Method and system for testing rollover stability of new energy vehicle

Abstract

The invention provides a rollover stability test method and system for a new energy vehicle, and relates to the technical field of vehicle detection, and the method comprises the steps: constructing static and dynamic rollover test scenes through the rollover scene backtracking learning of the new energy vehicle; constructing a finite element simulation model of the new energy vehicle; performing test parameter analysis on the static and dynamic rollover test scenes, constructing a static and dynamic scene test matrix, and loading the new energy vehicle finite element simulation model to execute a new energy vehicle rollover test to obtain static and dynamic scene test parameters; and performing multi-dimensional calculation evaluation on the static and dynamic scene test parameters based on the rollover stability evaluation index set, and determining a new energy vehicle rollover stability test result. The technical problem that the rollover risk in actual driving cannot be comprehensively simulated due to the fact that the prior art focuses on static scene testing and neglects complex factors under the dynamic driving condition is solved, and the accuracy, comprehensiveness and actual applicability of rollover stability testing are improved.

Description

Technical Field

[0001] This application relates to the field of vehicle testing technology, specifically to a method and system for testing the rollover stability of new energy vehicles. Background Technology

[0002] Due to their structural characteristics and center of gravity distribution, new energy vehicles are prone to rollover accidents when driving at high speeds, making sharp turns, or encountering uneven road surfaces, posing serious safety hazards to drivers and passengers. Rollover stability testing of new energy vehicles has gradually become a crucial aspect of ensuring driving safety.

[0003] Currently, rollover stability testing methods for new energy vehicles primarily assess rollover risk through static roll stability bench tests or theoretical calculation models. These methods calculate vehicle stability under different static conditions based on static parameters such as vehicle geometry and center of gravity height. However, static testing scenarios neglect dynamic factors such as vehicle speed, acceleration, steering angle, and road surface type, which significantly impact vehicle stability in real-world driving. Because these complex driving conditions cannot be simulated, test results cannot accurately reflect the rollover risk in actual use, leading to an incomplete safety assessment of new energy vehicles and failing to provide sufficient basis for design improvements. Summary of the Invention

[0004] This application provides a rollover stability test method and system for new energy vehicles, which solves the technical problem that existing technologies, which focus on static scenario testing and neglect complex factors under dynamic driving conditions, cannot fully simulate the rollover risk in actual driving. This achieves the technical effect of improving the accuracy, comprehensiveness and practical applicability of rollover stability testing.

[0005] In view of the above problems, this application provides a method for testing the rollover stability of new energy vehicles. The method includes: selecting a new energy vehicle to be tested; performing rollover scenario backtracking learning based on the new energy vehicle to be tested to construct N static rollover test scenarios and M dynamic rollover test scenarios; performing finite element analysis optimization based on the structural parameter information and material property information of the new energy vehicle to be tested to generate a finite element simulation model of the new energy vehicle; analyzing the test parameters of the N static rollover test scenarios and M dynamic rollover test scenarios to construct Q static scenario test matrices and Q dynamic scenario test matrices; using the Q static scenario test matrices and Q dynamic scenario test matrices, loading the finite element simulation model of the new energy vehicle and sequentially executing the rollover test of the new energy vehicle to obtain Q static scenario test parameters and Q dynamic scenario test parameters; obtaining a rollover stability evaluation index set; performing multi-dimensional calculation and evaluation on the Q static scenario test parameters and Q dynamic scenario test parameters based on the rollover stability evaluation index set to determine the rollover stability test result of the new energy vehicle.

