Oil-gas suspension stiffness and damping full working condition matching design method and system

Abstract

This application belongs to the technical field of hydraulic suspension stiffness and damping design optimization, specifically involving a hydraulic suspension stiffness and damping full-condition matching design method and system, including the following steps: S1: Establishing the structural parameters of the damping orifice and one-way valve. S The dataset, with 1 as input and flow coefficient as output, simultaneously establishes the cylinder. 、 Piston rod structural parameters S 2. Initial inflation pressure p For input, elastic force F 2( x, p, S 2) The output dataset; S2: Establish the structural parameters through the neural network. S 1. Speed v With damping force F 1( v, S 1) Mapping; Initial inflation pressure p Structural parameters S 2. Location x With elastic force F 2( x, p, S 2) Mapping; S3. Optimization using a hybrid genetic algorithm based on the LM algorithm; S4: Outputting the optimal parameters; The advantage is that, through the coordinated optimization of the damping orifice, one-way valve, cylinder, piston rod structure, and initial charging pressure, the precise design and efficient structural matching of the stiffness and damping of the oil-gas spring are achieved.

Description

Technical Field

[0001] This application belongs to the field of hydropneumatic suspension stiffness and damping design optimization technology, specifically involving a hydropneumatic suspension stiffness and damping full-condition matching design method and system. Background Technology

[0002] The damping force generated by the flow of fluid through the damping orifice and the one-way valve, and the elastic force generated by the expansion and compression of the inert gas, are key to the vibration reduction and support effects of automotive gas springs. Currently, in engineering practice, on the one hand, the flow coefficients of the damping orifice and the one-way valve are often estimated based on empirical formulas; on the other hand, the stiffness design of gas springs relies on simplified gas state equations, making it difficult to guarantee the accuracy of damping and elastic force calculations. This results in significant deviations between the product design values ​​and target values, often requiring multiple adjustments. Traditional design processes rely on experience and repeated trials, leading to low design efficiency and severely restricting the refined optimization and coordinated matching of gas spring stiffness and damping. Summary of the Invention

[0003] To address the above problems, this invention proposes a method and system for matching the stiffness and damping of an oil-gas suspension under all working conditions. The technical solution is as follows: A method for matching the stiffness and damping of an oil-gas suspension under all working conditions includes the following steps: S1: Establish structural parameters for the damping orifice and one-way valve S The dataset, with 1 as input and flow coefficient as output, simultaneously establishes the cylinder. 、 Piston rod structural parameters S 2. Initial inflation pressure p For input, elastic force This is the output dataset; S2: Establish the structural parameter vectors respectively S 1. Speed ​​of piston rod movement v Damping force Mapping; initial inflation pressure p Cylinder and piston rod structural parameters S 2. Location x With elastic force Mapping; ; ; S3: Hybrid genetic algorithm for optimization based on LM algorithm; S4: Output the optimal parameters.

[0004] Preferably, step S1 establishes the flow coefficient and elastic force. The data collection steps are as follows: S1.1: Establish parametric structural models for the damping orifice, one-way valve, cylinder, and piston rod respectively. (Damping orifice ( ), one-way valve ( Cylinder inner diameter Piston rod diameter D h Structural parameters; S1.2.1: Obtain flow coefficient data under different combinations of key parameters of damping orifice and check valve through simulation; S1.3.1: Establish the structural parameters of the damping orifice and the one-way valve. S The dataset consists of 1 as input and flow coefficient as output; S1.2.2: Obtain different positions through simulation x Different initial inflation pressures p Elastic force under different cylinder and piston rod key parameters F 2; S1.3.2: Establish cylinder barrel 、 Piston rod structural parameters S 2. Initial inflation pressure p For input, elastic force F 2 represents the output dataset.

[0005] Preferably, step S2 establishes the mapping between damping force and elastic force, and the steps are as follows: S2.1: Input piston rod speed v and structural parameters S 1. Based on the input structural parameters S 1. Query the flow coefficient dataset in step S1.3.1 to obtain , ,in The damping orifice flow coefficient, The flow coefficient of the check valve; ultimately determined by the flow coefficient. and Substitute into the mechanical formula to calculate the output damping force F 1; Enter initial inflation pressure p Cylinder and piston rod structural parameters S 2. Piston position x Elastic force is established by querying S1.3.2. F 2 datasets, output elastic force F 2; S2.2: Data Preparation: The input data is... The output data is { F 1, F 2}; Divide and process the data; S2.3: Training, verification, and testing ultimately yield a neural network surrogate model for predicting damping force and elastic force.

