Method and device for determining the stiffness and damping characteristics of an air spring

Abstract

This application relates to the field of air spring technology, and particularly to a method and apparatus for determining the stiffness-damping characteristic curve of an air spring. The method includes: acquiring operating data of the air spring under different operating conditions; determining key indicators of the air spring's impact on the overall dynamic performance of the vehicle based on the operating data corresponding to the vehicle data; analyzing the influence of positional change parameters corresponding to the vehicle data on the key indicators to determine local characteristic parameters; evaluating the air spring based on the operating data to obtain overall shape parameters; and determining the stiffness-damping characteristic curve based on the local characteristic parameters and the overall shape parameters. Therefore, the method for determining the stiffness-damping characteristic curve of an air spring provided by this application can solve the problem of neglecting the local characteristics of the air spring, which easily leads to conflicts between local optimization and global performance.

Description

Technical Field

[0001] This application belongs to the field of air spring technology, and in particular relates to a method and equipment for determining the stiffness-damping characteristic curve of an air spring. Background Technology

[0002] An air spring is an elastic element that uses compressed air as the elastic medium and a rubber air bladder as the load-bearing structure. It is widely used in automobiles, rail transportation, and industrial equipment. Its core advantage lies in the ability to dynamically adjust stiffness by regulating the internal air pressure, thereby balancing comfort and handling.

[0003] Existing technologies typically design experiments based on ideal operating conditions or single parameter variations, relying on simple curve fitting of overall experimental data. They fail to consider the local nonlinear effects of air springs caused by displacement and angle changes during actual operation, and lack constraints on the overall trend of the characteristic curve, easily leading to conflicts between local optimization and global dynamic performance. Therefore, existing methods for determining the stiffness and damping characteristic curves of air springs neglect their local characteristics, easily resulting in conflicts between local optimization and global performance. Summary of the Invention

[0004] This application provides a method and device for determining the stiffness and damping characteristic curve of an air spring, which can solve the problem of neglecting the local characteristics of the air spring, which can easily lead to conflicts between local optimization and global performance.

[0005] In a first aspect, embodiments of this application provide a method for determining the stiffness-damping characteristic curve of an air spring, including: The operation data of the air spring under different operating conditions are obtained; wherein, the operating conditions include vibration frequency, amplitude and temperature, and the operation data includes position change parameters and reaction force, and the position change parameters include displacement change and angle change. Based on the operating data under the operating conditions corresponding to the vehicle data, key indicators of the impact of air springs on the overall dynamic performance of the vehicle are determined; wherein, the vehicle data includes vehicle speed, load distribution, and environmental conditions; The influence of the position change parameters corresponding to the vehicle data on the key indicators is analyzed to determine local feature parameters; wherein, the local feature parameters are used to reflect the local characteristics of the stiffness-damping characteristic curve of the air spring. The overall shape parameters of the air spring are obtained by evaluating the operating data; wherein, the overall shape parameters are used to reflect the overall shape of the stiffness-damping characteristic curve. The stiffness-damping characteristic curve is determined based on the local characteristic parameters and the overall shape parameters.

[0006] The technical solutions described in this application embodiment have at least the following technical effects: The method for determining the stiffness-damping characteristic curve of an air spring provided in this application involves: acquiring operating data of the air spring under different operating conditions; determining key indicators of the air spring's impact on the overall dynamic performance of the vehicle based on the operating data corresponding to the vehicle data; analyzing the influence of position change parameters corresponding to the vehicle data on the key indicators to determine local characteristic parameters; evaluating the air spring based on each operating data to obtain overall shape parameters; and determining the stiffness-damping characteristic curve based on the local characteristic parameters and overall shape parameters. Therefore, the method for determining the stiffness-damping characteristic curve of an air spring provided in this application quantifies local stiffness-damping characteristics by analyzing the influence of displacement and angle changes on key indicators, extracting the main deformation modes of the stiffness-damping characteristic curve, and constraining the range of overall shape parameters.

[0007] In one possible implementation of the first aspect, the method further includes: When it is determined that the vehicle data has changed, the basic characteristic curve and the first characteristic curve corresponding to each target parameter in the operating conditions affected by the changed vehicle data are obtained; wherein, the basic characteristic curve refers to the characteristic curve of air spring stiffness and damping under ideal or reference operating conditions. The stiffness-damping characteristic curve is updated based on the basic characteristic curve and each of the first characteristic curves.

[0008] In one possible implementation of the first aspect, determining the key indicators of the air spring's impact on the overall dynamic performance of the vehicle based on the operating data under the operating conditions corresponding to the vehicle data includes: Obtain the basic design parameters of the air spring; wherein, the basic design parameters include the initial internal pressure, the diameter of the piston, and the angle of the frustum. Multiple sets of key parameters are determined based on the basic design parameters and the operating conditions; wherein, the key parameter sets include at least one parameter from the operating conditions; For each set of key parameters, the stiffness characteristic index of the air spring is obtained based on the operating data under the operating conditions corresponding to the vehicle data; wherein, the stiffness characteristic index includes static stiffness, dynamic stiffness and their ratio; The key indicators are selected from each of the key parameter sets based on the stiffness characteristic index.

[0009] In one possible implementation of the first aspect, determining multiple sets of key parameter sets based on the basic design parameters and the operating conditions includes: Multiple parameter samples are generated based on the parameter ranges corresponding to the basic design parameters and the operating conditions. Calculate the first influence index of each parameter in the operating conditions based on the parameter samples; wherein the first influence index is used to quantify the contribution rate of the parameter to the stiffness and damping of the air spring. Key parameters are selected from the various parameters of the operating conditions based on the first influence index; Multiple sets of the key parameters are constructed based on the key parameters.

