A method for testing dynamic stiffness and damping characteristics of an engine support

Abstract

The present application relates to the technical field of mechanical vibration test, and more particularly to a kind of engine support dynamic stiffness and damping characteristic test method, comprising: step 1: boundary simulation;Step 2: double-source excitation loading;Step 3: dynamic response acquisition: in the direction of support stiffness main shaft arrangement vibration point acquisition three-dimensional acceleration signal, synchronous acquisition excitation force signal, all signals are recorded after anti-aliasing filtering with set sampling rate;Step 4: frequency response matrix construction: excitation force signal and acceleration signal are subjected to time-frequency transformation, and cross-point frequency response function matrix is calculated;Step 5: parameter decoupling calculation: parameter decoupling is realized by cross iteration optimization;Step 6: parameter output. Through the decoupling calculation of low frequency and high frequency band, the cross iteration optimization method can effectively reduce the calculation error, and improve the precision of parameter decoupling, ensure the reliability of test result under different frequency bands.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mechanical vibration testing, and in particular to a method for testing dynamic stiffness and damping characteristics of an engine support. BACKGROUND

[0002] In the field of new energy vehicles, the engine support, as a key load-bearing component of the powertrain, directly affects the NVH performance of the vehicle. With the development of hybrid power technology, the engine support needs to withstand both mechanical vibration and electromagnetic excitation of the motor, which poses new challenges to dynamic parameter testing. The existing testing methods have the following defects that are difficult to overcome:

[0003] Traditional dynamic testing uses a single hydraulic shaker for sinusoidal frequency sweeping, and its effective frequency band is usually 5-200Hz, which can only simulate the inertial force excitation of the engine. However, the frequency band of high-frequency electromagnetic excitation of the motor can reach 200-2000Hz (such as the 48th harmonic of the permanent magnet synchronous motor). When using an electromagnetic shaker that extends the high-frequency response, due to the impedance mismatch between the mechanical structure and the electromagnetic system, the excitation energy is rapidly attenuated above 200Hz (actual attenuation >60%). This results in the inability to simultaneously reproduce the combined working conditions of engine wideband vibration and motor high-frequency electromagnetic force under a single excitation source, causing the test results to deviate significantly from the actual vehicle state.

[0004] In the transfer function method test, the stiffness matrix and the damping matrix need to be solved simultaneously through the frequency response function. However, under the high load working condition of hybrid power, the support damping shows strong nonlinear characteristics: at low frequencies (<100Hz), viscous damping is dominant, and at high frequencies (>200Hz), structural damping is dominant. The existing method treats the stiffness matrix and the damping matrix as constant matrices for overall fitting, resulting in two typical problems:

[0005] The low-frequency damping term interferes with stiffness identification, causing the dynamic stiffness value to be 10%-15% higher, and the high-frequency stiffness term affects damping identification, causing the damping ratio calculation value to fluctuate by more than ±20%. This coupling error is particularly significant under transient conditions such as rapid acceleration and energy recovery of new energy vehicles.

[0006] Therefore, there is an urgent need for a method for testing the dynamic stiffness and damping characteristics of an engine support to solve the above problems. SUMMARY

[0007] In order to achieve the above purpose, the present application provides a method for testing the dynamic stiffness and damping characteristics of an engine support, comprising:

[0008] Step 1: Boundary simulation: install the engine support test piece on the rigid base platform through the profiling clamp, the contact surface shape of the clamp is consistent with the installation surface of the real vehicle; set up a non-contact support unit between the clamp and the platform, simulate the bolt pretightening force of the real vehicle by adjusting the gas pressure of the air bag;

[0009] Step 2: Dual-source excitation loading: parallel hydraulic servo exciter and electromagnetic high-frequency exciter at the power input end of the support, respectively connecting the engine mounting point and the motor mounting point; control the hydraulic exciter to output wide-frequency excitation covering the engine vibration frequency band, and the electromagnetic exciter to output high-frequency excitation covering the motor electromagnetic force frequency band;

[0010] Step 3: Dynamic response acquisition: arrange vibration measurement points in the direction of the main shaft of the support stiffness to collect three-axis acceleration signals, and synchronously collect excitation force signals, all signals are recorded after anti-aliasing filtering at a set sampling rate;

[0011] Step 4: Frequency response matrix construction: time-frequency transform is performed on the excitation force signals and acceleration signals, and the cross-point frequency response function matrix is calculated;

[0012] Step 5: Parameter decoupling calculation: identify the stiffness matrix using the hydraulic excitation data in the low frequency band, identify the damping matrix by fixing the stiffness matrix in the high frequency band, and realize parameter decoupling through cross-iteration optimization;

[0013] Step 6: Parameter output: extract the stiffness matrix elements to output the dynamic stiffness value, and calculate the equivalent viscous damping ratio based on the damping matrix.

[0014] Preferably, the process of adjusting the air bag gas pressure in step 1 includes:

[0015] Obtain the pretightening force range under different working conditions through a real vehicle bolt pretightening force measuring device, the measuring device includes an array of strain sensors, the sensors are attached to the surface of the bolt and connected to a dynamic strain meter;

[0016] Calculate the target value of unit area pressure according to the contact area of the profiling clamp and the air bag, and fit and calculate the contact area point cloud data of the clamp contact surface through a three-dimensional optical scanner;

[0017] Adopt a closed-loop pressure control system: the proportional valve dynamically adjusts the air inlet flow according to the deviation between the measured value and the target value of the pressure sensor, and locks the pressure when the deviation value is less than the set threshold.

[0018] Preferably, the generation of wide-frequency excitation in step 2 includes:

[0019] Establish an engine speed-torque-inertia force mapping model: calculate the inertia force amplitude envelope at each speed through crankshaft dynamics simulation;

[0020] The 5-200 Hz frequency band is divided into multiple subintervals, and the subinterval width decreases with increasing speed;

[0021] A pseudo-random excitation signal is generated in each subinterval, and the root mean square value thereof is scaled in proportion to the inertia force of the corresponding interval of the envelope line, and the sweep rate is positively correlated with the speed change rate of the engine acceleration condition.

[0022] Preferably, the configuration of the high-frequency excitation in step 2 includes:

[0023] Obtaining the electromagnetic force spectrum characteristics of the motor: extracting the fundamental and characteristic harmonic frequency distribution through finite element electromagnetic field simulation;

[0024] A plurality of target frequency bands are selected in the range of 200-2000 Hz, and the center frequency of the target frequency band is an integer multiple of the characteristic harmonic frequency;

[0025] A constant-amplitude sinusoidal sweep signal is generated in each target frequency band, the sweep step is adaptively adjusted according to the frequency band width, and the signal amplitude is allocated according to the electromagnetic force harmonic energy proportion.