[0006] Preferably, step S3 aims to minimize the error between the target damping force / elastic force and the actual damping force / elastic force by performing optimization matching. The steps are as follows: Elastic force matching stage: matching the one-way valve and damping orifice structural parameters S 1. Fixed; Matching damping force stage: Obtaining structural parameters S 2 and initial inflation pressure p parameter; By using a hybrid genetic algorithm based on the LM algorithm to explore the optimal parameter set, the fitness between the target value and the actual value of elastic force / damping force is maximized. This allows for the matching of actual elastic force and damping force with the target elastic force and damping force, ultimately achieving the optimization of the parameters to be optimized. The algorithm incorporates LM local optimization, where the LM local optimization part is performed when the current iteration number is an integer multiple of the preset frequency of the local search.

[0007] The fitness calculation in the elastic force matching stage involves: for each individual in the population, based on the current structural parameters... S 2. Initial inflation pressure p and the given position x Elastic force is obtained using the established neural network model. F 2; Calculate fitness : ; in, The root mean square error, Let m be the target elastic force of the gas spring, and m be the number of working conditions of the gas spring at different positions. Damping force matching stage fitness calculation: based on current structural parameters S 1 and a given velocity v The established neural network model yields the damping force. F 1; Calculate fitness : ; in The root mean square error, The target damping force of the gas spring. m’ The number of operating conditions for the gas spring at different speeds; Preferably, local optimization of LM is performed for each elite, with the following steps: S3.6.1: Selecting elite individuals to form an elite set E ; Input neural network surrogate model: displacement x or speed v Target elastic force or damping force Initial parameters of individuals in the elite set Initial damping factor; in These represent the optimization parameters for the elastic force and damping force to be optimized. S3.6.2: Obtaining elastic force Damping force ; Input neural network surrogate model displacement x or speed v ;parameter Predicting elastic force F 2 or damping force F 1; S3.6.3: Calculate the sum of squared residuals; ; in , These are the sum of squares of the residuals of elastic force and damping force, respectively; These are the residual vectors of elastic force and damping force, respectively; ; The first i Target elastic damping force values ​​under each working condition; For the first i Actual elastic force / damping force values ​​under each working condition; S3.6.4: Calculate the parameter update step size, perform parameter update iteration, adjust the damping factor by comparing the residuals and squares before and after the iteration, and output the individual parameters when the termination condition is met.

[0008] Preferably, S4 outputs the optimal parameters, and the specific steps are as follows: S4.1 Outputs Optimal Structure Parameters 、 Optimal initial inflation pressure ; S4.2 Outputs Optimal Structure Parameters .

[0009] A hydraulic suspension stiffness and damping matching design system under all working conditions includes an information acquisition unit, a data processing unit, and an output unit; Information acquisition unit: Acquires the flow coefficients of damping orifices and check valves under different structures, and establishes the structural parameters of damping orifices and check valves. S The dataset, with 1 as input and flow coefficient as output, simultaneously establishes the cylinder. 、 Piston rod structural parameters S 2. Initial inflation pressure p For input, elastic force This is the output dataset; Data processing unit: Based on the flow coefficient dataset and combined with mechanical formulas, it establishes structural parameters through a neural network. S 1. Piston rod movement speed v With damping force Nonlinear mapping relationship between them; initial inflation pressure is established through neural network. p Structural parameters S 2. Location x With elastic force F The mapping relationship of 2; using a neural network as a proxy model, a hybrid genetic algorithm based on the LM algorithm is constructed to find the optimal parameters that best match the target damping force and the target elastic force; Output unit: Outputs the results.

[0010] Compared with the prior art, the beneficial effects of this application are as follows: This invention utilizes neural networks to establish nonlinear mapping relationships between damping force and structural parameters, motion velocity, and between elastic force and structural parameters, displacement, and initial inflation pressure. A hybrid genetic algorithm based on the LM algorithm is employed for optimization, achieving rapid local fine-tuning and accelerated global convergence of damping force and elastic force related parameters. This method achieves independent and precise matching and collaborative optimization of the stiffness and damping of the gas spring. Attached Figure Description

[0011] Figure 1 Optimize the overall flowchart for matching damping force and elastic force; Figure 2 Here is a flowchart of the local optimization process of the LM algorithm; Figure 3 This is a structural diagram of a hydraulic spring check valve and its damping orifice. Detailed Implementation

[0012] To enable those skilled in the art to better understand the present invention, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Obviously, the described embodiments are merely some embodiments of the present invention, and 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.