[0006] On the other hand, this application also provides a rollover stability testing system for new energy vehicles. The system includes: a rollover test scenario construction module, used to select a new energy vehicle to be tested, perform rollover scenario backtracking learning based on the new energy vehicle to be tested, and construct N static rollover test scenarios and M dynamic rollover test scenarios; a finite element analysis module, used to perform finite element analysis optimization based on the structural parameter information and material property information of the new energy vehicle to be tested, and generate a finite element simulation model of the new energy vehicle; a test parameter parsing module, used to parse the test parameters of the N static rollover test scenarios and M dynamic rollover test scenarios, and construct Q static scenario test matrices and Q dynamic scenario test matrices; a rollover test module, used to load the new energy vehicle finite element simulation model with the Q static scenario test matrices and Q dynamic scenario test matrices and sequentially execute the new energy vehicle rollover test, and obtain Q static scenario test parameters and Q dynamic scenario test parameters; and a test result calculation module, used to obtain a rollover stability evaluation index set, perform multi-dimensional calculation and evaluation on the Q static scenario test parameters and Q dynamic scenario test parameters based on the rollover stability evaluation index set, and determine the rollover stability test result of the new energy vehicle.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages: By backtracking through rollover scenarios, diverse test scenarios are constructed, including both static and dynamic conditions. This approach comprehensively covers the various complex situations that new energy vehicles may encounter in actual use, providing a more realistic scenario foundation for subsequent testing. Using finite element analysis (FEM) technology, a high-precision simulation model is generated based on the actual structural parameters and material properties of the new energy vehicle. The FEM model can accurately simulate the stress distribution, deformation, and stress state of various components during a rollover. Optimizing the model ensures the accuracy and reliability of test results while reducing destructive testing on actual vehicles, lowering testing costs and risks. The constructed test scenarios are further refined by analyzing the test parameters of each scenario to generate specific test matrices. These test matrices provide clear input conditions for subsequent simulation tests, ensuring that each scenario can be accurately simulated and evaluated. This method allows for systematic analysis of vehicle rollover stability under different scenarios, providing data support for subsequent multi-dimensional evaluation. Using the finite element simulation model and the parameters in the test matrices, rollover tests are executed sequentially. Through simulation testing, detailed test parameters of the vehicle under different scenarios can be obtained. These parameters are key data for evaluating vehicle rollover stability, providing a foundation for subsequent multidimensional calculations and evaluations. Multidimensional calculations and evaluations analyze the test parameters by integrating multiple evaluation indicators. Multidimensional evaluation can more comprehensively reflect the vehicle's rollover stability, avoiding misjudgments due to the limitations of a single indicator. In this way, the rollover stability test results of new energy vehicles can be accurately determined, providing a scientific basis for vehicle design optimization and safety assessment.

[0008] In summary, this application constructs diverse static and dynamic test scenarios through backtracking learning of rollover scenarios, optimizes and generates high-precision simulation models using finite element analysis, systematically executes simulation tests through a test matrix, and finally utilizes multi-dimensional calculations to evaluate and determine the rollover stability test results. This approach significantly improves the accuracy, comprehensiveness, and practical applicability of rollover stability testing. Through comprehensive evaluation of multiple scenarios and indicators, it can more realistically simulate the rollover risk of new energy vehicles in actual driving, providing strong support for vehicle safety design and improvement.

[0009] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0010] Figure 1 This is a flowchart illustrating a rollover stability test method for a new energy vehicle provided in an embodiment of this application.

[0011] Figure 2 This is a schematic diagram illustrating the process of constructing Q static scenario test matrices and Q dynamic scenario test matrices in a rollover stability test method for a new energy vehicle provided in an embodiment of this application.

[0012] Figure 3 This is a flowchart illustrating the process of determining the rollover stability test results of a new energy vehicle in a rollover stability test method provided in this application embodiment.

[0013] Figure 4 This is a schematic diagram of the structure of a rollover stability testing system for a new energy vehicle provided in an embodiment of this application.

[0014] Figure labeling: Side rollover test scenario construction module 10, finite element analysis module 20, test parameter analysis module 30, side rollover test module 40, test result calculation module 50. Detailed Implementation

[0015] This application provides a rollover stability testing method and system for new energy vehicles, which solves the technical problem that existing technologies, which focus on static scenario testing and neglect complex factors under dynamic driving conditions, cannot fully simulate the rollover risk in actual driving. This achieves the technical effect of improving the accuracy, comprehensiveness, and practical applicability of rollover stability testing.

[0016] Example 1, as Figure 1 As shown in the figure, this application provides a method for testing the rollover stability of a new energy vehicle, the method comprising: Step S1: Select the new energy vehicle to be tested, and perform rollover scenario backtracking learning based on the new energy vehicle to be tested to construct N static rollover test scenarios and M dynamic rollover test scenarios.

[0017] Specifically, the new energy vehicle to be tested can be any specific new energy vehicle that needs to undergo rollover stability testing. A static rollover test scenario refers to a rollover test when the new energy vehicle is in a relatively stationary state. A dynamic rollover test scenario simulates a rollover of a new energy vehicle while it is in motion.

[0018] First, identify the new energy vehicle to be tested. Then, collect rollover-related data from similar models or existing data on this model, including accident data and simulation test data. Use a backtracking algorithm to learn from rollover scenarios, constructing N static rollover test scenarios and M dynamic rollover test scenarios. Here, N and M are variable positive integers representing the number of test scenarios. For example, for constructing static rollover test scenarios, different tilt angles and support point positions can be set based on factors such as the vehicle's geometry and center of gravity. Dynamic rollover test scenarios consider variables such as speed, acceleration, and steering angle, using vehicle dynamics simulation software to construct different dynamic scenarios.

