A method and system for optimizing dynamic comfort of a commercial vehicle seat

Abstract

This invention discloses a method and system for optimizing the dynamic comfort of commercial vehicle seats. The method includes the following steps: acquiring the dynamic response signal of the commercial vehicle seat system through vehicle road testing and bench testing; obtaining the displacement excitation spectrum of the seat mounting point by inversely calculating the acquired dynamic response signal based on virtual iteration technology; establishing a constant-damping seat multibody dynamics model; inputting the seat mounting point displacement excitation spectrum into the constant-damping seat multibody dynamics model, traversing multiple sets of parameter combinations of air spring stiffness and shock absorber damping, and obtaining seat dynamic comfort evaluation indices under different parameter combinations; obtaining the globally optimal parameter combination of air spring stiffness and shock absorber damping based on the seat dynamic comfort evaluation indices. The method of this invention obtains the seat mounting point displacement excitation spectrum based on virtual iteration technology, ensuring the reliability of comfort optimization; and improves the practicality of seat comfort optimization by establishing a seat multibody dynamics model.

Description

Technical Field

[0001] This invention relates to the field of seat comfort technology, and in particular to a method and system for optimizing the dynamic comfort of commercial vehicle seats. Background Technology

[0002] In related technologies, seats are important and structurally complex automotive components that play a crucial role in the safety of drivers and passengers. During vehicle operation, the periodic vibration excitation caused by uneven road surfaces will simultaneously stimulate the elastic-damping characteristics of the seat suspension and the human body's sensitive frequency response, resulting in a significant amplification of the seat's vibration transmission. This deteriorates seat comfort, easily leads to fatigue driving, and causes traffic accidents. Summary of the Invention

[0003] This invention aims to at least solve one of the technical problems existing in the prior art. To this end, this invention proposes a method for optimizing the dynamic comfort of commercial vehicle seats, which can optimize seat comfort and reduce bumps caused by uneven road surfaces.

[0004] This invention also proposes a dynamic comfort optimization system for commercial vehicle seats.

[0005] The commercial vehicle seat dynamic comfort optimization method according to the first aspect of the present invention includes the following steps: S1. Obtain the dynamic response signals of the commercial vehicle seat system through vehicle road tests and bench tests; S2. Based on virtual iteration technology, the obtained dynamic response signal is inversely calculated to obtain the displacement excitation spectrum of the seat mounting point; S3. Establish a multibody dynamics model of a constant-damping seat; S4. Input the displacement excitation spectrum of the seat mounting point into the multibody dynamics model of the constant damping seat, traverse multiple sets of parameter combinations of air spring stiffness and damper damping, and obtain the seat dynamic comfort evaluation index under different parameter combinations. S5. Based on the seat dynamic comfort evaluation index, obtain the global optimal parameter combination of air spring stiffness and shock absorber damping.

[0006] In some embodiments, the method for vehicle road testing in step S1 is as follows: vibration sensors are arranged at the left front, right front, left rear, and right rear positions of the car seat slide rail, and at the left front and right front positions of the seat frame, respectively. A seat cushion sensor is arranged on the seat cushion of the car seat to collect the Z-axis acceleration signal of each test point under multiple working conditions on national highways and expressways. After resampling and filtering, the virtual iterative dynamic response signal is obtained.

[0007] In some embodiments, the method for inversely calculating the acquired dynamic response signal based on virtual iteration technology in step S2 includes: S21. Input a preset white noise excitation signal into the multibody dynamics model of the cab, obtain the system's response signal to the white noise, and calculate the system's transfer function. S22. Using the dynamic response signal as the target signal, the first external excitation is obtained by solving the inverse function of the system transfer function; S23. Iteratively update the external excitation to make the root mean square error between the response signal and the target signal obtained by the iterative simulation continuously converge until the convergence criterion is met. S24. Substitute the converged displacement excitation of the active end of the cab suspension back into the multibody dynamics model of the cab, and simulate and extract the vertical displacement response at the four mounting points of the seat to obtain the displacement excitation spectrum of the seat mounting points covering multiple working conditions.

[0008] In some embodiments, the convergence criteria are: the root mean square error between the iterative response and the target signal in the time domain is less than 5% of the root mean square value of the target signal; the coherence function value between the iterative response and the target signal is greater than 0.9 in the 0-80Hz key frequency band; and the relative damage ratio between the iterative response and the target signal is in the range of 0.5 to 2.0.

