if the accuracy of the first surrogate model is less than 95%, the DOE sampling calculation can be performed again, new second vehicle parameters can be generated, and then simulation, DOE sampling calculation, model training and verification, etc. can be performed again, and / or the model type of the first surrogate model can be adjusted, for example, from a convolutional model to a neural network model, and then simulation, DOE sampling calculation, model training and verification, etc. can be performed again using the second vehicle parameters until the first surrogate model meets the preset accuracy requirements. In this way, the first surrogate model with higher accuracy can be used to accurately determine the lateral stiffness of the vehicle.

[0038] In an embodiment of the present invention, before step 102, obtaining the second surrogate model, the method may further include: Create a vehicle multi-body simulation analysis model and a suspension multi-body simulation analysis model; the vehicle multi-body simulation analysis model is created according to the front suspension, rear suspension, steering system, powertrain, tires, body, and stabilizer bar of the vehicle, and is used to simulate the understeer degree of the vehicle, and the suspension multi-body simulation analysis model is created according to the rear suspension, steering system, test bench, and stabilizer bar of the vehicle, and is used to simulate the cornering compliance of the vehicle; Obtain the third vehicle parameters corresponding to the preset second design variables; the third vehicle parameters at least include the 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 spring stiffness; Input the third vehicle parameters into the multi-body simulation analysis model and the suspension multi-body simulation analysis model to obtain the simulated understeer degree and simulated cornering compliance simulated by the multi-body simulation analysis model and the suspension multi-body simulation analysis model; Sample the third vehicle parameters, the simulated understeer degree, and the simulated cornering compliance to obtain a third sampling space and a fourth sampling space; the data in the third sampling space and the fourth sampling space do not overlap; Train the second surrogate model to be trained according to the third sampling space; the second surrogate model is constructed according to the second design variables; Verify the trained second surrogate model using the fourth sampling space, and obtain the trained second surrogate model after verification passes.

[0039] In the embodiments of the present invention, a vehicle multi-body simulation analysis model for constructing vehicle body flexibility can be built to perform vehicle understeer degree simulation analysis, and a suspension multi-body simulation analysis model can be built to perform suspension C simulation analysis, such as performing simulation analysis of cornering flexibility.

[0040] Specifically, subsystems such as the front suspension, rear suspension, steering system, powertrain, tires, vehicle body, and stabilizer bar are established, a rigid-flexible coupled vehicle multi-body simulation analysis model for vehicle body flexibility is built, Matlab scripts are written to automatically extract the linear segment and non-linear cornering flexibility of the front and rear axles, subsystems such as the rear suspension, steering system, test bench, and stabilizer bar are established, a suspension multi-body simulation analysis model is built, Matlab scripts are written to automatically extract indexes such as rear suspension roll, lateral force, longitudinal force, and cornering flexibility. Among them, indexes such as lateral force (Side_F), initial value of rear wheel toe angle (R_TOE_I_x), and Y-direction position of the front suspension roll axis (F_LSA_y) can be used to determine the understeer degree. In the embodiments of the present invention, through contribution analysis, design variables with greater contributions to the understeer degree and the like are screened out, and such design variables are focused on for optimization. The contribution analysis results are as Figure 3 shown in the contribution analysis diagram. The vertical axis represents the design variables affecting the understeer degree, and the horizontal axis represents the influence values on the understeer degree. It can be seen that Side_F (lateral force) has a greater influence on the understeer degree, while R_TOE_I_x (initial value of rear wheel toe angle) and F_LSA_y (Y-direction position of the front suspension roll axis) have a smaller influence on the understeer degree. Then, the lateral force can be focused on for optimization to optimize parameters such as the understeer degree.

[0041] It should be added that both the vehicle multi-body simulation analysis model and the suspension multi-body simulation analysis model and the second surrogate model can predict the understeer degree and cornering flexibility of the vehicle. However, the vehicle multi-body simulation analysis model and the suspension multi-body simulation analysis model are simulation models, and the accuracy of the simulation models is higher than that of the second surrogate model. However, the simulation time is very long, usually at the level of days, while the prediction time of the second surrogate model is very short, usually at the millisecond level, and the calculation efficiency is very high.