[0010] In one possible implementation of the first aspect, the step of analyzing the impact of the position change parameters corresponding to the vehicle data on the key indicators and determining local feature parameters includes: The impact of the position change parameters on the key indicators is simulated using the Monte Carlo method to obtain a standardized data matrix; wherein, the standardized data matrix includes the key indicators of all samples and the corresponding position change parameters of the air springs; The local feature parameters are obtained by calculating the covariance matrix based on the standardized data matrix; wherein, the covariance matrix is ​​used to reflect the degree of linear correlation between variables.

[0011] In one possible implementation of the first aspect, updating the stiffness-damping characteristic curve based on the basic characteristic curve and each of the first characteristic curves includes: Multiple second characteristic curves are obtained based on the basic characteristic curves and each of the first characteristic curves; wherein, the second characteristic curves are characteristic curves corresponding to the simultaneous change of multiple parameters in the vehicle data; For each of the second characteristic curves, a characteristic difference parameter is obtained based on the basic characteristic curve and the first characteristic curve; wherein, the characteristic difference parameter is used to reflect the difference between the second characteristic curve and the basic characteristic curve and the first characteristic curve, respectively; The probability that the second characteristic curve is the updated stiffness-damping characteristic curve is obtained based on the characteristic difference parameters; The stiffness-damping characteristic curve is updated based on the probability.

[0012] In one possible implementation of the first aspect, before obtaining the basic characteristic curve and the first characteristic curve corresponding to each target parameter in the operating conditions affected by the changed vehicle data based on the overall shape parameter when it is determined that the vehicle data has changed, the method further includes: Calculate the correlation coefficient between the vehicle data and the operating conditions; A second influence index for the parameters in the operating conditions is selected and calculated based on the correlation coefficient; wherein, the second influence index is used to quantify the total contribution rate of the parameters in the operating conditions to the changes in the vehicle data; Each target parameter is selected from the parameters of the operating conditions based on the second influence index.

[0013] In one possible implementation of the first aspect, the step of evaluating the air spring based on the respective operating data to obtain the overall shape parameters includes: The overall shape parameters are obtained by weighted evaluation and curve fitting based on the operating data corresponding to different operating conditions.

[0014] In one possible implementation of the first aspect, determining the stiffness-damping characteristic curve based on the local characteristic parameters and the global shape parameters includes: A stiffness-damping characteristic surface is constructed based on the local feature parameters and the overall shape parameters; The stiffness-damping characteristic curve is obtained by contour projection of the stiffness-damping characteristic surface.

[0015] Secondly, embodiments of this application provide a device for determining the stiffness-damping characteristic curve of an air spring, comprising: The acquisition module is used to acquire the operating data of the air spring under different operating conditions; wherein, the operating conditions include vibration frequency, amplitude and temperature, and the operating data includes position change parameters and reaction force, and the position change parameters include displacement change and angle change. The key performance indicator module is used to determine the key indicators of the impact of air springs on the overall dynamic performance of the vehicle based on the operating data under the operating conditions corresponding to the vehicle data; wherein, the vehicle data includes vehicle speed, load distribution and environmental conditions; The local feature parameter module is used to analyze the impact of the position change parameters corresponding to the vehicle data on the key indicators and determine the local feature parameters; wherein, the local feature parameters are used to reflect the local characteristics of the stiffness-damping characteristic curve of the air spring. The overall shape parameter module is used to evaluate the air spring based on the aforementioned operating data to obtain the overall shape parameters; wherein, the overall shape parameters are used to reflect the overall shape of the stiffness-damping characteristic curve; The stiffness-damping characteristic curve module is used to determine the stiffness-damping characteristic curve based on the local characteristic parameters and the overall shape parameters.

[0016] Thirdly, embodiments of this application provide an air spring stiffness-damping characteristic curve determination device, 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 method as described in any one of the first aspects above.

[0017] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in any of the first aspects above.

[0018] Fifthly, embodiments of this application provide a computer program product that, when running on an air spring stiffness-damping characteristic curve determining device, causes the air spring stiffness-damping characteristic curve determining device to execute the method described in any one of the first aspects above.

[0019] It is understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating a method for determining the stiffness-damping characteristic curve of an air spring according to an embodiment of this application. Figure 2 This is a schematic diagram of the implementation process of steps S600 and S700 in the method for determining the stiffness and damping characteristic curve of an air spring provided in an embodiment of this application. Figure 3 This is a schematic diagram of the implementation process of steps S200 and S220 in the method for determining the stiffness and damping characteristic curve of an air spring provided in an embodiment of this application. Figure 4 This is a schematic diagram of the implementation process of steps S300, S400 and S500 in the method for determining the stiffness and damping characteristic curve of an air spring provided in an embodiment of this application. Figure 5 This is a schematic diagram of the influencing index in the method for determining the stiffness-damping characteristic curve of an air spring provided in an embodiment of this application; Figure 6 This is a schematic diagram of the structure of the air spring stiffness and damping characteristic curve determination device provided in the embodiments of this application; Figure 7 This is a schematic diagram of the structure of the air spring stiffness and damping characteristic curve determination device provided in the embodiments of this application. Detailed Implementation

[0022] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0023] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0024] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0025] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0026] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0027] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0028] In related technologies, experimental design is typically based on ideal operating conditions or single parameter variations, relying on simple curve fitting of overall experimental data. This approach fails to consider the local nonlinear effects of air springs caused by displacement and angle changes during actual operation. Furthermore, it lacks a constraint mechanism on the overall trend of the characteristic curve, easily leading to conflicts between local optimization and global dynamic performance. Therefore, existing methods for determining the stiffness and damping characteristic curves of air springs suffer from neglecting the local characteristics of the air spring, easily resulting in conflicts between local optimization and global performance.