[0019] By constructing various static and dynamic rollover test scenarios, we can more comprehensively cover the rollover situations that new energy vehicles may encounter in actual use, improve the comprehensiveness of rollover testing, and thus provide a scenario basis for subsequent accurate assessment of vehicle rollover stability.

[0020] Step S2: Based on the structural parameter information and material property information of the new energy vehicle to be tested, perform finite element analysis and optimization to generate a finite element simulation model of the new energy vehicle.

[0021] Specifically, the structural parameters and material properties of the new energy vehicle under test are collected through vehicle design drawings and material testing reports. Structural parameters include data related to the vehicle's structure, such as frame dimensions, wheelbase, track width, and weight distribution. Material properties refer to the characteristics of the various materials constituting the new energy vehicle, such as the strength and elastic modulus of the frame metal materials, and the hardness and toughness of the body plastic parts. Then, using finite element analysis software (such as ANSYS and ABAQUS), the structure of the new energy vehicle is divided into multiple finite elements according to certain rules, and corresponding mechanical properties are set for each element based on the material properties. By loading the entire model (e.g., applying forces and torques in different directions) and solving the model, the stress and strain of the model are analyzed, and the model is optimized based on the analysis results to more accurately reflect the performance of the actual new energy vehicle. For example, if excessive stress concentration is found in a certain part when simulating frame stress, the structure or material of that part can be adjusted to optimize the model. Through finite element analysis optimization, a finite element simulation model of the new energy vehicle is generated to simulate the mechanical behavior of the new energy vehicle under various working conditions.

[0022] The finite element simulation model generated based on the vehicle's own structure and material properties can more accurately reflect the physical characteristics of the tested new energy vehicle compared to a general model, providing a more realistic simulation object for subsequent rollover tests and improving the accuracy of rollover tests.

[0023] Step S3: Analyze the test parameters for the N static rollover test scenarios and M dynamic rollover test scenarios, and construct Q static scenario test matrices and Q dynamic scenario test matrices.

[0024] Specifically, for each static rollover test scenario, the relevant test parameters are analyzed, such as roll angle, load distribution, road conditions, and suspension settings. These parameters are arranged into a matrix, and values ​​are assigned to the matrix according to different parameter values, resulting in Q static scenario test matrices. For dynamic scenario test matrices, dynamic parameters such as vehicle speed, steering input, road conditions, crosswind, and vehicle status are considered. Similarly, data processing tools (such as Excel spreadsheets or specialized matrix processing software) are used to organize these parameters into a matrix form, and values ​​are assigned to the matrix according to different parameter values, resulting in Q dynamic scenario test matrices. By constructing scenario test matrices, the management of test parameters becomes more standardized and orderly, which helps to improve the repeatability of tests and the reliability of results.

[0025] Step S4: Using the Q static scene test matrices and Q dynamic scene test matrices, load the new energy vehicle finite element simulation model and perform the new energy vehicle rollover test sequentially to obtain Q static scene test parameters and Q dynamic scene test parameters.

[0026] Specifically, using simulation testing software (such as LS-DYNA), the parameters from the constructed Q static scenario test matrices and Q dynamic scenario test matrices are sequentially input into the finite element simulation model of the new energy vehicle. In each scenario, the model is operated according to the parameter settings in the matrix, for example, simulating the driving actions of a new energy vehicle according to set parameters such as speed and steering angle. Then, the model's performance in rollover tests is observed, and the stress, strain, displacement, and other relevant parameters of the model in each scenario are recorded as the Q static scenario test parameters and Q dynamic scenario test parameters. Each static or dynamic scenario test matrix represents a possible rollover situation in actual driving and corresponds to one static or dynamic scenario test parameter.

[0027] By loading the simulation model according to the matrix parameters and conducting rollover tests, test parameters for different scenarios can be obtained. These parameters are important bases for evaluating rollover stability and provide data support for comprehensively evaluating the rollover stability of new energy vehicles.

[0028] Step S5: Obtain the rollover stability evaluation index set, and perform multi-dimensional calculation and evaluation on the Q static scenario test parameters and Q dynamic scenario test parameters based on the rollover stability evaluation index set to determine the rollover stability test results of the new energy vehicle.

[0029] Specifically, the rollover stability assessment index set is a collection of multiple indicators used to evaluate the rollover stability of new energy vehicles. These indicators may cover multiple aspects, including the vehicle's physical characteristics (such as center of gravity height and wheelbase), driving conditions (such as vehicle speed and road conditions), and operational factors (such as steering). First, the rollover stability assessment index set is determined, which can be based on industry standards, previous research findings, or practical experience. Then, mathematical calculation software (such as Matlab) is used to perform multi-dimensional calculations on the acquired Q static scenario test parameters and Q dynamic scenario test parameters according to the requirements of the assessment index set. For example, the relationship between the rollover critical speed and the actual test speed under different scenarios, and the relationship between the center of gravity offset and the rollover angle, are calculated. By synthesizing these calculation results, the rollover stability of new energy vehicles is evaluated, and the rollover stability test results are determined, such as the stability level, the areas affecting stability, and possible safety improvement suggestions.