[0009] In some embodiments, the method for establishing a multibody dynamics model of a constant-damping seat in step S3 includes: S31. Quasi-static loading tests were conducted on the air springs using a universal testing machine to obtain the air spring stiffness and load-displacement curves under different inflation pressures; damping characteristic tests were conducted on the damper using an MTS hydraulic servo test bench to obtain the damping force and power curves at different speeds; and the mass, center of mass coordinates, and moment of inertia parameters of the seat sprung parts were obtained through modal testing. S32. Import the three-dimensional model of the constant damping seat into the ADAMS simulation environment, remove redundant decorative parts that do not affect vibration transmission, preserve the core moving parts, add moving joint and rotating joint constraint relationships according to the assembly logic of the actual vehicle, and match the motion degrees of freedom of the actual vehicle. S33. Assign the nonlinear stiffness curve of the air spring, the force-velocity characteristic curve of the shock absorber, and the parameters of sprung mass and moment of inertia obtained from the component test to the model, and add a 65Kg mass block to the seat to simulate the occupant mass. S34. Apply displacement excitation to the input end at the bottom of the seat, collect the acceleration response signals of the seat frame and the center of mass of the human body, compare the time domain and frequency domain characteristics of the simulated acceleration response with the measured signals of the road test, and determine the model verification is effective when the degree of agreement between the two meets the error requirements.

[0010] In some embodiments, the air spring stiffness in step S4 ranges from 10 to 55 N / mm, with a value interval of 1 N / mm; the damping force of the shock absorber ranges from 300 to 2000 N.

[0011] In some embodiments, the seat dynamic comfort evaluation indicators in step S4 include: in, The indicators for evaluating seat dynamic comfort are w1 and w2, which are weighting coefficients. The design variables are vectors, namely air spring stiffness, damper restoring force, and compressive force; RMS transfer rate; Peak transfer rate; Inequality constraints; Equality constraint; : Upper and lower limits for design variables.

[0012] A commercial vehicle seat dynamic comfort optimization system according to a second aspect of the present invention includes: The signal acquisition module is used to acquire the dynamic response signals of the seat mounting point during real vehicle road tests; The virtual iterative inverse calculation module is used to construct a multibody dynamics model of the cab-suspension system and, based on the dynamic response signal, uses virtual iterative technology to inversely calculate the displacement excitation spectrum of the seat mounting point. The multibody dynamics simulation module is used to construct a seat multibody dynamics model that includes an air spring nonlinear stiffness model and a shock absorber hysteresis damping model, and to perform parameter combination simulation using the displacement excitation spectrum as input. The parameter optimization module is used to determine the optimal parameter combination based on the vibration transmissibility under various parameter combinations.

[0013] The present invention has at least the following effects: 1. To address the challenges of directly measuring loads at commercial vehicle seat mounting points and the low correlation between traditional load spectra and actual operating conditions, a method based on virtual iteration technology is employed to inversely calculate the acquired dynamic response signals. This method yields a displacement excitation spectrum at seat mounting points covering multiple operating conditions. Unlike existing research that relies on standard road spectra or simplified loads, this approach ensures the reliability of comfort optimization from the outset.

[0014] 2. Overcoming the limitations of traditional simulations that simplify human body models and ignore the influence of cab flexibility, a multibody dynamics model of a constant-damping seat is established. This model deeply integrates the measured characteristics of components such as the nonlinear stiffness of air springs and the hysteresis damping of shock absorbers. Calibration is completed through modal and transmissivity tests, enabling accurate simulation of seat dynamic response and more realistically reflecting the correlation between human driving experience and vibration characteristics. This solves the core problem of "disconnect between simulation results and actual experience" in simplified models, making seat comfort optimization more in line with actual experience and improving the practicality of seat comfort optimization.

[0015] 3. Construct a dynamic comfort evaluation index for seats, breaking through the limitations of existing studies that focus solely on optimizing vibration transmissibility or neglecting resonance peak values. By listing "minimizing RMS transmissibility" and "minimizing peak transmissibility" as dual objectives, and using a weighted summation method to transform them into single-objective optimization, the optimal parameter combination obtained achieves a dual improvement in reducing the root mean square acceleration of the seat cushion and reducing the resonance peak transmissibility, forming an optimization method that takes into account both "global vibration isolation" and "local resonance suppression".

[0016] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a flowchart of a method for optimizing the dynamic comfort of commercial vehicle seats according to an embodiment of the present invention; Figure 2 This is a schematic diagram of the dynamic response signal of a national highway at 70km / h according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the dynamic response signal of a highway at 70 km / h according to an embodiment of the present invention; Figure 4 This is a schematic diagram of displacement excitation of the active end of the cab suspension on a national highway at 70km / h according to an embodiment of the present invention; Figure 5 This is a schematic diagram of displacement excitation of the active end of the cab suspension at 70km / h on a high-speed road according to an embodiment of the present invention; Figure 6 This is a seat excitation spectrum for a national highway at 70km / h according to an embodiment of the present invention; Figure 7 This is a seat excitation spectrum at 70 km / h on a high-speed road according to an embodiment of the present invention; Figure 8 This is a multibody dynamics model of a constant-damping seat according to an embodiment of the present invention; Figure 9 This is a three-dimensional response surface plot of the RMS transmissivity as a function of stiffness and damping according to an embodiment of the present invention. Figure 10 This is a three-dimensional response surface plot of the peak transmissivity as a function of stiffness and damping according to an embodiment of the present invention. Detailed Implementation