[0029] To address the aforementioned problems, this application provides a method and apparatus for determining the stiffness-damping characteristic curve of an air spring. The method involves acquiring operating data of the air spring under different operating conditions; determining key indicators of the air spring's impact on the overall dynamic performance of the vehicle based on the operating data corresponding to the vehicle data; analyzing the influence of positional change parameters corresponding to the vehicle data on the key indicators to determine local characteristic parameters; evaluating the air spring based on the operating data to obtain overall shape parameters; and determining the stiffness-damping characteristic curve based on the local characteristic parameters and the overall shape parameters. Therefore, the method for determining the stiffness-damping characteristic curve of an air spring provided in this application analyzes the influence of vertical / horizontal displacement changes and angle changes on key indicators, quantifies local stiffness-damping characteristics, extracts the main deformation modes of the stiffness-damping characteristic curve, and constrains the range of overall shape parameters.

[0030] The method for determining the stiffness and damping characteristic curve of an air spring provided in this application embodiment can be applied to an air spring stiffness and damping characteristic curve determination device. In this case, the air spring stiffness and damping characteristic curve determination device is the executing entity of the air spring stiffness and damping characteristic curve determination method provided in this application embodiment. This application embodiment does not impose any restrictions on the specific type of air spring stiffness and damping characteristic curve determination device.

[0031] For example, the device for determining the stiffness and damping characteristic curve of an air spring can be an industrial computer, a programmable logic controller, an embedded control system, a distributed control system, a tablet computer, a laptop computer, an ultra-mobile personal computer (UMPC), a netbook, a desktop computer, a laptop computer, a handheld computing device, etc., but is not limited to these.

[0032] To better understand the method for determining the stiffness and damping characteristic curve of an air spring provided in this application, the specific implementation process of the method for determining the stiffness and damping characteristic curve of an air spring provided in this application will be described by way of example below.

[0033] Figure 1A schematic flowchart of the method for determining the stiffness-damping characteristic curve of an air spring provided in an embodiment of this application is shown. The method for determining the stiffness-damping characteristic curve of an air spring includes: S100 acquires the operating data of the air spring under different operating conditions. The operating conditions include vibration frequency, amplitude, and temperature, and the operating data includes position change parameters and reaction force. The position change parameters include displacement change and angle change.

[0034] It is understandable that an air spring includes a frustum-shaped piston.

[0035] For example, the operating data of an air spring under different operating conditions can be obtained by performing multi-condition tests on the air spring using a dynamic testing machine (such as the Inston system). For instance, operating conditions could include setting the vibration frequency range to 1-10Hz (in 1Hz increments), amplitude ±5mm, temperature to 25-80℃ (in 15℃ intervals), and inflation pressure to 0.1-0.7MPa (in 0.1MPa increments). Displacement changes can be measured using laser displacement sensors, inductive displacement sensors, etc., angle changes can be measured using tilt sensors, and reaction forces can be recorded using piezoelectric force sensors, capacitive force sensors, etc.

[0036] S200 determines the key indicators of the air spring's impact on the overall dynamic performance of the vehicle based on operating data under corresponding operating conditions. Vehicle data includes vehicle speed, load distribution, and environmental conditions.

[0037] It is understandable that the operating conditions corresponding to vehicle data refer to the vibration frequency, amplitude, and temperature corresponding to the vehicle data. Key indicators are one or more parameters selected from multiple parameters of the operating conditions.

[0038] For example, based on the changes in position change parameters and reaction force under operating conditions corresponding to vehicle data, statistical analysis methods such as correlation analysis and variance analysis can be used to analyze the degree of influence of changes in each parameter under operating conditions on the changes in position change parameters and reaction force. For instance, the correlation coefficient between each parameter under operating conditions and the position change parameter and reaction force can be calculated using the Pearson correlation coefficient. Parameters whose correlation coefficients with the position change parameter or reaction force are greater than a preset threshold are selected from multiple parameters under operating conditions; these are considered key indicators of the air spring's impact on the overall dynamic performance of the vehicle.

[0039] S300 analyzes the impact of positional change parameters corresponding to vehicle data on key indicators to determine local characteristic parameters. These local characteristic parameters reflect the local characteristics of the air spring's stiffness-damping characteristic curve.

[0040] It is understandable that local characteristic parameters can refer to the local slope of the stiffness-damping characteristic curve, which is used to reflect the linear correlation between key indicators and positional change parameters.

[0041] For example, a first-order parameter sensitivity index can be used to quantify the impact of positional changes on key indicators. Based on the first-order parameter sensitivity index, a sensitivity index curve is obtained. Within the sensitive region where the first-order parameter sensitivity index exceeds a preset threshold, the slope of the sensitivity index curve can reflect the local slope of the stiffness-damping characteristic curve. For example, the first-order parameter sensitivity index is: Where Y is the position change parameter, X i This is a key indicator.

[0042] S400: The overall shape parameters of the air spring are obtained by evaluating various operating data. These overall shape parameters reflect the overall shape of the stiffness-damping characteristic curve.

[0043] It is understandable that the overall shape parameter can refer to the overall slope, curvature, or extreme value of the stiffness-damping characteristic curve.

