Multidisciplinary optimization design methods and storage media for suspension systems

Abstract

This invention relates to a multidisciplinary optimization design method for suspension systems, comprising the following steps: problem identification and P-chart analysis; establishing a vehicle road noise performance simulation analysis model and a driving performance C-characteristic simulation analysis model; determining the design variables that need to be optimized in the suspension system and parametrically processing each design variable; performing DOE sampling calculations on each design variable; extracting DOE sample points and calculation results to construct an approximate model of vehicle road noise performance and an approximate model of driving performance C-characteristics; performing multidisciplinary optimization of vehicle road noise performance and driving performance C-characteristics to obtain an optimization scheme; and verifying the optimization scheme by calling the vehicle road noise performance simulation analysis model and the driving performance C-characteristics simulation analysis model. This invention also proposes a storage medium. This invention can improve the efficiency of suspension system optimization design, enabling simultaneous optimization design of the suspension system based on both vehicle road noise performance and driving performance C-characteristics.

Description

Technical Field

[0001] This invention relates to multidisciplinary design optimization, specifically to a multidisciplinary optimization design method and storage medium for a suspension system. Background Technology

[0002] With the rapid development of automotive technology, NVH (noise, vibration, and harshness) performance and handling performance, as important attributes of vehicle quality, are increasingly valued by consumers. Driven by the trend of new energy vehicles, in-vehicle noise is no longer masked by engine noise, making road noise particularly prominent. Designing reasonable suspension parameters to improve road noise performance while also considering other performance aspects such as driving dynamics is of great significance to product development.

[0003] Road noise refers to the audible noise generated inside a vehicle during driving due to the interaction between the tires and the road surface, including collisions and friction. From a mechanistic perspective, road noise is generally divided into low-frequency structural road noise and high-frequency aerodynamic road noise. High-frequency aerodynamic road noise refers to the mid-to-high frequency noise generated by the interaction between the tires and the road surface that is transmitted into the vehicle through the air. Low-frequency structural road noise refers to the mid-to-low frequency noise radiated into the vehicle interior by the mid-to-low frequency noise caused by the road surface's excitation of the tires, transmitted through the suspension system to the vehicle body, resulting in vehicle vibration.

[0004] The C-characteristics of driving performance, also known as suspension elastic kinematics, are the changes in wheel alignment parameters caused by the forces and moments between the tires and the road surface. The C-characteristics of driving performance have a significant impact on the overall vehicle handling, steering, and ride comfort during individual impacts, and are mainly determined by factors such as suspension geometry, elastic element stiffness, and local stiffness at the contact points.

[0005] Low-frequency structural road noise and driving performance C-characteristics are both related to suspension system parameters, and the requirements for these parameters are often conflicting. Currently, the industry mainly adopts a "serial design" approach in product development, which involves analyzing and optimizing low-frequency structural road noise and driving performance C-characteristics separately. This results in repeated iterative verification of the suspension system design and makes it difficult to resolve existing performance conflicts.

[0006] For example, patent application CN202011581735.X proposes a method for diagnosing and optimizing in-vehicle structural noise. This method determines the engine torque frequency domain load and the wheel center frequency domain load, performs engine noise simulation calculations and road noise simulation calculations respectively. When engine noise or road noise exceeds design standards, it determines the main transmission path of in-vehicle noise based on the noise contribution of the transmission path. For each main transmission path, it performs chassis modal analysis and bushing vibration isolation rate analysis on the transmission force. When the transfer function of the main transmission path exceeds a set transfer function value, it also performs attachment point dynamic stiffness analysis and body panel noise contribution and modal analysis on each main transmission path. Based on the analysis results, it determines the structural optimization object and performs structural optimization. By determining the main transmission path through transmission path contribution analysis, and then analyzing the transmission force and transfer function of the main transmission path, it identifies the root cause of the noise exceeding the standard, and determines the object and method of structural optimization based on this cause. Based on noise transmission path analysis, body panel contribution, and modal analysis, it optimizes the chassis structure, bushing stiffness, attachment point stiffness, and main transmission panels. However, chassis structure and bushing stiffness not only affect low-frequency structural noise, but also have a significant impact on driving performance (C-characteristics). Considering only low-frequency structural noise may lead to a deterioration in other performance aspects such as driving performance. Summary of the Invention

[0007] The purpose of this invention is to propose a multidisciplinary optimization design method and storage medium for suspension systems, so as to improve the efficiency of suspension system optimization design and enable simultaneous optimization design of suspension systems based on vehicle road noise performance and driving performance C characteristics.