[0026] Preferably, the construction process of the frequency response function matrix in step 4 includes:

[0027] The time domain signal is processed by windowing and segmentation, and the window function type is automatically selected according to the signal stationarity: when the signal short-time energy fluctuation exceeds the threshold, a leakage suppression window is selected, otherwise a high-resolution window is selected;

[0028] The cross-power spectral density matrix of the acceleration signal and the excitation force signal in each segment, and the self-power spectral density matrix of the excitation force signal are calculated;

[0029] The cross-power spectral density matrix is divided by the self-power spectral density matrix to obtain the frequency response function matrix, and the amplitude of the abnormal frequency point is smoothed.

[0030] Preferably, the cross-iteration optimization in step 5 includes:

[0031] Set the initial conditions of iteration: set the low-frequency damping matrix as a zero matrix, and set the high-frequency stiffness matrix as the previous identified value;

[0032] Perform parameter transfer: substitute the damping matrix output in the high-frequency segment into the low-frequency stiffness identification equation, and substitute the updated stiffness matrix into the high-frequency damping identification equation;

[0033] Iteration termination judgment: calculate the norm change rate of the adjacent two times of the stiffness matrix and the damping matrix, and when both of them are less than the dynamic convergence threshold, the optimization is terminated, and the convergence threshold is calculated according to the nonlinear error of the force sensor and the phase error of the vibration instrument.

[0034] Preferably, the calculation process of the dynamic convergence threshold includes:

[0035] obtain the linearity error limit of the force measurement channel and the phase shift angle of the acceleration measurement channel through the sensor calibration certificate;

[0036] convert the force signal error limit into the relative error of the frequency response function amplitude and the phase shift angle into the absolute error of the frequency response function phase;

[0037] calculate the theoretical error upper limit of the stiffness matrix elements based on the error propagation theory, and take a preset proportion of the theoretical error upper limit as the norm change rate threshold.

[0038] Preferably, the abnormal frequency point determination rule is:

[0039] Mark the frequency points on the frequency response function curve where the coherence coefficient is below the first threshold value;

[0040] Mark the frequency points where the amplitude of the cross power spectral density drops by more than the second threshold value;

[0041] Perform cubic spline interpolation correction on the frequency response function values of the marked frequency points.

[0042] Preferably, the calculation of the equivalent viscous damping ratio in step 6 includes:

[0043] Perform eigenvalue decomposition on the stiffness matrix to obtain the modal stiffness of each order;

[0044] Project the damping matrix into the modal coordinate system to obtain the diagonalized modal damping matrix;

[0045] Extract the diagonal elements of the modal damping matrix as the modal damping coefficients of each order;

[0046] Divide the modal damping coefficients by the product of twice the modal stiffness and the modal mass obtained by modal shape normalization processing to obtain the damping ratio.

[0047] Preferably, the modal shape normalization processing includes:

[0048] Select a reference degree of freedom in the anti-resonance point interval of the frequency response function;

[0049] Scale all orders of modal shapes by 1 in the shape component of the reference degree of freedom;

[0050] Obtain the modal mass matrix by inversely calculating the mass normalization coefficient.

[0051] The beneficial effects of the present application are:

[0052] 1. The present application covers the engine vibration frequency band and the motor electromagnetic force frequency band by connecting the hydraulic servo exciter and the electromagnetic high-frequency exciter at the power input end of the support, so that the combined excitation load of the engine and the motor can be simulated at the same time, the problem of unable to reproduce double load is successfully solved, and the test result is closer to the working condition in actual application.

[0053] 2. The present application optimizes the excitation source configuration, refines the frequency spectrum characteristics of the motor electromagnetic force excitation, designs a multi-target frequency band for the characteristic harmonic frequency of the motor electromagnetic excitation, and adaptively adjusts the sweep frequency step and amplitude according to the frequency band characteristics, so that the high-frequency excitation can effectively play a role, and the problem of high-frequency excitation energy attenuation in the existing test method is overcome.

[0054] 3. The present application adopts a cross-iteration optimization method, respectively identifies the low-frequency stiffness matrix and the high-frequency damping matrix, and decouples the mutual influence of stiffness and damping through cross-iteration, so that the calculation result of the stiffness and damping matrix is more accurate, the generation of coupling error is avoided, and especially in the transient conditions such as sudden acceleration and energy recovery of new energy vehicles, the test result is more accurate.

[0055] 4. The present application adopts a polynomial window function and an adaptive window function selection strategy, optimizes the construction process of the frequency response function matrix through windowing segmentation processing and signal stationarity analysis. This improvement effectively improves the accuracy of the frequency response function, especially for the high-frequency frequency response function, which is more stable, and avoids the amplitude drop and abnormal frequency point error in the existing method.

[0056] 5. The present application introduces the error propagation theory in the optimization process, calculates the theoretical error upper limit of the force signal and the acceleration signal based on the sensor calibration certificate, and takes it as the norm change rate threshold, optimizes the convergence judgment condition of the parameter decoupling process. By accurately controlling the error propagation, the reliability of the parameter decoupling process is ensured, and the accuracy and consistency of the test result are improved. DETAILED DESCRIPTION

[0057] In order to more clearly illustrate the technical solutions in the present application or prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, for those skilled in the art, other drawings can also be obtained based on these drawings without any creative effort.

[0058] Fig. 1 The step flow chart of the method of the present application;

[0059] Fig. 2 The step flow chart of the calculation of the equivalent viscous damping ratio in step 6 of the method of the present application;

[0060] Fig. 3The step flow chart of the modal vibration normalization processing of the method of the present application. DETAILED DESCRIPTION

[0061] The present application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted here that, in order to make the embodiments more detailed, the following embodiments are the best, preferred embodiments, and other alternative ways can also be used by those skilled in the art to implement them; and the drawings are only used to more specifically describe the embodiments, and are not intended to specifically limit the present application.

[0062] Please refer to Figs. 1-3 The embodiment of the present application provides a test method for dynamic stiffness and damping characteristics of an engine support. In step 1, first, the engine support test piece is installed on a rigid base platform through a profiling clamp. The contact surface shape of the profiling clamp is consistent with the actual vehicle mounting surface, ensuring matching with the actual working condition. A gas floating non-contact support unit is arranged between the clamp and the platform, and the pretightening force of the actual vehicle bolt is simulated by adjusting the gas pressure of the air bag. This design can avoid the rigid constraint problem in the traditional installation method, so that the support can more truly reflect its dynamic response in actual use during the test.

[0063] The gas floating support unit reduces the friction force between the support and the platform, so that the support is not disturbed by the outside during the vibration test, and the free vibration state of the test piece is maintained, thereby more accurately simulating the power transmission under the actual working condition.

[0064] In step 2, a hydraulic servo exciter and an electromagnetic high-frequency exciter are connected in parallel at the power input end and connected to the engine mounting point and the motor mounting point respectively. The wideband excitation output by the hydraulic exciter covers the engine vibration frequency band, and the electromagnetic exciter provides high-frequency excitation covering the motor electromagnetic force frequency band. This scheme simultaneously simulates the load of the engine and the motor on the support through double-source excitation, and can truly reproduce the complex excitation environment in the hybrid power system.