[0044] For example, the fitting model (which can be a linear model, a polynomial model, an exponential model, or a piecewise function model, etc.) can be determined based on the data characteristics of each operational data (displacement-reaction force) (such as difference, skewness, kurtosis, the shape of the corresponding scatter plot, etc.). For linear and polynomial models, the least squares method can be used for fitting and the overall shape parameters can be calculated. For exponential models, the natural logarithm is taken to convert it into a linear mode before fitting and calculating the overall shape parameters. For piecewise function models, the nodes of the segments are first determined according to the data characteristics of each operational data, and then a suitable model (such as a linear model, a polynomial model, etc.) is selected for fitting in each segment. The fitting results of each segment are combined to obtain a complete piecewise function model, and the overall shape parameters are calculated based on the piecewise function model.

[0045] S500 determines the stiffness-damping characteristic curve based on local characteristic parameters and overall shape parameters.

[0046] For example, an initial stiffness-damping characteristic curve can be constructed based on the overall shape parameters and the selected fitting model. Local regions within the stiffness-damping characteristic curve that require adjustment are identified, such as regions where the first-order parameter sensitivity index is greater than a preset threshold. These local regions of the initial curve are then corrected based on local characteristic parameters, i.e., local slopes, to obtain the final stiffness-damping characteristic curve.

[0047] In one possible implementation, please refer to Figure 2 The methods also include: S600, upon determining that vehicle data has changed, acquires the basic characteristic curve and the first characteristic curve corresponding to each target parameter in the operating conditions affected by the changed vehicle data. The basic characteristic curve refers to the characteristic curve of air spring stiffness and damping under ideal or reference operating conditions.

[0048] For example, when it is determined that vehicle data has changed, the basic characteristic curve can be measured under ideal operating conditions (such as standard load distribution, standard environmental conditions, and standard vehicle speed). 500 sets of parameter samples are generated through Monte Carlo sampling, and the first characteristic curve corresponding to each target parameter in the operating conditions affected by the changed vehicle data is calculated.

[0049] S700 updates the stiffness and damping characteristic curves based on the basic characteristic curves and each first characteristic curve.

[0050] For example, a basic weight can be preset for the basic characteristic curve, and a conditional weight can be assigned to the corresponding first characteristic curve according to each target parameter in the operating conditions affected by the changed vehicle data (the conditional weight can be preset according to the importance of each target parameter in the operating conditions, or it can be calculated by normalizing the weighted average of the correlation coefficients between each target parameter and the position change parameter and the reaction force in the operating conditions). Based on the basic weight and the conditional weight, the stiffness damping characteristic curve is updated by weighted fusion of the basic characteristic curve and each first characteristic curve.

[0051] Through steps S600 to S700, precise mapping between vehicle data and operating conditions is achieved using parameter decoupling technology. The influence gradient of each parameter on stiffness and damping characteristics is quantified, providing a quantitative basis for curve updates. An improved weighted moving average algorithm is used to dynamically fuse the base curve and the first characteristic curve. Optimal weight coefficients are determined through particle swarm optimization, ensuring the updated curve remains continuously differentiable across the entire displacement range, thus improving prediction accuracy.

[0052] In one possible implementation, please refer to Figure 3 S200, based on operating data under corresponding operating conditions, determines the key indicators of the impact of air springs on the overall dynamic performance of the vehicle, including: S210, obtain the basic design parameters of the air spring. These parameters include the initial internal pressure, piston diameter, and frustum angle.

[0053] For example, a high-precision pressure sensor (range 0-1 MPa, accuracy 0.01 MPa) can be used to inflate the air spring to the design pressure (e.g., 0.5 MPa), and the internal pressure value after stabilization can be recorded. A laser scanner (accuracy 0.001 mm) is used to perform a 3D scan of the piston, and the maximum diameter (e.g., Φ200 mm) and minimum diameter (e.g., Φ180 mm) of the piston are extracted through reverse engineering. The average diameter is then calculated as a design parameter. Based on the difference between the upper and lower diameters of the piston and its height (e.g., H=50 mm), the angle between the side wall of the frustum and the vertical direction is calculated using trigonometric functions to obtain the frustum angle.

[0054] S220 determines multiple sets of key parameters based on basic design parameters and operating conditions. Each set of key parameters includes at least one parameter from the operating conditions.

[0055] For example, Latin hypercube sampling (LHS) can be used to sample each parameter in the operating conditions in multiple dimensions, and then combine the basic design parameters to generate a set of key parameters.

[0056] S230, for each set of key parameters, the stiffness characteristic index of the air spring is obtained based on the operating data under the operating conditions corresponding to the vehicle data. The stiffness characteristic index includes static stiffness, dynamic stiffness, and their ratio.

[0057] For example, for each set of key parameters, the air spring can be tested under multiple operating conditions using a dynamic testing machine (such as an Inston 8802) according to the operating conditions corresponding to the vehicle data, to obtain operating data and calculate stiffness characteristic indicators. For instance, the air spring is installed on the testing machine, the sampling frequency is set to 1000Hz, and it is preloaded to an initial internal pressure of 0.5MPa. Sinusoidal vibration excitation is applied to each set of key parameters, and the displacement change parameters and reaction forces are recorded. Under static load (e.g., displacement from 0mm to 10mm compression), the static stiffness is calculated; under dynamic load (e.g., frequency 5Hz), the fundamental frequency component is extracted by Fourier transform, and the dynamic stiffness is calculated; the stiffness ratio is obtained based on the static stiffness and dynamic stiffness.

[0058] S240 selects key indicators from the set of key parameters based on stiffness characteristic indicators.