[0042] In the embodiments of the present invention, the chassis elastic parameters of the vehicle are selected as the design variables of the second surrogate model, i.e., the second design variables. The chassis elastic parameters may at least include body stiffness, tire parameters, inertial parameters, four-wheel alignment parameters, bushing stiffness, and shock spring stiffness, etc. In an embodiment of the integrated development workflow of the understeer degree and suspension C characteristics of a whole vehicle in the present invention, parametric modeling of the design variables is implemented based on the Optimus platform. Among them, the 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 (complete parameter set of the rear suspension system), STI (lateral torque input), Elastic_parameter (elastic parameter), inertial_parameter (inertial parameter), HardPoint (hard point), tire_parameter (tire parameter). And cost models / parametric models, i.e., the second surrogate model, are respectively constructed according to the characteristics of the design variables. Then, the parametric models are used for constant-radius analysis and C characteristic analysis to obtain the understeer degree and cornering compliance in the non-linear section, lateral stiffness cost, understeer degree and cornering compliance in the linear section, inertial parameter cost, and tire cost, etc. In practical applications, the design variables can be divided into two categories: cost-type design variables and non-cost-type design variables. Among them, the cost-type design variables include body stiffness, inertial parameters, and tire parameters, etc. Specifically, a specific relationship between weight and body stiffness is established to represent the body stiffness cost with weight; the cost of adjusting the inertial parameters is represented by the percentage change in the centroid position; the cost of adjusting the tire parameters is represented by the percentage change in the tire cornering stiffness factor and friction coefficient factor. The non-cost-type design variables may include bushing stiffness, shock spring stiffness, four-wheel alignment parameters, and hard points, etc.

[0043] In some embodiments, the workflow can use the optimal Latin hypercube experimental design method for DOE sampling calculation. Through correlation analysis, the design variables strongly correlated with the above responses are screened out. After removing the insensitive design variables, DOE sampling calculation is performed on the remaining design variables.

[0044] Specifically, obtain the third vehicle parameters corresponding to the preset second design variables. The third vehicle parameters may at least include body stiffness, tire parameters, inertia parameters, four-wheel alignment parameters, bushing stiffness, and shock spring stiffness, etc. Then, the third vehicle parameters can be input into the vehicle multi-body simulation analysis model and the suspension multi-body simulation analysis model to obtain the simulation understeer degree and simulation cornering compliance obtained by simulation of the vehicle multi-body simulation analysis model and the suspension multi-body simulation analysis model. Then, the third vehicle parameters, the simulation understeer degree, and the simulation cornering compliance can be sampled to obtain a third sampling space and a fourth sampling space. The data in the third sampling space and the fourth sampling space do not overlap. The vehicle parameters in the third sampling space can be used to train the second surrogate model, and the vehicle parameters in the fourth sampling space can be used to verify the second surrogate model. After completing the training and passing the verification, the trained second surrogate model can be obtained. Subsequently, the understeer degree and cornering compliance can be quickly predicted based on the second surrogate model.

[0045] In an alternative embodiment of the present invention, 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.

[0046] In an embodiment of the present invention, to decouple the influence of the lateral stiffness of the body and the torsional stiffness of the bushing in the Z direction on the handling and stability performance, the lateral stiffness of the body can be equivalently replaced by the torsional stiffness of the bushing in the Z direction according to the constraint conditions of the vehicle lateral stiffness simulation analysis.

[0047] In some embodiments, the preset equivalent formula is as follows:

[0048]

[0049] Wherein, M represents torque; represents the torsional stiffness of the bushing in the Z direction; represents the angle of twist; represents the lateral stiffness of the body; F represents the loading force of the vehicle lateral stiffness simulation analysis; L represents the distance in the x direction between the bushing and the loading point of the lateral stiffness loading force F.

[0050] In an embodiment of the present invention, using the fourth sampling space to verify the trained second surrogate model, and obtaining the trained second surrogate model after passing the verification may include: Input the third vehicle parameters of the fourth sampling space into the trained second surrogate model to obtain the verified understeer degree and verified cornering compliance output by the trained second surrogate model; Determine the relative errors between the simulated lateral stiffness and simulated weight of the fourth sampling space and the calibrated understeer degree and calibrated cornering compliance; When the relative errors meet the preset accuracy requirements, it is determined that the verification is passed, and the trained second surrogate model is obtained; When the relative errors do not meet the preset accuracy requirements, adjust the model parameters of the second surrogate model, obtain the new third vehicle parameters corresponding to the second design variables, and return to execute 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 degree and simulated cornering compliance simulated by the multi-body simulation analysis model and the suspension multi-body simulation analysis model, and / or adjust the model type of the second surrogate model and return to execute the step of training the second surrogate model to be trained according to the third sampling space.