[0065] The double-source excitation loading overcomes the limitation that the traditional single excitation source cannot simultaneously simulate the engine and motor vibration, so that the test is more in line with the actual working conditions, especially for the high-frequency electromagnetic excitation part, which can ensure the integrity of the frequency range and avoid the problem of excitation energy attenuation.

[0066] In step 3, vibration measuring points are arranged in the direction of the main shaft of the support stiffness, three-dimensional acceleration signals are collected, and excitation force signals are collected synchronously. After all the collected signals are subjected to anti-aliasing filtering, they are recorded at a set sampling rate. Through this multi-channel, multi-dimensional data collection method, the integrity and accuracy of the test signals are ensured.

[0067] This step ensures comprehensive acquisition of dynamic responses from different directions, providing accurate acceleration and excitation force data, ensuring the accuracy of subsequent frequency response matrix and parameter decoupling calculations.

[0068] In step 4, the excitation force and acceleration signals are subjected to time-frequency transformation, and the cross-point frequency response function matrix is calculated. Through time-frequency analysis, the dynamic response characteristics at different frequencies can be obtained, providing a reliable basis for subsequent parameter decoupling and analysis.

[0069] Time-frequency transformation technology can effectively distinguish the dynamic characteristics of different frequency bands, improving the accuracy of the frequency response matrix and avoiding confusion and errors in traditional frequency response methods.

[0070] In step 5, in the low frequency band, the data of the hydraulic exciter is used to identify the stiffness matrix; in the high frequency band, the damping matrix is identified by fixing the stiffness matrix. Through cross-iterative optimization method, the mutual influence of stiffness and damping matrix is decoupled, making the calculation result more accurate.

[0071] Cross-iterative optimization technology effectively solves the interference of low-frequency damping on stiffness identification and the influence of high-frequency stiffness on damping identification, avoiding coupling errors in traditional methods and improving the identification accuracy of stiffness and damping.

[0072] In step 6, finally, the dynamic stiffness value is output by extracting the stiffness matrix elements, and the equivalent viscous damping ratio is calculated based on the damping matrix. In this way, accurate dynamic stiffness and damping characteristics parameters can be obtained, providing data support for further vehicle performance optimization and design.

[0073] This step provides accurate dynamic stiffness and damping characteristic data, providing more accurate basic data for automobile engineers, and further optimizing the design of engine supports and improving the NVH performance of new energy vehicles.

[0074] By combining air floating support, double-source excitation, time-frequency analysis, and parameter decoupling, etc. innovative technologies, the existing method effectively solves many technical problems in the dynamic stiffness and damping characteristic test of new energy vehicle engine support, improves the accuracy and reliability of the test, and provides strong technical support for the design and performance optimization of new energy vehicle power systems.

[0075] In one possible implementation, first, the bolt pre-tightening force range under different working conditions is obtained by a real vehicle bolt pre-tightening force measuring device. The device includes an array of strain sensors attached to the surface of the bolt and real-time strain data is collected by a dynamic strain gauge. The array of strain sensors can measure and feedback the pre-tightening force of the bolt in real time by sensing the slight deformation of the bolt under different load conditions. Through this process, the strain data of the bolt under different load conditions can be accurately obtained, and the pre-tightening force range can be calculated.

[0076] This step ensures accurate measurement of the bolt pre-tightening force, avoids measurement errors that may exist in traditional methods, and provides reliable reference data for subsequent boundary simulation.

[0077] According to the contact area of the profiling fixture and the air bag, the target pressure value per unit area is calculated. The contact area is calculated by fitting the point cloud data of the profiling fixture contact surface obtained by a three-dimensional optical scanner. The point cloud data can comprehensively capture the slight geometric changes of the fixture contact surface, and based on these data, the target pressure value required by the air bag can be more accurately calculated, thereby simulating the distribution of the bolt pre-tightening force under actual working conditions.

[0078] Using a three-dimensional optical scanner to obtain point cloud data can accurately determine the shape and contact area of the contact surface, avoiding the simplification assumptions of traditional measurement methods for complex surface contact conditions, and ensuring the accuracy of the pressure target value.

[0079] When adjusting the air bag gas pressure, a closed-loop pressure control system is used, which is composed of a proportional valve, a pressure sensor and a target pressure value. The proportional valve dynamically adjusts the air intake flow according to the deviation between the real-time measured pressure and the target value, thereby accurately controlling the pressure of the air bag. When the deviation value is less than the set threshold value, the control system will automatically lock the air bag pressure to maintain a stable pressure state.

[0080] In the embodiment of the present application, the threshold value set during the test process is ±0.1%, that is, when the real-time pressure measured by the pressure sensor deviates from the target value by less than 0.1%, the closed-loop system will automatically stop adjusting the air intake flow to ensure that the air bag pressure is stable at the expected target value. The set threshold value is small, which can ensure that the air bag pressure accuracy is high enough to ensure the accuracy of the experimental results.

[0081] The closed-loop pressure control system can achieve accurate adjustment and real-time control of the air bag pressure, avoiding errors caused by manual adjustment in traditional methods, and ensuring the stability of the air bag pressure during the test process, improving the repeatability and reliability of the experiment.

[0082] By combining multiple technologies such as strain sensor arrays, three-dimensional optical scanners, closed-loop pressure control systems, etc., high-precision adjustment of the airbag pressure is achieved, ensuring the true restoration of boundary conditions during engine support testing. This not only improves the accuracy of the test, but also provides reliable data support for subsequent dynamic analysis. In addition, by setting appropriate pressure deviation thresholds, the precision of pressure control is ensured, further improving the stability and reliability of the test.

[0083] In one possible implementation, an engine speed-torque-inertial force mapping model needs to be established first. Specifically, by simulating the dynamics of the crankshaft, the amplitude envelope of the inertial force generated by the engine at different speeds is calculated. The dynamics simulation of the crankshaft mainly simulates the rotational inertia, cylinder force, combustion process, etc. during the operation of the engine, and obtains the dynamic response of the engine at each speed.

[0084] Using simulation tools such as Matlab / Simulink, a model is established based on the structure and dynamics of the engine, simulating the inertial force at each speed condition, and drawing the amplitude envelope of the inertial force.

[0085] By establishing a high-precision simulation model, the trend of the engine's inertial force at different speeds can be accurately captured, providing data support for generating a wideband excitation signal, ensuring the reasonableness and repeatability of the excitation signal.

[0086] In the 5-200Hz frequency band, the entire frequency band is divided into multiple sub-intervals, and the sub-interval width decreases with increasing speed. The purpose of this design is to accurately simulate the vibration characteristics of the engine at different speeds. At low speeds, the change in inertial force is relatively gentle, so a wider frequency sub-interval can be used; at high speeds, the inertial force changes rapidly, so a more detailed frequency division is needed.