[0013] This invention relates to a design method and system for matching the stiffness and damping of a hydropneumatic suspension under all working conditions. Its core lies in optimizing the structural dimensional parameters of the damping orifice, one-way valve, cylinder, and piston rod, along with the initial inflation pressure parameters, thereby achieving the target value of the hydropneumatic spring damping force. f 1 and actual value F 1. Target value of elastic force f 2 and actual value F Minimize the error between 2.

[0014] A method for matching the stiffness and damping of an air suspension under all working conditions mainly includes the following steps: S1: Output the flow coefficients under different damping orifice and check valve structures, and establish the structural parameters of the damping orifice and check valve. S The dataset, with 1 as input and flow coefficient as output, simultaneously establishes the cylinder. 、 Piston rod structural parameters S 2. Initial inflation pressure p For input, elastic force F 2 represents the output dataset.

[0015] First, parameterized structural models of the damping orifice and the one-way valve are established respectively, and the design parameters include, for example: Figure 3 The damping orifice shown ( ), one-way valve ( The cylinder inner diameter is The piston rod diameter is .

[0016] Secondly, the flow coefficient data of the damping orifice and the check valve were obtained through CFD simulation; and the flow coefficient data at different positions were also obtained. x Initial inflation pressure p Elastic force under different cylinder and piston rod key parameters F 2.

[0017] Finally, the structural parameters of the damping orifice and one-way valve were established. S The dataset, with 1 as input and flow coefficient as output, simultaneously establishes the cylinder. 、 Piston rod structural parameters S 2. Initial inflation pressure p For input, elastic force F 2 represents the output dataset.

[0018] S2: Use neural networks to establish structural parameter vectors respectively. S 1. Speed ​​of piston rod movement v Damping force F Mapping of 1; initial inflation pressure p Structural parameters S 2. Location x elastic force F Mapping of 2; The specific steps are as follows: S2.1: Input piston rod speed v and structural parameters S 1. Based on the input structural parameters S Substituting the flow coefficient dataset from step S1.3.1 into the dataset yields... Ultimately determined by the flow coefficient Substitute into the mechanical formula to calculate the output damping force F 1; Enter initial inflation pressure p Cylinder and piston rod structural parameters S 2. Distance between the piston's current position and its equilibrium position x Substitute into step S1.3.2 to establish elastic force F 2 datasets, output elastic force F 2; S2.2: Data Preparation: Input data is The output data is { F 1, F 2) Divide and process the data; S2.3: Training, verification, and testing ultimately yield a neural network surrogate model for predicting damping force and elastic force.

[0019] S3: Hybrid genetic algorithm for optimization based on LM algorithm.

[0020] The specific steps are as follows: Matching elastic force stage: Obtaining structural parameters of the one-way valve and damping orifice. S 1; Matching damping force stage: Obtaining structural parameters S 2 and initial inflation pressure p ; The hybrid genetic algorithm based on the LM algorithm aims to maximize the fitness between the target value and the actual value of the elastic force / damping force, thereby matching the actual elastic force / damping force with the target elastic force / damping force, and finally achieving the optimization of the parameters to be optimized. The algorithm incorporates LM local optimization, and performs the LM local optimization part when the current iteration number is an integer multiple of the local search preset frequency.

[0021] The fitness calculation in the elastic force matching stage involves: for each individual in the population, based on the current structural parameters... S 2. Initial inflation pressure p and the given position x Elastic force is obtained using the established neural network model. F 2; Calculate fitness : ; in The root mean square error, Let m be the target elastic force of the gas spring, and m be the number of working conditions of the gas spring at different positions. Damping force matching stage fitness calculation: based on current structural parameters S 1 and a given velocity v The established neural network model yields the damping force. F1: Calculate fitness : ; in The root mean square error, The target damping force of the gas spring. m’ This represents the number of operating conditions for the gas spring at different speeds.

