Vehicle chassis hybrid simulation test system

Abstract

The invention provides a hybrid simulation test system for a vehicle chassis, and the system comprises a simulation test unit which is used for carrying out the simulation test of a to-be-tested vehicle in a complex environment; the human body data acquisition unit is used for acquiring human body data when the driver is on the to-be-tested vehicle; the discomfort type identification unit is used for identifying the discomfort type of the driver when the to-be-tested vehicle is tested based on the human body data; the parameter optimization unit is used for optimizing chassis parameters of the to-be-tested vehicle according to the discomfort types; while simulation tests of different complex environments are carried out on a to-be-tested vehicle, whether a driver feels uncomfortable or not is recognized by collecting human body data of the driver, and if the driver feels uncomfortable, chassis parameters are optimized and adjusted and fed back to the simulation test unit, simulation tests are carried out again, and the driver feels comfortable. And if the discomfort of the driver can be eliminated or relieved, the chassis parameter optimization is effective, so that simulation testing is continuously carried out, the driving experience of the driver is improved, and man-machine compatibility is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile detection, and in particular to a vehicle chassis hybrid simulation test system. Background Art

[0002] In the field of automotive engineering, the vehicle chassis is a key component of the vehicle, and its performance is directly related to the core performance indicators of the vehicle, such as handling stability, driving smoothness, and riding safety. With the rapid development of the automotive industry and the increasing demand of consumers for automobile quality, accurate and comprehensive performance evaluation and optimization of the vehicle chassis has become increasingly important. Chassis simulation testing, as a high-efficiency, low-cost technical means that can simulate a variety of complex working conditions, has emerged and has been widely used. By constructing a simulation model of the vehicle chassis, it is possible to simulate various conditions encountered by the vehicle during actual driving, such as the dynamic response of the chassis under different road conditions, vehicle speeds, and driving operations, thereby providing a solid theoretical basis and data support for the design, improvement, and performance optimization of the chassis system.

[0003] Traditional vehicle chassis simulation technology focuses on performance-oriented testing and optimization. In terms of handling stability, it focuses on simulating vehicle steering, braking, acceleration and other working conditions to study and optimize the chassis suspension system geometry, shock absorber damping characteristics, tire mechanical properties and other parameters to ensure that the vehicle has good driving stability and handling accuracy in scenarios such as high-speed driving and emergency avoidance. In the study of driving smoothness, the main focus is on the chassis vibration transmission characteristics under the excitation of road roughness. By adjusting parameters such as spring stiffness and shock absorber damping, the body vibration acceleration is reduced and the ride comfort is improved. However, these traditional The design concept and optimization direction of chassis simulation test technology mainly revolve around the mechanical performance of the vehicle itself, and often ignore the interaction between the driver and the vehicle and the driver's feelings, that is, the consideration of human-machine compatibility. In the actual driving process, the driver, as the operator and user of the vehicle, will have a significant impact on the physiological and psychological state of the vehicle chassis performance. The existing chassis simulation technology fails to fully consider these human-machine compatibility factors, so that the chassis optimized by simulation may perform well in some performance indicators in actual applications, but it may bring a bad driving experience to the driver and fail to achieve true human-machine compatibility. Summary of the Invention

[0004] In view of this, the present invention proposes a vehicle chassis hybrid simulation test system, which fully considers the driver's physiological and psychological state when testing the vehicle chassis to ensure the driver's driving experience.

[0005] The technical solution of the present invention is achieved as follows:

[0006] A vehicle chassis hybrid simulation test system, comprising:

[0007] Simulation test unit, used to conduct simulation tests on the vehicle under test in complex environments;

[0008] A human body data collection unit, used to collect human body data of the driver when he is on the vehicle to be tested;

[0009] a discomfort type identification unit, for identifying the type of discomfort of the driver when testing the vehicle to be tested based on human body data;

[0010] Parameter optimization unit, used to optimize the chassis parameters of the vehicle to be tested according to the type of discomfort;

[0011] The human body data includes muscle change data, sitting posture change data and head change data. The simulation test unit, human body data acquisition unit, discomfort type identification unit and parameter optimization unit are connected in sequence, and the parameter optimization unit is connected in data with the simulation test unit.

[0012] Preferably, the execution steps of the simulation test unit include:

[0013] Step S11: placing the vehicle to be tested on a multi-degree-of-freedom motion platform, and moving the driver into the cockpit of the vehicle to be tested;

[0014] Step S12: driving the vehicle to be tested to travel on the multi-degree-of-freedom motion platform, and driving the vehicle to be tested to perform pitch, roll, yaw and three-dimensional translation through the multi-degree-of-freedom motion platform.