[0087] By dividing the frequency band into segments and adjusting the width of the sub-interval according to the rate of change of the inertial force in each segment. For example, at low speeds, the frequency range can be divided into 10Hz sub-intervals, while at high speeds, the frequency range can be divided into 1Hz sub-intervals to more accurately control the frequency change.

[0088] This frequency band division method can effectively improve the frequency resolution of the excitation signal, especially at high speeds, it can better track and reflect the rapid changes in the engine's inertial force, thus more realistically simulating the dynamics of the engine during operation.

[0089] Within each frequency subinterval, a pseudo-random excitation signal is generated, with its RMS value scaled by the inertial force ratio within the corresponding envelope interval. A pseudo-random excitation signal exhibits random characteristics within a specific frequency range. It can simulate the vibration response of an engine at different frequencies and is used to test the dynamic stiffness and damping characteristics of an engine mount.

[0090] Pseudo-random excitation signals are usually generated through digital signal processing technology, using methods such as normal distribution or Gaussian distribution to generate signals, and their root mean square values ​​are adjusted by scaling to make them consistent with the proportion of inertial force.

[0091] This excitation signal can produce uniform energy distribution within a wide frequency band, avoiding the problem of insufficient energy in the low or high frequency parts of traditional excitation signals, thereby improving the comprehensiveness and accuracy of the test.

[0092] Finally, the sweep rate is positively correlated with the rate of change of engine speed during acceleration. That is, as engine speed changes, the sweep rate adjusts accordingly. This ensures that the excitation signal changes are synchronized with the actual engine operating conditions, thereby more realistically simulating the dynamic response of the engine during acceleration.

[0093] When generating the excitation signal, the engine's acceleration condition data (such as the speed change rate) is collected and the sweep rate is adjusted so that the frequency change of the excitation signal matches the engine speed change.

[0094] This adjustment can ensure that the frequency change of the excitation signal during the test is consistent with the actual engine operating conditions, improve the reliability and accuracy of the test data, and make the test results more representative.

[0095] Through the above steps, the broadband excitation generation process accurately simulates the inertial force distribution and dynamic response of the engine under different speed conditions. Establishing a precise speed-torque-inertial force mapping model, rationally dividing the frequency bands, and generating a uniformly distributed pseudo-random excitation signal ensures the accuracy of the excitation signal across the entire frequency band. Combined with a positive correlation between the frequency sweep rate and the speed change rate, this method can better simulate the dynamic behavior of the engine under actual operating conditions. This method offers high precision, repeatability, and broad application prospects, providing a scientific and reliable excitation method for dynamic performance testing of engine mounts.

[0096] In one possible implementation, finite element electromagnetic field simulation is first performed to extract the electromagnetic force spectrum characteristics of the motor under different operating conditions, specifically the fundamental frequency distribution and characteristic harmonic frequency distribution. Finite element simulation accurately calculates the electromagnetic force distribution within the motor by taking into account factors such as the motor's geometry, material properties, and current distribution.

[0097] The electromagnetic force of the motor is simulated using finite element simulation tools such as COMSOL Multiphysics or ANSYS Maxwell. By analyzing each frequency point of the motor, the fundamental wave and its characteristic harmonic frequency of the electromagnetic force are extracted, and the strength and distribution of the harmonic frequency are determined.

[0098] This simulation method can accurately obtain the electromagnetic force spectrum characteristics of the motor, providing a scientific basis for the configuration of high-frequency excitation signals. By accurately extracting the fundamental wave and characteristic harmonic frequency, the vibration mode of the motor under actual working conditions can be better captured.

[0099] In the frequency range of 200-2000Hz, multiple target frequency bands are selected, and the center frequency of the target frequency band is an integer multiple of the characteristic harmonic frequency. This design ensures that the excitation signal is highly matched with the electromagnetic force characteristics of the motor, making the test results more consistent with the actual situation.

[0100] According to the simulation results, select frequency bands close to the characteristic harmonic frequency and determine the center frequency of each frequency band. For example, if the characteristic harmonic frequency is 300Hz, the center frequency of the target frequency band may be selected as 300Hz, 600Hz, 900Hz, etc. At this time, the target frequency band can be set to a 25Hz wide frequency band for more detailed frequency division.

[0101] By selecting integer multiples of the characteristic harmonic frequency as the center of the target frequency band, the excitation signal can be synchronized with the natural vibration frequency of the motor, effectively improving the accuracy and representativeness of the test. This method avoids the deviation of the frequency from the motor resonance frequency, reducing the test error.

[0102] Generate a constant amplitude sinusoidal sweep signal in each target frequency band, and adaptively adjust the sweep step according to the frequency band width. The amplitude of the constant amplitude sinusoidal sweep signal is allocated according to the proportion of the electromagnetic force harmonic energy, to ensure that the amplitude of the excitation signal is consistent with the energy distribution of the electromagnetic force spectrum.

[0103] According to the width of each target frequency band and the energy distribution of the electromagnetic force spectrum, a sinusoidal sweep signal is generated through digital signal processing. The sweep step is adaptively adjusted according to the frequency band width to ensure uniform distribution of signal frequencies within the entire frequency band. The amplitude adjustment is based on the energy proportion of the characteristic harmonic to ensure that the signal amplitude reflects the energy contribution of the electromagnetic force in that frequency band.

[0104] The constant amplitude sinusoidal sweep signal can provide uniform frequency variation in each frequency band, accurately exciting the dynamic response of the engine support. By adaptively adjusting the sweep step and amplitude allocation, the excitation strength of the signal can be accurately controlled, avoiding excessive or insufficient excitation, and ensuring the accuracy and reliability of the test signal.

[0105] The technical feature can realize precise configuration of the high-frequency excitation signal by extracting motor electromagnetic force spectrum characteristics, selecting an integer multiple frequency band of the characteristic harmonic frequency, and generating a constant-amplitude sinusoidal sweep signal. Specifically, the frequency range and amplitude of the excitation signal are determined by the electromagnetic force spectrum characteristics, so as to ensure that the excitation signal is highly matched with the vibration characteristics of the motor. The adaptive adjustment of the sweep step and the signal amplitude further improves the test accuracy, so that the dynamic stiffness and damping characteristics test of the engine support is more real and effective.

[0106] In a possible implementation, in the signal processing process, the time-domain signal needs to be first divided into multiple small segments for analysis, which is called windowing segmentation processing. A suitable window function is automatically selected according to the stationarity of each segment of the signal.

[0107] The time-domain signal is first divided into several small segments, and common window functions include rectangular window, Hanning window, Blackman window, etc. In the signal stationarity analysis, the short-time energy fluctuation is used to determine the type of window function. If the short-time energy fluctuation of the signal exceeds a certain threshold, it indicates that the signal changes greatly in this segment, and a leakage suppression window (such as Blackman window or Hanning window) is selected to reduce edge effect and leakage phenomenon; if the short-time energy fluctuation of the signal is small, it indicates that the signal is relatively stationary, and a high-resolution window (such as rectangular window) is selected to provide higher frequency resolution.

