Hydraulic bushing modeling and fatigue life acceleration prediction method for new energy automobile suspension

Abstract

The invention relates to the technical field of hydraulic bushings, in particular to a hydraulic bushing modeling and fatigue life acceleration prediction method for a new energy automobile suspension, which comprises the following steps: S1, collecting data and analyzing the driving condition characteristics of a new energy automobile; s2, the structure of the hydraulic bushing is selected, and parameters of the hydraulic bushing are preliminarily determined; s3, checking the strength and stability of the hydraulic bushing; s4, analyzing the sensitivity of each parameter and determining an optimizable parameter, wherein the sensitivity analysis comprises local sensitivity analysis and global sensitivity analysis; s5, based on a simple harmonic excitation experiment, fitting model initial parameters by using a classic frequency domain identification method; and S6, fitting the parameters of the mixed lumped parameter model of the hydraulic bushing according to the experimental data. According to the technical scheme, through the innovative modeling method and the fatigue life prediction method, the dynamic performance prediction precision and the fatigue life evaluation accuracy of the hydraulic bushing for the new energy automobile suspension are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of hydraulic bushings, and particularly to a method for modeling and accelerating fatigue life prediction of hydraulic bushings for new energy vehicle suspensions. Background Technique

[0002] At present, with the booming development of new energy vehicles, their unique motor drive characteristics, frequent start-stop mechanisms, and energy recovery functions have made the NVH (noise, vibration, harshness) performance of vehicles a focus of attention. Different from traditional fuel vehicles, new energy vehicles lack the shielding effect of the engine, which makes the vibration and noise generated by the motor operation more likely to be transmitted into the vehicle, affecting the ride comfort. As a key component of the vehicle, the performance of the suspension system plays a decisive role in the comfort and safety of the whole vehicle. In new energy vehicles, due to the above characteristics, the requirements for vibration and noise control are higher. Traditional rubber bushings are difficult to meet these strict requirements, and hydraulic bushings have gradually become a research hotspot due to their superior vibration isolation performance.

[0003] A hydraulic bushing is a composite vibration isolation element that combines rubber and hydraulic damping, with excellent vibration isolation performance and energy absorption capacity. Its dynamic characteristics are not only related to frequency but also closely related to the excitation amplitude, showing significant nonlinear characteristics. Therefore, accurately modeling the dynamic characteristics of hydraulic bushings and predicting the fatigue life on this basis are of great significance for optimizing the suspension system design and improving the NVH performance of new energy vehicles.

[0004] At present, scholars at home and abroad have conducted a large number of studies on the dynamic characteristic modeling and fatigue life prediction of hydraulic bushings. Traditional modeling methods mostly use linear models, such as lumped parameter models and equivalent mechanical models. These models can better describe the dynamic characteristics of hydraulic bushings under a certain specific working condition, but it is difficult to reflect their amplitude correlation and frequency correlation. In recent years, some researchers have proposed nonlinear models, such as fractional derivative models and hyperelastic-viscoelastic-elastoplastic models. These models can better describe the nonlinear dynamic characteristics of hydraulic bushings, but the models are complex, parameter identification is difficult, and it is difficult to apply to engineering practice.

[0005] In terms of fatigue life prediction, the special driving conditions of new energy vehicles pose challenges to the durability of hydraulic bushings. Its load spectrum is affected by motor drive characteristics, frequent start-stop, and different road conditions, which is significantly different from that of traditional fuel vehicles. Under the action of complex and variable loads, the fatigue damage accumulation process of hydraulic bushings is complex. Traditional fatigue life prediction methods are mostly based on linear models and are difficult to accurately predict the fatigue life of hydraulic bushings under complex working conditions. Researchers have proposed a fatigue life prediction method based on the damage accumulation theory and used the rain flow counting method to compile the fatigue load spectrum. Such methods can consider the amplitude and frequency distribution of the load spectrum, but the calculation complexity is high, and it is difficult to achieve rapid prediction. Summary of the Invention

[0006] The object of the present invention is to solve the problems existing in the prior art, and to propose a method for modeling and accelerated prediction of fatigue life of a hydraulic bushing for a new energy vehicle suspension.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] A method for modeling and accelerated prediction of fatigue life of a hydraulic bushing for a new energy vehicle suspension, comprising the following steps:

[0009] S1. Collect data and analyze the driving condition characteristics of new energy vehicles;

[0010] S2. Select the structure of the hydraulic bushing and preliminarily determine its parameters;

[0011] S3. Check the strength and stability of the hydraulic bushing;

[0012] S4. Analyze the sensitivity of each parameter and determine the optimizable parameters, and the sensitivity analysis includes local sensitivity analysis and global sensitivity analysis;

[0013] S5. Based on the harmonic excitation experiment, use the classical frequency domain identification method to fit the initial parameters of the model;

[0014] S6. Fit the parameters of the hybrid lumped parameter model of the hydraulic bushing according to the experimental data;

[0015] S7. Fine-tune the model parameters based on fast time domain identification;

[0016] S8. Predict the dynamic performance of the hydraulic bushing under multiple working conditions through simulation;

[0017] S9. Conduct an accelerated performance degradation experiment for fatigue life;

[0018] S10. Predict the accelerated life of the bushing based on the accelerated life prediction method of the degradation amount distribution;

[0019] S11. Optimize and solve the design variables based on performance and life prediction.