[0059] For example, principal component analysis (PCA) can be used to reduce the dimensionality of stiffness characteristic indicators, and K-means clustering can be combined to select the most representative key indicators. For instance, Z-score standardization can be applied to static stiffness, dynamic stiffness, and stiffness ratio to eliminate the influence of dimensions. The covariance matrix and eigenvalues ​​are calculated, and principal components are extracted (e.g., the cumulative contribution rate of the first two principal components > 95%). K-means clustering (e.g., k=3) is then performed on the dimensionality-reduced data, and the sample closest to the cluster center is selected as the key indicator.

[0060] Through steps S210 to S240, LHS sampling and dynamic testing achieve comprehensive coverage of operating condition parameters and accurate quantification of stiffness characteristics, avoiding characteristic curve deviations caused by parameter omissions in traditional methods. PCA and cluster analysis compress multi-dimensional stiffness indices into a few key indices, significantly reducing the complexity of subsequent analysis while retaining the main operating condition characteristics. The selected key indices reflect the core characteristics of air springs under different operating conditions (such as high stiffness ratio and high dynamic stiffness), providing more engineering-meaning input for updating characteristic curves.

[0061] Optionally, please refer to Figure 3 S220, based on basic design parameters and operating conditions, determines multiple sets of key parameter sets, including: S221 generates multiple parameter samples based on the parameter ranges corresponding to the basic design parameters and operating conditions.

[0062] For example, orthogonal experimental design combined with uniform design method can be used to generate multiple sets of parameter samples within the range of basic design parameters (initial internal pressure, piston diameter, frustum angle) and operating conditions (vibration frequency, amplitude, temperature).

[0063] S222, calculate the first influence index of each parameter in the operating conditions based on the parameter samples. The first influence index is used to quantify the contribution rate of the parameter to the stiffness and damping of the air spring.

[0064] For example, variance decomposition can be used to quantify the contribution of operating conditions to the stiffness and damping of the air spring (i.e., the first influence index), such as... Figure 5 As shown, a Gaussian process regression (GPR) surrogate model can be trained based on parameter samples and corresponding stiffness and damping test data (such as static stiffness and dynamic stiffness) to replace complex finite element simulations. For example: Input: initial internal pressure, piston diameter, frustum angle, frequency, amplitude, temperature. Output: static stiffness (N / mm), dynamic stiffness (N / mm). The first-order sensitivity index (first influence index) of the parameter in each operating condition is calculated based on the Gaussian process regression surrogate model, representing the contribution rate of that parameter's individual variation to the output variance. For example, , where X i X is the i-th parameter. ~i For other parameters, Y is the output (such as static stiffness).

[0065] S223, select key parameters from various parameters of operating conditions based on the first influence index.

[0066] For example, a preset threshold (e.g., 0.3) can be set to filter parameters whose first influence index exceeds the preset threshold as key parameters, such as... Figure 5 As shown.

[0067] S224, construct multiple sets of key parameter sets based on key parameters.

[0068] For example, a combination of full factorial design and boundary value analysis can be used to construct multiple parameter sets based on key parameters. For instance, based on the range of key parameters (such as frequency, amplitude, and temperature), three levels (low, medium, and high) can be defined for each parameter. Combinations of all parameter levels are generated, and boundary value samples are added to cover extreme operating conditions. The full factorial combinations and boundary value samples are then merged to obtain multiple sets of key parameters.

[0069] Through steps S221 to S224, orthogonal and uniform design are combined to achieve uniform coverage of the parameter space, reducing the number of samples while maintaining representativeness. Quantifying parameter contribution rates avoids omissions of interactions found in traditional methods (such as single-factor analysis) and reduces the error of the first influence index. Full-factor design covers parameter combinations, and boundary value analysis supplements extreme working conditions. The constructed parameter set reduces the prediction error of the stiffness-damping characteristic curve at key working points.

[0070] In one possible implementation, please refer to Figure 4 S300, analyze the impact of vehicle data location change parameters on key indicators, and determine local feature parameters, including: S310 uses the Monte Carlo method to simulate the impact of position change parameters on key indicators, obtaining a standardized data matrix. This standardized data matrix includes the key indicators for all samples and the corresponding position change parameters of the air springs.

[0071] For example, the output parameters (position change parameters) and input indicators (key indicators) can be simulated using the Monte Carlo method. Multiple sets of input parameter samples can be sampled using Latin hypercube. For each set of input indicator samples, the corresponding output parameters can be calculated through finite element simulation (such as ABAQUS) or bench experiments, and then standardized to obtain a standardized data matrix.

[0072] S320: Calculate the covariance matrix based on the standardized data matrix to obtain local feature parameters. The covariance matrix reflects the degree of linear correlation between variables.

[0073] For example, the covariance of each pair of variables (between output parameters and input indicators) can be calculated on the standardized data matrix to obtain a covariance matrix. The covariance matrix is ​​then normalized to a correlation coefficient matrix. Based on the correlation coefficient matrix, variable pairs with correlation coefficients greater than a preset threshold are selected. Using key indicators as independent variables and location change parameters as dependent variables, a linear regression model is established based on the selected variable pairs. The regression coefficients of the linear regression model are then determined as local feature parameters.

[0074] Through steps S310 to S320, efficient coverage and dimensional unification of the parameter space are achieved, providing high-quality input data for covariance analysis. Quantifying parameter correlations and extracting principal components reveals the dynamic characteristics of air springs under different operating conditions. Sorting by absolute covariance value facilitates the rapid identification of parameters that significantly affect stiffness and damping, and shortens the design iteration cycle.

