Automobile leaf spring motion stroke parameter optimization matching method and system

Abstract

The application discloses a kind of automobile leaf spring motion stroke parameter optimization matching method and system.The method includes: to the basic car is carried out road spectrum collection, respectively obtains the load Fx, Fy, Dz of vehicle at wheel center in x, y, z direction, and the torque Mx, My, Mz around x, y, z axis;Establish the multi-body dynamics model of vehicle ADAMS, exert wheel center force Fx, Fy, Dz, Mx, My, Mz at wheel center, and solve the load of leaf spring at leaf spring seat;Stress field is solved to the fatigue analysis of leaf spring, and fatigue analysis is carried out to leaf spring;The maximum stroke of leaf spring is equivalent to the optimization of limit block hard point coordinate, the z direction coordinate of limit block hard point is parameterized, and the optimal leaf spring stroke parameter is determined based on Isight platform.The application can solve the problem of long optimization iteration cycle caused by artificial repeated trial and error in the prior art, and can speed up product development.

Description

Technical Field

[0001] This invention relates to the field of automotive technology, and in particular to a method and system for optimizing and matching the motion stroke parameters of automotive leaf springs. Background Technology

[0002] Leaf springs have advantages such as low cost, simple structure, and easy maintenance, and are therefore widely used in various vehicle models as a major component of the suspension system. With the improvement of design and manufacturing processes and the driving force of automotive lightweighting, leaf springs are usually composed of several steel leaf springs with variable cross-sections. Extreme lightweighting results in a small fatigue life margin for leaf springs, so the fatigue life of leaf springs has become an important indicator for evaluating their performance. During the design phase, the parameters of leaf springs need to be optimized to obtain the maximum allowable travel that will not cause the leaf spring to break during road testing. This allows the vehicle to have better NVH performance and avoids the body or frame fractures connected by the limit block (a larger travel reduces the load transmitted from the leaf spring to the limit block, thus reducing the impact excitation received by the body system, improving NVH comfort performance, and increasing the durability of the limit block connection system).

[0003] In engineering practice, there are three main methods for assessing the fatigue life of leaf springs:

[0004] The first method is bench testing, which uses sinusoidal loads and takes the number of cycles the leaf spring can withstand under sinusoidal excitation as a life indicator. This method is simple and easy to implement, but because bench fatigue testing ignores the bending moment and longitudinal force around the axle caused by the vehicle starting or braking during road testing, and longitudinal force and bending moment have a significant impact on the fatigue durability of the vehicle leaf spring, the bench test results have a poor correlation with the vehicle road test results.

[0005] The second method is the enhanced road durability test. This method relies entirely on road testing. After the leaf spring assembly is actually installed on the vehicle, it is driven on a reinforced road surface until it breaks. The lifespan is judged based on the mileage. Its advantage is that the conclusion is relatively accurate, but the verification cycle is long and the test cost is expensive. At the same time, multiple leaf spring assemblies must be tested to reflect the lifespan distribution. A change in design parameters requires driving hundreds of thousands of kilometers on the test track, which takes tens of days or even months, making it very costly.

[0006] The third method is to perform CAE simulation analysis on the leaf spring based on road spectrum loads to predict its fatigue life. This is an efficient and feasible approach. However, the fatigue life simulation of the leaf spring is significantly related to the CAE simulation modeling method and the manufacturing process of the leaf spring. Furthermore, since the fatigue life of the leaf spring is closely related to its maximum permissible travel in the suspension system, if the initial design of the maximum travel is unreasonable, the leaf spring life will not meet the requirements. This necessitates readjusting the leaf spring travel parameters and recalculating the fatigue life, resulting in lengthy manual trial-and-error adjustments that fail to yield optimal travel parameters. Therefore, optimizing and matching the leaf spring motion travel parameters to avoid long optimization iteration cycles due to repeated manual trial and error is a technical problem that needs to be solved by those skilled in the art. Summary of the Invention

[0007] To address this issue, one embodiment of the present invention proposes a method for optimizing and matching the motion stroke parameters of automotive leaf springs, thereby solving the problem of long optimization iteration cycles caused by repeated manual trial and error, and accelerating product development.

[0008] A method for optimizing and matching the motion stroke parameters of an automotive leaf spring according to an embodiment of the present invention includes:

[0009] Road spectrum data was collected from the base vehicle to obtain the loads Fx, Fy, and Dz of the whole vehicle in the x, y, and z directions at the wheel center, as well as the torques Mx, My, and Mz around the x, y, and z axes.

[0010] Establish a multibody dynamics model of the whole vehicle using ADAMS, apply wheel center forces Fx, Fy, Dz, Mx, My, and Mz at the wheel center, and solve for the load of the leaf spring at the leaf spring seat;

[0011] The stress field for fatigue analysis of leaf springs is solved, and fatigue analysis of leaf springs is performed.

[0012] The maximum stroke of the leaf spring is equivalently transformed into the optimization of the coordinates of the hard point of the limit block. The z-coordinate of the hard point of the limit block is parameterized, and the optimal leaf spring stroke parameters are determined based on the Isight platform.

[0013] According to embodiments of the present invention, a method for optimizing and matching the motion stroke parameters of automotive leaf springs is provided. First, road spectrum data is collected from a base vehicle to obtain the loads Fx, Fy, and Dz in the x, y, and z directions at the wheel center, and the torques Mx, My, and Mz around the x, y, and z axes. Then, an ADAMS multibody dynamics model of the vehicle is established, and corresponding loads are applied. Finally, the stress field for leaf spring fatigue analysis is solved, and leaf spring fatigue analysis is performed. The maximum stroke of the leaf spring is equivalently transformed into the optimization of the coordinates of the limiting block hard point. The z-axis coordinate of the limiting block hard point is parameterized, and the optimal leaf spring stroke parameters are determined based on the Isight platform. This method obtains leaf spring stroke parameters that satisfy fatigue life, achieving optimized matching of leaf spring motion stroke parameters. This avoids the problem of long optimization iteration cycles caused by repeated manual trial and error, accelerates product development, and significantly reduces costs and shortens the cycle compared to traditional physical testing.