[0019] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0020] The following disclosure provides numerous different embodiments or examples for implementing various structures of the invention. To simplify the disclosure, specific examples of components and arrangements are described below. These are merely examples and are not intended to limit the invention. Furthermore, reference numerals and / or letters may be repeated in different examples. Such repetition is for simplification and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed. Additionally, examples of various specific processes and materials are provided in this invention; however, those skilled in the art will recognize the applicability of other processes and / or the use of other materials.

[0021] Below, refer to Figure 1 The present invention describes a method for optimizing the dynamic comfort of commercial vehicle seats according to a first aspect, comprising the following steps: S1. Obtain the dynamic response signals of the commercial vehicle seat system through vehicle road tests and bench tests; S2. Based on virtual iteration technology, the obtained dynamic response signal is inversely calculated to obtain the displacement excitation spectrum of the seat mounting point; S3. Establish a multibody dynamics model of a constant-damping seat; S4. Input the displacement excitation spectrum of the seat mounting point into the multibody dynamics model of the constant damping seat, and iterate through multiple sets of parameter combinations of air spring stiffness and damper damping to obtain the seat dynamic comfort evaluation index under different parameter combinations. S5. Based on the seat dynamic comfort evaluation index, obtain the global optimal parameter combination of air spring stiffness and shock absorber damping.

[0022] In some embodiments, in step S1, the dynamic response signal of the commercial vehicle seat system is obtained through vehicle road testing and bench testing. Vehicle road testing can ensure the engineering practicality of the commercial vehicle seat dynamic comfort optimization method. Obtaining accurate and reliable dynamic response signals is the key to performing virtual iteration to inversely calculate the displacement excitation spectrum of the seat mounting point. The specific method of vehicle road testing in step S1 is as follows: vibration sensors are arranged at the left front, right front, left rear, and right rear positions of the car seat slide rail, and at the left front and right front positions of the seat frame, respectively. A seat cushion sensor is arranged on the seat cushion of the car seat. The Z-axis acceleration signals of each test point under multiple working conditions on national highways and expressways are collected. After resampling and filtering, the dynamic response signal of virtual iteration is obtained.

[0023] Each channel in each working condition corresponds to the Z-axis acceleration signal of the right rear, left rear, right front, left front, and the upper end of the right rear suspension, left rear suspension, and right front suspension, respectively.

[0024] In some embodiments, the method for inversely calculating the acquired dynamic response signal based on virtual iteration technology in step S2 includes: S21. Construct a multibody dynamics model of the cab, input a preset white noise excitation signal into the multibody dynamics model of the cab, obtain the system's response signal to the white noise, and calculate the system's transfer function. S22. Using the dynamic response signal as the target signal, the first external excitation is obtained by solving the inverse function of the system transfer function. S23. Iteratively update the external excitation to make the root mean square error between the response signal and the target signal obtained by the iterative simulation continuously converge until the convergence criterion is met. S24. Substitute the converged displacement excitation of the active end of the cab suspension back into the multibody dynamics model of the cab, and simulate and extract the vertical displacement response at the four mounting points of the seat to obtain the displacement excitation spectrum of the seat mounting points covering multiple working conditions.

[0025] The specific methods for constructing the multibody dynamics model of the driver's cab include: The essence of establishing a multibody dynamics model of the cab is to obtain the transfer function during virtual load iteration to reflect the system's response characteristics. The desired signal for the virtual iteration is the acceleration signal from road tests, while the direct result obtained through virtual load iteration is the displacement of the lower end of the cab suspension. Therefore, multibody dynamics modeling only needs to consider the motion transmission path from the lower end of the suspension to the cab; hence, a multibody dynamics model of the cab and suspension system is established.

[0026] 1. Construction Preparation: Before building the model, a high-fidelity cab model needs to be constructed. The model system-level testing is required, mainly to evaluate the dynamic characteristics of the seat mounting points, including origin dynamic stiffness (IPI) and vibration transfer function (VTF) tests.