[0020] Preferably, the step of collecting data and analyzing the driving condition characteristics of new energy vehicles includes the following steps:

[0021] S1. Install required sensors at key parts of the new energy vehicle and collect relevant data;

[0022] S2. Conduct preprocessing and filtering of the data. The preprocessing steps of the data include removing outliers, filling in missing data, and smoothing processing. The filtering of the data includes low-pass filtering, high-pass filtering, and band-pass filtering;

[0023] S3. Analyze the load spectrum of new energy vehicles.

[0024] Preferably, the structural selection of the hydraulic bushing and the preliminary determination of its parameters include the following steps:

[0025] S1. Select a suitable hydraulic bushing configuration according to the vehicle model characteristics. The hydraulic bushing configurations include single-chamber type, double-chamber type, and multi-chamber type.

[0026] S2. Preliminarily determine the key design parameters of the hydraulic bushing. The key design parameters include the stiffness K r of the rubber main spring, the volume V c of the liquid chamber, the length L and diameter D of the inertia channel, and the viscosity μ of the liquid.

[0027] Preferably, the strength and stability check of the hydraulic bushing include the following steps:

[0028] S1. Establish a three-dimensional geometric model of the hydraulic bushing and import it into the finite element analysis software.

[0029] S2. Apply corresponding boundary conditions and loads according to the load spectrum under actual working conditions.

[0030] Preferably, in step S5, apply harmonic excitations with different frequencies to the hydraulic bushing, measure its dynamic response, extract the dynamic characteristic parameters of the hydraulic bushing through frequency domain analysis method, and construct a frequency domain parameter model according to the dynamic stiffness K(ω, A) and the loss energy W(ω, A). The parameter model formula is:

[0031]

[0032] where p is the model parameter to be identified, ω is the vibration angular frequency, A is the displacement amplitude. Among them, p can be composed of the stiffness K r of the rubber main spring, the volume V c of the liquid chamber, the length L and diameter D of the inertia channel, and the viscosity μ of the liquid, that is, p = {K r , V c , L, D, μ};

[0033] The dynamic stiffness K(ω, A) and the loss energy W(ω, A) can be expressed as:

[0034] K * (ω, A) = K'(ω, A) + jK”(ω, A);

[0035] W(ω, A) = πA 2 K”(ω, A)

[0036] where K'(ω, A) is the storage stiffness, representing the elastic characteristic; K”(ω, A) is the loss stiffness, representing the damping characteristic.

[0037] Preferably, in the step S6, a weighted multi-objective minimization method is used to fit the parameter model, and the parameter model formula is:

[0038]

[0039] wherein, and are respectively the measured reference values of dynamic stiffness and loss energy. β is used to weigh the relative contributions of dynamic stiffness and loss energy in the optimization process. By adjusting the model parameter p, the difference between the model prediction value and the measured reference value is minimized to obtain the most suitable model parameters;

[0040] The hybrid lumped parameter model is obtained by weighting multiple parameter models,

[0041] wherein, the hybrid lumped parameter model is the Kelvin–Voigt model, and its model formula is:

[0042]

[0043] wherein, the hybrid lumped parameter model is the Berg model, and its model formula is:

[0044]

[0045] wherein, the hybrid lumped parameter model is the Mod-Berg model, and its model formula:

[0046]

[0047] where k is the stiffness and c is the damping; F e is the elastic component, F ve is the viscoelastic component, and F fr is the friction component.

[0048] Preferably, in the step S7, the time-domain identification is to identify the dynamic response of the hydraulic bushing under random loads through a time-domain analysis method. First, a random load is applied to the hydraulic bushing, and its dynamic response is measured. The Sigmoid-chirp relative displacement is applied to the bushing as an excitation signal using a fatigue testing machine. The frequency of this signal is scanned within a given range, and the amplitude gradually changes, exciting the full-range movement of the bushing sample in the compression and tension directions, ensuring at least one complete cycle in the low-frequency band to capture the friction effect and not exciting excessive viscous damping phenomena, while covering the entire frequency bandwidth and amplitude range of interest; then the reaction force generated by the bushing and the applied relative displacement are recorded.

[0049] Preferably, in the step S8, the multi-condition simulation is carried out through finite element analysis and numerical simulation, and the analyzed conditions include low-frequency large-amplitude excitation, high-frequency small-amplitude excitation, and random load excitation.

[0050] Preferably, in the step S10, the accelerated life prediction method based on the degradation quantity distribution establishes a degradation quantity distribution model by analyzing the performance degradation quantity distribution of the hydraulic bushing. In this method, the performance degradation quantity of the hydraulic bushing follows a Weibull distribution, and the distribution parameters are estimated by statistical methods.

[0051] First, based on all the obtained data, a global reliability model R * (t) is established. The reliability model formula based on the degradation quantity distribution is as follows:

[0052]

[0053] Then, cross-validation is performed on the experimental data. The data set is divided into several subsets, each subset contains a certain proportion of experimental data. One subset is taken as the test set in turn, and the rest are used as the training set. Then, the sub-reliability function is established using the training set, the results of the test set data are predicted, and the prediction error of the sub-reliability function is calculated. The error indicators include mean square error, mean absolute error, and coefficient of determination. Then, the prediction results and experimental data under different training set partitions are compared to evaluate the accuracy and reliability of the prediction results. If the prediction result error is less than the set threshold, the selected accelerated life prediction model is considered accurate;

[0054] Finally, based on the obtained reliability model, life curve extrapolation is carried out to finally obtain the product life prediction model.