[0014] Furthermore, the automotive leaf spring motion stroke parameter optimization matching method provided in the embodiments of the present invention also has the following technical features:

[0015] Furthermore, the specific steps of collecting road spectrum data from the base vehicle to obtain the loads Fx, Fy, and Dz in the x, y, and z directions at the wheel center, and the torques Mx, My, and Mz around the x, y, and z axes include:

[0016] The two loads Fx and Fy and the three torques Mx, My and Mz exerted on the tires by the road surface are measured by a six-component force sensor when the vehicle is driving on the actual road durability test surface. The vertical displacement load Dz of the wheel center is measured by a displacement sensor. Fx, Fy, Dz, Mx, My and Mz are used as the road surface excitation for the vehicle.

[0017] Furthermore, the steps of establishing a full-vehicle ADAMS multibody dynamics model, applying wheel center forces Fx, Fy, Dz, Mx, My, and Mz at the wheel center, and solving for the loads on the leaf springs at the leaf spring seats specifically include:

[0018] Based on the load spectrum, the actual connection relationship between various components of the vehicle is collected. The modal synthesis method is used, and the finite element software is used to calculate the frame neutral file containing modal information. The frame neutral file is used to complete the flexibility processing of the frame, thereby establishing the whole vehicle ADAMS multibody dynamics model. The model includes at least the front suspension, rear suspension, steering, frame, and body system.

[0019] K&C characteristic comparison analysis was performed on the front and rear suspensions, and the simulation results were compared with the experimental results. The main comparison types included the vertical stiffness, lateral stiffness and longitudinal stiffness of the front suspension, and the vertical stiffness, lateral stiffness and longitudinal stiffness of the rear suspension. Finally, based on the good comparison, wheel center forces Fx, Fy, Dz, Mx, My and Mz were applied at the wheel center, the response of the entire system was solved, and the load of the leaf spring at the leaf spring seat was output.

[0020] Furthermore, the specific steps for solving the stress field and performing fatigue analysis of the leaf spring include:

[0021] Import the leaf spring CAD model in the free clamping state after parameter confirmation into the finite element preprocessing software Hypermesh. Switch the Hypermesh software to the ABAQUS module, then mesh the leaf spring CAD model with 3-5mm mesh based on first-order shell elements, and assign the varying material thickness attribute to the leaf spring mesh nodes to realize the simulation of the variable cross section of the leaf spring. Set the elastic modulus, Poisson's ratio and density as three material properties, then make all the leaf springs into a set, and establish a general contact based on the set. The general contact is used to automatically simulate the actual contact behavior that may occur between the leaf springs during actual operation.

[0022] Data feature analysis was performed on the load of the multibody dynamics output of the unoptimized leaf spring. The analysis objects included the stroke Dzmax of the leaf spring seat from the free clamping state to the road test equilibrium state, and the maximum force and torque Fxmax, Fymax, Mxmax, Mymax, and Mzmax in the x, y, and z directions of the leaf spring seat, and the rationality of the load was ensured.

[0023] In the finite element model of the leaf spring, the front and rear lugs of the leaf spring are constrained. The rotational degree of freedom of the front lug is released, and the rotational degree of freedom and the translational degree of freedom along the length of the leaf spring are released of the rear lug. Load moments Fxmax, Fymax, Mxmax, Mymax, and Mzmax are applied to the leaf spring seat respectively, resulting in six finite element models under different loads. These models are then submitted to the ABAQUS module for solution calculation to obtain the stress field of the leaf spring under each load. The stress field is then correlated and superimposed with the leaf spring seat load calculated in the multibody dynamics model of the whole vehicle. The specific formula for calculating the stress field is as follows:

[0024]

[0025] Where σ(t) is the stress field expression, F k (t) represents the time-varying load at the leaf spring seat input by the multibody dynamics software, k = x / y / Mx / My / Mz, D z (t) represents the vertical displacement of the leaf spring seat as a function of time, σ Dzmaxσ represents the stress caused by applying a load Dzmax to a leaf spring. Fxmax σ represents the stress caused by applying a load Fxmax to a leaf spring. Fymax σ represents the stress caused by applying a load Fymax to a leaf spring. Mxmax σ represents the stress caused by applying a load Mxmax to a leaf spring. Mymax σ represents the stress caused by applying a load Mymax to a leaf spring. Mzmax This represents the stress caused by applying a load Mzmax to the leaf spring;

[0026] Then, the fatigue life of the leaf spring was analyzed in the fatigue software FEMFAT based on the SN method.

[0027] Furthermore, the steps of converting the maximum stroke of the leaf spring into an equivalent optimization of the coordinates of the hard point of the limiting block, parameterizing the z-coordinate of the hard point of the limiting block, and determining the optimal leaf spring stroke parameters based on the Isight platform specifically include:

[0028] Using the objective function that the fatigue damage of the leaf spring is less than a preset value, the maximum stroke of the leaf spring that satisfies the fatigue performance of the leaf spring is found based on the Isight platform and a multi-island genetic algorithm. That is, the z-coordinate of the hard point of the limit block is defined as the variable DVz, and then the optimal maximum stroke of the leaf spring is determined based on the Isight optimization algorithm.

[0029] Another embodiment of the present invention proposes an optimization matching system for the motion stroke parameters of automotive leaf springs to solve the problem of long optimization iteration cycles caused by repeated manual trial and error, thereby accelerating product development.

[0030] An automotive leaf spring motion stroke parameter optimization and matching system according to an embodiment of the present invention includes:

[0031] The data acquisition module is used to collect road spectrum data from the base vehicle, and obtain the loads Fx, Fy, and Dz of the whole vehicle in the x, y, and z directions at the wheel center, as well as the torques Mx, My, and Mz around the x, y, and z axes.