[0008] The multidisciplinary optimization design method for suspension systems described in this invention includes the following steps:

[0009] S1: Problem identification and P control chart analysis, based on the vehicle's road noise performance and driving performance characteristics, determine the relevant parameters for the multidisciplinary optimization design of the suspension system;

[0010] S2: Establish a simulation analysis model for the road noise performance of the whole vehicle and perform simulation analysis of the road noise performance of the whole vehicle; establish a simulation analysis model for the C-characteristics of driving performance and perform simulation analysis of the C-characteristics of driving performance; when establishing the simulation analysis model for the road noise performance of the whole vehicle, dynamic stiffness is used as the characterization of the stiffness of the elastic element; when establishing the simulation analysis model for the C-characteristics of driving performance, force-displacement curve is used as the characterization of the stiffness of the elastic element.

[0011] S3: Based on the relevant parameters determined in S1, determine the design variables that need to be optimized for the suspension system, and perform parameterization on each design variable; the parameterization on each design variable includes the following steps: introduce a dimensionless scaling factor into both the force-displacement curve and the dynamic stiffness, and use the dimensionless scaling factor as the design variable;

[0012] S4: Perform DOE sampling calculations for each design variable;

[0013] S5: Extract DOE sample points and calculation results, and construct an approximate model of the vehicle road noise performance and an approximate model of the driving performance C characteristics that meet the accuracy requirements;

[0014] S6: Based on the two approximate models in S5, perform multidisciplinary optimization of the vehicle's road noise performance and driving performance C characteristics, and obtain the optimization scheme;

[0015] S7: Call the vehicle road noise performance simulation analysis model and driving performance C characteristic simulation analysis model in S2 to verify the optimization scheme obtained in S6.

[0016] Optionally, the relevant parameters in S1 are as follows:

[0017] The input signals include: road spectrum under vehicle road noise conditions, load state and hard point corresponding state under driving performance C characteristic conditions;

[0018] The control factors include: stiffness and damping of each bushing in the front suspension, stiffness and damping of each bushing in the rear suspension, elastic modulus of the front suspension spring, and elastic modulus of the rear suspension spring.

[0019] Noise factors include: bushing stiffness fluctuation deviation and hard spot position deviation;

[0020] The output signals include: sound pressure level at the driver's right ear, suspension longitudinal flexibility, suspension lateral flexibility, lateral force deformation steering coefficient, and self-centering torque steering coefficient.

[0021] Optionally, the establishment of the whole vehicle road noise performance simulation analysis model and the performance simulation analysis of the whole vehicle road noise performance include the following steps: collecting original road spectrum PSD data, processing the data and converting it into whole vehicle road excitation as input signal, establishing a whole vehicle NVH finite element model as the simulation object, the whole vehicle NVH finite element model is composed of modal tire model, chassis model, acoustic cavity and TB interior body model, and using the sound pressure level at the driver's right ear as output signal to perform whole vehicle road noise performance simulation analysis.

[0022] Optionally, the establishment of the driving performance C-characteristic simulation analysis model and the driving performance C-characteristic simulation analysis include the following steps: using the load state of the actual vehicle test as the input signal for the driving performance C-characteristic simulation analysis, building a multi-body model of the suspension system in Adams software as the simulation object, and using the suspension longitudinal flexibility, suspension lateral flexibility, lateral force deformation steering coefficient and self-centering torque steering coefficient as output signals to perform driving performance C-characteristic simulation analysis.

[0023] Optionally, the design variables determined in S3 include: the stiffness and damping of each bushing of the front suspension, the stiffness and damping of each bushing of the rear suspension, the elastic modulus of the front suspension spring, and the elastic modulus of the rear suspension spring.

[0024] Optionally, before performing DOE sampling calculations for each design variable, the following steps are also included: building an integrated development workflow for the vehicle road noise performance and driving performance C characteristics on the Optimus platform.

[0025] Optionally, the DOE sampling calculation for each design variable includes the following steps: setting the design space and output signal constraints for each design variable; using the optimal Latin hypersquare experimental design method to perform DOE sampling calculation for each design variable; based on the DOE sampling calculation results, performing correlation analysis on the design variables and the performance of interest; selecting significant design variables that have a relatively significant correlation with the performance of interest; and then performing DOE sampling calculation for the significant design variables.

[0026] Optionally, S5 includes the following steps: extracting DOE sample points and calculation results, constructing an approximate model of vehicle road noise performance and an approximate model of driving performance C characteristics. When the accuracy of the approximate model of vehicle road noise performance or the approximate model of driving performance C characteristics is less than 95%, return to S4 to add DOE sampling points or change the approximate model type until the accuracy of the approximate model of vehicle road noise performance and the approximate model of driving performance C characteristics are both greater than or equal to 95%.