[0055] Preferably, in the step S11, based on the performance and life prediction results, an optimization model of the hydraulic bushing is constructed. The results of performance modeling provide key stress-strain data and dynamic performance information for fatigue optimization. The weak links and optimization directions determined in the fatigue optimization process guide the improvement of structural design. The objective function and constraint conditions of the optimization model are as follows:

[0056]

[0057] Among them, L(x) represents the fatigue life, P(x) represents the dynamic performance index, R(x) represents the reliability index, and x is the design variable;

[0058] After completing a round of fatigue optimization, the optimized structural parameters are fed back to the structural design stage, and steps such as re - performing design calculations, strength, and stability checks are carried out again. The update of the structural design triggers a new cycle of performance modeling and fatigue optimization. Through multiple iterations, the comprehensive performance of the hydraulic bushing is continuously improved until it meets the strict requirements of the vehicle suspension system for the hydraulic bushing in terms of fatigue life, dynamic performance, and reliability, realizing the continuous optimization of product design.

[0059] Compared with the existing technologies, the advantages of the present invention are as follows:

[0060] The technical solution of the present invention significantly improves the prediction accuracy of the dynamic performance and the evaluation accuracy of the fatigue life of the hydraulic bushing for new - energy vehicle suspensions through innovative modeling methods and fatigue life prediction methods.

[0061] 1. Conduct structural design and optimization according to the characteristics of new - energy vehicles: Fully consider the characteristics of new - energy vehicles, such as the driving states under different energy - transmission conditions, various driving operations, and complex road conditions, and select the structure type and determine the parameters of the hydraulic bushing. Select a suitable configuration of the hydraulic bushing according to the vehicle model characteristics, such as single - chamber, double - chamber, or multi - chamber, and preliminarily determine the key design parameters such as the rubber main - spring stiffness, liquid - chamber volume, inertia - channel size, and liquid viscosity. At the same time, considering the limited space of the new - energy vehicle suspension system, design a compact and easy - to - install structure. Through this targeted design, the hydraulic bushing can better adapt to the special working conditions of new - energy vehicles and improve its overall performance in the new - energy vehicle suspension system.

[0062] 2. Improve the accuracy of the dynamic - characteristic modeling of the hydraulic bushing: A series of advanced modeling methods are adopted, including fitting the initial parameters of the model using the classical frequency - domain identification method based on the harmonic - excitation experiment, fitting the parameters of the hybrid lumped - parameter model of the hydraulic bushing using the weighted multi - objective minimization method, and finely tuning the model parameters based on fast time - domain identification. These methods can effectively capture the non - linear dynamic characteristics of the hydraulic bushing, fully consider the correlation between its dynamic characteristics and the excitation amplitude and frequency, and solve the limitations of the traditional linear model in describing the dynamic characteristics of the hydraulic bushing. Through multi - step parameter fitting and fine - tuning, the accuracy of the dynamic - characteristic modeling of the hydraulic bushing is significantly improved, providing a reliable basis for accurately predicting its performance under different working conditions.

[0063] 3. Improve the accuracy of fatigue life prediction: In terms of fatigue life prediction, the impact of the special driving conditions of new energy vehicles on the durability of hydraulic bushings is comprehensively considered. By comprehensively collecting the load data of new energy vehicles under various conditions, preprocessing and analyzing it, an accurate load spectrum is obtained. Based on this load spectrum, a fatigue life accelerated performance degradation experiment is designed, and a multi-model accelerated life prediction cross-validation method is adopted, including accelerated life prediction methods based on random processes, degradation trajectories, and degradation quantity distributions. These methods describe the fatigue damage process of hydraulic bushings from different perspectives, and are preferentially used through cross-validation, effectively improving the accuracy of fatigue life prediction, being able to more accurately evaluate the fatigue life of hydraulic bushings in actual use, and providing strong support for the reliability design and maintenance of hydraulic bushings.

[0064] 4. Reduce R & D costs and cycle: On the one hand, during the modeling process, through accurate parameter identification and high-precision model establishment, the repeated experiments and adjustments caused by inaccurate models are reduced, saving experimental costs and time. On the other hand, in terms of fatigue life prediction, an accelerated life prediction method is adopted. By increasing the load amplitude or frequency, the experimental cycle is shortened, and fatigue life data can be quickly obtained. At the same time, multi-model cross-validation ensures the accuracy of the prediction results, avoiding additional R & D costs caused by inaccurate predictions. In addition, based on the optimization solution of design variables for performance and life prediction, through multiple iterations of optimization, while the hydraulic bushing meets the performance requirements, its structural parameters are continuously optimized, reducing unnecessary material waste and design changes, thus effectively reducing R & D costs and shortening the entire R & D cycle.

[0065] In summary, the technical solution of the present invention significantly improves the performance and reliability of hydraulic bushings by carrying out structural design and optimization according to the characteristics of new energy vehicles, improving the modeling accuracy of the dynamic characteristics of hydraulic bushings, enhancing the accuracy of fatigue life prediction, and reducing R & D costs and cycle, providing important technical support for the design and optimization of hydraulic bushings for new energy vehicle suspensions. Description of the Drawings

[0066] Figure 1 It is the framework flowchart of the modeling and fatigue life accelerated prediction method for the hydraulic bushing of the new energy vehicle suspension proposed by the present invention. Detailed Embodiments

[0067] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0068] Refer toFigure 1 , A method for modeling and accelerating fatigue life prediction of a hydraulic bushing for a new energy vehicle suspension, comprising the following steps:

[0069] S1. Collect data and analyze the driving condition characteristics of new energy vehicles;

[0070] S2. Select the structure of the hydraulic bushing and preliminarily determine its parameters;

[0071] S3. Check the strength and stability of the hydraulic bushing;