[0075] Optionally, please refer to Figure 2 S700 updates the stiffness-damping characteristic curves based on the basic characteristic curves and each first characteristic curve, including: S710, based on the basic characteristic curve and each first characteristic curve, obtains multiple second characteristic curves. Among them, the second characteristic curves are the characteristic curves corresponding to the simultaneous changes of multiple parameters in the vehicle data.

[0076] For example, different weighting coefficients can be assigned to the basic characteristic curve and each first characteristic curve, and then multiple second characteristic curves can be synthesized by weighted average.

[0077] S720, for each second characteristic curve, a characteristic difference parameter is obtained based on the basic characteristic curve and the first characteristic curve. The characteristic difference parameter reflects the differences between the second characteristic curve and both the basic and first characteristic curves.

[0078] For example, for each second characteristic curve, the root mean square error (RMSE) and maximum deviation (Max Deviation) between it and the basic characteristic curve and each first characteristic curve can be calculated to obtain the characteristic difference parameter.

[0079] S730, based on the characteristic difference parameters, the probability that the second characteristic curve is the updated stiffness-damping characteristic curve.

[0080] For example, a probabilistic model (such as Bayesian inference or logistic regression) can be used to map the variance parameters to update probabilities, thus constructing a probabilistic model: input variance parameters (RMSE, Max Deviation) output update probabilities. A logistic regression model can be chosen, collecting historical data containing variance parameters and corresponding update decisions (update = 1, no update = 0), and using maximum likelihood estimation (MLE) or gradient descent to optimize the model parameters to obtain the probabilistic model.

[0081] S740 updates the stiffness-damping characteristic curve based on probability.

[0082] For example, the second characteristic curve can be weighted and fused (with the basic characteristic curve) or directly replaced based on the update probability to generate the final stiffness-damping characteristic curve.

[0083] Through steps S710 to S740, weighted synthesis and difference analysis are used to accurately quantify the combined effects of multiple parameters on stiffness and damping. The data- and model-based update strategy improves reliability and reduces the number of design iterations. It also facilitates a smooth transition from simple to complex operating conditions, enhancing the vehicle's performance stability throughout its entire lifecycle.

[0084] Optionally, please refer to Figure 2 S600, before obtaining the basic characteristic curve based on the overall shape parameters and the first characteristic curve corresponding to each target parameter in the operating conditions affected by the changed vehicle data, when it is determined that the vehicle data has changed, the method further includes: S601, calculates the correlation coefficient between vehicle data and operating conditions.

[0085] For example, the correlation coefficient (such as the Pearson correlation coefficient) can be used. , where x i For the i-th type of data in the vehicle data, y i For the i-th type of parameter in the running conditions, Let be the mean value corresponding to the i-th type of data in the vehicle data. Calculate the correlation coefficient between vehicle data and operating conditions (the mean value corresponding to the i-th type of parameter in the operating conditions).

[0086] S602, select and calculate the second influence index of the parameters under operating conditions based on the correlation coefficient. The second influence index is used to quantify the total contribution rate of the parameters to the changes in vehicle data under operating conditions.

[0087] For example, the correlation coefficient can be normalized to obtain the weights of each parameter. , where r i is the correlation coefficient between the i-th operating condition parameter and the vehicle data, where m is the total number of parameters. Calculate the second influence index, such as... Figure 5 As shown: Where Var(X) is the variance of the vehicle data, representing the magnitude of the data variation.

[0088] S603, the target parameters are selected from the various parameters of the operating conditions based on the second influence index.

[0089] For example, the parameter corresponding to the second influence index being greater than a preset threshold can be selected as the target parameter.

[0090] Through steps S601 to S603, the impact of operating condition parameters on vehicle data is quantified using correlation coefficients and the second influence index, avoiding subjective judgment. The screening method based on the influence index can quickly locate key parameters and reduce unnecessary calculations. The method can dynamically adjust target parameters according to changes in vehicle data, making it applicable to different operating conditions. Combining variance and correlation coefficients, the second influence index considers not only correlation but also the magnitude of data changes, more closely reflecting the actual impact.

[0091] In one possible implementation, please refer to Figure 4 S400, based on various operating data, evaluates the air spring to obtain overall shape parameters, including: S410 performs weighted evaluation and curve fitting based on the operating data corresponding to different operating conditions to obtain the overall shape parameters.

[0092] For example, the data can be categorized according to operating conditions to form multiple subsets. A fitting model (such as linear, polynomial, exponential, or piecewise function) is selected for each subset, resulting in multiple curves. Overall shape parameters (such as slope, intercept, curvature, etc.) are then extracted from these curves.

[0093] Through step S410 above, the weighted evaluation can adjust the weights for different driving scenarios (such as urban and highway driving) to make the shape parameters more closely match actual usage needs. Parameters extracted from the fitted curve (such as slope and curvature) provide standardized performance indicators, facilitating comparisons across vehicle models or operating conditions. The fitted model is updated by combining real-time operating data to achieve dynamic adjustment of shape parameters, supporting adaptive control strategies. It can be extended to multivariate fitting, comprehensively revealing performance patterns under complex operating conditions.

[0094] In one possible implementation, please refer to Figure 4 S500, the stiffness-damping characteristic curve is determined based on local characteristic parameters and overall shape parameters, including: S510 constructs a stiffness-damping characteristic surface based on local feature parameters and global shape parameters.

[0095] For example, stiffness-damping characteristic surfaces can be obtained by fitting based on local characteristic parameters and global shape parameters using the polynomial response surface method (RSM) or Kriging interpolation.