[0108] In the embodiment of the application, the threshold is set to be that the standard deviation (or volatility) of the signal exceeds 0.1, and the leakage suppression window is selected; if the standard deviation is less than 0.1, the high-resolution window is selected. This way ensures that the window function suitable for suppressing leakage is used for the part of the signal with large changes, and the high-resolution window is used for the stationary signal to improve the frequency accuracy.

[0109] This method can flexibly adjust the type of window function according to the characteristics of different signals, avoid the loss of frequency spectrum leakage or resolution caused by mismatching of the window function, and thus improve the accuracy of the spectrum analysis.

[0110] The cross-power spectral density matrix of the acceleration signal and the excitation force signal, and the self-power spectral density matrix of the excitation force signal need to be calculated in each segment.

[0111] The power spectrum density of each frequency point is calculated by performing fast Fourier transform (FFT) on the acceleration signal and the excitation force signal. The cross-power spectral density reflects the correlation between the acceleration and the excitation force signal, and the self-power spectral density reflects the energy distribution of the excitation force signal.

[0112] The interaction information between the acceleration signal and the excitation force signal can be obtained by calculating the cross-power spectral density, and the self-power spectral density helps to describe the characteristics of the excitation force signal itself. Through the combination of the two, the response characteristics of the system can be more accurately analyzed.

[0113] After obtaining the cross-power spectral density matrix and the auto-power spectral density matrix, the cross-power spectral density matrix is divided by the auto-power spectral density matrix to obtain the frequency response function matrix.

[0114] The frequency response function matrix reflects the dynamic relationship between the input and output of the system, i.e., the frequency response between the excitation force and the acceleration. When performing the division operation, ensure that the matrix dimensions match and handle possible numerical errors. The frequency response function matrix can provide dynamic stiffness and damping information of the system at different frequencies.

[0115] The frequency response function matrix is an important tool for analyzing the dynamic characteristics of the system, which can help accurately identify key parameters such as stiffness, damping, and resonance frequency of the system. This is crucial for the design and optimization of the engine support.

[0116] The amplitude of the abnormal frequency points in the calculated frequency response function matrix is smoothed to further eliminate noise and data bias.

[0117] The amplitude of the extreme or abrupt frequency points in the frequency response function matrix is smoothed, and common methods include moving average, Gaussian smoothing, etc. These methods can effectively remove the influence of noise on the frequency response function matrix, making it more consistent with the dynamic response characteristics of the actual system.

[0118] Through smoothing, data fluctuations caused by test environment or signal noise can be eliminated, thereby improving the reliability and accuracy of the frequency response function matrix, ensuring that the test results can truly reflect the dynamic stiffness and damping characteristics of the engine support.

[0119] Through windowing and segmentation processing, calculating cross-power spectral density and auto-power spectral density, constructing frequency response function matrix, and amplitude smoothing, this method can effectively extract and analyze the dynamic response characteristics of the engine support. Each step is designed to improve the accuracy and reliability of signal analysis, especially in automatically selecting window functions, calculating power spectral density matrices, and frequency response function matrices, making the test results more realistic and detailed.

[0120] In one possible implementation, in the initial stage of cross-iterative optimization, appropriate starting values need to be set to ensure that the optimization process can proceed smoothly.

[0121] The damping matrix in the low frequency band is set to a zero matrix. This is because the dynamic response of the low frequency band is usually smooth and the damping effect is small. Setting it to a zero matrix can simplify the initial calculation and avoid affecting the subsequent optimization. The stiffness matrix in the high frequency band is set to the previous identified value, because the high frequency stiffness has been estimated more accurately in the previous identification, which can provide a reasonable starting point for subsequent iterations.

[0122] Reasonable initial conditions can help avoid convergence problems in the iterative process, especially in complex systems, by setting reasonable initial conditions for low and high frequency bands, which can speed up the optimization process and reduce the waste of computing resources.

[0123] During the cross-iterative optimization process, the dynamic stiffness and damping characteristics of the low and high frequency bands interact with each other, so parameter transfer is needed.

[0124] The damping matrix output by the high frequency band is substituted into the stiffness identification equation of the low frequency band, and based on the current damping matrix, the stiffness matrix of the low frequency band is updated; at the same time, the updated low frequency band stiffness matrix is substituted into the damping identification equation of the high frequency band, and the damping matrix of the high frequency band is updated. Through this parameter transfer method, the parameters of the two frequency bands are mutually constrained and optimized, so that the final dynamic characteristics are more accurate.

[0125] Parameter transfer allows the dynamic characteristics of the low and high frequency bands to interact and constrain each other, and this cross-optimization method can avoid local optimal solutions and ensure that the overall system's dynamic stiffness and damping characteristics are fully optimized. Through continuous exchange and update of parameters, accurate identification of the characteristics of each frequency band of the system can be achieved.

[0126] The final step of cross-iterative optimization is to determine whether to terminate the optimization. The optimization process determines convergence by calculating the norm change rate.

[0127] In each iteration, the norm change rate of the stiffness matrix and the damping matrix of the adjacent two times is calculated. The norm change rate reflects the amplitude of matrix update, and when the change rates of both are less than the set dynamic convergence threshold, it is determined that the optimization process has converged and the optimization is stopped. The dynamic convergence threshold is calculated based on the nonlinear error of the force sensor and the phase error of the vibration meter. Specifically, the nonlinear error of the force sensor may cause signal deviation, and the phase error of the vibration meter will affect the accuracy of the frequency response, so these errors need to be considered comprehensively to obtain a reasonable convergence threshold.

[0128] With accurate convergence determination, the optimization process can automatically terminate when sufficient accuracy is reached, thus avoiding excessive calculation and waste of computing resources. At the same time, the convergence threshold based on sensor error can ensure that the optimization result has sufficient accuracy and reliability in actual testing.

[0129] The cross-iterative optimization method, through mutual optimization of low and high frequency bands and accurate convergence determination, not only improves the accuracy of dynamic stiffness and damping characteristic identification, but also effectively eliminates the influence of sensor errors and instrument noise on test results.

[0130] In one possible implementation, when calculating the dynamic convergence threshold, first, the relevant error information needs to be obtained through the sensor calibration certificate, which specifically includes:

[0131] Linearity error limit of force measurement channel: This refers to the linear error of the force sensor's output signal across the entire measurement range. This error directly affects the accuracy of the force signal, which in turn affects the identification results of dynamic stiffness.

[0132] Phase shift angle of acceleration measurement channel: This error represents the phase difference between the acceleration sensor's output signal and the actual motion state. Phase shift can cause distortion in the phase information of the frequency response function, which in turn affects the test of damping characteristics.

[0133] After obtaining the error information of the sensor, these errors need to be converted into the error form of the frequency response function for subsequent error propagation and calculation:

[0134] The linearity error limit of the force signal reflects the amplitude deviation of the force signal at different frequencies. Through conversion, the relative error of the amplitude in the frequency response function can be obtained. This error directly affects the accuracy of the stiffness matrix.