[0072] S4. Analyze the sensitivity of each parameter and determine the parameters that can be optimized. The sensitivity analysis includes local sensitivity analysis and global sensitivity analysis;

[0073] Furthermore, through parametric modeling and simulation analysis, determine which parameters have the greatest impact on the performance of the hydraulic bushing. Commonly used sensitivity analysis methods include local sensitivity analysis and global sensitivity analysis. Local sensitivity analysis evaluates the impact on the performance of the hydraulic bushing by changing the value of a single parameter; global sensitivity analysis evaluates the impact of the interaction between multiple parameters on the performance of the hydraulic bushing by changing the values of multiple parameters. The results of the parameter sensitivity analysis provide an important basis for subsequent optimization design. Determine the parameters with higher sensitivity as the parameter space for subsequent performance modeling and fatigue optimization analysis, and conduct targeted optimization design to improve the performance and life of the hydraulic bushing. After completing the structural design of the hydraulic bushing in the first to fourth steps, according to the selected configuration and initially determined parameters, make samples of the hydraulic bushing for the experiments required for subsequent performance modeling and fatigue optimization.

[0074] S5. Based on the harmonic excitation experiment, use the classical frequency domain identification method to fit the initial parameters of the model;

[0075] S6. Fit the parameters of the hybrid lumped parameter model of the hydraulic bushing according to the experimental data;

[0076] S7. Fine-tune the model parameters based on fast time domain identification;

[0077] S8. Predict the dynamic performance of the hydraulic bushing under multiple working conditions through simulation;

[0078] S9. Conduct an accelerated performance degradation experiment for fatigue life;

[0079] S10. Predict the accelerated life of the bushing based on the accelerated life prediction method of the degradation amount distribution;

[0080] S11. Optimize and solve the design variables based on performance and life prediction.

[0081] Collecting data and analyzing the driving condition characteristics of new energy vehicles includes the following steps:

[0082] S1. Install the required sensors at the key parts of new energy vehicles and collect relevant data. At the key parts of new energy vehicles, such as the chassis, suspension, wheels, etc., install high-precision sensors, including acceleration sensors, displacement sensors, strain sensors, torque sensors, etc. The vehicle travels under various actual road conditions, covering urban congested roads, highways, rural bumpy roads, etc. The sensors collect various physical quantity data during different driving operations in various energy transmission conditions of the vehicle, such as pure electric driving, power feeding driving, mode switching, etc., such as acceleration, deceleration, turning, braking, etc. during the operation of the vehicle. Data collection not only needs to consider the normal driving conditions of the vehicle, but also cover extreme conditions, such as hard acceleration, hard braking, and high-speed cornering. The load data under these extreme conditions is particularly important for subsequent fatigue life prediction because it often causes stress concentration and increased fatigue damage of the hydraulic bushing. Through comprehensive load spectrum collection, rich data support is provided for subsequent modeling and prediction;

[0083] S2. Preprocess and filter the data. The collected load data usually contains a large amount of noise and outliers. These noises may come from sensor errors, environmental interference, or vibrations of other vehicle components. To ensure the accuracy of the data, the original data needs to be preprocessed first. The preprocessing steps include removing obvious outliers, filling in missing data, and smoothing. Commonly used filtering techniques include low-pass filtering, high-pass filtering, and band-pass filtering. These filtering methods can effectively remove high-frequency noise and low-frequency drift and retain useful signal components. Normalize the data to eliminate the dimensional differences between different sensors. The normalized data can better reflect the actual force condition of the hydraulic bushing and provide a reliable basis for subsequent load spectrum analysis. Data processing and filtering are key steps to ensure the accuracy of subsequent modeling and prediction. Only the data that has been strictly processed can accurately reflect the actual working state of the hydraulic bushing;

[0084] S3. Analyze the load spectrum of new energy vehicles. Load spectrum analysis is to perform statistical and frequency domain analysis on the processed data to extract the load characteristics of the hydraulic bushing under actual working conditions. First, segment the load data and divide the continuous time-domain signal into multiple load cycles. Each load cycle contains a complete load change process, such as from zero load to maximum load and then back to zero load. Through statistical analysis of these load cycles, information such as the amplitude distribution, frequency distribution, and number of cycles of the load is obtained. Load spectrum analysis provides input data for subsequent fatigue life prediction. By analyzing the load spectrum, determine the stress level and number of cycles of the hydraulic bushing under different working conditions, and then evaluate its fatigue damage accumulation. The results of load spectrum analysis are usually presented in the form of stress-time curves or stress-frequency curves. These curves can intuitively reflect the load characteristics of the hydraulic bushing and provide an important basis for subsequent modeling and prediction.

[0085] The structural selection of the hydraulic bushing and the preliminary determination of its parameters include the following steps:

[0086] S1. Select a suitable configuration for the hydraulic bushing according to the vehicle model characteristics. The configurations of the hydraulic bushing include single-chamber type, double-chamber type, and multi-chamber type. Further, the single-chamber type has a simple structure and is suitable for working conditions with low frequencies and small amplitudes; the double-chamber type can provide better vibration isolation performance by adding liquid chambers and inertia channels and is suitable for working conditions with medium frequencies and amplitudes; the multi-chamber type can adapt to more complex working conditions through the combination of multiple liquid chambers and inertia channels and is suitable for working conditions with high frequencies and large amplitudes. During the structural selection process, the installation space and connection method of the hydraulic bushing also need to be considered. The suspension system of new energy vehicles usually has limited space, so the structural design of the hydraulic bushing needs to be compact and easy to install. At the same time, the connection method of the hydraulic bushing also needs to match other components of the suspension system to ensure that it can withstand complex multi-axis loads;