[0051] In the embodiment of the present invention, based on the DOE calculation results, that is, the third sampling space and the fourth sampling space, a second surrogate model of understeer degree and cornering compliance is constructed, and it is verified whether the accuracy of the second surrogate model meets the preset accuracy requirements. Among them, the fourth sampling space can be 10-20 groups sampled outside the third sampling space, and the residual analysis is used to verify whether the second surrogate model meets the accuracy requirements. Specifically, reference can be made to Figure 4 the residual diagram of the understeer degree surrogate model shown, where the vertical axis represents the Residual (normalized) of the understeer degree, that is, the residual (normalized) of the understeer degree, and the horizontal axis represents multiple groups of data in the fourth sampling space. Exemplarily, if the accuracy of the second surrogate model is greater than or equal to 95%, it is determined that the second surrogate model passes the accuracy verification, and it can be determined that the trained second surrogate model is obtained after the verification is passed; if the accuracy of the first surrogate model is less than 95%, the DOE sampling calculation can be performed again, generate new third vehicle parameters, and then perform simulation, DOE sampling calculation, model training and verification, etc. again, and / or adjust the model type of the second surrogate model, for example, adjust from a convolutional model to a neural network model, and then re-use the third vehicle parameters for simulation, DOE sampling calculation, model training and verification, etc. until the second surrogate model meets the preset accuracy requirements. In this way, a first surrogate model with higher accuracy can be used to accurately determine the understeer degree and cornering compliance of the vehicle.

[0052] In an embodiment of the present invention, after step 107, when the candidate weight and the candidate understeer degree meet the optimization objective and the candidate cornering compliance is within the preset constraint conditions, and the candidate lateral stiffness is used as the target lateral stiffness, the method may further include: Input the first vehicle parameters corresponding to the target lateral stiffness into the finite element analysis model of the vehicle's lateral stiffness to obtain the target simulated lateral stiffness and the target simulated weight obtained by simulating the finite element analysis model of the vehicle's lateral stiffness; Input the first vehicle parameters corresponding to the target lateral stiffness into the multi-body simulation analysis model and the suspension multi-body simulation analysis model to obtain the target simulated understeer degree and the target simulated cornering 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 degree meet the optimization goal and the target simulated cornering compliance is within the preset constraint conditions, it is determined that the target lateral stiffness passes the simulation verification.

[0053] In the embodiment of the present invention, the accuracy of the simulation model is higher than that of the surrogate model. After obtaining the target lateral stiffness, the simulation model can be further used to verify the target lateral stiffness.

[0054] Specifically, the first vehicle parameters corresponding to the target lateral stiffness can be input into the finite element analysis model of the vehicle's lateral stiffness to obtain the target simulated lateral stiffness and the target simulated weight obtained by simulating the finite element analysis model of the vehicle's lateral stiffness. At the same time, the first vehicle parameters corresponding to the target lateral stiffness are input into the multi-body simulation analysis model and the suspension multi-body simulation analysis model to obtain the target simulated understeer degree and the target simulated cornering compliance 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 degree meet the optimization goal, and at the same time, the target simulated cornering compliance is within the preset constraint conditions, for example, when the target simulated cornering compliance 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 finally predicted lateral stiffness.

[0055] In an embodiment of the present invention, the method may further include: When the target simulated weight and the target simulated understeer degree do not meet the optimization goal, or the target simulated cornering compliance is within the preset constraint conditions, adjust the model parameters of the first surrogate model and the second surrogate model, retrain the first surrogate model and the second surrogate model, or retrain the first surrogate model and the second surrogate model after adjusting the model types of the first surrogate model and the second surrogate model.

[0056] In the embodiment of the present invention, the optimization result, that is, the target lateral stiffness, is verified through the simulation model. If the handling and stability performance and cost meet the requirements, the optimization is completed, that is, if the understeer degree and weight meet the optimization objectives, the optimization is completed, and the target lateral stiffness is the vehicle lateral stiffness target. If the understeer degree and weight do not meet the optimization objectives, the optimization fails, then the optimization algorithm parameters and / or model types are adjusted, or sampling points are added near the current optimal solution to construct a new surrogate model, and the balance optimization is carried out again until the handling and stability performance and cost meet the optimization objectives.