[0027] The IPI test is conducted using a force hammer excitation method. Vibration sensors are placed near the seat mounting points, and the test direction and sensitivity of the vibration acceleration sensors are set. A single-point excitation and single-point response modal test excitation method is employed. A rubber-tipped force hammer is used to ensure energy attenuation exceeds 50% and there is no double-click phenomenon. The VTF test excites the passive side of the cab suspension, measuring the acceleration response at the seat mounting points. Vibration sensors are attached near each mounting point of the car seat, the seat cushion frame, and the seat cushion itself. The vibration acceleration sensors are connected to the test front end, and the test direction and sensitivity of each vibration acceleration sensor are set. The test uses a single-point excitation and multi-point response method, with a frequency bandwidth of 0-128Hz and a frequency resolution of 0.13Hz. The excitation point is selected in the high-stiffness modal region to excite as many modes as possible in the structure. The response points are evenly distributed on the specimen to ensure that they reflect the shape characteristics of the seat structure.

[0028] 2. Establishment of the driver's cab model: The construction of the multibody dynamics model of the cab aims to restore the characteristics of the real vehicle and adapt iteratively in a virtual manner. The core input conditions required for modeling are shown in Table 1, which cover four key elements: numerical model, key parameters, motion relationships and software tools, providing basic support for the accurate construction of the model.

[0029] Table 1 Input Statistics Table

[0030] The core output of the modeling is a multibody dynamics model of the cab adapted for virtual iteration. The specific output information is shown in Table 2. This model will be used for subsequent co-simulation with FEMFAT-Lab.

[0031] Table 2 Output Statistics

[0032] Based on the above input conditions and modeling objectives, the specific modeling process consists of the following four key steps: (1) Model import and structural simplification: The 3D model of the cab is imported into ADAMS / View. Based on the vibration transmission path analysis, components such as pipes that do not affect the transmission are deleted, and the system is simplified into three major components: the cab, the front suspension assembly, and the rear suspension assembly. The front suspension assembly mainly includes brackets, supports, shock absorbers, lateral stabilizer bars, and various bushings; the rear suspension assembly includes brackets, hydraulic locks, gantry frames, and corresponding bushings.

[0033] (2) Establishment of motion relationships: Define the constraint relationships between components based on the actual connection method. The cab and the bracket are connected by a fixed pair; the hinge points in each suspension (such as the bushing installation point) are simulated by rubber bushings to accurately characterize their elastic and damping characteristics in all directions, rather than ideal hinges; the shock absorber and the connection point are defined by a combination of spring damping unit and cylindrical pair.

[0034] (3) Key parameter settings: The accuracy of the model depends on the input of key physical parameters: Mass attributes: The mass, center of mass position and moment of inertia of the cab body are obtained using finite element software and assigned values ​​in ADAMS.

[0035] Mechanical properties: Based on bench test data, the damping force-velocity curves and design stiffness (300 N / mm) of the front and rear helical spring dampers were obtained by fitting.

[0036] Bushing stiffness: Input the static stiffness values ​​of each rubber bushing (such as comfort bushing, flip bushing, each lug bushing, etc.) in six directions, which are measured experimentally, into the corresponding Bushing element.

[0037] (4) Input / output channel configuration: To interface with the virtual iteration software (FEMFAT-Lab), the model interface needs to be configured correctly. Displacement input channels are created at the four active suspension ends (using the Point Motion driving function defined via Spline); acceleration output channels are created at the four mounting points of the seat and the three passive suspension ends (using Request definition). Finally, export the ADAMSSolver (.adm) format model file for use in the virtual iteration.

[0038] The mathematical process of virtual iteration can be discretized to adapt to numerical simulation. Let the input of the nth iteration be... The output is Then the error signal The algorithm corrects the input by using the inverse of the system transfer function, and its iterative formula is as follows: in, It is the system transfer function The inverse matrix, This is the step size factor (usually 1, but can be adaptively adjusted to accelerate convergence). Transfer function. Preliminary calibration is required: generating white noise excitation. Get the response and calculate In practice, to avoid the singularity of frequency domain inverse operations, regularization techniques (such as Tikhonov regularization) are often used to transform the inverse operation into: in, For regularization parameters, It is the identity matrix. This indicates the conjugate transpose. This treatment ensures the numerical stability of the algorithm in the resonant frequency band.

