Passenger car riding comfort performance prediction system and method

Abstract

The invention discloses a passenger car riding comfort performance prediction system and method, and the system comprises a dynamic model construction module which is used for carrying out the automatic construction of a vehicle dynamic system, and obtaining a vehicle dynamic model; the proofreading and calibration module is used for proofreading and calibrating a system-level working condition and a whole vehicle-level working condition in the vehicle dynamics model respectively; the riding comfort prediction module outputs a comfort prediction working condition according to the corrected and calibrated vehicle dynamics model, and sets corresponding digital road surface characteristics and corresponding vehicle speed and steering wheel angle according to the comfort prediction working condition; and obtaining riding comfort prediction data according to the comfort prediction working condition, the digital pavement features, the vehicle speed and the steering wheel angle. The method can overcome the defects of long real vehicle test period and high cost, and has the characteristics of abundant typical user working conditions and consistent actual subjective feeling and objective indexes of the human body under corresponding working conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of passenger car riding comfort, and in particular to a passenger car riding comfort performance prediction system and method. Background Art

[0002] The ride comfort of passenger cars is one of the important performances of cars. In order to improve the ride comfort of cars, it is necessary to design and control the relevant key performances during the vehicle development process, and carry out multiple rounds of vehicle ride comfort simulation and chassis tuning. There are many factors that affect vehicle ride comfort, including springs, shock absorbers, bushings, tires and even hard points, and it is related to road roughness and vehicle speed. At present, the identification of ride comfort is mainly based on vehicle road test evaluation, which includes subjective expert evaluation method and objective test method.

[0003] The subjective expert evaluation method is that the chassis tuning engineers and the technical leaders of the enterprise conduct subjective evaluation and scoring of ride comfort. The subjective evaluation method is related to the experience, ability and emotions of the evaluators, and due to the complexity of human perception, the evaluators cannot clearly distinguish the current comfort vibration isolation level, body control level and sound isolation level, resulting in the lack of consistency in the evaluation results and inaccurate identification of problems. For example, the prior art discloses "a chassis smoothness evaluation method, device and storage medium". This method is a chassis smoothness evaluation method based on applied psychology. The evaluators wear and remove eye masks and noise reduction headphones respectively, and conduct front and rear row evaluation and scoring respectively. This method is a subjective evaluation method, which is related to the experience, ability and emotions of the evaluators, and due to the complexity of human perception, the evaluators cannot clearly distinguish the current comfort vibration isolation level, body control level and sound isolation level, resulting in the lack of consistency in the evaluation results and inaccurate identification of problems.

[0004] The objective test method is to use acceleration sensors on the seat, seat surface, guide rail and vehicle floor to measure the acceleration values ​​of x, y and z items. The objective test method only measures the parameters of several typical positions and only considers the analysis of the peak value of the acceleration. It lacks the measurement of the acceleration process and cannot represent the actual feeling of the human body. In addition, the measurement conditions are relatively single and lack representativeness. For example, the prior art discloses "a vehicle smoothness analysis method based on virtual road testing". In this method, in the CAE virtual environment, the same road surface characteristics, test conditions and data processing methods are used for model simulation and real vehicle testing, and a multi-body dynamics model of the basic vehicle and a virtual road model for smoothness evaluation are established. The simulation results are compared with the test results, and the model is corrected and verified, and a smoothness simulation is performed. This method is a simulation method based on CAE, which measures the parameters of several typical positions and only considers the analysis of the peak value of the acceleration. It cannot represent the actual feeling of the human body, and the measurement conditions are relatively single and lack representativeness.