[0027] Optionally, S6 includes the following steps: based on the two approximate models in S5, with the constraint that "each output signal of the driving performance C characteristic is in the target range" and the objective that "minimizes the sound pressure level at the driver's right ear", a global search algorithm is used to perform multi-objective optimization to obtain a set of optimal suspension system matching parameters as the optimization scheme.

[0028] Optionally, S7 includes the following steps: calling the vehicle road noise performance simulation analysis model and the driving performance C characteristic simulation analysis model in S2 to verify the optimization scheme obtained in S6. If the requirements of vehicle road noise performance and driving performance C characteristic are not met, return to S4 to recalculate the DOE sampling of each design variable.

[0029] Optionally, the elastic element is a bushing.

[0030] The present invention also proposes a storage medium storing a computer program that, when executed by a processor, can implement the multidisciplinary optimization design method for suspension systems described in any of the above claims.

[0031] This invention proposes a multidisciplinary optimization design method for suspension systems based on vehicle road noise performance and driving performance characteristics (C-characteristics). It replaces simulation analysis with approximate modeling techniques, enabling joint optimization under multiple operating conditions and significantly improving optimization efficiency. Compared to traditional "serial design" development methods, this invention fully considers the performance conflicts between vehicle road noise performance and driving performance characteristics (C-characteristics). By deterministically optimizing multiple design variables, it can simultaneously satisfy multiple performance requirements, significantly shortening the design cycle. This method is low-cost, highly efficient, and enables forward design of the suspension system in the early stages of automotive product development, reducing the risk of longer development cycles and increased development costs due to later design changes. This invention improves the efficiency of suspension system optimization design, enabling simultaneous optimization of the suspension system based on both vehicle road noise performance and driving performance characteristics (C-characteristics). Attached Figure Description

[0032] Figure 1 This is a flowchart of the multidisciplinary optimization design method for the suspension system described in the specific implementation embodiment;

[0033] Figure 2 A schematic diagram illustrating the parameterization of bushing stiffness under different working conditions;

[0034] Figure 3 An integrated development workflow for the vehicle's road noise performance and driving performance characteristics (C-characteristics);

[0035] Figure 4 Residual plot for approximate model;

[0036] Figure 5 Contribution analysis chart;

[0037] Figure 6 To optimize the rendering. Detailed Implementation

[0038] The embodiments of the present invention will be described below 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 illustrating the present invention and not for limiting the scope of protection of the present invention.

[0039] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0040] like Figure 1 The multidisciplinary optimization design method for a suspension system shown includes the following steps:

[0041] S1: Problem identification and P control chart analysis, based on the vehicle's road noise performance and driving performance characteristics, determine the relevant parameters for the multidisciplinary optimization design of the suspension system;

[0042] S2: Establish a simulation analysis model for the road noise performance of the whole vehicle and perform simulation analysis of the road noise performance of the whole vehicle; establish a simulation analysis model for the C-characteristics of driving performance and perform simulation analysis of the C-characteristics of driving performance; when establishing the simulation analysis model for the road noise performance of the whole vehicle, dynamic stiffness is used as the characterization of the stiffness of the elastic element; when establishing the simulation analysis model for the C-characteristics of driving performance, force-displacement curve is used as the characterization of the stiffness of the elastic element.

[0043] S3: Based on the relevant parameters determined in S1, determine the design variables that need to be optimized for the suspension system, and perform parameterization on each design variable; the parameterization on each design variable includes the following steps: introduce a dimensionless scaling factor into both the force-displacement curve and the dynamic stiffness, and use the dimensionless scaling factor as the design variable;

[0044] S4: Perform DOE sampling calculations for each design variable;

[0045] S5: Extract DOE sample points and calculation results, and construct an approximate model of the vehicle road noise performance and an approximate model of the driving performance C characteristics that meet the accuracy requirements;

[0046] S6: Based on the two approximate models in S5, perform multidisciplinary optimization of the vehicle's road noise performance and driving performance C characteristics, and obtain the optimization scheme;

[0047] S7: Call the vehicle road noise performance simulation analysis model and driving performance C characteristic simulation analysis model in S2 to verify the optimization scheme obtained in S6.

[0048] By adopting the above technical solution, the optimization efficiency is greatly improved by replacing simulation analysis with approximate model technology and performing joint optimization under multiple working conditions. By introducing dimensionless scaling factors into both the force-displacement curve and the dynamic stiffness, and using the dimensionless scaling factors as design variables, the possibility and convenience of joint simulation based on the vehicle's road noise performance and driving performance C characteristics are provided. Multiple performance requirements can be met simultaneously, and the design cycle is greatly shortened. This method is low-cost, highly efficient, and can realize the forward design of the suspension system in the early stage of automotive product development, reducing the risk of longer R&D cycles and increased R&D costs caused by later design changes.