[0039] The effect of virtual iteration is essentially whether the external excitation of the system can be completely reproduced from the system's internal response signal using an inverse function. This is achieved by setting white noise excitation (including setting the number of blocks, the 0-40Hz frequency range, and 1 / f). 2 The simulation process involves filtering features, matching displacement input types, generating white noise signals, and simulating to obtain a response file. It also involves generating an inverse transfer function (based on the white noise driving signal and simulation response file, selecting the corresponding driving and response channels, setting the frequency range, and generating the system's inverse transfer function), monitoring iteration trends (checking the damage value monitoring item and synchronously tracking RMS error), setting the iteration start position, iteration number, and driving signal gain coefficient, generating a driving signal, and repeatedly simulating and iterating until the iterative response highly matches the time and frequency domain trends of the target signal, and the relative damage values ​​of each channel are within the convergence range of 0.5 to 2.0. Finally, the converged displacement excitations of the four active suspension ends of the cab are substituted back into the ADAMS cab multibody dynamics model according to the corresponding operating conditions to drive the cab and suspension system to complete the simulation. Ultimately, the vertical displacement responses at the four seat mounting points are extracted, forming a seat excitation spectrum covering various vehicle speed conditions on national highways and expressways. For example, the specific processes of steps S22, S23, and S24 are as follows: (1) First, create white noise. White noise is input as an excitation signal into the system model to obtain the system's response to white noise. ; (2) Transfer function of the computational system ; (3) The acceleration signal obtained from the actual road test As the desired target signal, the first external excitation is obtained from the inverse function of the system transfer function. ; (4) Perform multiple iterations. The root mean square error of the response signal and the target signal in the nth iteration is compared and made to approach 0 to ensure the convergence characteristics of the channel. If the iterative convergence condition is satisfied, the iteration is considered successful and the real external stimulus can be obtained.

[0040] The convergence criteria are as follows: the root mean square error between the iterative response and the target signal in the time domain is less than 5% of the root mean square error of the target signal; the coherence function value between the iterative response and the target signal is greater than 0.9 in the 0-80Hz key frequency band; and the relative damage ratio between the iterative response and the target signal is in the range of 0.5 to 2.0.

[0041] In some embodiments, the method for establishing a multibody dynamics model of a constant-damping seat in step S3 includes: S31. Quasi-static loading tests were conducted on the air springs using a universal testing machine to obtain the air spring stiffness and load-displacement curves under different inflation pressures; damping characteristic tests were conducted on the damper using an MTS hydraulic servo test bench to obtain the damping force and power curves at different speeds; and the mass, center of mass coordinates, and moment of inertia parameters of the seat sprung parts were obtained through modal testing. S32. Import the three-dimensional model of the constant damping seat into the ADAMS simulation environment, remove redundant decorative parts that do not affect vibration transmission, preserve the core moving parts, add moving joint and rotating joint constraint relationships according to the assembly logic of the actual vehicle, and match the motion degrees of freedom of the actual vehicle. S33. Assign the nonlinear stiffness curve of the air spring, the force-velocity characteristic curve of the shock absorber, and the parameters of sprung mass and moment of inertia obtained from the component test to the model, and add a 65Kg mass block to the seat to simulate the occupant mass. S34. Apply displacement excitation to the input end at the bottom of the seat, collect the acceleration response signals of the seat frame and the center of mass of the human body, compare the time domain and frequency domain characteristics of the simulated acceleration response with the measured signals of the road test, and determine the model verification is effective when the degree of agreement between the two meets the error requirements.

[0042] For example, the specific process of step S31 includes: Air spring stiffness test: The stiffness of the air spring was obtained by performing a quasi-static loading test on the semi-air spring on a universal testing machine. The test was conducted at a standard height, with inflation pressures of 0.4 MPa, 0.6 MPa, and 0.8 MPa applied respectively, and the loading speed controlled at 120 mm / min.

[0043] Damping characteristics of the vibration damper were tested on an MTS hydraulic servo test bench. To accurately reproduce its nonlinearity in the model, this study adopted a semi-empirical model commonly used in the literature. The test data is fitted with the following parameters: , , This model can accurately describe the dynamic relationship between damping force and velocity. The test speed range covers 0.02 m / s to 0.6 m / s to simulate actual working conditions. The damping force at different speeds is recorded, and the work indication curve and force-velocity characteristic curve are plotted.

[0044] Sprout rotational inertia: First, adjust the seat back to the designed angle and the guide rail to the midpoint between the vertical and horizontal directions. Tighten the guide rail, angle adjuster, and other components requiring torque fixation. The excitation point is selected in the high stiffness region of the mode, and the response points are evenly distributed according to the grid division. The region of interest is densified, avoiding nodes of each modality. The sensor is fixed to the frame by making a 30×30mm opening at the corresponding frame of the seat foam (without affecting the foam wrapping performance). A single-point excitation and multi-point response method is adopted, and the excitation force does not exceed the linear range of the seat. Then, a three-dimensional test model containing the coordinates of the response point and the excitation point is established. The frequency bandwidth of 0-128Hz and the frequency resolution of 0.13Hz are set. The force hammer is used as the signal type and is tested an average of 5 times. Data such as transfer function (FRF) and correlation function are collected. After the test, it is ensured that the energy attenuation of the force hammer is at least 50%, there is no double-click phenomenon, and the resonance peak of the frequency response function curve is clear.

[0045] For example, the specific processes of steps S32, S33, and S34 include: Only by building a simulation model can the acceleration response of the seat under different working conditions be obtained based on experimental data, and the vibration response characteristics of the seat be analyzed, thereby optimizing seat comfort. The finite element analysis modeling of the seat was completed using Adams software version 2019.