[0005] Whether it is a subjective evaluation method or an objective test method, it can only be carried out after the prototype car is manufactured, resulting in a short development cycle for related components, high risk after problems occur, and difficulty in improvement. For example, the prior art discloses "a method and system for evaluating the comfort of riding in an automobile". This method arranges acceleration sensors and displacement sensors at the tester's foot, seat cushion, back, chest, and head positions and calculates the comfort evaluation index. This method is complex to operate in practice, cannot be predicted in advance, and does not involve the actual working conditions of users and improvement and optimization. Summary of the invention

[0006] The purpose of the present invention is to address the shortcomings of the prior art that the evaluation of the ride comfort of passenger cars is not accurate enough, and can only be evaluated after the production of the prototype, and the prediction cannot be carried out in advance, which is time-consuming and costly, and thus a passenger car ride comfort performance prediction system and method are proposed. The system first automatically establishes a vehicle dynamics model for predicting the dynamic performance of the vehicle. At the same time, in order to verify the accuracy of the vehicle dynamics model and improve the accuracy of the model, the vehicle dynamics model is proofread and calibrated through a specific working condition sequence and parameter adjustment, and then the proofread and calibrated vehicle dynamics model is used to predict the ride comfort performance, and the corresponding digital road surface characteristics and the corresponding vehicle speed and steering wheel angle are automatically set according to the specific prediction working condition, and the measurement data of the ride comfort performance is predicted and output. The passenger car ride comfort performance prediction system described in the present invention can not only solve the shortcomings of long actual vehicle test cycle and high cost, but also has the characteristics of rich typical user working conditions and the consistency of the actual subjective feelings of the human body and objective indicators under the corresponding working conditions.

[0007] To achieve this purpose, the present invention is designed to predict the ride comfort performance of a passenger car, comprising: a dynamic model building module, a proofreading and calibration module, and a ride comfort prediction module;

[0008] The dynamics model building module is used to automatically build the vehicle dynamics system to obtain the vehicle dynamics model;

[0009] The proofreading and calibration module is used to proofread and calibrate the system-level working conditions and the vehicle-level working conditions in the vehicle dynamics model respectively;

[0010] The ride comfort prediction module outputs a comfort prediction condition based on the proofread and calibrated vehicle dynamics model, and sets corresponding digital road surface characteristics and corresponding vehicle speed and steering wheel angle according to the comfort prediction condition, and obtains ride comfort prediction data according to the comfort prediction condition, digital road surface characteristics, vehicle speed and steering wheel angle.

[0011] Beneficial effects of the present invention:

[0012] The present invention first automatically establishes a vehicle dynamics model for predicting vehicle dynamic performance. At the same time, in order to verify the accuracy of the vehicle dynamics model and improve the accuracy of the model, the vehicle dynamics model is calibrated through a specific working condition sequence and parameter adjustment, and then the calibrated vehicle dynamics model is used to predict ride comfort performance. According to the specific predicted working condition, the corresponding digital road surface characteristics and the corresponding vehicle speed and steering wheel angle are automatically set to predict and output the measurement data of ride comfort performance. The passenger car ride comfort performance prediction system of the present invention can not only solve the shortcomings of long actual vehicle test cycle and high cost, but also has the characteristics of rich typical user working conditions and the actual subjective feelings of the human body under the corresponding working conditions and the consistency of objective indicators. At the same time, ride comfort can be predicted in advance, which can replace part of the field test work, reduce product development costs, shorten the development cycle, and bring higher economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 A structural diagram of a passenger car ride comfort performance prediction system of the present invention;

[0014] Figure 2 It is a schematic diagram of the acceleration curve of the vehicle in the Z direction changing with time in the present invention;

[0015] Figure 3 It is a schematic diagram of the time taken by the acceleration curve of the vehicle changing with time between the vibration peak value and the vibration peak value*10% in the present invention;

[0016] Figure 4 It is a schematic diagram of the peak-to-valley difference between acceleration curves of two vehicle types changing with time at A1-A5 in the present invention;

[0017] Figure 5 It is a schematic diagram comparing the amplitude-frequency curves of the transfer functions of the two vehicle types in the present invention. DETAILED DESCRIPTION

[0018] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments:

[0019] Example 1

[0020] The first aspect of the present invention provides a passenger car ride comfort performance prediction system, such as Figure 1 As shown, it includes: a dynamic model building module, a proofreading and calibration module, and a ride comfort prediction module;

[0021] The dynamics model building module is used to automatically build the vehicle dynamics system to obtain the vehicle dynamics model;

[0022] The proofreading and calibration module is used to proofread and calibrate the system-level working conditions and the vehicle-level working conditions in the vehicle dynamics model respectively;

[0023] The ride comfort prediction module outputs a comfort prediction condition based on the proofread and calibrated vehicle dynamics model, and sets corresponding digital road surface characteristics and corresponding vehicle speed and steering wheel angle according to the comfort prediction condition, and obtains ride comfort prediction data according to the comfort prediction condition, digital road surface characteristics, vehicle speed and steering wheel angle.