[0087] S2. Preliminarily determine the key design parameters of the hydraulic bushing. The key design parameters include the stiffness K of the rubber main spring r , the volume V of the liquid chamber c , the length L and diameter D of the inertia channel, and the viscosity μ of the liquid. Further, the stiffness of the rubber main spring directly affects the static and dynamic characteristics of the hydraulic bushing and is usually determined through experiments or simulations. The volume of the liquid chamber and the size of the inertia channel determine the damping characteristics of the hydraulic bushing and are usually optimized through hydrodynamic analysis. The process of initial parameter determination needs to combine the actual working conditions of new energy vehicles and the design requirements of the suspension system. By preliminarily determining the above parameters, basic data is provided for subsequent verification, performance optimization, and life prediction.

[0088] The strength and stability verification of the hydraulic bushing include the following steps:

[0089] S1. Establish a three-dimensional geometric model of the hydraulic bushing and import it into the finite element analysis software;

[0090] S2. Apply corresponding boundary conditions and loads according to the load spectrum under actual working conditions.

[0091] Through finite element analysis, the stress distribution and deformation of the hydraulic bushing under different loads are obtained. Evaluate the stress level of the hydraulic bushing under the ultimate load to ensure that it does not exceed the yield strength of the material. The stability verification is mainly carried out through modal analysis and buckling analysis. Modal analysis is used to determine the natural frequency and vibration mode of the hydraulic bushing to ensure that it does not resonate with other components of the suspension system. Buckling analysis is used to evaluate the stability of the hydraulic bushing under compressive loads to ensure that it does not undergo instability or buckling failure.

[0092] In step S5, a harmonic excitation with different frequencies is applied to the hydraulic bushing, and its dynamic response is measured. Through the frequency-domain analysis method, the dynamic characteristic parameters of the hydraulic bushing are extracted. A frequency-domain parameter model is constructed based on the dynamic stiffness K(ω,A) and the loss energy W(ω,A). The formula of the parameter model is as follows:

[0093]

[0094] where p is the model parameter to be identified, ω is the vibration angular frequency, and A is the displacement amplitude. Here, p can be composed of the stiffness K r of the rubber main spring, the volume V c of the liquid chamber, the length L and diameter D of the inertia channel, and the viscosity μ of the liquid, that is, p = {K r , V c , L, D, μ};

[0095] The dynamic stiffness K(ω,A) and the loss energy W(ω,A) can be expressed as:

[0096] K * (ω,A) = K'(ω,A) + jK”(ω,A);

[0097] W(ω,A) = πA 2 K”(ω,A)

[0098] where K'(ω,A) is the storage stiffness, representing the elastic characteristic; K”(ω,A) is the loss stiffness, representing the damping characteristic.

[0099] A steady-state harmonic test of the bushing with multiple combinations of frequencies and amplitudes is carried out. The steady-state part is extracted from the measured force and displacement signals and used to calculate the dynamic stiffness and loss energy as reference values. The dynamic stiffness is calculated as the ratio of the best-fitting force and the amplitude of the displacement harmonic signal, or obtained from the slope of the major axis of the quasi-elliptical hysteresis loop; the loss energy is obtained from the area of the hysteresis loop. In this way, the measured data is converted into physical quantities that can be directly used for model parameter identification, providing target values for the subsequent optimization process.

[0100] In step S6, a weighted multi-objective minimization method is used to fit the parameter model. The formula of the parameter model is as follows:

[0101]

[0102] where and are the measured reference values of the dynamic stiffness and loss energy respectively. β is used to weigh the relative contributions of the dynamic stiffness and loss energy in the optimization process. By adjusting the model parameter p, the difference between the model prediction value and the measured reference value is minimized to obtain the most suitable model parameters;

[0103] The hybrid lumped parameter model is obtained by weighting multiple parameter models.

[0104] Among them, the hybrid lumped parameter model is the Kelvin–Voigt model, and its model formula is:

[0105]

[0106] Among them, the hybrid lumped parameter model is the Berg model, and its model formula is:

[0107]

[0108] Among them, the hybrid lumped parameter model is the Mod-Berg model, and its model formula:

[0109]

[0110] Where k is the stiffness and c is the damping; F e is the elastic component, F ve is the viscoelastic component, F fr is the friction component.

[0111] In step S7, time-domain identification is to identify the dynamic response of the hydraulic bushing under random loads through time-domain analysis methods. First, a random load is applied to the hydraulic bushing, and its dynamic response is measured. The Sigmoid-chirp relative displacement is applied to the bushing as an excitation signal using a fatigue testing machine. The frequency of this signal is scanned within a given range, and the amplitude gradually changes. Its design purpose is to stimulate the full-range movement of the bushing sample in the compression and tensile directions, ensure at least one complete cycle in the low-frequency band to capture the friction effect and not stimulate excessive viscous damping phenomena, while covering the entire frequency bandwidth and amplitude range of interest. Record the reaction force F m (t) and the applied relative displacement x(t). For a bushing system with an obvious non-linear dynamic response, its dynamic characteristics are affected by factors such as frequency, amplitude, preload, and temperature. The time-domain identification method can effectively capture these non-linear phenomena. By adopting a special Sigmoid-chirp excitation signal, the full-range movement of the bushing under different working conditions is stimulated, covering the changes from low frequency to high frequency and different amplitudes, so as to obtain rich information to accurately identify the model parameters, which is suitable for studying the non-linear characteristics of the bushing, quickly determining the model parameters, and predicting the bushing force more quickly and accurately than the frequency-domain identification method.