[0049] In some embodiments, the relevant parameters in S1 are as follows:

[0050] The input signals include: road spectrum under vehicle road noise conditions, load state and hard point corresponding state under driving performance C characteristic conditions;

[0051] The control factors include: stiffness and damping of each bushing in the front suspension, stiffness and damping of each bushing in the rear suspension, elastic modulus of the front suspension spring, and elastic modulus of the rear suspension spring.

[0052] Noise factors include: bushing stiffness fluctuation deviation and hard spot position deviation;

[0053] The output signals include: sound pressure level at the driver's right ear, suspension longitudinal flexibility, suspension lateral flexibility, lateral force deformation steering coefficient, and self-centering torque steering coefficient.

[0054] In some embodiments, the establishment of a vehicle road noise performance simulation analysis model and the performance simulation analysis of the vehicle road noise include the following steps: collecting original road spectrum PSD data, processing the data and converting it into a vehicle road surface excitation as an input signal, establishing a vehicle NVH finite element model as the simulation object, the vehicle NVH finite element model consisting of a modal tire model, a chassis model, a acoustic cavity and a TB interior body model, and using the sound pressure level at the driver's right ear as the output signal to perform a vehicle road noise performance simulation analysis.

[0055] In some embodiments, the establishment of a driving performance C-characteristic simulation analysis model and the driving performance C-characteristic simulation analysis include the following steps: using the load state of the actual vehicle test as the input signal for the driving performance C-characteristic simulation analysis, building a multi-body model of the suspension system in Adams software as the simulation object, and using the suspension longitudinal flexibility, suspension lateral flexibility, lateral force deformation steering coefficient, and self-centering torque steering coefficient as output signals to perform driving performance C-characteristic simulation analysis.

[0056] In some embodiments, the design variables determined in S3 include: the stiffness and damping of each bushing of the front suspension, the stiffness and damping of each bushing of the rear suspension, the elastic modulus of the front suspension spring, and the elastic modulus of the rear suspension spring.

[0057] In some embodiments, before performing DOE sampling calculations for each design variable, the following steps are included: building an integrated development workflow for the vehicle road noise performance and driving performance C characteristics on the Optimus platform.

[0058] In some embodiments, the DOE sampling calculation of each design variable includes the following steps: setting the design space and output signal constraints for each design variable; using the optimal Latin hypersquare experimental design method to perform DOE sampling calculation for each design variable; based on the DOE sampling calculation results, performing correlation analysis on the design variables and the performance of interest; screening out the significant design variables that have a relatively significant correlation with the performance of interest; and then performing DOE sampling calculation for the significant design variables.

[0059] In some embodiments, S5 includes the following steps: extracting DOE sample points and calculation results, constructing an approximate model of vehicle road noise performance and an approximate model of driving performance C characteristics. When the accuracy of the approximate model of vehicle road noise performance or the approximate model of driving performance C characteristics is less than 95%, return to S4 to add DOE sampling points or change the approximate model type until the accuracy of the approximate model of vehicle road noise performance and the approximate model of driving performance C characteristics are both greater than or equal to 95%.

[0060] In some embodiments, S6 includes the following steps: based on the two approximate models in S5, with the constraint that "each output signal of the driving performance C characteristic is in the target range" and the objective of "minimizing the sound pressure level at the driver's right ear", a global search algorithm is used to perform multi-objective optimization to obtain a set of optimal suspension system matching parameters as the optimization scheme.

[0061] In some embodiments, S7 includes the following steps: calling the vehicle road noise performance simulation analysis model and the driving performance C characteristic simulation analysis model in S2 to verify the optimization scheme obtained in S6. If the requirements of vehicle road noise performance and driving performance C characteristic are not met, return to S4 to recalculate the DOE sampling of each design variable.

[0062] In some embodiments, the elastic element is a bushing.