[0057] Referring to Figure 5 , which is an optimization effect diagram provided in the embodiment of the present invention. Among them, the dashed line represents the data Base before optimization, and the solid line represents the data Opt after optimization. After optimizing the lateral stiffness and chassis parameters, the understeer degree increases by 19%, the front axle cornering flexibility increases by 1%, and the rear axle cornering flexibility decreases by 2.9%, meeting the handling and stability performance requirements. At the same time, the lateral stiffness increases by 10% relative to the base value, resulting in a 3 Kg (kilogram) increase in vehicle body weight, but the cost increase is within an acceptable range. Therefore, the lateral stiffness target of this vehicle model should be increased by 10% on the basis of the basic state.

[0058] In order to enable those skilled in the art to better understand the embodiments of the present invention, the following uses an example for illustration. Specifically, under the condition of comprehensively considering the handling and stability performance and cost, the present invention provides a method for designing the vehicle body lateral stiffness target, which can fully ensure the rear axle fishtailing performance of the vehicle and has the advantages of low cost and high efficiency. Referring to Figure 6 , which is a flowchart of a method for decomposing the vehicle lateral stiffness target provided in the embodiment of the present invention, and specifically may include the following steps: Step 1: Build a finite element analysis model for the vehicle's lateral stiffness and perform a lateral stiffness simulation analysis.

[0059] Specifically, build a finite element analysis model for the vehicle's lateral stiffness, that is, a simulation analysis model for the vehicle's lateral stiffness. After completing the finite element analysis, extract the lateral stiffness of the front axle and rear axle of the vehicle body.

[0060] Step 2: Build a simulation workflow for the vehicle's lateral stiffness, select the corresponding design variables, and perform DOE sampling calculations.

[0061] Specifically, based on the Optimus platform, integrate the vehicle's lateral stiffness performance and build a simulation workflow for the vehicle's lateral stiffness. Select the structural parameters of the key areas of the front axle and rear axle as design variables to drive the change of the vehicle's lateral stiffness. Among them, the workflow can use the optimal Latin hypercube experimental design method to perform DOE calculations.

[0062] Step 3: Construct a surrogate model for lateral stiffness and weight based on the DOE data and check the accuracy of the surrogate model.

[0063] Specifically, based on the DOE calculation results, a surrogate model of lateral stiffness and weight, i.e., the first surrogate model, is constructed, and the accuracy of the surrogate model is verified. If the accuracy of the surrogate model is greater than or equal to 95%, it passes the accuracy verification. If the accuracy of the surrogate model is less than 95%, return to step 2 to increase the DOE sampling points or change the surrogate model type until the surrogate model meets the accuracy requirements.

[0064] Step 4: Build a vehicle multi-body simulation analysis model with flexible body and conduct vehicle understeer simulation analysis, and build a suspension multi-body simulation analysis model and conduct suspension C simulation analysis.

[0065] Specifically, establish a rigid-flexible coupling multi-body dynamics vehicle model with flexible body, and extract the lateral compliance of the front and rear axles in the linear section and non-linear section. Based on the suspension system simulation analysis model, extract indicators such as suspension roll, lateral force, and longitudinal force.

[0066] Step 5: Set up the vehicle understeer and suspension C simulation workflows, select the corresponding design variables, build a cost model, and perform DOE sampling calculations.

[0067] Specifically, based on the Optimus platform, integrate performances such as vehicle understeer and suspension C, and set up the vehicle understeer and suspension C simulation workflows. Select chassis elastic parameters such as body stiffness, tire parameters, inertia parameters, four-wheel alignment parameters, hard points, bushing stiffness, and shock absorber spring stiffness as design variables, and build cost models according to the characteristics of the design variables. Among them, the workflow can perform DOE calculations using the optimal Latin hypercube experimental design method.

[0068] Step 6: Construct surrogate models of understeer and suspension C based on the DOE data, and check the accuracy of the surrogate models.

[0069] Specifically, based on the DOE calculation results, construct surrogate models of understeer and suspension C respectively, i.e., the second surrogate model, and verify the accuracy of the surrogate models. If the accuracy of the surrogate model is greater than or equal to 95%, it passes the accuracy verification. If the accuracy of the surrogate model is less than 95%, return to step 4 to increase the DOE sampling points or change the surrogate model type until the surrogate model meets the accuracy requirements.

[0070] Step 7: Based on the surrogate models in step 3 and step 6, conduct a balance optimization based on the vehicle understeer performance and cost, and verify the optimization results through the simulation model.