[0112] In step S8, based on the hybrid lumped parameter model, the dynamic performance of the hydraulic bushing under different working conditions is simulated. The multi-condition simulation is carried out through finite element analysis and numerical simulation. The analyzed working conditions include low-frequency large-amplitude excitation (such as a vehicle passing over a speed bump), high-frequency small-amplitude excitation (such as engine vibration), and random load excitation (such as random vibration caused by road unevenness), etc. Through the multi-condition simulation, the dynamic performance of the hydraulic bushing under different load conditions is predicted, including dynamic stiffness, damping, and hysteresis angle, etc. The results of the multi-condition simulation can be used to evaluate the performance of the hydraulic bushing under different working conditions, provide a reference for the optimization design, and provide an important basis for the subsequent fatigue life prediction. After the multi-condition simulation is completed, it is necessary to evaluate the dynamic performance of the hydraulic bushing. The main indicators for performance evaluation include dynamic stiffness, damping, and hysteresis angle. By comparing the simulation results with the design requirements, it is judged whether the hydraulic bushing meets the performance indicators. Finally, a sample of the optimized hydraulic bushing is manufactured, and a dynamic performance test is carried out. By comparing the experimental results with the simulation results, the feasibility and effectiveness of the optimization results are ensured.

[0113] In step S9, the actual force-bearing conditions of the hydraulic bushing in the suspension system of new energy vehicles are studied in depth. Combining vehicle dynamics analysis and actual road test data, the fatigue load spectrum of the new energy vehicle model compiled in the first step is analyzed in detail. Determine the main load types (axial force, radial force, torque, etc.) borne by the hydraulic bushing and their variation laws under different driving conditions (such as acceleration, braking, turning, driving on bumpy roads, etc.). According to the above analysis results, the fatigue load spectrum is reasonably classified and screened, and the load segments and cycles that play a key role in the fatigue damage of the hydraulic bushing are extracted. Using advanced signal processing techniques such as wavelet transform, the noise and secondary components in the load spectrum are removed to improve the accuracy and effectiveness of the load spectrum.

[0114] To achieve the accelerated prediction of fatigue life, in terms of increasing the load amplitude, according to the material properties and structural strength of the hydraulic bushing, a suitable amplitude amplification factor is determined. Referring to the fatigue test data of similar materials and structural components and relevant material mechanics theories, it is ensured that the amplified load amplitude can not only significantly shorten the experimental period but also not introduce new failure modes or change the original failure mechanism. For example, it is determined to increase the key load amplitude by 1.5 to 2 times on the premise of not exceeding a certain proportion of the material yield strength. In terms of increasing the frequency, considering the dynamic response characteristics and damping characteristics of the hydraulic bushing, a suitable frequency increase range is selected. Using the dynamic model of the hydraulic bushing, the stress and strain distribution at different frequencies are analyzed to avoid abnormal phenomena such as changes in the flow characteristics of the hydraulic oil or structural resonance caused by too high a frequency. Based on the original typical working condition frequency, the frequency is gradually increased by 2 - 3 times for the experiment, and at the same time, the temperature change of the hydraulic bushing is monitored to ensure that its working temperature is within the range allowed by the material.

[0115] During the accelerated experiment, the fatigue load with an increased amplitude or frequency is gradually applied according to the predetermined experimental plan. After each loading cycle, the collected data is promptly analyzed and processed to calculate the variation of key performance indicators (such as stiffness, damping, maximum stress, maximum strain, etc.). Regular visual inspections are carried out on the hydraulic bushing to observe whether there are phenomena such as cracks, rubber aging, and metal corrosion. Nondestructive testing techniques, such as ultrasonic testing and infrared thermography, are used to detect the internal structure of the bushing to discover potential defects and damages. According to the performance monitoring data and the results of visual inspections, the experimental parameters (such as load amplitude, frequency, loading time interval, etc.) are adjusted in a timely manner to ensure that the experiment can accurately simulate the fatigue damage process of the hydraulic bushing under actual working conditions and obtain reliable fatigue life accelerated performance degradation data in the shortest time. A large amount of collected fatigue life accelerated performance degradation data is systematically sorted and analyzed. Statistical analysis methods are used to calculate statistical parameters such as the mean, standard deviation, and coefficient of variation of key performance indicators to evaluate the discreteness and reliability of the data.

[0116] In step S10, the accelerated life prediction method based on the degradation amount distribution analyzes the performance degradation amount distribution of the hydraulic bushing to establish a degradation amount distribution model. In this method, the performance degradation amount of the hydraulic bushing follows a Weibull distribution, and the distribution parameters are estimated by statistical methods.

[0117] First, based on all the obtained data, a global reliability model R * (t) is established. The reliability model formula based on the degradation amount distribution is:

[0118]

[0119] Then, cross-validation is performed on the experimental data. The data set is divided into several subsets, each subset containing a certain proportion of experimental data. One subset is taken turns as the test set, and the rest are used as the training set. Then, the sub-reliability function is established using the training set, the results of the test set data are predicted, and the prediction error of the sub-reliability function is calculated. The error indicators include mean square error, mean absolute error, and coefficient of determination. Then, the prediction results and the experimental data under different training set partitions are compared to evaluate the accuracy and reliability of the prediction results. If the prediction result error is less than the set threshold, the selected accelerated life prediction model is considered accurate;

[0120] Finally, based on the obtained reliability model, life curve extrapolation is performed to ultimately obtain the product life prediction model.