[0046] The core input requirements for seat modeling cover four main categories: numerical model, measured data, excitation signals, and functional requirements, as shown in Table 3, providing comprehensive basic support for model construction.

[0047] Table 3 Modeling Inputs

[0048] The expected output of the modeling includes the model itself and key simulation data, as shown in Table 4, which directly serve the subsequent vibration comfort analysis and parameter optimization work.

[0049] Table 4 Modeling Output

[0050] Based on the above input conditions and output objectives, the construction of the seat multibody dynamics model is carried out in the following four steps: A. Model import and structural simplification: The three-dimensional digital model of the constant damping seat is imported into the simulation environment. Combined with vibration transmission path analysis, targeted simplification is carried out. Ten core components that affect vibration transmission are retained, and redundant structures such as decorative parts are eliminated. While simplifying the model, key mechanical properties are not lost. B. Definition of motion constraint relationship: Based on the connection form of the actual vehicle parts, precise constraint relationships are added - by simulating the motion characteristics of scissor mechanisms, springs and shock absorbers through prismatic joints, revolute joints, etc., the constraint type strictly matches the assembly logic of the actual vehicle to ensure that the model's motion freedom is consistent with the real seat; C. Key parameter assignment and calibration: The sprung mass (after the sprung parameters are set, a 65Kg mass block is added to the seat to replace the occupant's mass), moment of inertia, and the nonlinear stiffness curve of the air spring and the force-velocity characteristic curve of the damper are set by fitting experimental data. D. Excitation application and output channel configuration: At the input end at the bottom of the seat, a displacement excitation (including actual excitation and iterative excitation) obtained through virtual iteration is applied to simulate the vibration input during actual vehicle driving; at the same time, an output channel is set to collect the acceleration response signals of the seat frame and the human body center of mass in a targeted manner, and finally form a complete multibody dynamics model of the constant damping seat.

[0051] Finally, for effective parameter optimization, the seat model must be experimentally verified and proven reliable; therefore, the model's effectiveness must be verified. The verification method involves applying the displacement excitation obtained through virtual iteration to the bottom of the constant-damping seat's 3D model and simulating the acceleration response on the seat frame. This response is then compared with the measured signals from road tests to verify the model's effectiveness.

[0052] In some embodiments, in step S4, the displacement excitation spectrum of the seat mounting point is input into the multibody dynamics model of the constant damping seat. Multiple combinations of air spring stiffness and damper damping parameters are iterated to obtain the seat dynamic comfort evaluation index under different parameter combinations. This method employs a full factorial design approach. The stiffness design space is set as follows: Damping design space is The number of experiments for the complete combination is: Total number of simulations (in this study) Group stiffness, Group damping, Second-rate); Number of discrete groups in the stiffness design space; Number of discrete groups in the damping design space; The advantages of this method include: (1) Global convergence: It covers all vertices and internal points in the design space, avoiding gradient-based algorithms from getting trapped in local optima.

[0053] (2) Data completeness: Generate complete response surfaces to provide a basis for parameter sensitivity analysis and coupling effect research.

[0054] (3) Engineering friendliness: No complex optimization algorithm is required. It can be directly implemented through parallel simulation, which is easy to verify and debug.

[0055] However, its computational cost increases exponentially with the number of variables, making it suitable for scenarios with fewer variables (3) in this study. This study utilized ADAMS batch processing technology and a high-performance computing cluster to keep the 7912 simulations within a feasible timeframe.

[0056] Specifically, the air spring stiffness ranges from 10-55 N / mm, with values ​​in 1 N / mm increments, totaling 46 levels. This resulted in 46 load-displacement curves for different stiffnesses. During simulation, these 46 curves were added to ADAMS using spline curves, and the air spring stiffness was adjusted by changing the spline curve name in the air spring settings interface. The damping force of the vibration damper ranges from 300-2000 N. Based on engineering practice, the compressive damping force of the vibration damper was set to be greater than the restoring damping force. The damper testing introduced the fitting of damper damping, generating 172 force-velocity curves for different damping forces. These 172 damping curves were added to ADAMS using spline curves. During simulation, the damping force was changed by altering the name in the air damper damping settings interface, thus forming 46 stiffnesses × 172 complete parameter combinations. For each set of parameters, the force-displacement characteristic curve of the air spring was simulated, and a total of 7912 dynamic simulation calculations were completed to comprehensively explore the quantitative impact of key independent variables on seat comfort evaluation indicators.