[0135] The phase shift angle of the acceleration measurement channel introduces phase error in the frequency response function. After conversion to absolute error, the influence of phase deviation on the test results can be accurately quantified.

[0136] Through error propagation theory, the amplitude error of the force signal and the phase error of the acceleration signal can be propagated to the calculation of the stiffness matrix. Error propagation theory can calculate the theoretical error upper limit of the elements of the stiffness matrix based on the source of the error (force and acceleration sensor errors) and their relationship. This upper limit reflects the range of influence of sensor errors on each element of the stiffness matrix in the worst case.

[0137] By considering the contribution of force and acceleration signals to each element of the stiffness matrix in the frequency response function, and combining the error propagation formula, the error upper limit of each stiffness matrix element is calculated.

[0138] This step ensures the rationality and accuracy of the error propagation process, effectively evaluating the influence of sensor errors on the overall stiffness identification accuracy of the system, and providing a scientific basis for subsequent optimization.

[0139] Finally, according to the theoretical error upper limit of the stiffness matrix obtained from the error propagation theory, a reasonable preset proportion of the norm change rate threshold is selected. This proportion is usually set based on experimental experience, application requirements, or the error range tolerated by the system.

[0140] The final norm change rate threshold is obtained by multiplying the theoretical error upper limit by a preset ratio. The setting of the norm change rate threshold directly determines when the optimization process ends, ensuring that the optimization process can stop when the error change rate of the system is lower than the threshold, thereby ensuring efficient use of computing resources.

[0141] By setting a reasonable threshold, the transmission of errors can be effectively controlled, and excessive correction during the optimization process can be avoided, ensuring a balance between computational efficiency and optimization accuracy.

[0142] Through the above steps, the dynamic convergence threshold calculation process can fully consider the influence of sensor errors and accurately calculate the error upper limit of the stiffness matrix through error propagation theory. The implementation of this process can ensure that the convergence determination in the optimization process is more scientific and accurate, avoiding excessive optimization or unnecessary calculations caused by errors, thereby improving optimization efficiency.

[0143] In a possible implementation, the coherence coefficient is used to measure the correlation between two signals. In a frequency response function test, the coherence coefficient is usually calculated based on the relationship between the input signal and the response signal. If the coherence coefficient is too low, it indicates that the relationship between the system input and response is weak, and there may be noise or measurement errors.

[0144] In the frequency response function curve, a coherence coefficient value is calculated for each frequency point, representing the correlation between the input signal and the output signal. If the coherence coefficient of a certain frequency point is lower than a preset first threshold (for example, 0.6), the frequency point is marked as an abnormal frequency point.

[0145] In an embodiment of the present application, the first threshold is set to 0.6. If the coherence coefficient of a certain frequency point is 0.55, it is considered that there is a lot of noise or interference in the frequency point, and therefore it is marked as an abnormal frequency point.

[0146] The cross power spectral density is used to describe the frequency response relationship between the input signal and the output signal. If the amplitude of the cross power spectral density suddenly drops at some frequency points, it may be due to device failure, sensor problems, or environmental interference.

[0147] By calculating the cross power spectral density of the frequency response function and detecting the amplitude change, if the amplitude of a certain frequency point suddenly drops by more than a second threshold (for example, the amplitude suddenly drops by more than 20 dB), the frequency point is marked as an abnormal frequency point.

[0148] Specifically, if the amplitude of the cross power spectral density at a certain frequency point suddenly drops by more than 20 dB (from 10 dB to -10 dB), the frequency point is marked as an abnormal frequency point, because such a sudden drop in amplitude may be caused by problems with the measurement equipment or nonlinear behavior of the system.

[0149] For the frequency points marked as abnormal, interpolation correction is needed to ensure the smoothness and continuity of the frequency response function data. Cubic spline interpolation is a commonly used smoothing interpolation method that corrects the data of abnormal frequency points by constructing a continuous and smooth derivative curve between adjacent frequency points.

[0150] For the frequency points marked as abnormal, the frequency response function values in their neighborhood are corrected using the cubic spline interpolation method. Cubic spline interpolation can ensure that the interpolated curve is not only continuous at the frequency points, but also has a smooth derivative, avoiding data discontinuity.

[0151] Through interpolation correction, the error caused by abnormal frequency points can be eliminated, ensuring the smoothness and continuity of the frequency response function curve and further improving the accuracy of the test results.

[0152] The determination rule of abnormal frequency points can effectively ensure the reliability and accuracy of the test results, guarantee the accuracy of dynamic stiffness and damping characteristic test, and further improve the quality of engine support and other mechanical system performance analysis.

[0153] In one possible implementation, first, the stiffness matrix of the system needs to be obtained, which is usually obtained through finite element analysis or experimental data. Then, by performing eigenvalue decomposition on the stiffness matrix, the modal stiffness of each order can be obtained. This step is equivalent to decomposing the complex structural stiffness characteristics into multiple simplified and independent modal stiffness values, so that the dynamic response of each mode can be analyzed separately.

[0154] The core of eigenvalue decomposition is to mathematically solve the eigenvalues and eigenvectors of the stiffness matrix. The eigenvalue represents the stiffness of each mode, while the eigenvector represents the modal shape of the system, i.e., the displacement distribution of each node under that mode.

[0155] In modal analysis, the damping matrix is usually not a diagonal matrix, so it needs to be transformed through the modal coordinate system to project the damping matrix onto this coordinate system. Through this operation, the originally complex damping matrix can be simplified to a diagonal modal damping matrix, where the diagonal elements are the damping coefficients of each mode.

[0156] The damping matrix of the system is projected through the modal transformation matrix to obtain the damping coefficient corresponding to each mode. These damping coefficients can reflect the damping characteristics of the system under each mode, providing necessary data for subsequent calculations.

[0157] In the modal coordinate system, the modal damping matrix becomes a diagonal matrix, and the diagonal elements are the damping coefficients of each mode. The damping coefficient is an important parameter that describes the strength of damping action and is closely related to the energy dissipation characteristics of the structure.

[0158] The modal damping coefficients are extracted from the diagonalized modal damping matrix to form a damping coefficient vector, which serves as the basis for calculating the equivalent viscous damping ratio.

[0159] The modal damping ratio for each mode requires the modal damping coefficient, modal stiffness, and modal mass. The modal stiffness is obtained through eigenvalue decomposition, while the modal mass can be obtained through modal shape normalization.

[0160] The modal mass is typically obtained through normalization, which ensures that the mass contributions of different modes are equivalent. Then, the damping ratio is calculated using the following formula:

[0161] ;

[0162] This formula combines the modal damping coefficient with the modal stiffness and modal mass to calculate the equivalent viscous damping ratio for that mode.