[0032] An application module is established to create a multibody dynamics model of the whole vehicle using ADAMS, apply wheel center forces Fx, Fy, Dz, Mx, My, and Mz at the wheel center, and solve for the load on the leaf spring at the leaf spring seat.

[0033] The solution and analysis module is used to solve the stress field for leaf spring fatigue analysis and to perform leaf spring fatigue analysis.

[0034] The optimization analysis module is used to convert the maximum stroke of the leaf spring into the equivalent optimization of the coordinates of the hard point of the limit block, parameterize the z-coordinate of the hard point of the limit block, and determine the optimal leaf spring stroke parameters based on the Isight platform.

[0035] The automotive leaf spring motion stroke parameter optimization and matching system provided in this embodiment of the invention first collects road spectrum data on the base vehicle to obtain the loads Fx, Fy, and Dz in the x, y, and z directions at the wheel center, as well as the torques Mx, My, and Mz around the x, y, and z axes. Then, it establishes a multibody dynamics model of the vehicle using ADAMS and applies the corresponding loads. Finally, it solves the stress field for leaf spring fatigue analysis and performs leaf spring fatigue analysis, converting the maximum stroke of the leaf spring into an equivalent optimization of the coordinates of the limiting block hard point. The z-axis coordinate of the limiting block hard point is parameterized, and the optimal leaf spring stroke parameters are determined based on the Isight platform. This system can obtain leaf spring stroke parameters that meet fatigue life requirements, achieving optimized matching of leaf spring motion stroke parameters. It avoids the problem of long optimization iteration cycles caused by repeated manual trial and error, accelerates product development, and significantly reduces costs and shortens the cycle compared to traditional physical testing.

[0036] Furthermore, the automotive leaf spring motion stroke parameter optimization and matching system provided according to embodiments of the present invention also has the following technical features:

[0037] Furthermore, the acquisition module is specifically used for:

[0038] The two loads Fx and Fy and the three torques Mx, My and Mz exerted on the tires by the road surface are measured by a six-component force sensor when the vehicle is driving on the actual road durability test surface. The vertical displacement load Dz of the wheel center is measured by a displacement sensor. Fx, Fy, Dz, Mx, My and Mz are used as the road surface excitation for the vehicle.

[0039] Furthermore, the establishment and application module is specifically used for:

[0040] Based on the load spectrum, the actual connection relationship between various components of the vehicle is collected. The modal synthesis method is used, and the finite element software is used to calculate the frame neutral file containing modal information. The frame neutral file is used to complete the flexibility processing of the frame, thereby establishing the whole vehicle ADAMS multibody dynamics model. The model includes at least the front suspension, rear suspension, steering, frame, and body system.

[0041] K&C characteristic comparison analysis was performed on the front and rear suspensions, and the simulation results were compared with the experimental results. The main comparison types included the vertical stiffness, lateral stiffness and longitudinal stiffness of the front suspension, and the vertical stiffness, lateral stiffness and longitudinal stiffness of the rear suspension. Finally, based on the good comparison, wheel center forces Fx, Fy, Dz, Mx, My and Mz were applied at the wheel center, the response of the entire system was solved, and the load of the leaf spring at the leaf spring seat was output.

[0042] Furthermore, the solution analysis module is specifically used for:

[0043] Import the leaf spring CAD model in the free clamping state after parameter confirmation into the finite element preprocessing software Hypermesh. Switch the Hypermesh software to the ABAQUS module, then mesh the leaf spring CAD model with 3-5mm mesh based on first-order shell elements, and assign the varying material thickness attribute to the leaf spring mesh nodes to realize the simulation of the variable cross section of the leaf spring. Set the elastic modulus, Poisson's ratio and density as three material properties, then make all the leaf springs into a set, and establish a general contact based on the set. The general contact is used to automatically simulate the actual contact behavior that may occur between the leaf springs during actual operation.

[0044] Data feature analysis was performed on the load of the multibody dynamics output of the unoptimized leaf spring. The analysis objects included the stroke Dzmax of the leaf spring seat from the free clamping state to the road test equilibrium state, and the maximum force and torque Fxmax, Fymax, Mxmax, Mymax, and Mzmax in the x, y, and z directions of the leaf spring seat, and the rationality of the load was ensured.

[0045] In the finite element model of the leaf spring, the front and rear lugs of the leaf spring are constrained. The rotational degree of freedom of the front lug is released, and the rotational degree of freedom and the translational degree of freedom along the length of the leaf spring are released of the rear lug. Load moments Fxmax, Fymax, Mxmax, Mymax, and Mzmax are applied to the leaf spring seat respectively, resulting in six finite element models under different loads. These models are then submitted to the ABAQUS module for solution calculation to obtain the stress field of the leaf spring under each load. The stress field is then correlated and superimposed with the leaf spring seat load calculated in the multibody dynamics model of the whole vehicle. The specific formula for calculating the stress field is as follows:

[0046]

[0047] Where σ(t) is the stress field expression, F k (t) represents the time-varying load at the leaf spring seat input by the multibody dynamics software, k = x / y / Mx / My / Mz, D z (t) represents the vertical displacement of the leaf spring seat as a function of time, σ Dzmax σ represents the stress caused by applying a load Dzmax to a leaf spring. Fxmax σ represents the stress caused by applying a load Fxmax to a leaf spring. Fymax σ represents the stress caused by applying a load Fymax to a leaf spring. Mxmax σ represents the stress caused by applying a load Mxmax to a leaf spring. Mymax σ represents the stress caused by applying a load Mymax to a leaf spring. Mzmax This represents the stress caused by applying a load Mzmax to the leaf spring;

[0048] Then, the fatigue life of the leaf spring was analyzed in the fatigue software FEMFAT based on the SN method.