[0121] In step S11, based on the performance and life prediction results, an optimization model of the hydraulic bushing is constructed. The results of performance modeling provide key stress-strain data and dynamic performance information for fatigue optimization. The weak links and optimization directions determined during the fatigue optimization process guide the improvement of the structural design. The objective function and constraint conditions of the optimization model are as follows:

[0122]

[0123] Among them, L(x) represents the fatigue life, P(x) represents the dynamic performance index, R(x) represents the reliability index, and x is the design variable;

[0124] After completing one round of fatigue optimization, the optimized structural parameters are fed back to the structural design link, and steps such as re-design calculation, strength and stability check are carried out again; the update of the structural design triggers a new round of cycle of performance modeling and fatigue optimization. Through multiple iterations, the comprehensive performance of the hydraulic bushing is continuously improved until the strict requirements of the vehicle suspension system for the hydraulic bushing in terms of fatigue life, dynamic performance and reliability are met, realizing the continuous optimization of product design.

[0125] In the specific optimization process, it is necessary to first clarify the optimization objectives and constraint conditions. The optimization objectives are generally the dynamic performance indexes of the hydraulic bushing, such as dynamic stiffness, damping and hysteresis angle, and the bushing life predicted by the established life model. For example, reducing the dynamic stiffness under low-frequency and large-amplitude excitation to reduce the vibration transmission of the suspension system; increasing the damping under high-frequency and small-amplitude excitation to effectively attenuate the vibrations of the power system and road surface unevenness. The constraint conditions include the strength, stability, installation space and material cost of the hydraulic bushing. For example, ensuring that the stress level of the hydraulic bushing does not exceed the yield strength of the material and its natural frequency does not resonate with other components of the suspension system. After determining the optimization objectives and constraint conditions, a suitable optimization algorithm is selected for parameter optimization. Commonly used optimization algorithms include genetic algorithm, particle swarm algorithm and gradient descent method, etc. According to the previously established hybrid lumped parameter model of the hydraulic bushing, the key design parameters (such as the stiffness of the rubber main spring, the volume of the liquid chamber, the size of the inertia channel, etc.) are used as optimization variables. Then, through the optimization algorithm, the values of the selected parameters are adjusted to make the dynamic performance indexes of the hydraulic bushing reach the optimum. The feasibility and effectiveness of the optimization results are verified through simulation and experiment. The optimization results are verified by comparing the performance indexes before and after optimization to evaluate the optimization effect. If the optimization results meet the design requirements, it indicates that the optimization effect is good; if there is a large deviation, the optimization model needs to be further adjusted.

[0126] The above are only the preferred specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention should cover within the protection scope of the present invention according to the technical solution and inventive concept of the present invention for equivalent replacement or change.

Claims

1. A method for modeling a hydraulic bushing for a new energy vehicle suspension and accelerating the prediction of fatigue life, characterized in that, It includes the following steps: S1. Collect data and analyze the driving condition characteristics of new energy vehicles; S2. Select the structure type of the hydraulic bushing and preliminarily determine its parameters; S3. Check the strength and stability of the hydraulic bushing; S4. Analyze the sensitivity of each parameter and determine the optimizable parameters. The sensitivity analysis includes local sensitivity analysis and global sensitivity analysis; S5. Based on the harmonic excitation experiment, use the classical frequency domain identification method to fit the initial parameters of the model; S6. Fit the parameters of the hybrid lumped parameter model of the hydraulic bushing according to the experimental data; S7. Fine-tune the model parameters based on fast time domain identification; S8. Predict the dynamic performance of the hydraulic bushing under multiple working conditions through simulation; S9. Conduct an accelerated performance degradation experiment on the fatigue life; S10. Predict the accelerated life of the bushing based on the accelerated life prediction method of the degradation quantity distribution; S11. Optimize and solve the design variables based on performance and life prediction.

2. The method for modeling and accelerated prediction of fatigue life of a hydraulic bushing for a new energy vehicle suspension according to claim 1, wherein The step of collecting data and analyzing the driving condition characteristics of new energy vehicles includes the following steps: S1. Install the required sensors at the key parts of the new energy vehicle and collect relevant data; S2. Conduct data preprocessing and filtering. The data preprocessing steps include removing outliers, filling in missing data, and smoothing processing. The data filtering includes low-pass filtering, high-pass filtering, and band-pass filtering; S3. Analyze the load spectrum of the new energy vehicle.

3. The method for modeling and accelerated prediction of fatigue life of a hydraulic bushing for a new energy vehicle suspension according to claim 1, wherein, The step of selecting the structure type of the hydraulic bushing and preliminarily determining its parameters includes the following steps: S1. Select a suitable hydraulic bushing configuration according to the vehicle model characteristics. The hydraulic bushing configurations include single-chamber type, double-chamber type, and multi-chamber type; S2. Preliminary determination of the key design parameters of the hydraulic bushing. The key design parameters include the stiffness K of the rubber main spring r , the volume V of the liquid chamber c , the length L and diameter D of the inertia passage, and the viscosity μ of the liquid.

4. The method for modeling and accelerated fatigue life prediction of a hydraulic bushing for a new energy vehicle suspension according to claim 1, wherein, The step of checking the strength and stability of the hydraulic bushing includes the following steps: S1. Establish a three-dimensional geometric model of the hydraulic bushing and import it into the finite element analysis software; S2. Apply the corresponding boundary conditions and loads according to the load spectrum under the actual working conditions.

