Safety belt and airbag detection method and system for new energy vehicle

Abstract

The application provides a safety belt and air bag detection method and system for a new energy vehicle, and relates to the field of safety belt and air bag detection. The method comprises the following steps: determining a plurality of groups of collision simulation parameters of a new energy vehicle to be detected based on safety belt parameter information and safety air bag system parameter information of the new energy vehicle to be detected, safety belt parameter information and safety air bag system parameter information of a plurality of sample new energy vehicles, and safety belt and air bag test data; for each group of collision simulation parameters, obtaining a safety belt detection data set and an air bag detection data set corresponding to the collision simulation parameters of the new energy vehicle to be detected; and performing collaborative performance evaluation of the safety belt and the air bag of the new energy vehicle to be detected according to the safety belt detection data set and the air bag detection data set corresponding to each group of collision simulation parameters of the new energy vehicle to be detected, so that the collaborative detection of the safety belt and the air bag is realized.

Description

Technical Field

[0001] This invention relates to the field of seat belt and airbag testing, and particularly to a method and system for testing seat belts and airbags in new energy vehicles. Background Technology

[0002] The Supplemental Restraint System (SRS) is an auxiliary protection system that includes a sensor assembly, inflator, folding airbag, igniter, solid nitrogen, and warning lights. When a car is subjected to a high-speed collision at a certain angle from the front, the collision sensor mounted on the front of the vehicle detects the sudden deceleration and transmits this signal to the airbag system's control computer (central processing module) within 10 milliseconds. After analysis and confirmation, the computer immediately detonates the electrothermal igniter (electric detonator) inside the airbag, causing it to explode. This process typically takes only about 50 milliseconds. After the igniter detonates, the solid nitrogen particles rapidly vaporize, and a large amount of nitrogen immediately inflates the airbag. Under the powerful impact, the airbag bursts through the cover on the steering wheel and fully deploys. This creates an "air cushion" in front of the occupants. When the occupants impact the airbag, the nitrogen inside is compressed and expelled through small holes in the airbag, thus reducing the impact force and preventing injury to the occupants. Car seat belts, also known as seat belts, are a type of occupant restraint device. They are designed to restrain occupants during a collision and prevent them from secondary impacts with the steering wheel and dashboard, or from being ejected from the vehicle, resulting in injury or death. Airbags must be used in conjunction with seat belts to provide optimal protection. In a collision, seat belts reduce the risk of passengers hitting objects inside the vehicle or being ejected, while airbags work in conjunction with seat belts to further reduce the severity of injuries.

[0003] In existing technologies, when conducting comprehensive performance tests on airbag systems, the assessment of their ability to work in conjunction with seat belts is often neglected, resulting in incomplete testing. This may lead to problems in the coordination between safety devices during actual use.

[0004] Therefore, there is a need to provide a method and system for detecting seat belts and airbags in new energy vehicles, so as to achieve coordinated detection of seat belts and airbags. Summary of the Invention

[0005] This invention provides a method for testing seat belts and airbags in new energy vehicles, comprising: acquiring seat belt parameter information, airbag system parameter information, and seat belt and airbag test data of multiple sample new energy vehicles, wherein the seat belt and airbag test data includes the coordinated performance of seat belts and airbags corresponding to multiple sets of simulated collision parameters for the sample new energy vehicles; acquiring seat belt parameter information and airbag system parameter information of the new energy vehicle to be tested; determining multiple sets of collision simulation parameters for the new energy vehicle to be tested based on the seat belt parameter information and airbag system parameter information of the new energy vehicle to be tested, the seat belt parameter information and airbag system parameter information of multiple sample new energy vehicles, and the seat belt and airbag test data, wherein the collision simulation parameters include at least collision angle and collision speed; for each set of collision simulation parameters, controlling a collision test bench to simulate a collision with the new energy vehicle to be tested based on the collision simulation parameters, and acquiring seat belt detection datasets and airbag detection datasets corresponding to the collision simulation parameters of the new energy vehicle to be tested; and evaluating the coordinated performance of seat belts and airbags of the new energy vehicle to be tested based on the seat belt detection datasets and airbag detection datasets corresponding to each set of collision simulation parameters of the new energy vehicle to be tested.

[0006] Furthermore, seat belt parameter information, airbag system parameter information, and seat belt and airbag test data of multiple sample new energy vehicles are obtained, including: determining multiple sets of simulated collision parameters; for each set of simulated collision parameters, controlling the collision test bench to simulate collisions on the sample new energy vehicles based on the simulated collision parameters, and obtaining seat belt detection datasets and airbag detection datasets corresponding to the simulated collision parameters of the sample new energy vehicles; and determining the cooperative performance of seat belts and airbags for each set of simulated collision parameters of the sample new energy vehicles based on the seat belt detection datasets and airbag detection datasets corresponding to each set of simulated collision parameters of the sample new energy vehicles.

[0007] Furthermore, based on the seat belt and airbag system parameter information of the new energy vehicle under test, the seat belt and airbag system parameter information of multiple sample new energy vehicles, and seat belt and airbag test data, multiple sets of collision simulation parameters for the new energy vehicle under test are determined. This includes: determining the influence weight of each seat belt parameter and each airbag system parameter based on the seat belt and airbag parameter information of multiple sample new energy vehicles and the seat belt and airbag test data; for any two sample new energy vehicles, based on the influence weight of each seat belt parameter and the influence weight of the airbag system parameter, and based on the seat belt and airbag system parameter information of the two sample new energy vehicles, calculating the consistency of the seat belt and airbag parameters between the two sample new energy vehicles. Coefficients; Based on the consistency coefficient of seat belt and airbag parameters of any two sample new energy vehicles, multiple sample new energy vehicles are clustered using a clustering algorithm to determine multiple clusters; For each cluster, based on the seat belt and airbag test data of each sample new energy vehicle included in the cluster, the performance difference coefficient of the cluster corresponding to each set of test simulation collision parameters is calculated, and the test simulation collision parameters corresponding to the cluster are determined based on the performance difference coefficient of the cluster corresponding to each set of test simulation collision parameters; Based on the seat belt parameter information and airbag system parameter information of the new energy vehicle to be tested, as well as the influence weight of each seat belt parameter and airbag system parameter, the cluster matching the new energy vehicle to be tested is determined, and multiple sets of collision simulation parameters of the new energy vehicle to be tested are determined according to the test simulation collision parameters corresponding to the cluster matching the new energy vehicle to be tested.