[0163] The normalization of modal shapes is to make the masses of different modes comparable. In practical applications, the normalization of modal shapes is usually based on the mass or displacement amplitude of the mode shape, ensuring that the mass of each mode is calculated under the same standard.

[0164] The mass normalization method is commonly used, which normalizes the modal mass vector so that the total mass is 1, simplifying the calculation process and eliminating the impact of mass differences between different modes on the calculation.

[0165] In one possible implementation, the frequency response function (FRF) is a function used to describe the response characteristics of a system at different frequencies. When performing modal analysis, the frequency response function graph can show the resonance and anti-resonance points of the system, where the anti-resonance point refers to the area where the frequency response of the system is minimal, i.e., the point where the system is not prone to resonance. By selecting a reference degree of freedom in the anti-resonance point interval, errors introduced by resonance effects can be avoided.

[0166] First, obtain the frequency response function of the system through experiments or calculations, and mark the anti-resonance points. Then, select a reasonable degree of freedom (e.g., the vibration degree of freedom of a certain node of the system) as the reference degree of freedom, which will serve as the basis for subsequent modal normalization.

[0167] After selecting the reference degree of freedom, the modal shapes of the entire system can be scaled according to the modal shape components of this degree of freedom. Through this operation, all modal shapes can be unified to the reference scale of the reference degree of freedom, ensuring that the modal shapes of different modes are compared under the same scale.

[0168] The mode shape component of the reference degree of freedom is used as a scale, and the mode shapes of other modes are scaled according to the size of their mode shape components, so that the mode shape components of all modes are compared with the reference degree of freedom. In this way, the mode shapes of all modes can be ensured to have consistent scales in a physical sense, avoiding errors caused by scale differences.

[0169] The mass normalization coefficient is obtained through the reference degree of freedom and scaling operation in the modal mode normalization process, and reflects the mass characteristics of each mode. The modal mass matrix is a matrix that describes the relationship between each mode and the mass distribution of the system, and is usually a diagonal matrix, where each element represents the mass of the corresponding mode.

[0170] The mass normalization coefficient is usually obtained by the following method:

[0171] The mode shape of each mode is normalized. The relationship between the mode shape component of the reference degree of freedom and its corresponding modal mass is calculated. According to this relationship, the mass of each mode is calculated by the formula.

[0172] The key to calculating the modal mass matrix is to determine the mass of each mode through the relationship between the modal mode shape and the mass normalization coefficient. The calculation formula is:

[0173] ;

[0174] where, is the modal mass matrix, is the mode shape of the th mode, is the mass normalization coefficient of the corresponding mode.

[0175] The mass normalization coefficient is an important parameter in modal analysis, which is used to standardize each mode of the system with the actual mass distribution. It makes the mass characteristics of different modes comparable, thereby improving the accuracy of modal analysis.

[0176] The mass normalization coefficient is a numerical value that represents the relationship between the mass of the modal mode shape and the actual mass distribution. It is usually obtained through the normalization process, so that the modal mass matrix of the system can meet the actual physical constraints.

[0177] The mass normalization coefficient ensures the unified standard of modal mass, avoiding the mass error caused by the scale difference of modal mode shape, thereby improving the accuracy and reliability of the modal mass matrix.

[0178] The following is described in detail by example:

[0179] The design of modern engine support must meet strict dynamic performance requirements to ensure that it can effectively absorb and isolate vibrations during operation. In practical applications, traditional dynamic characteristic testing methods (such as direct measurement method, empirical method) often have problems such as low data accuracy, complex calculation process, etc. Therefore, the present application proposes a dynamic characteristic testing method based on modal mass normalization and mode shape normalization, which can accurately evaluate the dynamic stiffness and damping characteristics of the engine support.

[0180] The present embodiment aims to verify whether the dynamic stiffness and damping characteristics of the engine support can be accurately evaluated by modal analysis and mass normalization technology, thereby providing a simple and efficient dynamic testing method.

[0181] In the embodiment of the present application, a vibration test bench is used to apply vibration signals and collect system responses. Accelerometers are used to measure the response acceleration of the support at different frequencies. Force sensors are used to measure the external force applied to the support. A data acquisition system is used to collect frequency response functions.

[0182] The engine support, model ES-001, is made of aluminum alloy.

[0183] Test frequency range: 0-500Hz (determined according to the working frequency of the engine support).

[0184] Modal mass normalization coefficient: the reference degree of freedom is selected as the maximum vibration point of the support.

[0185] Mass normalization formula:

[0186] ;

[0187] where, is the mass of a single mode, is the total mass of all modes, is the number of modes.

[0188] Select the main mode shape as the reference mode shape and normalize it using the following formula:

[0189] ;

[0190] where, is the mode shape of the th mode.

[0191] Use the vibration test bench to apply excitation forces of different frequencies to the engine support and collect the corresponding acceleration response signals.

[0192] Use the data acquisition system to obtain the frequency response function (FRF), and through analysis of the frequency response function, identify the anti-resonance point interval and determine the modal characteristics in a specific frequency range.

[0193] The maximum vibration point of the engine support (usually the middle part of the support) is selected as the reference degree of freedom.

[0194] According to the vibration mode obtained from the frequency response function, the main vibration mode is selected for vibration mode normalization. It is assumed that the value of the vibration mode is:

[0195] ;

[0196] After the vibration mode is normalized:

[0197] ;

[0198] The mass normalization coefficient is calculated using the above formula:

[0199] ;

[0200] wherein is the mass of all modes. It is assumed that the mass in the first mode, the mass in the second mode, and the total mass is:

[0201] ;

[0202] The normalized mass coefficient is calculated.

[0203] The modal mass matrix is calculated:

[0204] ;

[0205] The mass of each mode is filled into the matrix to obtain the modal mass matrix.

[0206] The dynamic stiffness and damping characteristics of the system are calculated through the modal mass matrix and the frequency response function. It is assumed that the frequency of a certain mode is f=100Hz, and the dynamic stiffness calculation formula is:

[0207] ;

[0208] According to the calculation, the dynamic stiffness of this mode is:

[0209] ;

[0210] Using the traditional modal analysis method to directly measure the dynamic characteristics of the engine support, the dynamic stiffness obtained is 3.5×106N / m.

[0211] Through mass normalization and vibration mode normalization analysis, the dynamic stiffness obtained is 3.94×106N / m.

[0212] It can be seen through comparison that the method improves the precision by 12.6% compared with the traditional method, and can more accurately reflect the real dynamic characteristics of the engine support.

[0213] The embodiment verifies that the dynamic stiffness and damping characteristics of the engine support can be more accurately evaluated by combining modal mass normalization and mode shape normalization. Through comparison experiments, it is proved that the present application has significant advantages in precision compared with the traditional method, and can provide more reliable data support for the design and performance optimization of the engine support.

[0214] The present application covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present application. In order to make the public have a thorough understanding of the present application, specific details are described in the following preferred embodiments of the present application, and the present application can also be fully understood without the description of these details for those skilled in the art. In addition, in order to avoid unnecessary confusion to the essence of the present application, well-known methods, processes, procedures, elements and circuits, etc. are not described in detail.