[0071] Specifically, taking the inertia parameters, the change amounts of tire parameters, and the cornering compliances of the front and rear axles as preset constraint conditions, and taking the understeer degree and cost as optimization objectives, the optimization weight ratio can be 1:1. The understeer degree is expected to be large, and the cost is expected to be small. An adaptive optimization algorithm is used to balance and optimize performance and cost, obtain the lateral stiffness of the vehicle under the best balance condition of handling and stability performance and cost, and verify it in the simulation model. If it passes in the simulation model, the optimization is completed, and this lateral stiffness is the lateral stiffness target of the vehicle. If the requirements of handling and stability performance and cost are not met, adjust the parameters of the optimization algorithm or the type of surrogate model and restart the balance optimization.

[0072] It can be seen that the present invention proposes a method for decomposing the lateral stiffness target of a vehicle. After obtaining the relationship that the understeer degree of the whole vehicle increases with the increase of the vehicle's lateral stiffness through data analysis, on the premise of meeting the handling and stability performance of the vehicle, the cost is minimized, thereby realizing the decomposition of the vehicle's lateral stiffness target. The present invention greatly improves the optimization efficiency, has a low optimization cost, and can quickly realize the forward design of the vehicle's lateral stiffness target by jointly optimizing the understeer degree and cost of the whole vehicle and the surrogate model technology.

[0073] It should be noted that for the method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the embodiments of the present invention are not limited by the described action sequences, because according to the embodiments of the present invention, some steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily essential to the embodiments of the present invention.

[0074] As Figure 7 shown, the present invention discloses a device for determining the lateral stiffness of a vehicle. The vehicle is equipped with at least one sensor and components. The device includes: A first acquisition module 901, configured to acquire a first surrogate model; the first surrogate model is a surrogate model for predicting the lateral stiffness and weight of the vehicle; A second acquisition module 902, configured to acquire a second surrogate model; the second surrogate model is a surrogate model for predicting the understeer degree and cornering compliance of the vehicle; An optimization target determination module 903, configured to determine the optimization target of the vehicle; A third acquisition module 904, configured to acquire at least one set of first vehicle parameters of the vehicle; A first candidate module 905, configured to input the first vehicle parameters into the first surrogate model to obtain a candidate lateral stiffness and a candidate weight; A second candidate module 906, configured to input the first vehicle parameters into the second surrogate model to obtain a candidate understeer degree and a candidate cornering compliance; A determination module 907, configured to use the candidate lateral stiffness as the target lateral stiffness when the candidate weight and the candidate understeer degree meet the optimization objective and the candidate cornering compliance is within a preset constraint condition.

[0075] In an embodiment of the present invention, the apparatus further includes: a first construction module, configured to: Create a finite element analysis model for the vehicle's lateral stiffness; the finite element analysis model for the vehicle's lateral stiffness is created according to the vehicle body, interior and exterior trims, and the rigidly connected chassis, and is used to simulate the lateral stiffness and weight of the vehicle; Obtain second vehicle parameters corresponding to a preset first design variable; the second vehicle parameters at least include the structural parameters of the key areas of the front axle and the rear axle of the vehicle body. Input the second vehicle parameters into the finite element analysis model for the vehicle's lateral stiffness to obtain the simulated lateral stiffness and simulated weight obtained by simulating the finite element analysis model for the vehicle's lateral stiffness. Sample the second vehicle parameters, the simulated lateral stiffness, and the simulated weight to obtain a first sampling space and a second sampling space; the data in the first sampling space and the second sampling space do not overlap. Train the first surrogate model to be trained according to the first sampling space; the first surrogate model is constructed according to the first design variable. Use the second sampling space to verify the trained first surrogate model, and obtain the trained first surrogate model after the verification passes.

[0076] In an embodiment of the present invention, the first construction module is further configured to: Input the second vehicle parameters in the second sampling space into the trained first surrogate model to obtain the verified lateral stiffness and verified weight output by the trained first surrogate model. Determine the relative error between the simulated lateral stiffness and the simulated weight in the second sampling space and the verified lateral stiffness and verified weight. When the relative error meets the preset accuracy requirement, it is determined that the verification passes and the trained first surrogate model is obtained. When the relative error does not meet the preset accuracy requirement, adjust the model parameters of the first surrogate model, obtain the new second vehicle parameters corresponding to the first design variable, and return to execute the step of inputting the second vehicle parameters into the vehicle lateral stiffness finite element analysis model to obtain the simulated lateral stiffness and simulated weight obtained by simulating the vehicle lateral stiffness finite element analysis model, and / or, adjust the model type of the first surrogate model and return to execute the step of training the first surrogate model to be trained according to the first sampling space.