[0024] In the above technical solution, the vehicle dynamics system is automatically constructed, and the specific method for obtaining the vehicle dynamics model is: the suspension system, steering system, body system, tire system and powertrain system in the vehicle dynamics system are automatically established and selected. First, a model library of each subsystem is established. For example, different types of suspension libraries are established for the suspension system: McPherson, three-link, torsion beam, four-link, five-link, etc. The user selects the corresponding type according to the actual vehicle model. The system will pop up the corresponding input requirements according to the corresponding suspension type. The user enters the corresponding hard point and elastic element stiffness and damping to complete the automatic modeling of the corresponding subsystem. The steering system, body tires and other systems are also set in the same way.

[0025] The key working conditions related to the accuracy of the vehicle dynamics model are proofread and calibrated. In this article, the key working conditions related to the accuracy include system-level conditions and vehicle-level conditions. The system-level conditions include vertical jump conditions, reverse return torque conditions, same-direction return torque conditions, roll conditions, reverse lateral force conditions, same-direction lateral force conditions, longitudinal force conditions and steering conditions in the order and process of benchmarking. The key indicators of the system-level conditions include suspension stiffness, toe angle change rate, camber angle change rate, longitudinal displacement change rate and lateral displacement change rate.

[0026] In the above technical solution, the specific method for respectively checking and calibrating the system-level working conditions and vehicle-level working conditions in the vehicle dynamics model is: comparing the vertical jump working conditions, reverse return moment working conditions, same direction return moment working conditions, roll working conditions, reverse lateral force working conditions, same direction lateral force working conditions, longitudinal force working conditions and steering working conditions in the system-level working conditions with the simulation tests under the corresponding working conditions in turn, and checking the suspension stiffness, toe angle change rate, camber angle change rate, longitudinal displacement change rate and lateral displacement change rate in the system-level working conditions according to the comparison results. In the actual operation process, for example, the same direction jump working condition test data of the competitor or trial-produced sample vehicle is imported into the vehicle design software, and the relevant suspension parameters are fine-tuned so that the suspension performance indicators output by the suspension model are consistent with the experimental test curve.

[0027] The simulation and calibration of steady-state rotation conditions, angular step conditions, angular pulse conditions, frequency sweep conditions, steering return conditions and center steering conditions in the vehicle-level working conditions are carried out with the actual vehicle handling stability test. At the same time, the steering system damping and shock absorber damping and other parameters are calibrated using transient conditions, the steering assist and steering system friction and other parameters are calibrated using center steering conditions, and the tire damping and limit block clearance and other parameters are calibrated using frequency domain conditions, which greatly improves the accuracy of the vehicle dynamics model.

[0028] In the above technical scheme, the specific method for setting the corresponding digital road surface characteristics and the corresponding vehicle speed and steering wheel angle according to the comfort prediction working condition is: establish a digital road surface library related to comfort, and select the road surface and vehicle speed according to the automatic setting of the actual comfort prediction working condition: for example, impact comfort automatically selects the road surface as a triangular bump with a height of 40mm and a width of 400mm, and drives straight through the triangular bump at speeds of 20km / h, 30km / h, and 40km / h respectively; for example, random road surface comfort will automatically select cement road and rough asphalt road, and drives straight through the road surface at speeds of 60km / h, 70km / h, and 80km / h respectively.