[0063] As a specific embodiment, a multidisciplinary optimization design method for a suspension system includes the following steps:

[0064] S1: Problem identification and P control chart analysis, based on the vehicle's road noise performance and driving performance characteristics, determine the relevant parameters for the multidisciplinary optimization design of the suspension system;

[0065] Specifically, numerous factors influence the overall vehicle road noise performance and driving performance (C-characteristics), such as: time effects, external ambient temperature, interactions between various vehicle systems (suspension system changes, tire pressure changes), system errors, and user usage. Through P-control chart analysis, the relevant parameters for the multidisciplinary optimization design of the suspension system based on the overall vehicle road noise performance and driving performance (C-characteristics) were determined as follows: Input signals include: road spectrum under overall vehicle road noise conditions, load state and hard point corresponding state under driving performance (C-characteristics) conditions; Control factors include: stiffness and damping of each bushing in the front suspension, stiffness and damping of each bushing in the rear suspension, elastic modulus of the front suspension spring, and elastic modulus of the rear suspension spring; Noise factors include: bushing stiffness fluctuation deviation and hard point position deviation; Output signals include: sound pressure level at the driver's right ear, suspension longitudinal flexibility, suspension lateral flexibility, lateral force deformation steering coefficient, and self-centering torque steering coefficient.

[0066] S2: Establish a simulation analysis model for the road noise performance of the whole vehicle and perform simulation analysis of the road noise performance of the whole vehicle; establish a simulation analysis model for the C-characteristics of driving performance and perform simulation analysis of the C-characteristics of driving performance; when establishing the simulation analysis model for the road noise performance of the whole vehicle, dynamic stiffness is used as the characterization of the stiffness of the elastic element; when establishing the simulation analysis model for the C-characteristics of driving performance, force-displacement curve is used as the characterization of the stiffness of the elastic element.

[0067] Specifically, the establishment of a vehicle road noise performance simulation analysis model and the subsequent simulation analysis include the following steps: collecting original road spectrum PSD data, processing the data to convert it into a vehicle road surface excitation as the input signal, establishing a vehicle NVH finite element model as the simulation object, the vehicle NVH finite element model consisting of a modal tire model, chassis model, acoustic cavity, and TB interior body model, using the sound pressure level at the driver's right ear as the output signal, and performing a vehicle road noise performance simulation analysis. The establishment of a driving performance C-characteristic simulation analysis model and the subsequent simulation analysis include the following steps: using the load state of a real vehicle test as the input signal for the driving performance C-characteristic simulation analysis, building a multi-body model of the suspension system in Adams software as the simulation object, examining the changes in wheel center position and positioning parameters under the action of braking force or driving force, lateral force in the same or opposite direction, and self-centering force in the same or opposite direction, and using the suspension longitudinal flexibility, suspension lateral flexibility, lateral force deformation steering coefficient, and self-centering torque steering coefficient as output signals, and performing a driving performance C-characteristic simulation analysis.

[0068] S3: Based on the relevant parameters determined in S1, determine the design variables that need to be optimized for the suspension system, and perform parameterization on each design variable; the parameterization on each design variable includes the following steps: introduce a dimensionless scaling factor into both the force-displacement curve and the dynamic stiffness, and use the dimensionless scaling factor as the design variable;

[0069] Specifically, based on the relevant parameters in S1, the design variables for the multidisciplinary optimization design of the suspension system are determined, and each design variable is parameterized. The design variables include: the stiffness and damping of each bushing in the front suspension, the stiffness and damping of each bushing in the rear suspension, the elastic modulus of the front suspension spring, and the elastic modulus of the rear suspension spring. Since the representation of bushing stiffness differs under vehicle road noise conditions and driving performance C-characteristic conditions, in the vehicle road noise simulation analysis model, the stiffness value at a certain frequency is used as the representation of bushing stiffness, i.e., dynamic stiffness is used as the representation of bushing stiffness; in the driving performance C-characteristic simulation analysis model, the force-displacement curve is used as the representation of bushing stiffness. For example... Figure 2As shown, in order to achieve the purpose of joint simulation of vehicle road noise and driving performance C characteristics, a dimensionless scaling factor φ is introduced as a design variable in both the force-displacement curve and the dynamic stiffness. The force-displacement curve is φF / L; the dynamic stiffness is φK; thereby realizing the synchronous update of bushing stiffness under the vehicle road noise analysis condition and the driving performance C characteristic analysis condition.

[0070] In practical implementation, the parameterization of each design variable includes the following steps: The model files included in the vehicle road noise performance simulation analysis and the driving performance C characteristic simulation analysis can be parameterized through the Excel and text parameterization interfaces of Optimus software, so as to realize the automatic update of all design variables, thereby driving the automatic update of the model files of the above-mentioned vehicle road noise performance simulation analysis and driving performance C characteristic simulation analysis.

[0071] S4: Build an integrated development workflow for the vehicle road noise performance and driving performance C characteristics on the Optimus platform, and perform DOE sampling calculations for various design variables;

[0072] The DOE sampling calculation for each design variable includes the following steps: setting the design space and output signal constraints for each design variable; using the optimal Latin hypersquare experimental design method to perform DOE sampling calculation for each design variable; based on the DOE sampling calculation results, performing correlation analysis on the design variables and the performance of interest; selecting significant design variables that have a relatively significant correlation with the performance of interest; and then performing DOE sampling calculation for the significant design variables to achieve the purpose of dimensionality reduction.