[0008] Furthermore, based on the seat belt parameter information, airbag system parameter information, and seat belt and airbag test data of multiple sample new energy vehicles, the influence weight of each seat belt parameter and each airbag system parameter is determined, including: for each seat belt parameter, calculating the correlation coefficient between the seat belt parameter and the synergistic performance of the seat belt and airbag based on the seat belt parameter information and seat belt and airbag test data of multiple sample new energy vehicles; for each airbag system parameter, calculating the correlation coefficient between the airbag system parameter and the synergistic performance of the seat belt and airbag based on the seat belt parameter information and seat belt and airbag test data of multiple sample new energy vehicles; and determining the influence weight of each seat belt parameter and each airbag system parameter based on the correlation coefficient between the seat belt and airbag synergistic performance of each sample new energy vehicle.

[0009] Furthermore, the collision test bench includes a fixed platform, an acceleration component, a braking component, and a vibration component, wherein the new energy vehicle to be tested is fixed on the vibration component, the vibration component is disposed on the fixed platform, the fixed platform is disposed on the acceleration component, and the braking component is disposed on the fixed platform.

[0010] Furthermore, the collision test bench is controlled based on collision simulation parameters to simulate collisions with the new energy vehicle under test, including: generating acceleration curves based on collision simulation parameters; generating vibration simulation parameters based on acceleration curves and collision simulation parameters; controlling the acceleration and braking components to simulate collision speeds according to the acceleration curves; and simulating collision vibrations according to the vibration simulation parameters.

[0011] Further, acquiring the seatbelt detection dataset and airbag detection dataset corresponding to the collision parameters of the new energy vehicle to be tested includes: determining multiple seatbelt tightening detection positions and multiple dummy acceleration detection positions based on the seatbelt parameter information of the new energy vehicle to be tested; installing pressure detection devices at the multiple seatbelt tightening detection positions; installing acceleration detection devices at the multiple dummy acceleration detection positions; acquiring the seatbelt detection dataset during the simulated collision using the pressure detection devices and acceleration detection devices; and acquiring airbag images during the simulated collision using an image acquisition device, wherein the airbag detection dataset includes airbag images at multiple consecutive time points.

[0012] Furthermore, based on the seatbelt detection dataset and airbag detection dataset corresponding to each set of collision simulation parameters for the new energy vehicle under test, the collaborative performance evaluation of the seatbelt and airbag of the new energy vehicle under test is conducted. This includes: for each set of collision simulation parameters, determining the scores of the seatbelt and airbag of the new energy vehicle under test in multiple collaborative performance evaluation indicators for the corresponding collision simulation parameters based on the seatbelt detection dataset and airbag detection dataset for the corresponding collision simulation parameters; and conducting a collaborative performance evaluation of the seatbelt and airbag of the new energy vehicle under test based on the scores of the seatbelt and airbag of the new energy vehicle under test in multiple collaborative performance evaluation indicators for each set of collision simulation parameters.

[0013] Furthermore, the collaborative performance evaluation index includes at least the maximum displacement index; determining the scores of the seat belts and airbags of the new energy vehicle under test for the corresponding collision simulation parameters on multiple collaborative performance evaluation indexes includes: performing variational mode decomposition on the dummy acceleration curves obtained by each acceleration detection device during the simulated collision to obtain multiple dummy acceleration mode components, and extracting the features of each dummy acceleration mode component; performing variational mode decomposition on the acceleration curves to obtain multiple acceleration mode components, and extracting the features of each acceleration mode component; determining the maximum displacement of the dummy based on the features of each dummy acceleration mode component corresponding to each acceleration detection device and the features of each acceleration mode component of the acceleration curve; and determining the scores of the seat belts and airbags of the new energy vehicle under test on the maximum displacement index based on the maximum displacement of the dummy.

[0014] This invention provides a seatbelt and airbag testing system for new energy vehicles, applying the aforementioned seatbelt and airbag testing method for new energy vehicles, comprising: an information acquisition module, used to acquire seatbelt parameter information, airbag system parameter information, and seatbelt and airbag test data from multiple sample new energy vehicles, wherein the seatbelt and airbag test data includes the coordinated performance of the seatbelt and airbag corresponding to multiple sets of simulated collision parameters in the sample new energy vehicles, and is also used to acquire the seatbelt parameter information and airbag system parameter information of the new energy vehicle to be tested; and a parameter determination module, used to determine the parameters based on the seatbelt parameter information and airbag system parameter information of the new energy vehicle to be tested, and the safety performance of the seatbelt and airbag system from multiple sample new energy vehicles. The system includes parameter information, airbag system parameter information, and seat belt and airbag test data to determine multiple sets of collision simulation parameters for the new energy vehicle under test. These collision simulation parameters include at least the collision angle and collision speed. A collision simulation module is used to control a collision test bench to simulate a collision with the new energy vehicle under test based on each set of collision simulation parameters, obtaining seat belt and airbag test datasets corresponding to the collision simulation parameters. A performance analysis module is used to evaluate the coordinated performance of the seat belts and airbags of the new energy vehicle under test based on the seat belt and airbag test datasets corresponding to each set of collision simulation parameters.