[0029] In the above technical scheme, the specific method for obtaining ride comfort prediction data based on comfort prediction conditions, digital road surface characteristics, vehicle speed and steering wheel angle is: under the comfort prediction conditions and corresponding digital road surface characteristics, vehicle speed and steering wheel angle, the vehicle's body ups and downs, pitch balance, roll sway, road surface scanning, road surface replication, impact strength, aftershock, comfort point vibration, pull-up and compression limit impact are calculated to obtain ride comfort prediction data.

[0030] In the above technical solution, the system also includes a result output and evaluation module, which is used to calculate the ride comfort prediction data, obtain the impact suitability index and the random vibration comfort evaluation index, and evaluate and analyze the impact suitability index and the random vibration comfort evaluation index. In this article, impact comfort refers to the indicators E (peak weighted size), D (decay rate) and T (decay time) at different measurement points, and random comfort refers to the energy size of the transfer function curve at different measurement points in the two intervals of 4-8Hz and 8-12.5Hz.

[0031] The specific method for calculating the ride comfort prediction data to obtain the impact suitability index and the random vibration comfort evaluation index is as follows: the impact suitability index is the peak weighted size, attenuation time and attenuation rate, and the calculation formula of the peak weighted size is as follows:

[0032] E=2 / 3*A+1 / 3*B

[0033] Where E is the peak weighted value, A is the vibration peak value of the acceleration curve that changes with time in the Z direction, and the acceleration curve that changes with time in the Z direction is as follows: Figure 2 As shown in the figure, B is the vibration peak value of the acceleration curve in the X direction that changes with time. The size of the E value directly reflects the sensitivity of the system to the impact load and the maximum degree of damage that may be suffered. The larger the E value, the stronger the system's response to the impact and the more serious the damage that may be suffered. In this article, the weighting coefficient requires a large amount of data accumulation and comprehensive setting. Considering that the human body is more directly sensitive to vertical vibration, it is currently set at 2 / 3 (Z direction) and 1 / 3 (X direction). Whether it needs to be adjusted will be considered later.

[0034] The decay time is the time T taken for the vibration peak value of the acceleration curve that changes with time in the Z direction to decay to 10% of the vibration peak value, such as Figure 3 As shown in the figure, the length of the T value can evaluate the system's ability to dissipate impact loads and the speed of recovering to a stable state. The shorter the T value, the faster the system can dissipate impact energy and recover to a stable state, thereby reducing the long-term impact of the impact on the system.

[0035] The calculation formula of the attenuation rate is as follows:

[0036] D=A1 / (A1+A2+A3+A4+A5)

[0037] In the formula, D is the attenuation rate, A1 is the vibration peak value of the acceleration curve that changes with time, and A2-A5 are the peak-to-valley values ​​adjacent to A1, such as Figure 4 As shown, the size of the D value reflects the system's efficiency and stability in dissipating impact energy. The larger the D value, the faster the system response decays, and the system can dissipate impact energy more effectively and maintain stability, which is of great significance for improving the system's impact resistance and extending its service life.

[0038] In the above technical solution, the random vibration comfort evaluation index includes road surface scanning and road surface replication. The random vibration comfort evaluation index is obtained by obtaining a transfer function amplitude-frequency curve based on the ride comfort prediction data, and performing sensitive frequency range and attenuation level average analysis based on the transfer function amplitude-frequency curve.

[0039] In this paper, the ratio of the Fourier transform amplitude of the test output signal to the input signal is solved, and the formula of the transfer function is obtained as follows:

[0040] |H(jω)|=|(a_out(jω)) / (a_in(jω))|

[0041] Where H(jω) is the transfer function, a_out(jω) is the output signal, and a_in(jω) is the input signal.

[0042] The unit of the transfer function is converted into dB according to H = 20log(|H(jω)|), and the amplitude-frequency curve of the transfer function is obtained, as shown in Figure 5 As shown, the comfort of model 1 is better than that of model 2.