[0049] Furthermore, the optimization analysis module is specifically used for:

[0050] Using the objective function that the fatigue damage of the leaf spring is less than a preset value, the maximum stroke of the leaf spring that satisfies the fatigue performance of the leaf spring is found based on the Isight platform and a multi-island genetic algorithm. That is, the z-coordinate of the hard point of the limit block is defined as the variable DVz, and then the optimal maximum stroke of the leaf spring is determined based on the Isight optimization algorithm.

[0051] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by means of embodiments of the invention. Attached Figure Description

[0052] The above and / or additional aspects and advantages of the embodiments of the present invention will become apparent and readily understood from the description of the embodiments in conjunction with the following drawings, wherein:

[0053] Figure 1 This is a flowchart of a method for optimizing and matching the motion stroke parameters of an automotive leaf spring according to an embodiment of the present invention;

[0054] Figure 2 This is an exemplary schematic diagram of a finite element model;

[0055] Figure 3 This is a schematic diagram of the leaf spring's stroke.

[0056] Figure 4 This is a schematic diagram of the Isight optimization process;

[0057] Figure 5 This is a structural block diagram of an automotive leaf spring motion stroke parameter optimization and matching system according to an embodiment of the present invention. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0059] Please see Figure 1 An embodiment of the present invention proposes a method for optimizing and matching the motion stroke parameters of an automotive leaf spring, comprising steps S101 to S104:

[0060] S101 collects road spectrum data from the base vehicle to obtain the loads Fx, Fy, and Dz in the x, y, and z directions at the wheel center, as well as the torques Mx, My, and Mz around the x, y, and z axes.

[0061] Obtaining the fatigue load of leaf springs during product development is crucial for fatigue simulation and prediction. To achieve this, it's essential to first obtain the external excitation of the entire vehicle during road durability testing. Therefore, by collecting road load spectrum data from the base vehicle (or competitor vehicles) of the newly developed model, we obtain load spectrum data for the base vehicle under development on various rough roads, such as Belgian roads and washboard roads, including displacement, force, and torque. Specifically, a six-component force sensor measures the two loads Fx and Fy and three torques Mx, My, and Mz exerted on the tires by the road surface when the vehicle is traveling on the actual road durability test surface. A displacement sensor measures the vertical displacement load Dz at the wheel center. Fx, Fy, Dz, Mx, My, and Mz are used as the road excitation for the vehicle. A multi-body dynamics model of the newly developed vehicle is then used to obtain the loads Fx, Fy, Dz, Mx, My, and Mz on the leaf spring seat, which are then used for predicting the fatigue durability life of the leaf springs.

[0062] S102, establish the ADAMS multibody dynamics model of the whole vehicle, apply wheel center forces Fx, Fy, Dz, Mx, My, Mz at the wheel center, and solve the load of the leaf spring at the leaf spring seat.

[0063] Specifically, based on the load spectrum, the actual connection relationships between various vehicle components are collected. Modal synthesis is applied, and finite element software is used to calculate a neutral frame file containing modal information. This neutral frame file is then used to perform flexibility processing on the frame, thereby establishing a full-vehicle ADAMS multibody dynamics model. The model includes at least the front suspension, rear suspension, steering, frame, and body systems. For example, the front suspension is a double wishbone structure, and the rear suspension is an electric drive axle leaf spring suspension. The front suspension system consists of components such as the upper control arm, lower control arm, steering knuckle, stabilizer bar, limit block, coil spring, and shock absorber. The rear suspension system includes the electric drive solid axle, leaf springs, and shock absorbers. The suspension system is connected to the frame via bushings, and the stiffness data of these bushings are all derived from actual test data. Since the chassis will bend and torsion under the excitation of uneven road surface, it is necessary to make the chassis flexible. In this embodiment, the modal synthesis method is used to calculate the chassis neutral file containing modal information using finite element software. The chassis flexibility is completed using the modal neutral file, thereby establishing a rigid-flexible coupled multibody dynamics model of the whole vehicle.

[0064] To verify the accuracy of the front and rear suspension models, a K&C characteristic comparison analysis was performed on the front and rear suspensions, and the simulation results were compared with the experimental results. The main comparison types included the vertical stiffness, lateral stiffness, and longitudinal stiffness of the front suspension, and the vertical stiffness, lateral stiffness, and longitudinal stiffness of the rear suspension. Finally, based on the good comparison, wheel center forces Fx, Fy, Dz, Mx, My, and Mz were applied at the wheel center, the response of the entire system was solved, and the load of the leaf spring at the leaf spring seat was output.

[0065] S103 solves the stress field for leaf spring fatigue analysis and performs leaf spring fatigue analysis.

[0066] Specifically, step S103 includes:

[0067] Import the leaf spring CAD model in its free clamping state, after parameter confirmation, into the finite element preprocessing software Hypermesh. Switch Hypermesh to the ABAQUS module, then mesh the leaf spring CAD model using first-order shell elements with a 3-5mm mesh. Assign varying material thickness attributes to the leaf spring mesh nodes to simulate the variable cross-section of the leaf spring. An example of the completed finite element model is shown below. Figure 2 As shown, three material properties are set: elastic modulus, Poisson's ratio, and density. The numerical values ​​of these material properties are as follows: density is 7.85 × 10⁻⁶. -9 tons / mm 3 The elastic modulus E is 2.1 × 10⁻⁶. 5 MPa, Poisson's ratio is 0.3; then all the reeds are made into a set, and a general contact is established based on the set. The general contact is used to automatically simulate the actual contact behavior that may occur between the reeds during the actual operation of the leaf spring;

[0068] Data feature analysis was performed on the load of the multibody dynamics output of the unoptimized leaf spring. The analysis objects included the stroke Dzmax of the leaf spring seat from the free clamping state to the road test equilibrium state, and the maximum force and torque Fxmax, Fymax, Mxmax, Mymax, and Mzmax in the x, y, and z directions of the leaf spring seat, and the rationality of the load was ensured.