[0015] Compared with existing technologies, the seat belt and airbag detection method and system for new energy vehicles provided by this invention have at least the following beneficial effects:

[0016] 1. By acquiring seatbelt parameter information, airbag system parameter information, and test data of seatbelts and airbags from multiple sample new energy vehicles, a rich reference database is provided for the new energy vehicles to be tested. This helps to more accurately simulate real-world collision scenarios, thereby improving the efficiency and accuracy of testing. Based on the specific parameter information of the new energy vehicles to be tested, multiple sets of collision simulation parameters, such as collision angle and collision speed, can be determined individually. This customized testing scheme is closer to actual use and helps to more accurately evaluate the collaborative performance of seatbelts and airbags. Through simulated collision tests, seatbelt test datasets and airbag test datasets corresponding to different collision simulation parameters of the new energy vehicles to be tested can be obtained. These datasets cover various performance characteristics of seatbelts and airbags during the collision process, thereby achieving a comprehensive evaluation of collaborative performance.

[0017] 2. By determining multiple sets of simulated collision parameters and conducting simulated collision tests on sample new energy vehicles, abundant test data on seat belts and airbags can be obtained. This data provides a solid foundation for subsequent collaborative performance evaluation, making the tests more targeted and accurate. It not only considers the specific parameter information of the new energy vehicle under test but also combines test data from multiple sample new energy vehicles. By calculating the influence weights of seat belt parameters and airbag system parameters, as well as the consistency coefficient of seat belt and airbag parameters, the collision simulation parameters are optimized and determined. This helps to more accurately simulate real collision scenarios and improve the accuracy and effectiveness of the tests. By clustering multiple sample new energy vehicles using a clustering algorithm and calculating the performance difference coefficient based on the seat belt and airbag test data of each cluster, the simulated collision parameters corresponding to the cluster can be determined. This allows for a more comprehensive consideration of the differences between different sample new energy vehicles, thereby improving the reliability of collaborative performance evaluation. Based on the specific parameter information of the new energy vehicle under test and the influence weights of each seat belt parameter and airbag system parameter, the cluster matching the new energy vehicle under test can be determined, and multiple sets of collision simulation parameters can be determined accordingly. This personalized testing approach can better reflect the actual conditions of the new energy vehicles being tested, thus improving the relevance and practicality of the testing.

[0018] 3. The crash test bench design (including a fixed platform, acceleration components, braking components, and vibration components) can simulate various crash scenarios, including different crash speeds, angles, and vibration modes. This ability to highly simulate real-world crash environments makes test results more accurate and reliable. By generating acceleration curves and vibration simulation parameters based on crash simulation parameters, this method can precisely control the acceleration, braking, and vibration components to achieve the desired crash speed and vibration mode. This precise control helps obtain consistent and repeatable test results. Installing detection devices at multiple seatbelt tightening detection positions and dummy acceleration detection positions allows for comprehensive capture of the dynamic responses of the seatbelt and passenger during the crash. Simultaneously, acquiring airbag images through image acquisition devices allows for detailed analysis of the airbag deployment process and effects.

[0019] 4. Variational mode decomposition technology can decompose complex dummy acceleration curves and overall acceleration curves into multiple modal components and extract features from them. This helps to more accurately capture and analyze the dynamic response of dummies and vehicles during collisions, thereby improving the accuracy of determining the maximum displacement of the dummy. Attached Figure Description

[0020] This specification will be further described by way of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting; in these embodiments, the same reference numerals denote the same structures, wherein:

[0021] Figure 1 This is a schematic flowchart illustrating a method for testing seat belts and airbags in new energy vehicles, based on some embodiments of this specification.

[0022] Figure 2 This is a schematic diagram of a module for a seat belt and airbag detection system for new energy vehicles, according to some embodiments of this specification. Detailed Implementation

[0023] To more clearly illustrate the technical solutions of the embodiments in this specification, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of this specification. For those skilled in the art, these drawings can be applied to other similar scenarios without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0024] Figure 1 This is a flowchart illustrating a method for testing seat belts and airbags in new energy vehicles, based on some embodiments of this specification. Figure 1 As shown, the method for testing seat belts and airbags in new energy vehicles may include the following steps.

[0025] S101. Obtain seat belt parameter information, airbag system parameter information, and seat belt and airbag test data from multiple sample new energy vehicles.

[0026] Specifically, the seat belt parameter information of the sample new energy vehicles may include seat belt type (e.g., three-point seat belt, pre-tensioned seat belt, force-limiting seat belt, etc.), seat belt material (e.g., high-strength polyester, polypropylene or nylon, etc.), seat belt size (e.g., width, length and buckle size, etc.), load capacity, etc.

[0027] The airbag system parameter information of the sample new energy vehicles may include type (such as front airbags, side airbags, knee airbags, curtain airbags, etc.), quantity, location, gas generator type, airbag material, deployment speed, etc.

[0028] It is understandable that there are at least some differences in the seat belt parameters and airbag system parameters of different samples of new energy vehicles.

[0029] The test data for seat belts and airbags includes the coordinated performance of seat belts and airbags in multiple sets of simulated collision parameters for sample new energy vehicles.

[0030] In some embodiments, S101 specifically includes:

[0031] Determine multiple sets of experimental collision simulation parameters;

[0032] For each set of simulated collision parameters, the collision test bench is controlled to simulate collisions with the sample new energy vehicles based on the simulated collision parameters, and the seat belt detection dataset and airbag detection dataset corresponding to the simulated collision parameters of the sample new energy vehicles are obtained.

[0033] Based on the seat belt test dataset and airbag test dataset for each set of simulated collision parameters for the sample new energy vehicles, the coordinated performance of the seat belt and airbag for each set of simulated collision parameters for the sample new energy vehicles is determined.