[0015] Preferably, before step S12, the method further includes:

[0016] Step S13: According to the needs of the tester, the road surface of the multi-degree-of-freedom motion platform on which the vehicle to be tested is placed is switched to a road surface of different properties.

[0017] Preferably, the execution steps of the human body data acquisition unit include:

[0018] Step S21: attaching flexible EMG electrodes to the driver's neck and waist to collect electromyographic signals during the test and output them as muscle change data;

[0019] Step S22: Integrate a flexible pressure sensor array on the seat, steering wheel, and pedals to collect pressure data during the test and output it as sitting posture change data;

[0020] Step S23: Using an inertial measurement unit to collect head change data during the test.

[0021] Preferably, the execution steps of the human body data acquisition unit further include:

[0022] Step S24: Calculate a motion sickness index for the head change data based on the motion sickness model. If the motion sickness index is greater than a preset threshold, stop the simulation test.

[0023] Preferably, it also includes:

[0024] A preprocessing unit, used for preprocessing human body data;

[0025] The preprocessing includes denoising, time sequence alignment and standardization, and the human body data acquisition unit is connected to the discomfort type recognition unit through the preprocessing unit.

[0026] Preferably, the steps executed by the discomfort type identification unit include:

[0027] Step S31: extracting features from the muscle change data, the sitting posture change data, and the head change data, and forming a feature matrix;

[0028] Step S32: input the feature matrix into the random forest model, and output the probability of each discomfort type through voting of each decision tree;

[0029] Step S33: Output the discomfort type with the highest probability.

[0030] Preferably, in step S31, when forming the feature matrix, the subjective feeling input by the driver is obtained, the driver's discomfort location is determined based on the subjective feeling, and at least one of the muscle change data, sitting posture change data or head change data is assigned a value based on the discomfort location.

[0031] Preferably, the execution steps of the parameter optimization unit include:

[0032] Step S41: evaluating the correlation between the chassis parameters of the vehicle to be tested and the discomfort type based on the Pearson correlation coefficient method;

[0033] Step S42: Arrange the chassis parameters in descending order of relevance to form an arrangement table;

[0034] Step S43: Optimize multiple times based on the chassis parameters in the arrangement table, and perform an incremental selection strategy on the selected chassis parameters during each optimization.

[0035] Preferably, it also includes:

[0036] A comparison unit, used to compare human body data before and after parameter optimization to evaluate the optimization effect;

[0037] The comparison unit is data-connected to the pre-processing unit.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] A vehicle chassis hybrid simulation test system of the present invention can perform simulation tests on the vehicle to be tested in different complex environments. During the simulation test, the driver's human body data can be collected. The discomfort type identification unit can evaluate the driver's discomfort type based on the human body data, such as muscle fatigue, dizziness, back pain, etc. Based on different discomfort types, the parameter optimization unit can optimize and adjust the corresponding parameters of the vehicle to be tested. Then, when the simulation test is repeated, the driver's discomfort can be alleviated, while ensuring that the chassis performance meets the requirements, the driver's driving experience is improved, and human-machine adaptation is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only preferred embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0041] Figure 1 This is a schematic diagram of a vehicle chassis hybrid simulation test system of the present invention;

[0042] Figure 2 A diagram showing the execution steps of a simulation test unit of a vehicle chassis hybrid simulation test system according to the present invention;

[0043] Figure 3 A diagram showing the execution steps of a human body data acquisition unit of a vehicle chassis hybrid simulation test system according to the present invention;

[0044] Figure 4 A diagram showing the execution steps of an discomfort type identification unit of a vehicle chassis hybrid simulation test system according to the present invention;

[0045] Figure 5 A diagram showing the execution steps of a parameter optimization unit of a vehicle chassis hybrid simulation test system according to the present invention;

[0046] In the figure, 1. Simulation test unit; 2. Human body data acquisition unit; 3. Discomfort type identification unit; 4. Parameter optimization unit; 5. Preprocessing unit; 6. Comparison unit. DETAILED DESCRIPTION

[0047] In order to better understand the technical content of the present invention, a specific embodiment is provided below, and the present invention is further described in conjunction with the accompanying drawings.