[0073] Under the Optimus integration and optimization platform, build such as Figure 3 The integrated development workflow for the vehicle's road noise performance and driving performance C characteristics is shown. It integrates the parametric model and design variables from S3, sets the design space for the design variables, and uses batch processing to call the corresponding solver software to extract the response information from the result file to obtain the output signal. This integrated development workflow improves work efficiency.

[0074] Specifically, for the overall vehicle road noise condition, Nastran software is used to analyze the overall vehicle road noise condition, extract key information from the pch file, perform sound pressure level conversion, output the sound pressure level at the driver's right ear in the critical frequency band, and set constraints at the same time.

[0075] Specifically, the C-characteristic analysis of driving performance involves calling Adams software to perform the analysis, extracting key information from the result file using Matlab, performing corresponding data processing, and outputting the suspension's longitudinal flexibility, lateral flexibility, lateral force deformation steering coefficient, and self-centering torque steering coefficient, while also setting constraint conditions.

[0076] S5: Extract DOE sample points and calculation results, and construct an approximate model of the vehicle road noise performance and an approximate model of the driving performance C characteristics that meet the accuracy requirements;

[0077] Specifically, DOE sample points and calculation results are extracted to construct approximate models of vehicle road noise performance and driving performance C-characteristics. When the accuracy of either the approximate model or the approximate model is below 95%, the process returns to step S4 to add more DOE sample points or change the approximate model type until the accuracy of both the approximate models is greater than or equal to 95%.

[0078] In practical implementation, based on the DOE sample points and calculation results after dimensionality reduction in S4, an approximate model of the vehicle's road noise performance and an approximate model of its driving performance C-characteristics are constructed. When the accuracy of the approximate model is greater than or equal to 95%, S6 is executed; when the accuracy of the approximate model is less than 95%, S4 is returned, and the sample points from the DOE sampling are increased or the approximate model type is changed until the accuracy of the approximate model is greater than or equal to 95%. In this embodiment, the accuracy of the approximate model is verified based on residual analysis. In addition to the DOE samples used to construct the approximate model, 10 to 20 more sets of samples are calculated, and their relative errors with the predicted values ​​of the approximate model are calculated. The relative error is required to be within 5%. The residual plot of the approximate model is shown below. Figure 4 As shown, the results indicate that the accuracy of the approximate models is all above 95%, which meets the accuracy requirements.

[0079] S6: Based on the two approximate models in S5, perform multidisciplinary optimization of the vehicle's road noise performance and driving performance C characteristics, and obtain the optimization scheme;

[0080] In practical implementation, contribution analysis is used to identify design variables that contribute significantly to the response, especially those that contribute to multiple responses simultaneously. Subsequent optimization will focus on these design variables. The contribution analysis results are as follows: Figure 5 As shown. Based on the two approximate models in S5, with the constraint that "each output signal of the driving performance C characteristic is within the target range" and the objective of "minimizing the sound pressure level at the driver's right ear", a global search algorithm is used for multi-objective optimization to obtain a set of optimal suspension system matching parameters as the optimization scheme.

[0081] S7: Call the vehicle road noise performance simulation analysis model and driving performance C characteristic simulation analysis model in S2 to verify the optimization scheme obtained in S6.

[0082] Specifically, the vehicle road noise performance simulation analysis model and driving performance C characteristic simulation analysis model in S2 are called to verify the optimization scheme obtained in S6. If the requirements of vehicle road noise performance and driving performance C characteristic are not met, the process returns to S4 to recalculate the DOE sampling of each design variable.

[0083] In practical implementation, the optimized solution in S6 is substituted into the vehicle road noise performance simulation analysis model and the driving performance C characteristic simulation analysis model in S2 to obtain the actual results corresponding to the optimal solution. If the deviation between the actual results and the optimization results of the approximate model is less than 5%, and the output signals of the driving performance C characteristic are within the target range, the optimization solution is considered qualified; if the optimization solution is unqualified, the process returns to S4 to add DOE sample points near the optimal solution or adjust the design space of the design variables, and then performs multidisciplinary optimization of the vehicle road noise and driving performance C characteristics again. The final optimization result of this embodiment is as follows: Figure 6 As shown, while ensuring the C-characteristic requirements of driving performance, the road noise response in the critical frequency band (75-140Hz) is reduced by 1.4dB(A).