[0069] In the finite element model of the leaf spring, the front and rear lugs of the leaf spring are constrained. The rotational degree of freedom of the front lug is released, and the rotational degree of freedom and the translational degree of freedom along the length of the leaf spring are released of the rear lug. Load moments Fxmax, Fymax, Mxmax, Mymax, and Mzmax are applied to the leaf spring seat respectively, resulting in six finite element models under different loads. These models are then submitted to the ABAQUS module for solution calculation to obtain the stress fields (Fx.odb, Fy.odb, Dz.odb, Mx.odb, My.odb, Mz.odb) of the leaf spring under the above loads. The stress fields are then correlated and superimposed with the leaf spring seat loads calculated in the multibody dynamics model of the whole vehicle. The specific calculation formula for the stress field is as follows:

[0070]

[0071] Where σ(t) is the stress field expression, F k (t) represents the time-varying load at the leaf spring seat input by the multibody dynamics software, k = x / y / Mx / My / Mz, D z (t) represents the vertical displacement of the leaf spring seat as a function of time, σ Dzmax σ represents the stress caused by applying a load Dzmax to a leaf spring. Fxmax σ represents the stress caused by applying a load Fxmax to a leaf spring. Fymax σ represents the stress caused by applying a load Fymax to a leaf spring. Mxmax σ represents the stress caused by applying a load Mxmax to a leaf spring. Mymax σ represents the stress caused by applying a load Mymax to a leaf spring. Mzmax This represents the stress caused by applying a load Mzmax to the leaf spring. This represents the stress caused by a unit force in the Fx direction, multiplied by the load F output by the multibody dynamics. x (t), the result is the actual load F in the x-direction. x The stress increment caused by (t) and so on, the superposition of the stress fields in the six directions is the actual stress of the leaf spring.

[0072] Then, in the fatigue software FEMFAT, the fatigue life of the leaf spring is analyzed based on the SN method. For example, with the material surface correction factor set to 1.1, the fatigue damage of a certain leaf spring is 1.704, which exceeds 1, indicating that the leaf spring will break after completing the road durability test. Therefore, it is necessary to optimize the maximum stroke of the leaf spring (the initial maximum stroke is, for example, 213 mm).

[0073] S104 transforms the maximum stroke of the leaf spring into an equivalent optimization of the coordinates of the hard point of the limit block, parameterizes the z-coordinate of the hard point of the limit block, and determines the optimal leaf spring stroke parameters based on the Isight platform.

[0074] like Figure 3 As shown, the smaller the distance between the leaf spring and the limiting block, the smaller the maximum stroke of the leaf spring, the earlier the leaf spring contacts the limiting block, and the more directly the load transferred from the axle to the leaf spring is transferred to the frame. This results in lower maximum stress and a longer fatigue life for the leaf spring. Conversely, a larger maximum stroke increases the maximum stress on the leaf spring under random loads, shortening its fatigue life. In the initial parameter design stage, the leaf spring's strength is assessed solely based on material mechanics strength theory, and the stroke parameters are designed based only on layout boundaries and experience. An unreasonable stroke parameter design will lead to fatigue fracture of the leaf spring. According to the fatigue simulation calculation results from the previous step, the maximum stroke of the leaf spring is too large, resulting in a shorter fatigue life. Therefore, a fine-tuning design of the leaf spring's stroke parameters is necessary.

[0075] If the leaf spring stroke is adjusted manually time and time again, the fatigue load of the leaf spring is recalculated, the fatigue life of the leaf spring is calculated, and then the leaf spring stroke parameters are re-evaluated based on the fatigue results, the cycle is long and the efficiency is low.

[0076] In this embodiment, the objective function is to reduce the fatigue damage of the leaf spring to a preset value. Based on the Isight platform, a multi-island genetic algorithm is used to find the maximum stroke of the leaf spring that satisfies the fatigue performance of the leaf spring. That is, the z-coordinate of the hard point of the limit block is defined as the variable DVz. Then, the optimal maximum stroke of the leaf spring is determined based on the Isight optimization algorithm.

[0077] For example, using a leaf spring fatigue damage value of less than 0.286 (fatigue damage of 1 indicates that the leaf spring just meets the life requirement for completing the road test, and 0.286 indicates a safety factor of 3.5) as the objective function, the maximum leaf spring stroke that satisfies the fatigue performance requirement is quickly found based on the Isight platform and using a multi-island genetic algorithm. Specifically, the Z-axis coordinate of the hard point of the limit block is defined as the variable DVz, with a range of 0 ≤ DVz ≤ 50. Then, the optimal maximum leaf spring stroke is determined based on the Isight optimization algorithm. The Isight optimization process is as follows: Figure 4 As shown, the maximum stroke parameter of the leaf spring changed from 213mm to 201mm after optimization. After road testing, the leaf spring passed the durability test on the first try, verifying the feasibility of the optimization scheme.

[0078] In summary, the automotive leaf spring motion stroke parameter optimization and matching method provided by this invention first collects road spectrum data from the base vehicle to obtain the loads Fx, Fy, and Dz in the x, y, and z directions at the wheel center, as well as the torques Mx, My, and Mz around the x, y, and z axes. Then, an ADAMS multibody dynamics model of the vehicle is established, and the corresponding loads are applied. Finally, the stress field for leaf spring fatigue analysis is solved, and leaf spring fatigue analysis is performed. The maximum stroke of the leaf spring is equivalently transformed into the optimization of the coordinates of the limiting block hard point. The z-axis coordinate of the limiting block hard point is parameterized, and the optimal leaf spring stroke parameters are determined based on the Isight platform. This method can obtain leaf spring stroke parameters that meet the fatigue life requirements, achieving optimized matching of leaf spring motion stroke parameters. It avoids the problem of long optimization iteration cycles caused by repeated manual trial and error, accelerates product development, and significantly reduces costs and shortens the cycle compared to traditional physical testing.