[0034] Specifically, to comprehensively evaluate the coordinated performance of seat belts and airbags in sample new energy vehicles under different collision conditions, multiple sets of different simulated collision parameters need to be set. These simulated collision parameters can include collision speed, collision angle, collision type (such as frontal collision, side collision, rear-end collision, etc.), and the mass and shape of the colliding object. By adjusting these parameters, various possible collision scenarios can be simulated.

[0035] The sample new energy vehicles can be subjected to crash simulations using a crash test bench to obtain seat belt and airbag detection datasets for each set of simulated crash parameters. The coordinated performance of the seat belts and airbags for each set of simulated crash parameters is determined using multiple coordinated performance evaluation indicators. Further details regarding the crash test bench and multiple coordinated performance evaluation indicators can be found in section S105 and will not be repeated here.

[0036] S102. Obtain the seat belt parameter information and airbag system parameter information of the new energy vehicle to be tested.

[0037] S103. Based on the seat belt parameter information and airbag system parameter information of the new energy vehicle to be tested, the seat belt parameter information and airbag system parameter information of multiple sample new energy vehicles, and the test data of seat belts and airbags, determine multiple sets of collision simulation parameters of the new energy vehicle to be tested.

[0038] The collision simulation parameters include at least the collision angle and collision velocity, and may also include the collision type (such as frontal collision, side collision, rear-end collision, etc.), the mass and shape of the colliding objects, etc.

[0039] In some embodiments, S103 specifically includes:

[0040] Based on the seat belt parameter information, airbag system parameter information, and seat belt and airbag test data of multiple sample new energy vehicles, the influence weight of each seat belt parameter and the influence weight of each airbag system parameter are determined.

[0041] For any two sample new energy vehicles, based on the influence weight of each seat belt parameter and the influence weight of the airbag system parameter, the consistency coefficient of the seat belt and airbag parameters of the two sample new energy vehicles is calculated.

[0042] Multiple new energy vehicles are clustered based on the consistency coefficient of seat belt and airbag parameters between any two sample new energy vehicles using clustering algorithms (such as K-means clustering) to determine multiple clusters.

[0043] For each cluster, based on the test data of seat belts and airbags of each sample new energy vehicle included in the cluster, calculate the performance difference coefficient of the cluster corresponding to each set of test simulation collision parameters. Based on the performance difference coefficient of the cluster corresponding to each set of test simulation collision parameters, determine the test simulation collision parameters corresponding to the cluster. For example, test simulation collision parameters with a performance difference coefficient greater than the performance difference coefficient threshold can be used as the test simulation collision parameters corresponding to the cluster.

[0044] Based on the seat belt parameter information and airbag system parameter information of the new energy vehicle to be tested, as well as the influence weight of each seat belt parameter and airbag system parameter, the cluster matching the new energy vehicle to be tested is determined. According to the test simulation collision parameters corresponding to the cluster matching the new energy vehicle to be tested, multiple sets of collision simulation parameters of the new energy vehicle to be tested are determined.

[0045] In some embodiments, based on seat belt parameter information, airbag system parameter information, and seat belt and airbag test data from multiple sample new energy vehicles, the influence weight of each seat belt parameter and the influence weight of each airbag system parameter are determined, including:

[0046] For each seat belt parameter, based on seat belt parameter information from multiple sample new energy vehicles and seat belt and airbag test data, the correlation coefficient between the seat belt parameter and the synergistic performance of the seat belt and airbag is calculated.

[0047] For each airbag system parameter, based on the seat belt parameter information of multiple sample new energy vehicles and the test data of seat belts and airbags, the correlation coefficient between the airbag system parameters and the synergistic performance of seat belts and airbags is calculated.

[0048] Based on the correlation coefficients between each seatbelt parameter and the synergistic performance of the seatbelt and airbag, as well as the correlation coefficients between each airbag system parameter and the synergistic performance of the seatbelt and airbag, the influence weights of each seatbelt parameter and each airbag system parameter are determined.

[0049] Specifically, for each seatbelt parameter, the value of each seatbelt parameter for the sample new energy vehicles can be determined based on the seatbelt parameter information of the sample new energy vehicles. For non-continuous seatbelt parameters, the value of the seatbelt parameter can be determined by coding. For example, for seatbelt type, a three-point seatbelt can be coded as 11, a pretensioned seatbelt can be coded as 12, and a force-limiting seatbelt can be coded as 13.

[0050] For non-continuous seat belt parameters (such as seat belt type and material), regression analysis is performed using statistical software (such as SPSS, R, Python, etc.) to obtain the correlation coefficient between seat belt parameters and the synergistic performance of seat belts and airbags. For continuous variables (such as size and load capacity), the Pearson correlation coefficient is used to calculate the correlation coefficient between seat belt parameters and the synergistic performance of seat belts and airbags.

[0051] The method for calculating the correlation coefficient between airbag system parameters and the synergistic performance of seat belts and airbags is similar to the method for calculating the correlation coefficient between seat belt parameters and the synergistic performance of seat belts and airbags, and will not be repeated here.

[0052] The influence weights of seat belt parameters can be calculated using the following formula:

[0053] in, Let i be the influence weight of the i-th seat belt parameter. Let be the correlation coefficient between the i-th seat belt parameter and the synergistic performance of the seat belt and airbag. Let M be the correlation coefficient between the m-th seatbelt parameter and the synergistic performance of the seatbelt and airbag, where M is the total number of seatbelt parameters. Let be the correlation coefficient between the nth airbag system parameter and the synergistic performance of the seat belt and airbag, where N is the total number of airbag system parameters.

[0054] The method for calculating the influence weights of airbag system parameters is similar to that for calculating the influence weights of seat belt parameters, and will not be repeated here.