[0048] See also Figures 1 to 5 The present invention provides a vehicle chassis hybrid simulation test system, comprising:

[0049] Simulation test unit 1, used to perform simulation tests on the vehicle under test in a complex environment;

[0050] A human body data collection unit 2 is used to collect human body data of the driver when he is on the vehicle to be tested;

[0051] The discomfort type identification unit 3 is used to identify the type of discomfort of the driver when testing the vehicle to be tested based on the human body data;

[0052] A parameter optimization unit 4 is used to optimize the chassis parameters of the vehicle to be tested according to the type of discomfort;

[0053] The human body data includes muscle change data, sitting posture change data and head change data. The simulation test unit 1, the human body data acquisition unit 2, the discomfort type identification unit 3 and the parameter optimization unit 4 are connected in sequence, and the parameter optimization unit 4 is connected in sequence.

[0054] The present invention provides a vehicle chassis hybrid simulation test system for performing simulation tests on a vehicle chassis in a variety of different environments, wherein the simulation test unit 1 includes a test cabin, in which the vehicle to be tested is tested, and the simulation test unit 1 can provide a variety of different complex environments to simulate the test conditions of the vehicle chassis in different environments. When performing the chassis test, the driver will be in the driving seat of the vehicle to be tested to operate the vehicle, and before performing the simulation test, a sensor array can be integrated on the driver to collect the driver's human body data during the test of the vehicle to be tested through the sensor array. The human body data can be transmitted to the discomfort type recognition unit 3, and the discomfort type recognition unit Yuan 3 can evaluate the driver's discomfort type based on human body data, such as muscle fatigue, back pain or dizziness. Based on different types of discomfort, the parameter optimization unit 4 can optimize the vehicle under test, and then transmit the parameter optimization command to the simulation test unit 1. The simulation test unit 1 can adjust the vehicle under test based on the adjusted parameters, and then re-simulate the vehicle under test. At the same time, it can reconfirm the changes in the driver's human body data to evaluate whether the parameter adjustment can effectively alleviate the driver's discomfort. Through continuous testing and optimization, the driver's driving experience can be fully considered on the basis of ensuring chassis performance to achieve human-machine adaptation.

[0055] Preferably, the execution steps of the simulation test unit 1 include:

[0056] Step S11: placing the vehicle to be tested on a multi-degree-of-freedom motion platform, and moving the driver into the cockpit of the vehicle to be tested;

[0057] Step S13: switching the road surface of different properties on the surface of the multi-degree-of-freedom motion platform on which the vehicle to be tested is placed according to the needs of the tester;

[0058] Step S12: driving the vehicle to be tested to travel on the multi-degree-of-freedom motion platform, and driving the vehicle to be tested to perform pitch, roll, yaw and three-dimensional translation through the multi-degree-of-freedom motion platform.

[0059] A multi-degree-of-freedom motion platform is integrated in the test cabin of the simulation test unit 1. The vehicle to be tested can be located on the multi-degree-of-freedom motion platform, and the driver can be located in the driving seat of the vehicle to be tested to control the vehicle to be tested. The multi-degree-of-freedom motion platform is integrated with a track structure, so that the vehicle can move in place on the multi-degree-of-freedom motion platform to realize the movement of the vehicle to be tested. The simulation test unit 1 can provide a variety of different complex environments. According to the needs of the tester, different types of road surfaces can be switched on the multi-degree-of-freedom motion platform, such as dirt roads, sandy roads, and flooded roads. In addition, the multi-degree-of-freedom motion platform can drive the vehicle to be tested to swing at different angles, such as pitching 15° front and back, rolling 20° to both sides, and yaw 30°. At the same time, a temperature regulator can be integrated in the test cabin to adjust the temperature in the test cabin during the simulation test to provide a variety of complex environments.

[0060] Preferably, the steps executed by the human body data acquisition unit 2 include:

[0061] Step S21: attaching flexible EMG electrodes to the driver's neck and waist to collect electromyographic signals during the test and output them as muscle change data;

[0062] Step S22: Integrate a flexible pressure sensor array on the seat, steering wheel, and pedals to collect pressure data during the test and output it as sitting posture change data;

[0063] Step S23: Using an inertial measurement unit to collect head change data during the test.

[0064] When the driver is driving the vehicle to be tested, the vehicle to be tested will change at different angles due to the multi-degree-of-freedom motion platform, so the driver's body and sitting posture will also change. Before conducting the simulation test, the sensor array needs to be installed first, and flexible EMG electrodes are attached to the driver's neck, waist, back, etc. The flexible EMG electrodes can collect electromyographic signals during the test, and the electromyographic signals can be output as muscle change data. In addition to collecting muscle change data, a pressure sensor array is also integrated inside the vehicle to be tested. The pressure sensor array is integrated in the positions of the vehicle to be tested that come into contact with the driver, including the seat, steering wheel and pedals. When the driver operates the steering wheel and pedals, some pressure data can be collected. When the vehicle to be tested deviates on the multi-degree-of-freedom motion platform, the pressure sensor array on the seat can also collect the changes in the pressure applied to the seat by the driver, so as to evaluate the changes in the driver's sitting posture, and output the collected pressure data as sitting posture change data. In addition, the head change data during the test can also be collected through the inertial measurement unit. The collected muscle change data, sitting posture change data and head change data can be used as inputs of the discomfort type identification unit 3 to identify the driver's discomfort type, such as muscle fatigue, back pain, dizziness, etc.