[0057] In some embodiments, the seat dynamic comfort evaluation indicators in step S4 include: in: Objective function (comprehensive comfort index, the smaller the better); Design variable vector (in this study) (i.e., air spring stiffness, shock absorber restoring force, and compressive force). RMS transmission rate (dimensionless, weighted in this study) (Focusing on overall vibration isolation) Peak transfer rate (dimensionless, weighted in this study) (Focusing on resonance suppression). Inequality constraints (such as "mechanical limit displacement") (”); Equality constraints (e.g., "seat height = 450 mm"); Design variable upper and lower limits (e.g.) ).

[0058] A commercial vehicle seat dynamic comfort optimization system according to a second aspect of the present invention includes: The signal acquisition module is used to acquire the dynamic response signals of the seat mounting point during real vehicle road tests; The virtual iterative inverse calculation module is used to construct a multibody dynamics model of the cab-suspension system and to inversely calculate the displacement excitation spectrum of the seat mounting point based on the dynamic response signal using virtual iterative technology. The multibody dynamics simulation module is used to construct a seat multibody dynamics model that includes an air spring nonlinear stiffness model and a shock absorber hysteresis damping model, and to perform parameter combination simulation with the displacement excitation spectrum as input. The parameter optimization module is used to determine the optimal parameter combination based on the vibration transmissibility under various parameter combinations.

[0059] The following specific embodiment illustrates the method for optimizing the dynamic comfort of commercial vehicle seats according to the present invention.

[0060] Based on the acceleration test data of each test point under each working condition, the root mean square value of vertical (Z-direction) acceleration (m / s2), seat vertical (Z-direction) 0-80Hz transmissibility and peak transmissibility of each test point were calculated. The results are shown in Table 5.

[0061] Table 5. Root mean square acceleration, 0-80Hz transmissibility, and peak transmissibility

[0062] After resampling, filtering, and other processing, the final dynamic response signals for ten operating conditions of national highways and expressways are available for virtual iteration. Taking a national highway and expressway at 70km / h as an example, ... Figure 2 and 3 As shown, each channel in each working condition corresponds to the Z-axis acceleration signal of the right rear, left rear, right front, left front, right rear suspension top, left rear suspension top, and right front suspension top of the seat slide rail.

[0063] Using road test signals under different operating conditions as targets and a multibody dynamics model of the cab as the carrier, virtual iteration is performed to obtain the displacement excitation of the four active suspension ends of the cab. Taking national highways and expressways at 70km / h as examples, the iteration results are as follows: Figure 4 and Figure 5 As shown, the iterative response is highly consistent with the target signal in both the time and frequency domains. This fully demonstrates that the displacement excitation spectrum obtained through virtual iteration is highly reliable, providing a solid input foundation for seat simulation.

[0064] The displacement excitation of the active suspension end obtained after iterative convergence is substituted back into the ADAMS model for simulation, and finally the vertical displacement time history at the four mounting points of the seat is extracted, i.e., the high-precision seat excitation spectrum. Again, taking a national highway and expressway at 70km / h as examples, such as... Figure 6 and Figure 7 .

[0065] Based on the method described above for constructing a multibody dynamics model of a constant-damping seat, the completed model is as follows: Figure 8 The results were compared with the measured signals from road tests, and the time and frequency domain characteristics showed a high degree of agreement, thus verifying the effectiveness of the model and providing a reliable model for subsequent optimization.

[0066] Through the above steps, the 0-80Hz transmissivity and peak transmissivity for each simulation condition can be obtained. With spring stiffness as the x-axis and damper damping as the y-axis, and the transmissivity and peak transmissivity for each of the 1972 simulation conditions as the z-axis, transmissivity and peak transmissivity graphs are plotted. The transmissivity graphs are shown below. Figure 9 As shown, the surface clearly indicates the existence of a global optimum, with a distinct "valley," which is the global optimum. The parameters corresponding to this point are: stiffness 19 N / mm, damper damping (at 0.1 m / s), restoring force 400 N, and compressive force 500 N. Under this configuration, the RMS transmissibility reaches its minimum value of 0.8757.

[0067] Peak transfer rate image as Figure 10 As shown, from the analysis of peak transmissibility, the main function of damping is to control the resonance peak. There exists an optimal damping range that can effectively suppress the resonance peak without worsening high-frequency vibration isolation.

[0068] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0069] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A method for optimizing the dynamic comfort of commercial vehicle seats, characterized in that, Includes the following steps: S1. Obtain the dynamic response signals of the commercial vehicle seat system through vehicle road tests and bench tests; S2. Based on virtual iteration technology, the obtained dynamic response signal is inversely calculated to obtain the displacement excitation spectrum of the seat mounting point; S3. Establish a multibody dynamics model of a constant damping seat; S4. Input the displacement excitation spectrum of the seat mounting point into the multibody dynamics model of the constant damping seat, traverse multiple sets of parameter combinations of air spring stiffness and damper damping, and obtain the seat dynamic comfort evaluation index under different parameter combinations. S5. Based on the seat dynamic comfort evaluation index, obtain the global optimal parameter combination of air spring stiffness and shock absorber damping.