[0084] This invention proposes a multidisciplinary optimization design method for suspension systems based on vehicle road noise performance and driving performance characteristics (C-characteristics). It replaces simulation analysis with approximate modeling techniques, enabling joint optimization under multiple operating conditions and significantly improving optimization efficiency. Compared to traditional "serial design" development methods, this invention fully considers the performance conflicts between vehicle road noise performance and driving performance characteristics (C-characteristics). By deterministically optimizing multiple design variables, it can simultaneously satisfy multiple performance requirements, significantly shortening the design cycle. This method is low-cost, highly efficient, and enables forward design of the suspension system in the early stages of automotive product development, reducing the risk of longer development cycles and increased development costs due to later design changes. This invention improves the efficiency of suspension system optimization design, enabling simultaneous optimization of the suspension system based on both vehicle road noise performance and driving performance characteristics (C-characteristics).

[0085] The multidisciplinary optimization design method for suspension systems proposed in this invention has been verified in the development of a certain vehicle model. Conventional serial optimization methods require three iterations to find the optimal solution that simultaneously satisfies two performance requirements. However, by applying the multidisciplinary optimization design method for suspension systems proposed in this invention, the optimal solution can be found in one iteration, reducing the optimization time by two-thirds. Furthermore, conventional optimization methods may fail to find the optimal solution.

[0086] This invention establishes a multidisciplinary optimization design method for suspension systems that jointly optimizes vehicle road noise and driving performance C-characteristics. It effectively overcomes the problem of repeated iterative verification and low efficiency caused by the "serial design" mode, and can be used for performance balance optimization of vehicle road noise and driving performance C-characteristics.

[0087] In some embodiments, the present invention also provides a computer including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the multidisciplinary optimization design method for suspension systems described in any of the preceding embodiments.

[0088] The memory includes at least one type of storage medium, such as flash memory, hard disk, multimedia card, card-type memory (e.g., SD or DX memory), magnetic storage, magnetic disk, optical disk, etc. In some embodiments, the memory can be an internal storage unit of a computer, such as the computer's hard disk. In other embodiments, the memory can be an external storage device, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. Furthermore, the memory can include both internal and external storage units of the computer. The memory can be used not only to store application software and various types of data installed on the computer, but also to temporarily store data that has been output or will be output.

[0089] In some embodiments, the processor may be an electronic control unit (ECU, also known as a vehicle computer), a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip, used to run program code stored in memory or process data, such as executing access restriction programs.

[0090] In some embodiments, the present invention also provides a storage medium storing a computer program that, when executed by a processor, is capable of implementing the multidisciplinary optimization design method for suspension systems described in any of the preceding embodiments.

[0091] Those skilled in the art will understand that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequential list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can mean any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0092] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0093] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0094] The above embodiments are merely preferred embodiments provided to fully illustrate the present invention, and the scope of protection of the present invention is not limited thereto. Equivalent substitutions or modifications made by those skilled in the art based on the present invention are all within the scope of protection of the present invention. In the description of this specification, the reference to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., means that a specific feature, structure, material, or characteristic associated with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any suitable manner in one or more embodiments or examples. Furthermore, those skilled in the art can combine and integrate the different embodiments or examples described in this specification.

Claims

1. A multidisciplinary optimization design method for a suspension system, characterized in that, Includes the following steps: S1: Problem identification and P control chart analysis, based on the vehicle's road noise performance and driving performance characteristics, determine the relevant parameters for the multidisciplinary optimization design of the suspension system; S2: Establish a simulation analysis model for the road noise performance of the whole vehicle and perform simulation analysis of the road noise performance of the whole vehicle; establish a simulation analysis model for the C-characteristics of driving performance and perform simulation analysis of the C-characteristics of driving performance. When establishing the simulation analysis model of the whole vehicle road noise performance, dynamic stiffness is used as the characterization of the stiffness of the elastic element, and when establishing the simulation analysis model of the driving performance C characteristic, the force-displacement curve is used as the characterization of the stiffness of the elastic element. S3: Based on the relevant parameters determined in S1, determine the design variables that the suspension system needs to be optimized, and perform parameterization on each design variable; The parameterization of each design variable includes the following steps: introducing a dimensionless scaling factor into both the force-displacement curve and the dynamic stiffness, and using the dimensionless scaling factor as the design variable; S4: Perform DOE sampling calculations for each design variable; S5: Extract DOE sample points and calculation results, and construct an approximate model of the vehicle road noise performance and an approximate model of the driving performance C characteristics that meet the accuracy requirements; S6: Based on the two approximate models in S5, perform multidisciplinary optimization of the vehicle's road noise performance and driving performance C characteristics, and obtain the optimization scheme; S7: Call the vehicle road noise performance simulation analysis model and driving performance C characteristic simulation analysis model in S2 to verify the optimization scheme obtained in S6.