5. The method for modeling and accelerating fatigue life prediction of a hydraulic bushing for a new energy vehicle suspension according to claim 1, characterized in that, In step S5, apply harmonic excitations with different frequencies to the hydraulic bushing, measure its dynamic response, extract the dynamic characteristic parameters of the hydraulic bushing through the frequency domain analysis method, and construct a frequency domain parameter model according to the dynamic stiffness K(ω,A) and the loss energy W(ω,A). The parameter model formula is: Among them, p is the model parameter to be identified, ω is the vibration angular frequency, and A is the displacement amplitude. Among them, p can be composed of the stiffness K of the rubber main spring r , the volume V of the liquid chamber c , the length L and diameter D of the inertia channel, and the viscosity μ of the liquid, that is, p = {K r , V c , L, D, μ}; The dynamic stiffness K(ω,A) and the loss energy W(ω,A) can be expressed as: K * (ω, A) = K'(ω, A) + jK”(ω, A); W(ω,A) = πA 2 K”(ω,A) Among them, K'(ω,A) is the storage stiffness, representing the elastic characteristic; K”(ω,A) is the loss stiffness, representing the damping characteristic.

6. The method for modeling and accelerated fatigue life prediction of a hydraulic bushing for a new energy vehicle suspension according to claim 1, characterized in that, In step S6, use the weighted multi-objective minimization method to fit the parameter model. The parameter model formula is: wherein, and are respectively the measured dynamic stiffness and loss energy reference values, β is used to weigh the relative contributions of the dynamic stiffness and loss energy during the optimization process, and by adjusting the model parameter p, the difference between the model prediction value and the measured reference value is minimized to obtain the most suitable model parameter; The hybrid lumped parameter model is obtained by weighting multiple parameter models. Among them, the hybrid lumped parameter model is the Kelvin–Voigt model, and its model formula is: Among them, the hybrid lumped parameter model is the Berg model, and its model formula is: Among them, the hybrid lumped parameter model is the Mod-Berg model, and its model formula: where k is the stiffness and c is the damping; F e is the elastic component, F ve is the viscoelastic component, F fr is the frictional component.

7. The method for modeling and accelerated fatigue life prediction of a hydraulic bushing for a new energy vehicle suspension according to claim 1, wherein In the step S7, the time-domain identification is to identify the dynamic response of the hydraulic bushing under random loads through time-domain analysis methods. First, a random load is applied to the hydraulic bushing, and its dynamic response is measured. The Sigmoid-chirp relative displacement is applied to the bushing as an excitation signal using a fatigue testing machine. The frequency of this signal is scanned within a given range, and the amplitude gradually changes, stimulating the full-range movement of the bushing sample in the compression and tensile directions, ensuring at least one complete cycle in the low-frequency band to capture the friction effect and not stimulating excessive viscous damping phenomena, while covering the entire frequency bandwidth and amplitude range of interest; then the reaction force generated by the bushing and the applied relative displacement are recorded.

8. The method for modeling and accelerated prediction of fatigue life of a hydraulic bushing for a new energy vehicle suspension according to claim 1, wherein In the step S8, the multi-condition simulation is carried out through finite element analysis and numerical simulation. The conditions for analysis include low-frequency large-amplitude excitation, high-frequency small-amplitude excitation, and random load excitation.

9. The method for modeling and accelerated prediction of fatigue life of a hydraulic bushing for a new energy vehicle suspension according to claim 1, characterized in that, In the step S10, the accelerated life prediction method based on the degradation quantity distribution establishes a degradation quantity distribution model by analyzing the performance degradation quantity distribution of the hydraulic bushing. In this method, the performance degradation quantity of the hydraulic bushing follows a Weibull distribution, and the distribution parameters are estimated through statistical methods. First, establish the global reliability model R * (t) based on all the obtained data. The reliability model formula based on the distribution of the degradation amount is as follows: Then, cross-validation is performed on the experimental data. The data set is divided into several subsets, each subset containing a certain proportion of experimental data. One subset is taken as the test set in turn, and the rest are taken as the training set. Then, a sub-reliability function is established using the training set, the results of the test set data are predicted, and the prediction error of the sub-reliability function is calculated. The error indicators include mean square error, mean absolute error, and coefficient of determination; then, the prediction results under different training set partitions are compared with the experimental data to evaluate the accuracy and reliability of the prediction results. If the prediction result error is less than the set threshold, the selected accelerated life prediction model is considered accurate; Finally, based on the obtained reliability model, the life curve is extrapolated to finally obtain the product life prediction model.

10. The method for modeling and accelerated fatigue life prediction of a hydraulic bushing for a new energy vehicle suspension according to claim 1, wherein, In the step S11, based on the performance and life prediction results, an optimization model of the hydraulic bushing is constructed. The results of performance modeling provide key stress-strain data and dynamic performance information for fatigue optimization. The weak links and optimization directions determined in the fatigue optimization process guide the improvement of structural design. The objective function and constraint conditions of the optimization model are as follows: where L(x) represents the fatigue life, P(x) represents the dynamic performance index, R(x) represents the reliability index, and x is the design variable; After completing one round of fatigue optimization, the optimized structural parameters are fed back to the structural design link, and steps such as re-design calculation, strength and stability check are carried out again; the update of the structural design triggers a new round of cycle of performance modeling and fatigue optimization. Through multiple iterations, the comprehensive performance of the hydraulic bushing is continuously improved until the strict requirements of the vehicle suspension system for the hydraulic bushing in terms of fatigue life, dynamic performance, and reliability are met, realizing the continuous optimization of product design.

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