[0065] Preferably, the execution steps of the human body data acquisition unit 2 further include:

[0066] Step S24: Calculate a motion sickness index for the head change data based on the motion sickness model. If the motion sickness index is greater than a preset threshold, stop the simulation test.

[0067] For head change data, a motion sickness model can be used to calculate the motion sickness index. If the driver's dizziness is severe, the simulation test can be stopped to avoid affecting the accuracy of the simulation test results due to the driver's physical condition.

[0068] Preferably, it also includes:

[0069] A preprocessing unit 5, used for preprocessing human body data;

[0070] The preprocessing includes denoising, time sequence alignment and standardization. The human body data acquisition unit 2 is data-connected with the discomfort type recognition unit 3 via the preprocessing unit 5 .

[0071] The preprocessing unit 5 is used to preprocess the human body data collected by the human body data collection unit 2, including removing noise, standardizing data, and aligning different types of human body data in time sequence, to ensure that the discomfort type recognition unit 3 can accurately identify the discomfort type.

[0072] Preferably, the steps executed by the discomfort type identification unit 3 include:

[0073] Step S31: extracting features from the muscle change data, the sitting posture change data, and the head change data, and forming a feature matrix;

[0074] Step S32: input the feature matrix into the random forest model, and output the probability of each discomfort type through voting of each decision tree;

[0075] Step S33: Output the discomfort type with the highest probability.

[0076] For the identification of discomfort types, the present invention introduces a random forest model. After standardizing and classifying a large amount of collected historical data, the random forest model is trained. After the training is completed, human body data can be identified. Before the human body data is input into the random forest model, feature extraction can be performed first, and the extracted feature vectors can be composed into a feature matrix. The feature matrix is ​​then used as the input of the random forest model. The random forest model can output the probability of each discomfort type through a decision tree voting mechanism, and the discomfort type with the highest probability is recorded as the discomfort symptom currently experienced by the driver. By introducing the random forest model, the accuracy of discomfort type identification can be greatly improved.

[0077] Preferably, in step S31, when forming the feature matrix, the subjective feeling input by the driver is obtained, the driver's discomfort location is determined based on the subjective feeling, and at least one of the muscle change data, sitting posture change data or head change data is assigned a value based on the discomfort location.

[0078] When identifying the type of discomfort, the present invention introduces the driver's subjective feelings. After extracting the characteristic vector of each human body data, the driver's subjective feelings are collected, keywords are extracted through the subjective feelings, and then the discomfort location is determined from the extracted keywords. The discomfort location will correspond to at least one item of human body data, so at least one of the muscle change data, sitting posture change data or head change data can be assigned a value. After introducing the driver's subjective feelings, the accuracy of discomfort type identification can be further improved.

[0079] Preferably, the execution steps of the parameter optimization unit 4 include:

[0080] Step S41: evaluating the correlation between the chassis parameters of the vehicle to be tested and the discomfort type based on the Pearson correlation coefficient method;

[0081] Step S42: Arrange the chassis parameters in descending order of relevance to form an arrangement table;

[0082] Step S43: Optimize multiple times based on the chassis parameters in the arrangement table, and perform an incremental selection strategy on the selected chassis parameters during each optimization.

[0083] After determining the type of discomfort, the chassis parameters need to be adjusted and optimized. First, the correlation between the discomfort type and the chassis parameters needs to be evaluated. The present invention adopts the Pearson correlation coefficient method to perform calculations, and sorts the correlation of each chassis parameter after obtaining it, and obtains a ranking table. At this time, the chassis parameters can be selected according to the order in the ranking table for optimization and adjustment. During the first optimization and adjustment, the chassis parameter with the largest correlation is selected for optimization. If the simulation test result is not ideal, during the second optimization and adjustment, the chassis parameter with the second largest correlation is introduced, that is, the first two chassis parameters in the ranking table are optimized synchronously to verify the simulation test result again. By analogy, a chassis parameter will be added each time the optimization is performed. If the result of the simulation test can eliminate or significantly reduce the driver's discomfort, it proves that the selected chassis parameter is correct, and the optimization of the chassis parameter can be stopped at this time.