[0079] Please see Figure 5 An embodiment of the present invention proposes an automotive leaf spring motion stroke parameter optimization and matching system, comprising:

[0080] The data acquisition module is used to collect road spectrum data from the base vehicle, and obtain the loads Fx, Fy, and Dz of the whole vehicle in the x, y, and z directions at the wheel center, as well as the torques Mx, My, and Mz around the x, y, and z axes.

[0081] An application module is established to create a multibody dynamics model of the whole vehicle using ADAMS, apply wheel center forces Fx, Fy, Dz, Mx, My, and Mz at the wheel center, and solve for the load on the leaf spring at the leaf spring seat.

[0082] The solution and analysis module is used to solve the stress field for leaf spring fatigue analysis and to perform leaf spring fatigue analysis.

[0083] The optimization analysis module is used to convert the maximum stroke of the leaf spring into the equivalent optimization of the coordinates of the hard point of the limit block, parameterize the z-coordinate of the hard point of the limit block, and determine the optimal leaf spring stroke parameters based on the Isight platform.

[0084] In this embodiment, the acquisition module is specifically used for:

[0085] The two loads Fx and Fy and the three torques Mx, My and Mz exerted on the tires by the road surface are measured by a six-component force sensor when the vehicle is driving on the actual road durability test surface. The vertical displacement load Dz of the wheel center is measured by a displacement sensor. Fx, Fy, Dz, Mx, My and Mz are used as the road surface excitation for the vehicle.

[0086] In this embodiment, the establishment and application module is specifically used for:

[0087] Based on the load spectrum, the actual connection relationship between various components of the vehicle is collected. The modal synthesis method is used, and the finite element software is used to calculate the frame neutral file containing modal information. The frame neutral file is used to complete the flexibility processing of the frame, thereby establishing the whole vehicle ADAMS multibody dynamics model. The model includes at least the front suspension, rear suspension, steering, frame, and body system.

[0088] K&C characteristic comparison analysis was performed on the front and rear suspensions, and the simulation results were compared with the experimental results. The main comparison types included the vertical stiffness, lateral stiffness and longitudinal stiffness of the front suspension, and the vertical stiffness, lateral stiffness and longitudinal stiffness of the rear suspension. Finally, based on the good comparison, wheel center forces Fx, Fy, Dz, Mx, My and Mz were applied at the wheel center, the response of the entire system was solved, and the load of the leaf spring at the leaf spring seat was output.

[0089] In this embodiment, the solution analysis module is specifically used for:

[0090] Import the leaf spring CAD model in the free clamping state after parameter confirmation into the finite element preprocessing software Hypermesh. Switch the Hypermesh software to the ABAQUS module, then mesh the leaf spring CAD model with 3-5mm mesh based on first-order shell elements, and assign the varying material thickness attribute to the leaf spring mesh nodes to realize the simulation of the variable cross section of the leaf spring. Set the elastic modulus, Poisson's ratio and density as three material properties, then make all the leaf springs into a set, and establish a general contact based on the set. The general contact is used to automatically simulate the actual contact behavior that may occur between the leaf springs during actual operation.

[0091] Data feature analysis was performed on the load of the multibody dynamics output of the unoptimized leaf spring. The analysis objects included the stroke Dzmax of the leaf spring seat from the free clamping state to the road test equilibrium state, and the maximum force and torque Fxmax, Fymax, Mxmax, Mymax, and Mzmax in the x, y, and z directions of the leaf spring seat, and the rationality of the load was ensured.

[0092] In the finite element model of the leaf spring, the front and rear lugs of the leaf spring are constrained. The rotational degree of freedom of the front lug is released, and the rotational degree of freedom and the translational degree of freedom along the length of the leaf spring are released of the rear lug. Load moments Fxmax, Fymax, Mxmax, Mymax, and Mzmax are applied to the leaf spring seat respectively, resulting in six finite element models under different loads. These models are then submitted to the ABAQUS module for solution calculation to obtain the stress field of the leaf spring under each load. The stress field is then correlated and superimposed with the leaf spring seat load calculated in the multibody dynamics model of the whole vehicle. The specific formula for calculating the stress field is as follows:

[0093]

[0094] Where σ(t) is the stress field expression, F k (t) represents the time-varying load at the leaf spring seat input by the multibody dynamics software, k = x / y / Mx / My / Mz, D z (t) represents the vertical displacement of the leaf spring seat as a function of time, σ Dzmax σ represents the stress caused by applying a load Dzmax to a leaf spring. Fxmax σ represents the stress caused by applying a load Fxmax to a leaf spring. Fymax σ represents the stress caused by applying a load Fymax to a leaf spring. Mxmax σ represents the stress caused by applying a load Mxmax to a leaf spring. Mymax σ represents the stress caused by applying a load Mymax to a leaf spring. Mzmax This represents the stress caused by applying a load Mzmax to the leaf spring;

[0095] Then, the fatigue life of the leaf spring was analyzed in the fatigue software FEMFAT based on the SN method.

[0096] In this embodiment, the optimization analysis module is specifically used for:

[0097] Using the objective function that the fatigue damage of the leaf spring is less than a preset value, the maximum stroke of the leaf spring that satisfies the fatigue performance of the leaf spring is found based on the Isight platform and a multi-island genetic algorithm. That is, the z-coordinate of the hard point of the limit block is defined as the variable DVz, and then the optimal maximum stroke of the leaf spring is determined based on the Isight optimization algorithm.

[0098] The automotive leaf spring motion stroke parameter optimization and matching system provided by this invention first collects road spectrum data from a base vehicle to obtain the loads Fx, Fy, and Dz in the x, y, and z directions at the wheel center, as well as the torques Mx, My, and Mz around the x, y, and z axes. Then, an ADAMS multibody dynamics model of the vehicle is established, and the corresponding loads are applied. Finally, the stress field for leaf spring fatigue analysis is solved, and leaf spring fatigue analysis is performed. The maximum stroke of the leaf spring is equivalently transformed into the optimization of the coordinates of the hard point of the limiting block. The z-coordinate of the hard point of the limiting block is parameterized, and the optimal leaf spring stroke parameters are determined based on the Isight platform. This system can obtain leaf spring stroke parameters that meet the fatigue life requirements, achieving optimized matching of leaf spring motion stroke parameters. It avoids the problem of long optimization iteration cycles caused by repeated manual trial and error, accelerates product development, and significantly reduces costs and shortens the cycle compared to traditional physical testing.