[0096] S520, the stiffness-damping characteristic curve is obtained by contour projection of the stiffness-damping characteristic surface.

[0097] For example, the three-dimensional stiffness-damping-performance surface can be projected onto a plane to extract the performance change curve under equal damping or equal stiffness conditions, thus obtaining the stiffness-damping characteristic curve.

[0098] Through steps S510 to S520 above, the contour line clusters can visually demonstrate the interactions between parameters. By combining contour lines under different operating conditions, the dynamic characteristics of the system can be analyzed.

[0099] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0100] Corresponding to the air spring stiffness-damping characteristic curve determination method described in the above embodiments, this application also provides an air spring stiffness-damping characteristic curve determination device. Each module of the device can realize each step of the air spring stiffness-damping characteristic curve determination method. Figure 6 A structural block diagram of the air spring stiffness damping characteristic curve determination device provided in the embodiments of this application is shown. For ease of explanation, only the parts related to the embodiments of this application are shown.

[0101] Reference Figure 6 The device includes: The acquisition module is used to acquire the operating data of the air spring under different operating conditions; wherein, the operating conditions include vibration frequency, amplitude and temperature, and the operating data includes position change parameters and reaction force, and the position change parameters include displacement change and angle change. The key performance indicator module is used to determine the key indicators of the impact of air springs on the overall dynamic performance of the vehicle based on the operating data under the operating conditions corresponding to the vehicle data; wherein, the vehicle data includes vehicle speed, load distribution and environmental conditions; The local feature parameter module is used to analyze the impact of the position change parameters corresponding to the vehicle data on the key indicators and determine the local feature parameters; wherein, the local feature parameters are used to reflect the local characteristics of the stiffness-damping characteristic curve of the air spring. The overall shape parameter module is used to evaluate the air spring based on the aforementioned operating data to obtain the overall shape parameters; wherein, the overall shape parameters are used to reflect the overall shape of the stiffness-damping characteristic curve; The stiffness-damping characteristic curve module is used to determine the stiffness-damping characteristic curve based on the local characteristic parameters and the overall shape parameters.

[0102] It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0103] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above device can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0104] This application also provides a device for determining the stiffness-damping characteristic curve of an air spring. Figure 7 This is a schematic diagram of the structure of a device for determining the stiffness and damping characteristic curve of an air spring according to an embodiment of this application. Figure 7 As shown, the air spring stiffness-damping characteristic curve determination device 7 of this embodiment includes: at least one processor 70 ( Figure 7 Only one is shown in the image), at least one memory 71 ( Figure 7 (Only one is shown in the image) and a computer program 72 stored in the at least one memory 71 and executable on the at least one processor 70. When the processor 70 executes the computer program 72, it causes the air spring stiffness damping characteristic curve determination device 7 to implement the steps in any of the above-described air spring stiffness damping characteristic curve determination method embodiments, or causes the air spring stiffness damping characteristic curve determination device 7 to implement the functions of each module / unit in the above-described device embodiments.

[0105] For example, the computer program 72 may be divided into one or more modules / units, which are stored in the memory 71 and executed by the processor 70 to complete this application. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program 72 in the air spring stiffness-damping characteristic curve determination device 7.

[0106] The air spring stiffness-damping characteristic curve determination device 7 can be a computing device such as an industrial computer, programmable logic controller, desktop computer, laptop, handheld computer, or cloud server. This air spring stiffness-damping characteristic curve determination device may include, but is not limited to, a processor 70 and a memory 71. Those skilled in the art will understand that... Figure 7 This is merely an example of the air spring stiffness-damping characteristic curve determination device 7 and does not constitute a limitation on the air spring stiffness-damping characteristic curve determination device 7. It may include more or fewer components than shown in the figure, or combine certain components, or different components, such as input / output devices, network access devices, buses, etc.

[0107] The processor 70 can be a Central Processing Unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0108] In some embodiments, the memory 71 may be an internal storage unit of the air spring stiffness-damping characteristic curve determination device 7, such as a hard disk or memory of the air spring stiffness-damping characteristic curve determination device 7. In other embodiments, the memory 71 may be an external storage device of the air spring stiffness-damping characteristic curve determination device 7, such as a plug-in hard disk, SmartMedia Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the air spring stiffness-damping characteristic curve determination device 7. Furthermore, the memory 71 may include both internal storage units and external storage devices of the air spring stiffness-damping characteristic curve determination device 7. The memory 71 is used to store operating systems, applications, bootloaders, data, and other programs, such as the program code of computer programs. The memory 71 can also be used to temporarily store data that has been output or will be output.

[0109] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps in any of the above method embodiments.

[0110] This application provides a computer program product that, when running on an air spring stiffness-damping characteristic curve determination device, enables the air spring stiffness-damping characteristic curve determination device to implement the steps in any of the above method embodiments.

[0111] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of this application can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to the air spring stiffness-damping characteristic curve determination device, recording media, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks.