[0055] The consistency coefficient of seat belt and airbag parameters between two sample new energy vehicles can be calculated using the following formula:

[0056] in, Let be the consistency coefficient of the seat belt and airbag parameters between the i-th sample new energy vehicle and the j-th sample new energy vehicle. Let m be the influence weight of the m-th seat belt parameter. Let m be the value of the seat belt parameter corresponding to the i-th sample new energy vehicle. Let m be the value of the seat belt parameter corresponding to the j-th sample new energy vehicle. Let n be the influence weight of the nth airbag system parameter. Let n be the value of the airbag system parameter corresponding to the i-th sample new energy vehicle. Let be the value of the airbag system parameter corresponding to the i-th sample new energy vehicle.

[0057] The performance difference coefficient of each cluster corresponding to the simulated collision parameters in the test can be calculated using the following formula:

[0058] in, Let be the performance difference coefficient of the cluster corresponding to the simulated collision parameters of the i-th group of experiments. The coordinated performance of seat belts and airbags for the k-th sample new energy vehicle in the cluster, corresponding to the i-th group of simulated collision parameters. K This represents the total number of new energy vehicles included in the cluster.

[0059] For each cluster, the average value of each seat belt parameter for the sample new energy vehicles included in the cluster can be used as the value of each seat belt parameter for the cluster. Similarly, the average value of each airbag system parameter for the sample new energy vehicles included in the cluster can be used as the value of each airbag system parameter for the cluster.

[0060] The consistency coefficient of seat belt and airbag parameters between the new energy vehicle under test and each cluster can be calculated using the same method as calculating the consistency coefficient of seat belt and airbag parameters between two sample new energy vehicles. Clusters with a consistency coefficient greater than the threshold for seat belt and airbag parameter consistency coefficients are selected as the clusters to be matched with the new energy vehicle under test. After deduplication of the test simulation collision parameters corresponding to the clusters matched with the new energy vehicle under test, these parameters are used as multiple sets of collision simulation parameters for the new energy vehicle under test.

[0061] S104. For each set of collision simulation parameters, control the collision test bench to simulate a collision with the new energy vehicle to be tested based on the collision simulation parameters, and obtain the seat belt detection dataset and airbag detection dataset corresponding to the collision simulation parameters of the new energy vehicle to be tested.

[0062] In some embodiments, the crash test bench includes a fixed platform, an acceleration component, a braking component, and a vibration component, wherein the new energy vehicle to be tested is fixed on the vibration component, the vibration component is disposed on the fixed platform, the fixed platform is disposed on the acceleration component, and the braking component is disposed on the fixed platform.

[0063] Specifically, the stationary platform is the foundation of the entire crash test bench, providing a stable and robust platform to ensure the stability and accuracy of the entire system during simulated collisions. The stationary platform's design considers various factors, including load-bearing capacity, vibration resistance, and ease of adjustment and maintenance. The acceleration assembly simulates the vehicle's speed before a collision. It can precisely adjust the test bench's speed according to a preset acceleration curve to simulate collisions at different speeds. The acceleration assembly typically includes a motor, transmission, and control system, which work together to achieve precise acceleration and deceleration. The braking assembly simulates the vehicle's braking action upon impact, mimicking the natural deceleration and stopping process caused by the impact force after a collision. It typically includes a hydraulic or electric braking system, as well as sensors and controllers for monitoring and controlling the braking force.

[0064] Vibration assemblies are used to simulate the impact and vibration experienced by a vehicle during a collision. They can precisely adjust the vibration mode of a test bench based on vibration simulation parameters to simulate the dynamic response in a real collision. A vibration assembly may include exciters, sensors, and a control system, which work together to produce the desired vibration effect.

[0065] In some embodiments, the collision test bench is controlled to simulate a collision with the new energy vehicle under test based on collision simulation parameters, specifically including:

[0066] Acceleration curves are generated based on collision simulation parameters;

[0067] Vibration simulation parameters are generated based on acceleration curves and collision simulation parameters;

[0068] The acceleration and braking components are controlled based on the acceleration curve to simulate the collision speed.

[0069] Collision vibration simulation is performed based on vibration simulation parameters.

[0070] Specifically, based on collision simulation parameters (such as collision speed and collision angle), an acceleration curve is first generated. This curve describes the acceleration change throughout the entire process from pre-collision acceleration to post-collision deceleration. Based on the acceleration curve and collision simulation parameters, vibration simulation parameters are further generated. These parameters describe the types and intensities of vibrations the vehicle may experience during the collision. According to the acceleration curve, the acceleration and braking components are controlled to work together to simulate the vehicle's acceleration before and deceleration after the collision. This ensures that the test bench can reach the required collision speed during the simulated collision. Based on the vibration simulation parameters, the vibration components are controlled to produce corresponding vibration effects. This simulates the impact and vibration experienced by the vehicle during the collision, thus allowing for the evaluation of the vehicle's seatbelt and airbag systems in real-world accidents. The acceleration curve can be generated based on the collision simulation parameters using a curve generation model, or the vibration simulation parameters can be generated based on the acceleration curve and collision simulation parameters using a parameter generation model. Both the curve generation model and the parameter generation model can be convolutional neural network models.

[0071] In some embodiments, obtaining the seatbelt detection dataset and airbag detection dataset corresponding to the collision parameters of the new energy vehicle to be tested includes:

[0072] Based on the seat belt parameter information of the new energy vehicle to be tested, multiple seat belt tightening detection positions and multiple dummy acceleration detection positions are determined.

[0073] Pressure detection devices were installed at multiple seat belt tension detection points;

[0074] Acceleration detection devices were installed at multiple dummy acceleration detection locations;

[0075] Seatbelt detection datasets were obtained during a simulated collision using pressure and acceleration detection devices.

[0076] Airbag images are acquired during a simulated collision using image acquisition devices. The airbag detection dataset includes airbag images at multiple consecutive time points. Specifically, to obtain the airbag detection dataset, image acquisition devices (such as high-speed cameras) need to be installed within the test area. These devices should have high resolution and high-speed shooting capabilities to capture the instantaneous deployment and dynamic changes of the airbags during the collision.