[0099] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0100] 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.

[0101] 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.

[0102] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0103] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A method for optimizing and matching the motion stroke parameters of an automotive leaf spring, characterized in that, include: Road spectrum data was collected from the base vehicle to obtain the loads Fx, Fy, and Dz of the whole vehicle in the x, y, and z directions at the wheel center, as well as the torques Mx, My, and Mz around the x, y, and z axes. Establish a multibody dynamics model of the whole vehicle using ADAMS, apply wheel center forces Fx, Fy, Dz, Mx, My, and Mz at the wheel center, and solve for the load of the leaf spring at the leaf spring seat; The stress field for fatigue analysis of leaf springs is solved, and fatigue analysis of leaf springs is performed. The maximum stroke of the leaf spring is equivalently transformed into the optimization of the coordinates of the hard point of the limit block. The z-coordinate of the hard point of the limit block is parameterized, and the optimal leaf spring stroke parameters are determined based on the Isight platform. The steps of converting the maximum travel of the leaf spring into an equivalent optimization of the coordinates of the hard point of the limiting block, parameterizing the z-coordinate of the hard point of the limiting block, and determining the optimal leaf spring travel parameters based on the Isight platform specifically include: Using the objective function that the fatigue damage of the leaf spring is less than a preset value, the maximum stroke of the leaf spring that satisfies the fatigue performance of the leaf spring is found based on the Isight platform and a multi-island genetic algorithm. That is, the z-coordinate of the hard point of the limit block is defined as the variable DVz, and then the optimal maximum stroke of the leaf spring is determined based on the Isight optimization algorithm.

2. The method for optimizing and matching the motion stroke parameters of an automotive leaf spring according to claim 1, characterized in that, The specific steps for collecting road spectrum data from the base vehicle to obtain the loads Fx, Fy, and Dz in the x, y, and z directions at the wheel center, and the torques Mx, My, and Mz around the x, y, and z axes include: The two loads Fx and Fy and the three torques Mx, My and Mz exerted on the tires by the road surface are measured by a six-component force sensor when the vehicle is driving on the actual road durability test surface. The vertical displacement load Dz of the wheel center is measured by a displacement sensor. Fx, Fy, Dz, Mx, My and Mz are used as the road surface excitation for the vehicle.

3. The method for optimizing and matching the motion stroke parameters of an automotive leaf spring according to claim 2, characterized in that, The specific steps for establishing a full-vehicle ADAMS multibody dynamics model, applying wheel center forces Fx, Fy, Dz, Mx, My, and Mz at the wheel center, and solving for the loads on the leaf springs at the leaf spring seats include: Based on the load spectrum, the actual connection relationship between various components of the vehicle is collected. The modal synthesis method is used, and the finite element software is used to calculate the frame neutral file containing modal information. The frame neutral file is used to complete the flexibility processing of the frame, thereby establishing the whole vehicle ADAMS multibody dynamics model. The model includes at least the front suspension, rear suspension, steering, frame, and body system. K&C characteristic comparison analysis was performed on the front and rear suspensions, and the simulation results were compared with the experimental results. The main comparison types included the vertical stiffness, lateral stiffness and longitudinal stiffness of the front suspension, and the vertical stiffness, lateral stiffness and longitudinal stiffness of the rear suspension. Finally, based on the good comparison, wheel center forces Fx, Fy, Dz, Mx, My and Mz were applied at the wheel center, the response of the entire system was solved, and the load of the leaf spring at the leaf spring seat was output.

4. The method for optimizing and matching the motion stroke parameters of an automotive leaf spring according to claim 3, characterized in that, The specific steps for solving the stress field and performing fatigue analysis on a leaf spring include: Import the leaf spring CAD model in the free clamping state after parameter confirmation into the finite element preprocessing software Hypermesh. Switch the Hypermesh software to the ABAQUS module, then mesh the leaf spring CAD model with 3-5mm mesh based on first-order shell elements, and assign the varying material thickness attribute to the leaf spring mesh nodes to realize the simulation of the variable cross section of the leaf spring. Set the elastic modulus, Poisson's ratio and density as three material properties, then make all the leaf springs into a set, and establish a general contact based on the set. The general contact is used to automatically simulate the actual contact behavior that may occur between the leaf springs during actual operation. Data feature analysis was performed on the load of the multibody dynamics output of the unoptimized leaf spring. The analysis objects included the stroke Dzmax of the leaf spring seat from the free clamping state to the road test equilibrium state, and the maximum force and torque Fxmax, Fymax, Mxmax, Mymax, and Mzmax in the x, y, and z directions of the leaf spring seat, and the rationality of the load was ensured. In the finite element model of the leaf spring, the front and rear lugs of the leaf spring are constrained. The rotational degree of freedom of the front lug is released, and the rotational degree of freedom and the translational degree of freedom along the length of the leaf spring are released of the rear lug. Load moments Fxmax, Fymax, Mxmax, Mymax, and Mzmax are applied to the leaf spring seat respectively, resulting in six finite element models under different loads. These models are then submitted to the ABAQUS module for solution calculation to obtain the stress field of the leaf spring under each load. The stress field is then correlated and superimposed with the leaf spring seat load calculated in the multibody dynamics model of the whole vehicle. The specific formula for calculating the stress field is as follows: Where σ(t) is the stress field expression, F k (t) represents the time-varying load at the leaf spring seat input by the multibody dynamics software, k = x / y / Mx / My / Mz, D z (t) represents the vertical displacement of the leaf spring seat as a function of time, σ Dzmax σ represents the stress caused by applying a load Dzmax to a leaf spring. Fxmax σ represents the stress caused by applying a load Fxmax to a leaf spring. Fymax σ represents the stress caused by applying a load Fymax to a leaf spring. Mxmax σ represents the stress caused by applying a load Mxmax to a leaf spring. Mymax σ represents the stress caused by applying a load Mymax to a leaf spring. Mzmax This represents the stress caused by applying a load Mzmax to the leaf spring; Then, the fatigue life of the leaf spring was analyzed in the fatigue software FEMFAT based on the SN method.