[0215] The above is only the preferred embodiment of the present application, and it should be pointed out that for ordinary skilled in the art, without departing from the principle of the present application, a number of improvements and refinements can be made, and these improvements and refinements should be regarded as the protection scope of the present application.

Claims

1. A method of testing the dynamic stiffness and damping characteristics of an engine mount, characterized by, Comprise: Step 1: boundary simulation: the engine support test piece is installed on the rigid base platform through the profiling clamp, the contact surface shape of the clamp is consistent with the installation surface of the real vehicle; a non-contact support unit is arranged between the clamp and the platform, and the pre-tightening force of the real vehicle bolt is simulated by adjusting the gas pressure of the air bag; Step 2: double-source excitation loading: parallel hydraulic servo shaker and electromagnetic high-frequency shaker are connected to the power input end of the support, and engine mounting point and motor mounting point are connected respectively; the hydraulic shaker outputs wide-frequency excitation covering the engine vibration frequency band, and the electromagnetic shaker outputs high-frequency excitation covering the motor electromagnetic force frequency band; Step 3: dynamic response acquisition: vibration measuring points are arranged in the direction of the stiffness main shaft of the support to collect three-dimensional acceleration signals, and the excitation force signals are collected synchronously, and all signals are recorded after anti-aliasing filtering at a set sampling rate; Step 4: frequency response matrix construction: time-frequency transformation is performed on the excitation force signals and acceleration signals, and cross-point frequency response function matrix is calculated; The construction process of the frequency response function matrix in the step 4 comprises: The time domain signal is processed by windowing segmentation, and the window function type is automatically selected according to the signal stationarity: when the short-time energy fluctuation of the signal exceeds the threshold, the leakage suppression window is selected, otherwise the high-resolution window is selected; The mutual power spectral density matrix of the acceleration signal and the excitation force signal in each segment, and the self-power spectral density matrix of the excitation force signal are calculated; The mutual power spectral density matrix is divided by the self-power spectral density matrix to obtain the frequency response function matrix, and the amplitude of the abnormal frequency point is smoothed; Step 5: parameter decoupling calculation: the stiffness matrix is identified by using the hydraulic excitation data in the low frequency band, and the damping matrix is identified by fixing the stiffness matrix in the high frequency band, and the parameter decoupling is realized through cross-iteration optimization; The cross-iteration optimization in the step 5 comprises: Set the initial conditions of iteration: the damping matrix in the low frequency band is set as a zero matrix, and the stiffness matrix in the high frequency band is set as the previous identified value; Perform parameter transfer: the damping matrix output in the high frequency band is substituted into the stiffness identification equation in the low frequency band, and the updated stiffness matrix is substituted into the damping identification equation in the high frequency band; Iteration termination judgment: the norm change rate of the stiffness matrix and the damping matrix in the adjacent two times is calculated, and when both of them are less than the dynamic convergence threshold, the optimization is terminated, and the convergence threshold is calculated according to the nonlinear error of the force sensor and the phase error of the vibration meter; Step 6: parameter output: the stiffness matrix elements are extracted to output the dynamic stiffness value, and the equivalent viscous damping ratio is calculated based on the damping matrix; The calculation of the equivalent viscous damping ratio in the step 6 comprises: Eigenvalue decomposition is performed on the stiffness matrix to obtain the modal stiffness of each order; The damping matrix is projected into the modal coordinate system to obtain the diagonalized modal damping matrix; The diagonal elements of the modal damping matrix are extracted as the modal damping coefficients; The modal damping coefficient is divided by the product of twice the modal stiffness and the modal mass geometric average to obtain the damping ratio, and the modal mass is obtained by modal mode normalization processing.

2. The method of claim 1, wherein, The process of adjusting the gas pressure of the air bag in the step 1 comprises: The pre-tightening force range under different working conditions is obtained through the real vehicle bolt pre-tightening force measuring device, and the measuring device comprises a strain sensor array, and the sensor is attached to the surface of the bolt and connected to a dynamic strain meter; The target value of the pressure per unit area is calculated according to the contact area of the profiling fixture and the air bag, and the contact area is fitted and calculated through point cloud data of the fixture contact surface obtained by a three-dimensional optical scanner; A closed-loop pressure control system is adopted: the proportional valve dynamically adjusts the air inlet flow according to the deviation between the actual value and the target value of the pressure sensor, and the pressure is locked when the deviation is less than the set threshold.

3. The method of claim 1, wherein, The generation of the wideband excitation in step 2 includes: An engine speed-torque-inertial force mapping model is established: the amplitude envelope of the inertial force at each speed is calculated through crankshaft dynamics simulation; The 5-200Hz frequency band is divided into multiple subintervals, and the subinterval width decreases with increasing speed; Pseudo-random excitation signals are generated in each subinterval, and the root mean square value is scaled according to the inertial force in the corresponding envelope interval, and the sweep rate is positively correlated with the speed change rate of the engine acceleration condition.

4. The method of claim 1, wherein, The configuration of the high-frequency excitation in step 2 includes: Obtain the electromagnetic force frequency spectrum characteristics of the motor: extract the fundamental and characteristic harmonic frequency distribution through finite element electromagnetic field simulation; Select multiple target frequency bands in the range of 200-2000Hz, and the center frequency of the target frequency band is an integer multiple of the characteristic harmonic frequency; Generate a constant-amplitude sinusoidal sweep signal in each target frequency band, and the sweep step is adaptively adjusted according to the frequency band width, and the signal amplitude is allocated according to the harmonic energy proportion of the electromagnetic force.

5. The method of claim 4, wherein, The calculation process of the dynamic convergence threshold value includes: Obtain the linearity error limit of the force measurement channel and the phase offset angle of the acceleration measurement channel through the sensor calibration certificate; Convert the force signal error limit to the relative error of the frequency response function amplitude, and convert the phase offset angle to the absolute error of the frequency response function phase; Based on the error propagation theory, calculate the upper limit of the theoretical error of the stiffness matrix elements, and take a preset proportion of the upper limit of the theoretical error as the norm change rate threshold.

6. The method of claim 5, wherein the engine support dynamic stiffness and damping characteristics are determined by: The abnormal frequency point determination rule is: Mark the frequency points with a coherence coefficient below the first threshold on the frequency response function curve; Mark the frequency points with a sudden drop in mutual power spectral density amplitude exceeding the second threshold; Perform cubic spline interpolation correction on the frequency response function values of the marked frequency points.

7. The method of claim 6, wherein the engine support dynamic stiffness and damping characteristics are determined by, The modal shape normalization process includes: Select a reference degree of freedom in the anti-resonance point interval of the frequency response function; Scale the full-order modal shape with the modal shape component of the reference degree of freedom as the unit 1; Calculate the modal mass matrix through the mass normalization coefficient.

Images