[0077] Specifically, a detailed understanding of the seatbelt structure of the new energy vehicle being tested is necessary, including the webbing material, retractor type, and the working principle of the pretensioner (if applicable). Based on the seatbelt's design characteristics, potential tightening points should be identified. These points are typically the seatbelt's anchor point to the vehicle body, the pretensioner's activation point, or key locations on the seatbelt webbing. At these identified tightening points, multiple tightening detection locations should be determined based on the seatbelt's tightening characteristics and the pretensioner's effective range. These locations should accurately reflect the seatbelt's tightening and stress state during a collision. For example, pressure sensors can be installed at the seatbelt's mounting point with the seat, near the B-pillar, and at the pretensioner's trigger point to monitor the seatbelt's tightening pressure. When determining the seatbelt tightening detection locations, the passenger's protection needs must also be considered. For example, for front-seat passengers, special attention should be paid to the seatbelt's tightening in the chest and pelvic areas; for rear-seat passengers, attention may need to be paid to the shoulder and lumbar areas.

[0078] Based on crash test standards and the characteristics of the crash test dummy model, this study analyzes the potential acceleration impact on the dummy during a collision. The focus is on acceleration changes in key areas such as the head, chest, abdomen, and pelvis. Multiple acceleration detection locations are determined at these critical stress points on the dummy, according to the installation requirements of the acceleration sensors and the accuracy requirements of the test. These locations should be able to accurately measure the acceleration changes of the dummy during the collision and reflect its impact on the passenger. For example, acceleration sensors can be installed at the dummy's head, chest, abdomen, and pelvis to monitor acceleration changes during the collision.

[0079] S105. Based on the seat belt test dataset and airbag test dataset corresponding to each set of collision simulation parameters of the new energy vehicle to be tested, evaluate the coordinated performance of the seat belt and airbag of the new energy vehicle to be tested.

[0080] In some embodiments, S105 specifically includes:

[0081] For each set of collision simulation parameters, based on the seat belt detection dataset and airbag detection dataset of the new energy vehicle to be tested corresponding to the collision simulation parameters, the scores of the seat belt and airbag of the new energy vehicle to be tested in multiple collaborative performance evaluation indicators are determined. Among them, the collaborative performance evaluation indicators include at least the maximum displacement index.

[0082] Based on the scores of the seat belts and airbags of the new energy vehicle under test in multiple collaborative performance evaluation indicators corresponding to each set of collision simulation parameters, the collaborative performance of the seat belts and airbags of the new energy vehicle under test is evaluated.

[0083] Specifically, multiple collaborative performance evaluation indicators may also include:

[0084] 1. Maximum acceleration:

[0085] Definition: The maximum acceleration experienced by a point near the seatbelt or airbag (such as a critical part of a dummy) during a collision.

[0086] Importance: It reflects the impact intensity of a collision on passengers and is an important indicator for assessing the risk of passenger injury.

[0087] 2. Pressure distribution:

[0088] Definition: The distribution of pressure exerted on a passenger by a seatbelt during a collision.

[0089] Importance: Even pressure distribution helps reduce the risk of injury to passengers, especially in critical areas such as the shoulders and chest.

[0090] 3. Response time:

[0091] Definition: The time from the occurrence of a collision to the activation of the seatbelt pretensioner or the deployment of the airbag.

[0092] Importance: A fast response time can more effectively protect passengers from collision injuries.

[0093] 4. Airbag deployment configuration:

[0094] Definition: The shape and size of an airbag during a collision.

[0095] Importance: The correct deployment configuration ensures effective contact between the airbag and the passenger, providing optimal cushioning.

[0096] 5. Seatbelt restraint force:

[0097] Definition: The restraining force of a seatbelt on a passenger during a collision.

[0098] Importance: Appropriate restraint can prevent passengers from being thrown out or suffering secondary injuries during a collision.

[0099] 6. Passenger injury index:

[0100] Definition: A comprehensive indicator for assessing the risk of injury to passengers during a collision, typically calculated based on sensor data from a dummy.

[0101] Importance: The lower the score of the passenger injury index, the lower the risk of passenger injury during a collision.

[0102] 7. Interaction between airbags and seat belts:

[0103] Definition: To assess whether airbag deployment interferes with seatbelt operation and the impact of this interference on passenger protection.

[0104] Importance: To ensure that airbags and seat belts work together to provide the best protection for passengers.

[0105] Each performance synergy indicator can be assigned a weight based on its importance. A weighted average score is then calculated as the overall score for the synergistic performance of the seatbelts and airbags in the new energy vehicle under test.

[0106] A radar chart is generated to represent the score of each synergy performance evaluation indicator. By observing the shape and area of ​​the radar chart, the synergy performance of the seat belts and airbags in the tested new energy vehicle can be visually assessed. The larger the area of ​​the radar chart, the better the synergy performance. The overall score or radar chart of the synergy performance of the seat belts and airbags of the tested new energy vehicle is compared with industry standards, competing models, or historical test data. Through comparative analysis, it is assessed whether the synergy performance of the seat belts and airbags of the tested new energy vehicle reaches the industry average level, is better than competing models, or shows improvement.

[0107] In some embodiments, determining the scores of the seat belts and airbags of the new energy vehicle under test for corresponding collision simulation parameters on multiple collaborative performance evaluation indicators includes:

[0108] Variational mode decomposition is performed on the dummy acceleration curves obtained by each acceleration detection device during the simulated collision to obtain multiple dummy acceleration mode components, and the features of each dummy acceleration mode component are extracted.

[0109] Variational mode decomposition is performed on the acceleration curve to obtain multiple acceleration mode components, and the features of each acceleration mode component are extracted.