[0043] In the study of human sensitivity, the foot surface is the Z direction and the seat surface is the Z direction. The human body sensitive frequency range is 4-12.5Hz. Therefore, the Z-direction transfer function describes the vibration transfer characteristics from the wheel center to the body (suspension vibration isolation) and the floor to the seat (seat vibration isolation). Therefore, the focus is on the vibration isolation within the frequency range of 4-12.5Hz, and the average is taken as the evaluation index for evaluation and judgment. Specifically, the evaluation and judgment method is: 4-8Hz human body feels swaying, 8-12.5Hz human body feels shaking, and the energy size (area of ​​the curve) of the transfer function curve in the two intervals of 4-8Hz and 8-12.5Hz is statistically analyzed. The greater the energy, the worse the comfort, and the smaller the energy, the better the comfort. For the places that do not meet the standards in the evaluation, the problems are checked according to the FTA method and corresponding suggestions and methods are given. At the same time, the results of this prediction and the processed indicators are entered into the database for future query and comparison.

[0044] Example 2

[0045] The second aspect of the present invention provides a method for predicting the riding comfort performance of a passenger car, which comprises the following steps:

[0046] Automatically construct the vehicle dynamics system to obtain the vehicle dynamics model;

[0047] Proofreading and calibrating the system-level working condition and the vehicle-level working condition in the vehicle dynamics model respectively;

[0048] The comfort prediction working condition is output according to the proofread and calibrated vehicle dynamics model, and the corresponding digital road surface characteristics and the corresponding vehicle speed and steering wheel angle are set according to the comfort prediction working condition, and the ride comfort prediction data is obtained according to the comfort prediction working condition, the digital road surface characteristics, the vehicle speed and the steering wheel angle.

[0049] In the present invention, the work of the passenger car ride comfort performance prediction method is completed in dynamics simulation software (Adams / Car software).

[0050] Example 3

[0051] A third aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

[0052] Example 4

[0053] A fourth aspect of the present invention provides a computer program product, comprising a computer program, characterized in that when the computer program is executed by a processor, the steps of the method described in the claim are implemented.

[0054] The present invention first automatically establishes a vehicle dynamics model for predicting vehicle dynamic performance. At the same time, in order to verify the accuracy of the vehicle dynamics model and improve the accuracy of the model, the vehicle dynamics model is calibrated through a specific working condition sequence and parameter adjustment, and then the calibrated vehicle dynamics model is used to predict ride comfort performance. According to the specific predicted working condition, the corresponding digital road surface characteristics and the corresponding vehicle speed and steering wheel angle are automatically set, and the measured data of ride comfort performance are predicted and output. At the same time, the output predicted data is processed and output with key indicators, and the processed indicators are compared and evaluated with the target indicator values. The passenger car ride comfort performance prediction system described in the present invention can not only solve the shortcomings of long actual vehicle test cycle and high cost, but also has the characteristics of rich typical user working conditions and the actual subjective feelings of the human body under the corresponding working conditions and the consistency of objective indicators. It can replace part of the field test work, reduce product development costs, shorten the development cycle, and bring higher economic benefits.

[0055] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0056] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or multiple boxes.

[0057] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction system, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0058] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0059] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit its protection scope. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that after reading the present invention, those skilled in the art can still make various changes, modifications or equivalent substitutions to the specific implementation methods of the invention, but these changes, modifications or equivalent substitutions are all within the protection scope of the pending claims of the invention.

[0060] The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in this field.

Claims

1. A passenger car ride comfort performance prediction system, characterized in that: include: Dynamics model building module, proofreading and calibration module, and ride comfort prediction module; The dynamics model building module is used to automatically build the vehicle dynamics system to obtain the vehicle dynamics model; The proofreading and calibration module is used to proofread and calibrate the system-level working conditions and the vehicle-level working conditions in the vehicle dynamics model respectively; The ride comfort prediction module outputs a comfort prediction condition based on the proofread and calibrated vehicle dynamics model, and sets corresponding digital road surface characteristics and corresponding vehicle speed and steering wheel angle according to the comfort prediction condition, and obtains ride comfort prediction data according to the comfort prediction condition, digital road surface characteristics, vehicle speed and steering wheel angle.