2. The method for optimizing the dynamic comfort of commercial vehicle seats according to claim 1, characterized in that, The specific method for the vehicle road test in step S1 is as follows: Vibration sensors are arranged at the left front, right front, left rear, and right rear positions of the car seat slide rail, and at the left front and right front positions of the seat frame. A seat cushion sensor is arranged on the seat cushion of the car seat to collect the Z-axis acceleration signal of each test point under multiple working conditions on national highways and expressways. After resampling and filtering, the virtual iterative dynamic response signal is obtained.

3. The method for optimizing the dynamic comfort of commercial vehicle seats according to claim 2, characterized in that, The method for inversely calculating the acquired dynamic response signal based on virtual iteration technology in step S2 includes: S21. Input a preset white noise excitation signal into the multibody dynamics model of the cab, obtain the system's response signal to the white noise, and calculate the system's transfer function. S22. Using the dynamic response signal as the target signal, the first external excitation is obtained by solving the inverse function of the system transfer function; S23. Iteratively update the external excitation to make the root mean square error between the response signal and the target signal obtained by the iterative simulation continuously converge until the convergence criterion is met. S24. Substitute the converged displacement excitation of the active end of the cab suspension back into the multibody dynamics model of the cab, and simulate and extract the vertical displacement response at the four mounting points of the seat to obtain the displacement excitation spectrum of the seat mounting points covering multiple working conditions.

4. The method for optimizing the dynamic comfort of commercial vehicle seats according to claim 3, characterized in that, The convergence criteria are as follows: the root mean square error between the iterative response and the target signal in the time domain is less than 5% of the root mean square error of the target signal; the coherence function value between the iterative response and the target signal is greater than 0.9 in the 0-80Hz key frequency band; and the relative damage ratio between the iterative response and the target signal is in the range of 0.5 to 2.

0.

5. The method for optimizing the dynamic comfort of commercial vehicle seats according to claim 1, characterized in that, The method for establishing the multibody dynamics model of the constant damping seat in step S3 includes: S31. Quasi-static loading tests were conducted on the air springs using a universal testing machine to obtain the air spring stiffness and load-displacement curves under different inflation pressures; damping characteristic tests were conducted on the damper using an MTS hydraulic servo test bench to obtain the damping force and power curves at different speeds; and the mass, center of mass coordinates, and moment of inertia parameters of the seat sprung parts were obtained through modal testing. S32. Import the three-dimensional model of the constant damping seat into the ADAMS simulation environment, remove redundant decorative parts that do not affect vibration transmission, preserve the core moving parts, add moving joint and rotating joint constraint relationships according to the assembly logic of the actual vehicle, and match the motion degrees of freedom of the actual vehicle. S33. Assign the nonlinear stiffness curve of the air spring, the force-velocity characteristic curve of the shock absorber, and the parameters of sprung mass and moment of inertia obtained from the component test to the model, and add a 65Kg mass block to the seat to simulate the occupant mass. S34. Apply displacement excitation to the input end at the bottom of the seat, collect the acceleration response signals of the seat frame and the center of mass of the human body, compare the time domain and frequency domain characteristics of the simulated acceleration response with the measured signals of the road test, and determine the model verification is effective when the degree of agreement between the two meets the error requirements.

6. The method for optimizing the dynamic comfort of commercial vehicle seats according to claim 1, characterized in that, In step S4, the air spring stiffness ranges from 10 to 55 N / mm, with a value interval of 1 N / mm; the damping force of the shock absorber ranges from 300 to 2000 N.

7. The method for optimizing the dynamic comfort of commercial vehicle seats according to claim 1, characterized in that, The seat dynamic comfort evaluation indicators in step S4 include: in, The indicators for evaluating seat dynamic comfort are w1 and w2, which are weighting coefficients. The design variables are vectors, namely air spring stiffness, damper restoring force, and compressive force; RMS transfer rate; Peak transfer rate; Inequality constraints; For equality constraints; : Upper and lower limits for design variables.

8. A dynamic comfort optimization system for commercial vehicle seats, characterized in that, include: The signal acquisition module is used to acquire the dynamic response signals of the seat mounting point during real vehicle road tests; The virtual iterative inverse calculation module is used to construct a multibody dynamics model of the cab-suspension system and, based on the dynamic response signal, uses virtual iterative technology to inversely calculate the displacement excitation spectrum of the seat mounting point. The multibody dynamics simulation module is used to construct a seat multibody dynamics model that includes an air spring nonlinear stiffness model and a shock absorber hysteresis damping model, and to perform parameter combination simulation using the displacement excitation spectrum as input. The parameter optimization module is used to determine the optimal parameter combination based on the vibration transmissibility under various parameter combinations.

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