[0110] Based on the characteristics of each dummy acceleration mode component corresponding to each acceleration detection device and the characteristics of each acceleration mode component of the acceleration curve, the maximum displacement of the dummy is determined;

[0111] Based on the maximum displacement of the dummy, the scores of the seat belt and airbag of the new energy vehicle under test in terms of maximum displacement index are determined.

[0112] Specifically, the maximum displacement of the dummy can be determined by a displacement determination model based on the characteristics of each dummy acceleration mode component corresponding to each acceleration detection device and the characteristics of each acceleration mode component of the acceleration curve. The displacement determination model can be a convolutional neural network model.

[0113] Figure 2 This is a schematic diagram of a module for a seatbelt and airbag detection system for new energy vehicles, as shown in some embodiments of this specification. Figure 2 As shown, a seat belt and airbag detection system for new energy vehicles may include an information acquisition module, a parameter determination module, a collision simulation module, and a performance analysis module.

[0114] The information acquisition module is used to acquire seat belt parameter information, airbag system parameter information, and seat belt and airbag test data of multiple sample new energy vehicles. Among them, the seat belt and airbag test data includes the coordinated performance of seat belt and airbag corresponding to multiple sets of simulated collision parameters of sample new energy vehicles. It is also used to acquire seat belt parameter information and airbag system parameter information of the new energy vehicle to be tested.

[0115] The parameter determination module is used to determine multiple sets of collision simulation parameters for the new energy vehicle under test based on the seat belt parameter information and airbag system parameter information of the new energy vehicle under test, the seat belt parameter information and airbag system parameter information of multiple sample new energy vehicles, and the test data of seat belt and airbag. The collision simulation parameters include at least the collision angle and the collision speed.

[0116] The collision simulation module is used to control the collision test bench to simulate collisions with the new energy vehicle under test based on each set of collision simulation parameters, and to obtain the seat belt detection dataset and airbag detection dataset of the new energy vehicle under test corresponding to the collision simulation parameters.

[0117] The performance analysis module is used to evaluate the coordinated performance of the seat belts and airbags of the new energy vehicle under test based on the seat belt test dataset and airbag test dataset corresponding to each set of collision simulation parameters.

[0118] Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments described herein. Other variations may also fall within the scope of this specification. Therefore, alternative configurations of the embodiments described herein are intended to be illustrative rather than limiting, and should be considered consistent with the teachings of this specification. Accordingly, the embodiments described herein are not limited to those explicitly introduced and described herein.

Claims

1. A method for testing seat belts and airbags in new energy vehicles, characterized in that, include: The system acquires seat belt parameter information, airbag system parameter information, and seat belt and airbag test data from multiple sample new energy vehicles. The seat belt and airbag test data includes the coordinated performance of the seat belt and airbag for multiple sets of simulated collision parameters corresponding to the sample new energy vehicles. Obtain the seat belt parameter information and airbag system parameter information of the new energy vehicle to be tested; Based on the seat belt parameter information and airbag system parameter information of the new energy vehicle to be tested, the seat belt parameter information and airbag system parameter information of multiple sample new energy vehicles, and the test data of seat belt and airbag, multiple sets of collision simulation parameters of the new energy vehicle to be tested are determined. The collision simulation parameters include at least the collision angle and the collision speed. For each set of collision simulation parameters, the collision test bench is controlled to simulate a collision with the new energy vehicle to be tested based on the collision simulation parameters, and the seat belt detection dataset and airbag detection dataset corresponding to the collision simulation parameters of the new energy vehicle to be tested are obtained. Based on the seat belt test dataset and airbag test dataset corresponding to each set of collision simulation parameters of the new energy vehicle under test, the collaborative performance evaluation of the seat belt and airbag of the new energy vehicle under test is carried out.

2. The method for detecting seat belts and airbags in new energy vehicles according to claim 1, characterized in that, Obtain seat belt parameter information, airbag system parameter information, and seat belt and airbag test data from multiple sample new energy vehicles, including: Determine multiple sets of experimental collision simulation parameters; For each set of simulated collision parameters, the collision test bench is controlled to simulate collisions with the sample new energy vehicles based on the simulated collision parameters, and the seat belt detection dataset and airbag detection dataset corresponding to the simulated collision parameters of the sample new energy vehicles are obtained. Based on the seat belt test dataset and airbag test dataset for each set of simulated collision parameters for the sample new energy vehicles, the coordinated performance of the seat belt and airbag for each set of simulated collision parameters for the sample new energy vehicles is determined.

3. The method for detecting seat belts and airbags in new energy vehicles according to claim 2, characterized in that, Based on the seat belt and airbag system parameter information of the new energy vehicle under test, the seat belt and airbag system parameter information of multiple sample new energy vehicles, and seat belt and airbag test data, multiple sets of collision simulation parameters for the new energy vehicle under test are determined, including: Based on the seat belt parameter information, airbag system parameter information, and seat belt and airbag test data of multiple sample new energy vehicles, the influence weight of each seat belt parameter and the influence weight of each airbag system parameter are determined. For any two sample new energy vehicles, based on the influence weight of each seat belt parameter and the influence weight of the airbag system parameter, the consistency coefficient of the seat belt and airbag parameters of the two sample new energy vehicles is calculated. Based on the consistency coefficient of seat belt and airbag parameters between any two sample new energy vehicles, a clustering algorithm is used to cluster multiple sample new energy vehicles to determine multiple clusters. For each cluster, based on the test data of seat belts and airbags of each sample new energy vehicle included in the cluster, calculate the performance difference coefficient of the cluster corresponding to each set of test simulation collision parameters, and determine the test simulation collision parameters corresponding to the cluster based on the performance difference coefficient of the cluster corresponding to each set of test simulation collision parameters. Based on the seat belt parameter information and airbag system parameter information of the new energy vehicle to be tested, as well as the influence weight of each seat belt parameter and airbag system parameter, the cluster matching the new energy vehicle to be tested is determined. According to the test simulation collision parameters corresponding to the cluster matching the new energy vehicle to be tested, multiple sets of collision simulation parameters of the new energy vehicle to be tested are determined.