[0022] The specific steps of the LM local optimization process are as follows: S3.6.1: Selecting elite individuals to form an elite set E ; Input neural network surrogate model: displacement x or speed v Target elastic force or damping force Initial parameters of individuals in the elite set Initial damping factor; in These represent the optimization parameters for the elastic force and damping force to be optimized. S3.6.2: Obtaining elastic force Damping force ; Input neural network surrogate model; input displacement x or speed v ;parameter Output elastic force F 2 or damping force F 1; S3.6.3: Calculate the sum of squared residuals; ; in These are the sum of squares of the residuals of elastic force and damping force, respectively; These are the residual vectors of elastic force and damping force, respectively, representing the difference between the target elastic force and damping force and the actual elastic force and damping force.

[0023] ; The first i Values ​​of target elastic force and damping force under various working conditions; The first i Actual elastic force and damping force values ​​under each working condition; S3.6.4: Calculate the parameter update step size, perform parameter update iteration, output individual parameters when the termination condition is met, otherwise compare the residual sum of squares under the new and old parameters, adjust the damping factor, and continue the calculation.

[0024] S4: Output the optimal parameters. The specific steps are as follows: S4.1 Outputs Optimal Structure Parameters 、 Optimal initial inflation pressure ; S4.2 Outputs Optimal Structure Parameters .

[0025] A hydraulic suspension stiffness and damping matching design system under all working conditions includes an information acquisition unit, a data processing unit, and an output unit; Information acquisition unit: Acquires the flow coefficients of the damping orifice and the check valve, and establishes the structural parameters of the damping orifice and the check valve. S The dataset, with 1 as input and flow coefficient as output, simultaneously establishes the cylinder. 、 Piston rod structural parameters S 2. Initial inflation pressure p For input, elastic force F 2 represents the output dataset; Data processing unit: Based on the flow coefficient dataset and combined with mechanical formulas, it establishes structural parameters through a neural network. S 1. Piston rod movement speed v With damping force F The mapping relationship between 1 and 2; the initial inflation pressure is established through a neural network. p Structural parameters S 2. Piston position x With elastic force F The mapping relationship of 2; using a neural network as a proxy model, a hybrid genetic algorithm based on the LM algorithm is constructed to find the optimal value and output the parameters that best match the target damping force and the target elastic force; Output unit: Output result.

[0026] This application achieves optimized matching between damping force and one-way valve, damping orifice structural parameters, elastic force and cylinder, piston rod structural parameters, and initial inflation pressure.

[0027] It should be understood that the application of the present invention is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A method for matching the stiffness and damping of an oil-gas suspension under all working conditions, characterized in that, Includes the following steps: S1: Establish structural parameters for the damping orifice and one-way valve S A dataset with 1 as input and flow coefficient as output is established, along with a cylinder... 、 Piston rod structural parameters S 2. Initial inflation pressure p For input, elastic force This is the output dataset; S2: Establishing the damping hole, one-way valve structure parameters respectively S 1, the velocity of the piston rod v and the damping force ; initial inflation pressure p , structure parameters S 2, the piston position x and the elastic force ; ； ； wherein is the cylinder bore diameter, is the piston rod diameter; Using a neural network model to establish the mapping relationship between different structural parameters S 1. Different movement speeds v and damping force ; establish the mapping relationship between different structural parameters S 2. Piston position x , different initial inflation pressure p and elastic force , the specific steps are as follows: S2.1: Input speed of movement between the hydraulic spring cylinder and the piston rod v and structural parameters S 1. Based on each structural parameter in the input dataset S 1. Obtain the flow coefficients of the damping orifice and the one-way valve respectively; finally, substitute the flow coefficients into the mechanical formula to calculate the output damping force. F 1; Input initial inflation pressure p Structural parameters S 2. Piston position x Obtaining elastic force F 2. Establishing elastic force F 2. Data set; S2.2: Data preparation: input data is , output data is , data is divided and processed; S2.3: Through training, verification, and testing, a neural network surrogate model for predicting damping force and elastic force is finally obtained; S3: Optimization using a hybrid genetic algorithm based on the LM algorithm, with the following specific steps: Cylinder inner diameter Piston rod diameter Initial charge pressure p is a parameter for elastic force matching, and is not used as an optimization parameter in the damping force matching stage, so the matching of damping force and elastic force is divided into two design stages. First, the structure parameters , , p are optimized, and then the structure parameters S 1 are optimized. The specific steps are as follows: Matching elastic force phase: Obtain structure parameters S 1; Matching damping force phase: obtaining structure parameters S 2 with initial inflation pressure p ; By exploring the optimal design parameters using a hybrid genetic algorithm based on the LM algorithm, the fitness between the actual and target values ​​of elastic force and damping force is maximized, thereby matching the actual elastic force and damping force with the target elastic force and damping force, and finally achieving the optimization of the parameters to be optimized. It incorporates local optimization of the LM model; if the current iteration number is an integer multiple of the preset frequency of the local search, the local optimization part of the LM model is performed. Wherein the elastic force matching stage fitness calculation: for each individual in the population, according to the current structure parameters S 2, initial inflation pressure p And the given piston position x , the actual value of the elastic force is obtained by using the established neural network model F 2; Computing fitness : ； ； wherein is the root mean square error, is the target value of the elastic force of the oil gas spring, and m is the number of working conditions of the oil gas spring at different positions. Damping force matching phase fitness calculation: according to the current structure parameters S 1 and given motion speed v , the actual value of the damping force obtained by the established neural network model F 1: Computing fitness : ； ； wherein is the root mean square error, is the oil gas spring damping force target value, m’ is the number of working conditions of the oil gas spring at different speeds; For each elite, perform local optimization using the LM algorithm, following these steps: S3.1: Selecting elite individuals to form an elite set E ; Input neural network surrogate model: piston position x or speed of movement v Target value of elastic force Or target value of damping force Initial parameters of individuals in the elite set Initial damping factor; S3.2: Obtain actual value of elastic force , actual value of damping force ; Input neural network surrogate model: piston position x or speed of movement v Initial parameters Predict the actual value of elastic force F 2 or actual value of damping force F 1; S3.3: Compute the sum of squared residuals or ; ； ； in , These are the sum of squares of the residuals of elastic force and damping force, respectively; , These are the residual vectors of elastic force and damping force, respectively; ； respectively the target elastic force and damping force value under the i th working condition; The first i Actual elastic force and damping force values ​​under each working condition; S3.4: Calculate the parameter update step size, perform parameter update iteration, adjust the damping factor by comparing the residuals and squares before and after the iteration, and output the individual parameters when the termination condition is met; S4: Output optimal parameters: S4.1 outputting the optimal structure parameter , optimal initial inflation pressure ; S4.2 output optimal structure parameters .