2. The passenger car ride comfort performance prediction system according to claim 1, characterized in that: The specific method for automatically constructing the vehicle dynamics system and obtaining the vehicle dynamics model is: automatically establishing and selecting the suspension system, steering system, body system, tire system and powertrain system in the vehicle dynamics system.

3. The passenger car ride comfort performance prediction system according to claim 2, characterized in that: The specific method for respectively checking and calibrating the system-level working conditions and the vehicle-level working conditions in the vehicle dynamics model is as follows: comparing the vertical jump working condition, the reverse return moment working condition, the same direction return moment working condition, the roll working condition, the reverse lateral force working condition, the same direction lateral force working condition, the longitudinal force working condition and the steering working condition in the system-level working condition with the simulation test under the corresponding working conditions in turn, and checking the suspension stiffness, the toe angle change rate, the camber angle change rate, the longitudinal displacement change rate and the lateral displacement change rate in the system-level working condition according to the comparison results; The steady-state rotation condition, angular step condition, angular pulse condition, frequency sweep condition, steering return condition and center steering condition in the vehicle-level working conditions are simulated and calibrated with the actual vehicle handling stability test.

4. The passenger car ride comfort performance prediction system according to claim 3, characterized in that: The specific method for obtaining ride comfort prediction data based on comfort prediction conditions, digital road surface characteristics, vehicle speed and steering wheel angle is as follows: under the comfort prediction conditions and corresponding digital road surface characteristics, vehicle speed and steering wheel angle, the vehicle's body ups and downs, pitch balance, roll sway, road surface scanning, road surface replication, impact strength, aftershock, comfort point vibration, pull-up and compression limit impact are calculated to obtain ride comfort prediction data.

5. The passenger car ride comfort performance prediction system according to claim 4, characterized in that: The system also includes a result output and evaluation module, which is used to calculate the ride comfort prediction data, obtain an impact suitability index and a random vibration comfort evaluation index, and evaluate and analyze the impact suitability index and the random vibration comfort evaluation index.

6. The passenger car ride comfort performance prediction system according to claim 5, characterized in that: The specific method for calculating the ride comfort prediction data to obtain the impact suitability index and the random vibration comfort evaluation index is as follows: the impact suitability index is the peak weighted size, attenuation time and attenuation rate, and the calculation formula of the peak weighted size is as follows: E=2 / 3*A+1 / 3*B Where, E is the peak weighted value, A is the vibration peak value of the acceleration curve changing with time in the Z direction, and B is the vibration peak value of the acceleration curve changing with time in the X direction; The decay time is the time taken for the vibration peak value of the acceleration curve changing with time in the Z direction to decay to 10% of the vibration peak value; The calculation formula of the attenuation rate is as follows: D=A1 / (A1+A2+A3+A4+A5) Where D is the attenuation rate, A1 is the vibration peak value of the acceleration curve that changes with time, and A2-A5 are the peak and valley values ​​adjacent to A1 respectively; The random vibration comfort evaluation index is obtained by obtaining a transfer function amplitude-frequency curve according to the ride comfort prediction data, and performing a sensitive frequency range and an attenuation level average value analysis according to the transfer function amplitude-frequency curve.

7. The passenger car ride comfort performance prediction system according to claim 6, characterized in that: The formula of the transfer function in the transfer function amplitude-frequency curve is as follows: |H(jω)|=|(a_out(jω)) / (a_in(jω))| Where H(jω) is the transfer function, a_out(jω) is the output signal, and a_in(jω) is the input signal.

8. A method for predicting the riding comfort performance of a passenger car, characterized in that: It includes the following steps: Automatically construct the vehicle dynamics system to obtain the vehicle dynamics model; Proofreading and calibrating the system-level working condition and the vehicle-level working condition in the vehicle dynamics model respectively; The comfort prediction working condition is output according to the proofread and calibrated vehicle dynamics model, and the corresponding digital road surface characteristics and the corresponding vehicle speed and steering wheel angle are set according to the comfort prediction working condition, and the ride comfort prediction data is obtained according to the comfort prediction working condition, the digital road surface characteristics, the vehicle speed and the steering wheel angle.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to claim 8 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to claim 8 are implemented.

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