2. The multidisciplinary optimization design method for suspension systems according to claim 1, characterized in that, The relevant parameters in S1 are as follows: The input signals include: road spectrum under vehicle road noise conditions, load state and hard point corresponding state under driving performance C characteristic conditions; The control factors include: stiffness and damping of each bushing in the front suspension, stiffness and damping of each bushing in the rear suspension, elastic modulus of the front suspension spring, and elastic modulus of the rear suspension spring. Noise factors include: bushing stiffness fluctuation deviation and hard spot position deviation; The output signals include: sound pressure level at the driver's right ear, suspension longitudinal flexibility, suspension lateral flexibility, lateral force deformation steering coefficient, and self-centering torque steering coefficient.

3. The multidisciplinary optimization design method for suspension systems according to claim 1, characterized in that, The steps for establishing a vehicle road noise performance simulation analysis model and performing vehicle road noise performance simulation analysis are as follows: collecting original road spectrum PSD data, processing the data, converting it into vehicle road surface excitation as input signal, establishing a vehicle NVH finite element model as the simulation object, the vehicle NVH finite element model consists of a modal tire model, chassis model, acoustic cavity and TB interior body model, and using the sound pressure level at the driver's right ear as the output signal to perform vehicle road noise performance simulation analysis.

4. The multidisciplinary optimization design method for suspension systems according to claim 1, characterized in that, The establishment of the driving performance C-characteristic simulation analysis model and the driving performance C-characteristic simulation analysis include the following steps: using the load state of the actual vehicle test as the input signal for the driving performance C-characteristic simulation analysis, building a multi-body model of the suspension system in Adams software as the simulation object, and using the suspension longitudinal flexibility, suspension lateral flexibility, lateral force deformation steering coefficient and self-centering torque steering coefficient as the output signals to perform driving performance C-characteristic simulation analysis.

5. The multidisciplinary optimization design method for suspension systems according to claim 1, characterized in that, The design variables determined in S3 include: stiffness and damping of each bushing of the front suspension, stiffness and damping of each bushing of the rear suspension, elastic modulus of the front suspension spring, and elastic modulus of the rear suspension spring.

6. The multidisciplinary optimization design method for suspension systems according to claim 1, characterized in that, Before performing DOE sampling calculations for each design variable, the following steps are also included: building an integrated development workflow for the vehicle road noise performance and driving performance C characteristics on the Optimus platform.

7. The multidisciplinary optimization design method for suspension systems according to claim 1, characterized in that, The DOE sampling calculation for each design variable includes the following steps: setting the design space and output signal constraints for each design variable; using the optimal Latin hypersquare experimental design method to carry out DOE sampling calculation for each design variable; based on the DOE sampling calculation results, performing correlation analysis on the design variables and the performance of interest; screening out the significant design variables that have a relatively significant correlation with the performance of interest; and then carrying out DOE sampling calculation for the significant design variables.

8. The multidisciplinary optimization design method for suspension systems according to claim 1, characterized in that, S5 includes the following steps: extracting DOE sample points and calculation results, constructing an approximate model of vehicle road noise performance and an approximate model of driving performance C characteristics. When the accuracy of the approximate model of vehicle road noise performance or the approximate model of driving performance C characteristics is less than 95%, return to S4 to add DOE sampling points or change the approximate model type until the accuracy of the approximate model of vehicle road noise performance and the approximate model of driving performance C characteristics are both greater than or equal to 95%.

9. The multidisciplinary optimization design method for suspension systems according to claim 1, characterized in that, S6 includes the following steps: Based on the two approximate models in S5, with the constraint that "each output signal of the driving performance C characteristic is in the target range" and the objective that "minimizes the sound pressure level at the driver's right ear", a global search algorithm is used to perform multi-objective optimization to obtain a set of optimal suspension system matching parameters as the optimization scheme.

10. The multidisciplinary optimization design method for suspension systems according to claim 1, characterized in that, S7 includes the following steps: calling the whole vehicle road noise performance simulation analysis model and the driving performance C characteristic simulation analysis model in S2 to verify the optimization scheme obtained in S6. If the requirements of the whole vehicle road noise performance and driving performance C characteristic are not met, return to S4 to recalculate the DOE sampling of each design variable.

11. The multidisciplinary optimization design method for suspension systems according to claim 1, characterized in that, The elastic element is a bushing.

12. A storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program is able to implement the multidisciplinary optimization design method for suspension systems as described in any one of claims 1-11.

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