[0077] In an embodiment of the present invention, the device further includes: a second construction module, configured to: Create a vehicle multi-body simulation analysis model and a suspension multi-body simulation analysis model; the vehicle multi-body simulation analysis model is created according to the front suspension, rear suspension, steering system, powertrain, tires, body, and stabilizer bar of the vehicle, and is used to simulate the understeer degree of the vehicle, and the suspension multi-body simulation analysis model is created according to the rear suspension, steering system, test bench, and stabilizer bar of the vehicle, and is used to simulate the cornering compliance of the vehicle; Obtain the third vehicle parameters corresponding to the preset second design variable; the third vehicle parameters at least include the 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 spring stiffness; Input the third vehicle parameters into the multi-body simulation analysis model and the suspension multi-body simulation analysis model to obtain the simulated understeer degree and simulated cornering compliance obtained by simulating the multi-body simulation analysis model and the suspension multi-body simulation analysis model; Sample the third vehicle parameters, the simulated understeer degree, and the simulated cornering compliance to obtain a third sampling space and a fourth sampling space; the data in the third sampling space and the fourth sampling space do not overlap; Train the second surrogate model to be trained according to the third sampling space; the second surrogate model is constructed according to the second design variable; Verify the trained second surrogate model using the fourth sampling space, and obtain the trained second surrogate model after passing the verification.

[0078] In an embodiment of the present invention, the second construction module is further configured to: Input the third vehicle parameters in the fourth sampling space into the trained second surrogate model to obtain the verified understeer degree and verified cornering compliance output by the trained second surrogate model; Determine the relative error between the simulated lateral stiffness and simulated weight in the fourth sampling space and the verified understeer degree and verified cornering compliance; When the relative error meets the preset accuracy requirement, it is determined that the verification is passed, and the trained second surrogate model is obtained. When the relative error does not meet the preset accuracy requirement, adjust the model parameters of the second surrogate model, obtain the new third vehicle parameters corresponding to the second design variables, and return to execute 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 simulation understeer degree and the simulation cornering compliance obtained by simulation of the multi-body simulation analysis model and the suspension multi-body simulation analysis model, and / or adjust the model type of the second surrogate model and return to execute the step of training the second surrogate model to be trained according to the third sampling space.

[0079] In an embodiment of the present invention, the understeer degree is affected by the lateral stiffness, and the lateral stiffness is equivalently replaced by the torsional stiffness in the Z direction of the bushing according to a preset equivalent formula, and the preset equivalent formula is a formula for converting the lateral stiffness and the torsional stiffness in the Z direction of the bushing.

[0080] In an embodiment of the present invention, some of the first vehicle parameters are within preset constraint conditions, and at least the inertia parameters and the tire parameters are included in the some of the first vehicle parameters.

[0081] In an embodiment of the present invention, the device further includes: a simulation verification module, configured to: Input the first vehicle parameters corresponding to the target lateral stiffness into the vehicle lateral stiffness finite element analysis model to obtain the target simulation lateral stiffness and the target simulation weight obtained by simulation of the vehicle lateral stiffness finite element analysis model; Input the first vehicle parameters corresponding to the target lateral stiffness into the multi-body simulation analysis model and the suspension multi-body simulation analysis model to obtain the target simulation understeer degree and the target simulation cornering compliance obtained by simulation of the multi-body simulation analysis model and the suspension multi-body simulation analysis model; When the target simulation weight and the target simulation understeer degree meet the optimization target and the target simulation cornering compliance is within the preset constraint conditions, it is determined that the target lateral stiffness passes the simulation verification.

[0082] In an embodiment of the present invention, the simulation verification module is further configured to: When the target simulation weight and the target simulation understeer degree do not meet the optimization target, or the target simulation cornering compliance is within the preset constraint conditions, adjust the model parameters of the first surrogate model and the second surrogate model, retrain the first surrogate model and the second surrogate model, or retrain the first surrogate model and the second surrogate model after adjusting the model types of the first surrogate model and the second surrogate model.