[0084] Preferably, it also includes:

[0085] Comparison unit 6, used for comparing human body data before and after parameter optimization to evaluate the optimization effect;

[0086] The comparison unit 6 is data-connected to the pre-processing unit 5 .

[0087] The comparison unit 6 is configured to receive the human body data processed by the pre-processing unit 5 . Based on the human body data before and after parameter optimization, it is possible to evaluate whether the driver's discomfort is eliminated or alleviated, thereby evaluating the effect of parameter optimization.

[0088] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A vehicle chassis hybrid simulation test system, characterized in that: include: Simulation test unit, used to conduct simulation tests on the vehicle under test in complex environments; A human body data collection unit, used to collect human body data of the driver when he is on the vehicle to be tested; a discomfort type identification unit, for identifying the type of discomfort of the driver when testing the vehicle to be tested based on human body data; Parameter optimization unit, used to optimize the chassis parameters of the vehicle to be tested according to the type of discomfort; The human body data includes muscle change data, sitting posture change data and head change data. The simulation test unit, human body data acquisition unit, discomfort type identification unit and parameter optimization unit are connected in sequence, and the parameter optimization unit is connected in data with the simulation test unit.

2. A vehicle chassis hybrid simulation test system according to claim 1, characterized in that: The execution steps of the simulation test unit include: Step S11: placing the vehicle to be tested on a multi-degree-of-freedom motion platform, and moving the driver into the cockpit of the vehicle to be tested; Step S12: driving the vehicle to be tested to travel on the multi-degree-of-freedom motion platform, and driving the vehicle to be tested to perform pitch, roll, yaw and three-dimensional translation through the multi-degree-of-freedom motion platform.

3. A vehicle chassis hybrid simulation test system according to claim 2, characterized in that: Before step S12, the method further includes: Step S13: According to the needs of the tester, the road surface of the multi-degree-of-freedom motion platform on which the vehicle to be tested is placed is switched to a road surface of different properties.

4. A vehicle chassis hybrid simulation test system according to claim 1, characterized in that: The execution steps of the human body data acquisition unit include: Step S21: attaching flexible EMG electrodes to the driver's neck and waist to collect electromyographic signals during the test and output them as muscle change data; Step S22: Integrate a flexible pressure sensor array on the seat, steering wheel, and pedals to collect pressure data during the test and output it as sitting posture change data; Step S23: Using an inertial measurement unit to collect head change data during the test.

5. A vehicle chassis hybrid simulation test system according to claim 4, characterized in that: The execution steps of the human body data acquisition unit also include: Step S24: Calculate a motion sickness index for the head change data based on the motion sickness model. If the motion sickness index is greater than a preset threshold, stop the simulation test.

6. The vehicle chassis hybrid simulation test system according to claim 1, characterized in that: Also includes: A preprocessing unit, used for preprocessing human body data; The preprocessing includes denoising, time sequence alignment and standardization, and the human body data acquisition unit is connected to the discomfort type recognition unit through the preprocessing unit.

7. The vehicle chassis hybrid simulation test system according to claim 1, characterized in that: The steps of executing the discomfort type identification unit include: Step S31: extracting features from the muscle change data, the sitting posture change data, and the head change data, and forming a feature matrix; Step S32: input the feature matrix into the random forest model, and output the probability of each discomfort type through voting of each decision tree; Step S33: Output the discomfort type with the highest probability.

8. The vehicle chassis hybrid simulation test system according to claim 7, characterized in that: In step S31, when forming the feature matrix, the subjective feeling input by the driver is obtained, the driver's discomfort location is determined based on the subjective feeling, and at least one of the muscle change data, sitting posture change data or head change data is assigned a value based on the discomfort location.

9. The vehicle chassis hybrid simulation test system according to claim 1, characterized in that: The execution steps of the parameter optimization unit include: Step S41: evaluating the correlation between the chassis parameters of the vehicle to be tested and the discomfort type based on the Pearson correlation coefficient method; Step S42: Arrange the chassis parameters in descending order of relevance to form an arrangement table; Step S43: Optimize multiple times based on the chassis parameters in the arrangement table, and perform an incremental selection strategy on the selected chassis parameters during each optimization.

10. The vehicle chassis hybrid simulation test system according to claim 6, characterized in that: Also includes: A comparison unit, used to compare human body data before and after parameter optimization to evaluate the optimization effect; The comparison unit is data-connected to the pre-processing unit.

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