2. The hydropneumatic suspension stiffness and damping full-condition matching design method according to claim 1, characterized in that, Step S1: Establish structural parameters S A dataset with 1 as input and flow coefficient as output is established, along with a cylinder... 、 Piston rod structural parameters S 2. Initial inflation pressure p For input, elastic force To obtain the output dataset, the steps are as follows: S1.1: Establish parametric structural models for the damping orifice, one-way valve, cylinder, and piston rod respectively. The parameters of the damping orifice are ( The parameters of the one-way valve are () The cylinder inner diameter is The diameter of the piston rod is D h 、 The parameters of the large hole are ( , The parameters of the small hole are () ), angle is ( ), height is h The diameter of the sphere is ; S1.2: Obtain flow coefficient data for different combinations of key parameters of damping orifice and check valve through simulation; Piston position obtained through simulation x Different initial inflation pressures p Elastic force under different cylinder and piston rod key parameters ; S1.3: Establish structural parameters S The dataset consists of 1 as input and flow coefficient as output. Establish structural parameters S 2. Initial inflation pressure p For input, elastic force The output dataset.

3. A hydropneumatic suspension stiffness and damping full-condition matching design system, configured in the hydropneumatic suspension stiffness and damping full-condition matching design method according to any one of claims 1-2, characterized in that, It includes an information acquisition unit, a data processing unit, and an output unit; Information acquisition unit: Acquires the flow coefficients of damping orifices and check valves under different structures, and establishes the structural parameters of damping orifices and check valves. S A dataset with 1 as input and flow coefficient as output is established, along with a cylinder... 、 Piston rod structural parameters S 2. Initial inflation pressure p For input, elastic force This is the output dataset; Data processing unit: Based on the flow coefficient dataset, combined with the piston rod movement speed v And mechanical formulas, and establish structural parameters through neural networks. S 1. Speed ​​of movement v With damping force Nonlinear mapping relationship between them; initial inflation pressure is established through neural network. p Structural parameters S 2. Piston position x With the elastic force of the gas spring F The mapping relationship of 2; using a neural network as a proxy model, a hybrid genetic algorithm based on the LM algorithm is constructed to find the optimal parameters that best match the target damping force and the target elastic force; Output unit: Outputs the results.

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