[0083] In an embodiment of the present invention, during the vehicle R & D process, a first surrogate model and a second surrogate model can be obtained. Among them, the first surrogate model is a surrogate model for predicting the lateral stiffness and weight of a vehicle, and the second surrogate model is a surrogate model for predicting the understeer degree and cornering compliance of a vehicle. After determining the optimization objective of the vehicle, at least one set of first vehicle parameters of the vehicle can be obtained. Among them, some of the first vehicle parameters have corresponding value ranges. Then, the first vehicle parameters can be input into the first surrogate model to obtain a candidate lateral stiffness and a candidate weight, and the first vehicle parameters can be input into the second surrogate model to obtain a candidate understeer degree and a candidate cornering compliance. When the candidate weight and the candidate understeer degree meet the optimization objective and the candidate cornering compliance is within the preset constraint conditions, the candidate lateral stiffness can be used as the target lateral stiffness. In the embodiment of the present invention, during the vehicle R & D process, the lateral stiffness of the vehicle can be determined according to the first surrogate model and the second surrogate model without obtaining it through simulation, without consuming a large amount of time, with high calculation efficiency, and shortening the vehicle R & D cycle.

[0084] In addition, the weight and understeer degree corresponding to the vehicle stiffness meet the optimization objective, and the candidate cornering compliance is within the preset constraint conditions. Thus, by jointly optimizing the understeer degree and weight of the whole vehicle and the surrogate model technology, the optimization efficiency of the vehicle is greatly improved, the optimization cost is low, and the forward design of the vehicle lateral stiffness target can be quickly realized.

[0085] For the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple. For related parts, refer to the partial description of the method embodiment. An embodiment of the present invention also provides an electronic device, as Figure 8 shown, including a processor 1001, a device interface 1002, a memory 1003, and a bus 1004; The memory 1003 is used to store a computer program; The processor 1001 is used to implement the above steps when executing the program stored on the memory 1003.

[0086] The bus mentioned in the above terminal can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into an address bus, a data bus, a control bus, etc. For the convenience of representation, only a thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0087] The memory may include a Random Access Memory (RAM), or may also include non-volatile memory, such as at least one disk memory. Optionally, the memory may also be at least one storage device located far from the aforementioned processor.

[0088] The aforementioned processor may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it may 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, discrete hardware components.

[0089] The present invention also provides a storage medium. When the instructions in the storage medium are executed by the processor of an electronic device, the electronic device can execute the vehicle lateral stiffness determination method in the foregoing embodiments.

[0090] For the device embodiments, since they are basically similar to the method embodiments, the description is relatively simple. For the relevant parts, refer to the partial description of the method embodiments.

[0091] The algorithms and displays provided herein are not inherently related to any particular computer, virtual device, or other equipment. According to the above description, the structures required to construct such devices are obvious. In addition, the present invention is not directed to any specific programming language. It should be understood that the content of the present invention described herein can be implemented using various programming languages, and the description of the specific language above is to disclose the best implementation manner of the present invention.

[0092] In the specification provided herein, a large number of specific details are set forth. However, it can be understood that the embodiments of the present invention can be practiced without these specific details. In some instances, well-known methods, structures, and technologies are not shown in detail so as not to obscure the understanding of this specification.

[0093] Similarly, it should be understood that, in order to streamline the present invention and assist in understanding one or more of the various inventive aspects, in the foregoing description of the exemplary embodiments of the present invention, the various features of the present invention are sometimes grouped together into a single embodiment, figure, or description thereof. However, the disclosed method should not be construed as reflecting an intention that the claimed invention requires more features than are expressly recited in each claim. Rather, as reflected in the following claims, the inventive aspects lie in less than all the features of the single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate embodiment of the present invention.

[0094] Those skilled in the art will appreciate that the modules in the devices in the embodiments can be adaptively changed and disposed in one or more devices different from the embodiments. The modules or units or components in the embodiments can be combined into a module or unit or component, and in addition, they can be divided into multiple sub-modules or sub-units or sub-components. Except that at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all the features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all the processes or units of any method or device so disclosed. Unless otherwise expressly stated, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) can be replaced by an alternative feature that provides the same, equivalent, or similar purpose.

[0095] The various component embodiments of the present invention can be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art should understand that a microprocessor or a digital signal processor (DSP) can be used in practice to implement some or all of the functions of some or all of the components in the sorting device according to the present invention. The present invention can also be implemented as a device or apparatus program for performing part or all of the methods described herein. Such a program for implementing the present invention can be stored on a computer-readable medium, or can be in the form of one or more signals. Such signals can be downloaded from an Internet website, or provided on a carrier signal, or in any other form.