5. A system for optimizing and matching the motion stroke parameters of an automotive leaf spring, characterized in that, include: The data acquisition module is used to collect road spectrum data from the base vehicle, and obtain the loads Fx, Fy, and Dz of the whole vehicle in the x, y, and z directions at the wheel center, as well as the torques Mx, My, and Mz around the x, y, and z axes. An application module is established to create a multibody dynamics model of the whole vehicle using ADAMS, apply wheel center forces Fx, Fy, Dz, Mx, My, and Mz at the wheel center, and solve for the load on the leaf spring at the leaf spring seat. The solution and analysis module is used to solve the stress field for leaf spring fatigue analysis and to perform leaf spring fatigue analysis. The optimization analysis module is used to convert the maximum stroke of the leaf spring into the equivalent optimization of the coordinates of the hard point of the limit block, parameterize the z-coordinate of the hard point of the limit block, and determine the optimal leaf spring stroke parameters based on the Isight platform. The optimization analysis module is specifically used for: Using the objective function that the fatigue damage of the leaf spring is less than a preset value, the maximum stroke of the leaf spring that satisfies the fatigue performance of the leaf spring is found based on the Isight platform and a multi-island genetic algorithm. That is, the z-coordinate of the hard point of the limit block is defined as the variable DVz, and then the optimal maximum stroke of the leaf spring is determined based on the Isight optimization algorithm.

6. The automotive leaf spring motion stroke parameter optimization and matching system according to claim 5, characterized in that, The acquisition module is specifically used for: The two loads Fx and Fy and the three torques Mx, My and Mz exerted on the tires by the road surface are measured by a six-component force sensor when the vehicle is driving on the actual road durability test surface. The vertical displacement load Dz of the wheel center is measured by a displacement sensor. Fx, Fy, Dz, Mx, My and Mz are used as the road surface excitation for the vehicle.

7. The automotive leaf spring motion stroke parameter optimization and matching system according to claim 6, characterized in that, The establishment and application module is specifically used for: Based on the load spectrum, the actual connection relationship between various components of the vehicle is collected. The modal synthesis method is used, and the finite element software is used to calculate the frame neutral file containing modal information. The frame neutral file is used to complete the flexibility processing of the frame, thereby establishing the whole vehicle ADAMS multibody dynamics model. The model includes at least the front suspension, rear suspension, steering, frame, and body system. K&C characteristic comparison analysis was performed on the front and rear suspensions, and the simulation results were compared with the experimental results. The main comparison types included the vertical stiffness, lateral stiffness and longitudinal stiffness of the front suspension, and the vertical stiffness, lateral stiffness and longitudinal stiffness of the rear suspension. Finally, based on the good comparison, wheel center forces Fx, Fy, Dz, Mx, My and Mz were applied at the wheel center respectively, the response of the whole system was solved, and the load of the leaf spring at the leaf spring seat was output.

8. The automotive leaf spring motion stroke parameter optimization and matching system according to claim 7, characterized in that, The solution and analysis module is specifically used for: Import the leaf spring CAD model in the free clamping state after parameter confirmation into the finite element preprocessing software Hypermesh. Switch the Hypermesh software to the ABAQUS module, then mesh the leaf spring CAD model with 3-5mm mesh based on first-order shell elements, and assign the varying material thickness attribute to the leaf spring mesh nodes to realize the simulation of the variable cross section of the leaf spring. Set the elastic modulus, Poisson's ratio and density as three material properties, then make all the leaf springs into a set, and establish a general contact based on the set. The general contact is used to automatically simulate the actual contact behavior that may occur between the leaf springs during actual operation. Data feature analysis was performed on the load of the multibody dynamics output of the unoptimized leaf spring. The analysis objects included the stroke Dzmax of the leaf spring seat from the free clamping state to the road test equilibrium state, and the maximum force and torque Fxmax, Fymax, Mxmax, Mymax, and Mzmax in the x, y, and z directions of the leaf spring seat, and the rationality of the load was ensured. In the finite element model of the leaf spring, the front and rear lugs of the leaf spring are constrained. The rotational degree of freedom of the front lug is released, and the rotational degree of freedom and the translational degree of freedom along the length of the leaf spring are released of the rear lug. Load moments Fxmax, Fymax, Mxmax, Mymax, and Mzmax are applied to the leaf spring seat respectively, resulting in six finite element models under different loads. These models are then submitted to the ABAQUS module for solution calculation to obtain the stress field of the leaf spring under each load. The stress field is then correlated and superimposed with the leaf spring seat load calculated in the multibody dynamics model of the whole vehicle. The specific formula for calculating the stress field is as follows: Where σ(t) is the stress field expression, F k (t) represents the time-varying load at the leaf spring seat input by the multibody dynamics software, k = x / y / Mx / My / Mz, D z (t) represents the vertical displacement of the leaf spring seat as a function of time, σ Dzmax σ represents the stress caused by applying a load Dzmax to a leaf spring. Fxmax σ represents the stress caused by applying a load Fxmax to a leaf spring. Fymax σ represents the stress caused by applying a load Fymax to a leaf spring. Mxmax σ represents the stress caused by applying a load Mxmax to a leaf spring. Mymax σ represents the stress caused by applying a load Mymax to a leaf spring. Mzmax This represents the stress caused by applying a load Mzmax to the leaf spring; Then, the fatigue life of the leaf spring was analyzed in the fatigue software FEMFAT based on the SN method.

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