[0112] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0113] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0114] In the embodiments provided in this application, it should be understood that the disclosed air spring stiffness-damping characteristic curve determination device and method can be implemented in other ways. For example, the air spring stiffness-damping characteristic curve determination device embodiments described above are merely illustrative. For instance, the division of modules or units is merely a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0115] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0116] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for determining the stiffness-damping characteristic curve of an air spring, characterized in that, include: The operation data of the air spring under different operating conditions are obtained; wherein, the operating conditions include vibration frequency, amplitude and temperature, and the operation data includes position change parameters and reaction force, and the position change parameters include displacement change and angle change. Based on the operating data under the operating conditions corresponding to the vehicle data, key indicators of the impact of air springs on the overall dynamic performance of the vehicle are determined; wherein, the vehicle data includes vehicle speed, load distribution, and environmental conditions; The influence of the position change parameters corresponding to the vehicle data on the key indicators is analyzed to determine local feature parameters; wherein, the local feature parameters are used to reflect the local characteristics of the stiffness-damping characteristic curve of the air spring. The overall shape parameters of the air spring are obtained by evaluating the operating data; wherein, the overall shape parameters are used to reflect the overall shape of the stiffness-damping characteristic curve. The stiffness-damping characteristic curve is determined based on the local characteristic parameters and the overall shape parameters.

2. The method for determining the stiffness-damping characteristic curve of an air spring as described in claim 1, characterized in that, The method further includes: When it is determined that the vehicle data has changed, the basic characteristic curve and the first characteristic curve corresponding to each target parameter in the operating conditions affected by the changed vehicle data are obtained; wherein, the basic characteristic curve refers to the characteristic curve of air spring stiffness and damping under ideal or reference operating conditions. The stiffness-damping characteristic curve is updated based on the basic characteristic curve and each of the first characteristic curves.

3. The method for determining the stiffness-damping characteristic curve of an air spring as described in claim 1, characterized in that, The key indicators for determining the impact of air springs on the overall dynamic performance of the vehicle based on the operating data under the operating conditions corresponding to the vehicle data include: Obtain the basic design parameters of the air spring; wherein, the basic design parameters include the initial internal pressure, the diameter of the piston, and the angle of the frustum. Multiple sets of key parameters are determined based on the basic design parameters and the operating conditions; wherein, the key parameter sets include at least one parameter from the operating conditions; For each set of key parameters, the stiffness characteristic index of the air spring is obtained based on the operating data under the operating conditions corresponding to the vehicle data; wherein, the stiffness characteristic index includes static stiffness, dynamic stiffness and their ratio; The key indicators are selected from each of the key parameter sets based on the stiffness characteristic index.

4. The method for determining the stiffness-damping characteristic curve of an air spring as described in claim 3, characterized in that, The determination of multiple sets of key parameters based on the basic design parameters and the operating conditions includes: Multiple parameter samples are generated based on the parameter ranges corresponding to the basic design parameters and the operating conditions. Calculate the first influence index of each parameter in the operating conditions based on each parameter sample; wherein the first influence index is used to quantify the contribution rate of the parameter to the stiffness and damping of the air spring. Key parameters are selected from the various parameters of the operating conditions based on the first influence index; Multiple sets of key parameters are constructed based on the key parameters.

5. The method for determining the stiffness-damping characteristic curve of an air spring as described in claim 1, characterized in that, The analysis of the impact of the position change parameters corresponding to the vehicle data on the key indicators, and the determination of local feature parameters, includes: The impact of the position change parameters on the key indicators is simulated using the Monte Carlo method to obtain a standardized data matrix; wherein, the standardized data matrix includes the key indicators of all samples and the corresponding position change parameters of the air springs; The local feature parameters are obtained by calculating the covariance matrix based on the standardized data matrix; wherein, the covariance matrix is ​​used to reflect the degree of linear correlation between variables.

6. The method for determining the stiffness-damping characteristic curve of an air spring as described in claim 2, characterized in that, The step of updating the stiffness-damping characteristic curve based on the basic characteristic curve and each of the first characteristic curves includes: Multiple second characteristic curves are obtained based on the basic characteristic curves and each of the first characteristic curves; wherein, the second characteristic curves are characteristic curves corresponding to the simultaneous change of multiple parameters in the vehicle data; For each of the second characteristic curves, a characteristic difference parameter is obtained based on the basic characteristic curve and the first characteristic curve; wherein, the characteristic difference parameter is used to reflect the difference between the second characteristic curve and the basic characteristic curve and the first characteristic curve, respectively; The probability that the second characteristic curve is the updated stiffness-damping characteristic curve is obtained based on the characteristic difference parameters; The stiffness-damping characteristic curve is updated based on the probability.

7. The method for determining the stiffness-damping characteristic curve of an air spring as described in claim 2, characterized in that, Before obtaining the basic characteristic curve and the first characteristic curve corresponding to each target parameter in the operating conditions affected by the changed vehicle data based on the overall shape parameters when it is determined that the vehicle data has changed, the method further includes: Calculate the correlation coefficient between the vehicle data and the operating conditions; A second influence index for the parameters in the operating conditions is selected and calculated based on the correlation coefficient; wherein, the second influence index is used to quantify the total contribution rate of the parameters in the operating conditions to the changes in the vehicle data; Each target parameter is selected from the parameters of the operating conditions based on the second influence index.

8. The method for determining the stiffness-damping characteristic curve of an air spring as described in claim 1, characterized in that, The process of evaluating the air spring based on the aforementioned operating data to obtain overall shape parameters includes: The overall shape parameters are obtained by weighted evaluation and curve fitting based on the operating data corresponding to different operating conditions.

9. The method for determining the stiffness-damping characteristic curve of an air spring as described in claim 1, characterized in that, The step of determining the stiffness-damping characteristic curve based on the local characteristic parameters and the overall shape parameters includes: A stiffness-damping characteristic surface is constructed based on the local feature parameters and the overall shape parameters; The stiffness-damping characteristic curve is obtained by contour projection of the stiffness-damping characteristic surface.

10. A device for determining the stiffness-damping characteristic curve of an air spring, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 9.

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