[0096] It should be noted that the above embodiments are illustrative of the present invention rather than restrictive thereof, and those skilled in the art can design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in the claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The present invention can be implemented by means of hardware including several different elements and by means of a suitably programmed computer. In a unit claim enumerating several devices, several of these devices can be embodied by the same item of hardware. The use of the words first, second, and third, etc. does not denote any order. These words can be interpreted as names.

[0097] Those skilled in the art can 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 foregoing method embodiments and will not be elaborated herein.

[0098] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

[0099] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed by the present invention and shall be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

[0100] It should be noted that in the embodiments of the present application, all processes related to obtaining various data are carried out on the premise of complying with the corresponding data protection regulations and policies of the country where the location is located and with the authorization given by the owner of the corresponding device.

Claims

1. A method for determining the lateral stiffness of a vehicle, characterized in that: The method comprises: Acquire 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 parameter into the first proxy model to obtain a candidate lateral stiffness and a candidate weight; Inputting the first vehicle parameter into the second proxy model to obtain a candidate understeer degree and a candidate lateral compliance degree; 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 acquiring the first proxy model, the method further includes: Creating a finite element analysis model of the lateral stiffness of the whole vehicle; the finite element analysis model of the lateral stiffness of the whole vehicle is created based on the body, interior and exterior trim, and rigidly connected chassis of the vehicle, and is used to simulate and obtain the lateral stiffness and weight of the vehicle; Obtaining a second vehicle parameter corresponding to the preset first design variable; the second vehicle parameter at least includes structural parameters of key areas of the front axle and rear axle of the vehicle body; Inputting the second vehicle parameter into the whole vehicle lateral stiffness finite element analysis model to obtain a simulated lateral stiffness and a 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 of 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 The trained first proxy model is verified by using the second sampling space, and the trained first proxy model is obtained after the verification is passed, including: Inputting the second vehicle parameter of the second sampling space into the trained first proxy model to obtain the verified lateral stiffness and verified weight output by the trained first proxy model; Determining a relative error between the simulated lateral stiffness and the simulated weight and the verified lateral stiffness and the verified weight of the second sampling space; When the relative error meets the preset accuracy requirement, determining the first proxy model as the first proxy model that 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 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 is returned, and / or, the model type of the first proxy model is adjusted and the step of training the first proxy model to be trained according to the first sampling space is returned.

4. The method according to claim 1, characterized in that: Before acquiring the second proxy model, the method further includes: Creating a whole vehicle multi-body simulation analysis model and a suspension multi-body simulation analysis model; the whole vehicle multi-body simulation analysis model is created based on the front suspension, rear suspension, steering system, powertrain, tires, body and stabilizer bar of the vehicle, and is used to simulate the understeer of the vehicle; the suspension multi-body simulation analysis model is created based on the rear suspension, steering system, test bench and stabilizer bar of the vehicle, and is used to simulate the lateral compliance of the vehicle; Obtaining a third vehicle parameter corresponding to the preset second design variable; the third vehicle parameter at least includes a chassis elastic parameter of the vehicle; the chassis elastic parameter at least includes a vehicle body stiffness, a tire parameter, an inertia parameter, a four-wheel alignment parameter, a bushing stiffness, and a shock absorbing 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 degree and a simulated corner 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 degree and the simulated lateral compliance to obtain a third sampling space and a fourth sampling space; 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 variables; 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 The trained second proxy model is verified by using the fourth sampling space, and the trained second proxy model is obtained after the verification is passed, including: Inputting the third vehicle parameter of the fourth sampling space into the trained second proxy model to obtain the verified understeer degree and the verified lateral compliance output by the trained second proxy model; Determining a relative error 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 the second proxy model as the second proxy model that 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 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 cornering flexibility simulated by the multi-body simulation analysis model and the suspension multi-body simulation analysis model is returned, and / or, the model type of the second proxy model is adjusted and the step of training the second proxy model to be trained according to the third sampling space is returned.

6. The method according to claim 4, characterized in that 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.

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 parameters and the tire parameters.

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 the preset constraint, after taking 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 whole 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 whole 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 cornering 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 cornering compliance is within preset constraints, the 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 the 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, used to acquire a first proxy model; the first proxy model is a proxy model used to predict the lateral stiffness and weight of the vehicle; A second acquisition module, used for acquiring 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, used to determine the optimization target of the vehicle; A third acquisition module, used for acquiring at least one set of first vehicle parameters of the vehicle; 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; A second candidate module, configured to input the first vehicle parameter into the second proxy model to obtain a candidate understeer and a candidate lateral compliance; A determination module is used 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 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 the 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 as claimed in claim 11.

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