4. The method for detecting seat belts and airbags in new energy vehicles according to claim 3, characterized in that, Based on seat belt parameter information, airbag system parameter information, and seat belt and airbag test data from multiple samples of new energy vehicles, the influence weight of each seat belt parameter and the influence weight of each airbag system parameter are determined, including: For each seat belt parameter, based on seat belt parameter information from multiple sample new energy vehicles and seat belt and airbag test data, the correlation coefficient between the seat belt parameter and the synergistic performance of the seat belt and airbag is calculated. For each airbag system parameter, based on the seat belt parameter information of multiple sample new energy vehicles and the test data of seat belts and airbags, the correlation coefficient between the airbag system parameters and the synergistic performance of seat belts and airbags is calculated. Based on the correlation coefficients between each seatbelt parameter and the synergistic performance of the seatbelt and airbag, as well as the correlation coefficients between each airbag system parameter and the synergistic performance of the seatbelt and airbag, the influence weights of each seatbelt parameter and each airbag system parameter are determined.

5. The method for detecting seat belts and airbags in new energy vehicles according to any one of claims 1-4, characterized in that, The collision test bench includes a fixed platform, an acceleration component, a braking component, and a vibration component. The new energy vehicle to be tested is fixed on the vibration component, the vibration component is mounted on the fixed platform, the fixed platform is mounted on the acceleration component, and the braking component is mounted on the fixed platform.

6. The method for detecting seat belts and airbags in new energy vehicles according to claim 5, characterized in that, The collision test bench, based on collision simulation parameters, simulates collisions with the new energy vehicle under test, including: Acceleration curves are generated based on collision simulation parameters; Vibration simulation parameters are generated based on acceleration curves and collision simulation parameters; The acceleration and braking components are controlled based on the acceleration curve to simulate the collision speed. Collision vibration simulation is performed based on vibration simulation parameters.

7. The method for detecting seat belts and airbags in new energy vehicles according to claim 6, characterized in that, Obtain the seatbelt detection dataset and airbag detection dataset for the corresponding collision parameters of the new energy vehicle to be tested, including: Based on the seat belt parameter information of the new energy vehicle to be tested, multiple seat belt tightening detection positions and multiple dummy acceleration detection positions are determined. Pressure detection devices were installed at multiple seat belt tension detection points; Acceleration detection devices were installed at multiple dummy acceleration detection locations; Seatbelt detection datasets were obtained during a simulated collision using pressure and acceleration detection devices. Airbag images are acquired during a simulated collision using an image acquisition device. The airbag detection dataset includes airbag images at multiple consecutive time points.

8. The method for detecting seat belts and airbags in new energy vehicles according to claim 7, characterized in that, Based on the seatbelt test dataset and airbag test dataset corresponding to each set of collision simulation parameters for the new energy vehicle under test, the coordinated performance evaluation of the seatbelt and airbag of the new energy vehicle under test is carried out, including: For each set of collision simulation parameters, based on the seat belt detection dataset and airbag detection dataset of the new energy vehicle to be tested corresponding to the collision simulation parameters, the scores of the seat belt and airbag of the new energy vehicle to be tested in multiple collaborative performance evaluation indicators are determined. Based on the scores of the seat belts and airbags of the new energy vehicle under test in multiple collaborative performance evaluation indicators corresponding to each set of collision simulation parameters, the collaborative performance of the seat belts and airbags of the new energy vehicle under test is evaluated.

9. The method for detecting seat belts and airbags in new energy vehicles according to claim 8, characterized in that, The collaborative performance evaluation indicators include at least the maximum displacement indicator; Determine the scores of the seat belts and airbags of the new energy vehicle under test for the corresponding collision simulation parameters on multiple collaborative performance evaluation indicators, including: Variational mode decomposition is performed on the dummy acceleration curves obtained by each acceleration detection device during the simulated collision to obtain multiple dummy acceleration mode components, and the features of each dummy acceleration mode component are extracted. Variational mode decomposition is performed on the acceleration curve to obtain multiple acceleration mode components, and the features of each acceleration mode component are extracted. Based on the characteristics of each dummy acceleration mode component corresponding to each acceleration detection device and the characteristics of each acceleration mode component of the acceleration curve, the maximum displacement of the dummy is determined; Based on the maximum displacement of the dummy, the scores of the seat belt and airbag of the new energy vehicle under test in terms of maximum displacement index are determined.

10. A seatbelt and airbag detection system for new energy vehicles, characterized in that, The method for testing seat belts and airbags in new energy vehicles according to any one of claims 1-9 includes: The information acquisition module is used to acquire seat belt parameter information, airbag system parameter information, and seat belt and airbag test data of multiple sample new energy vehicles. Among them, the seat belt and airbag test data includes the coordinated performance of seat belt and airbag corresponding to multiple sets of simulated collision parameters of sample new energy vehicles. It is also used to acquire seat belt parameter information and airbag system parameter information of the new energy vehicle to be tested. The parameter determination module is used to determine multiple sets of collision simulation parameters of the new energy vehicle under test based on the seat belt parameter information and airbag system parameter information of the new energy vehicle under test, the seat belt parameter information and airbag system parameter information of multiple sample new energy vehicles, and the test data of seat belt and airbag. The collision simulation parameters include at least the collision angle and the collision speed. The collision simulation module is used to control the collision test bench to simulate collisions with the new energy vehicle under test based on each set of collision simulation parameters, and to obtain the seat belt detection dataset and airbag detection dataset of the new energy vehicle under test corresponding to the collision simulation parameters. The performance analysis module is used to evaluate the coordinated performance of the seat belts and airbags of the new energy vehicle under test based on the seat belt test dataset and airbag test dataset corresponding to each set of collision simulation parameters.

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