[0030] Through multi-dimensional calculation and evaluation, the rollover stability of new energy vehicles under different test conditions can be accurately determined, so as to make targeted improvements to the vehicles and thus improve their safety.

[0031] Furthermore, step S1 includes: Step S11: Mining and obtaining a dataset of new energy vehicle rollover accidents, extracting rollover factors from the dataset to obtain a set of key factors for new energy vehicle rollovers.

[0032] Step S12: Based on the set of key factors for new energy vehicle rollover, perform a backtracking of related scenarios on the dataset of new energy vehicle rollover accidents to obtain a knowledge base of new energy vehicle rollover factor scenarios.

[0033] Step S13: Determine the static rollover test targets and dynamic rollover test targets for new energy vehicles.

[0034] Step S14: Based on the knowledge base of new energy vehicle rollover factors and scenarios, perform factor scenario association design for the static rollover test target and the dynamic rollover test target of new energy vehicles respectively, and construct the N static rollover test scenarios and M dynamic rollover test scenarios.

[0035] Specifically, the new energy vehicle rollover accident dataset is a collection containing information related to multiple rollover accidents involving new energy vehicles, such as the time, location, vehicle condition, and road conditions of the accident. Rollover accident datasets of the same model of new energy vehicle being tested are collected from accident reports, vehicle records, and on-site investigation records. Then, data analysis algorithms (such as decision tree algorithms and association rule mining) are used to analyze the dataset, identifying various factors that may lead to rollovers and generating a set of key factors for new energy vehicle rollovers. These factors include vehicle-related factors (such as vehicle structure and tire condition), environmental factors (such as road conditions and weather), and human factors (such as driver operation). For example, the decision tree algorithm can filter out important factors based on the relationship between different factors in the accident dataset and the rollover outcome, thus obtaining the set of key factors for new energy vehicle rollovers. By mining the accident dataset and extracting key factors, we can focus on factors that have a significant impact on rollovers, providing a foundation for building a more targeted rollover scenario knowledge base and helping to improve the accuracy and effectiveness of rollover testing scenarios.

[0036] Based on a set of key factors for new energy vehicle rollovers, a backtracking algorithm is used to perform contextual backtracking on a new energy vehicle rollover accident dataset. This involves searching the existing accident dataset for scenario information related to each key factor. The specific backtracking process is as follows: One or more initial conditions (e.g., vehicle speed and road type) are selected from the accident dataset. Then, other factors that may affect the occurrence of the accident, such as load and driver operation, are recursively analyzed. During the backtracking process, whenever a factor is selected, the algorithm checks whether the currently selected factor meets preset conditions (e.g., whether the vehicle speed is too high, whether the road is slippery, etc.). If the currently selected factor violates the constraints, the algorithm backtracks to the previous step and selects other possible factors. Through step-by-step backtracking, it is deduced which combinations of factors are most likely to cause a rollover. For example, if the key factor is overloading, all accident scenario records related to overloading are searched, including the road conditions, vehicle speed, and driver operation at the time. This scenario knowledge is stored and managed using a database management system (such as MySQL), and this information is organized into a new energy vehicle rollover factor scenario knowledge base. This knowledge base contains various rollover factors and related rollover scenarios, providing rich reference materials for building rollover test scenarios, making the constructed test scenarios closer to actual rollover situations, and improving the realism of rollover tests.

[0037] The static and dynamic rollover test objectives for new energy vehicles are determined based on their usage characteristics, industry standards, and safety requirements. The static rollover test objective refers to the expected outcome of a rollover test conducted on a stationary vehicle; the dynamic rollover test objective refers to the expected outcome of a rollover test conducted while the vehicle is in motion. For example, referring to the design specifications of new energy vehicles, the static rollover test objective is to measure the tilt angle at which the vehicle will roll over; based on actual road conditions, the dynamic rollover test objective is set to the rollover situation at different speeds (e.g., 20 km / h, 30 km / h) and different steering angles (e.g., 30°, 45°). Clearly defining the static and dynamic rollover test objectives provides direction for subsequently constructing specific test scenarios based on a knowledge base, ensuring that the constructed test scenarios meet testing requirements and contributing to improved relevance and effectiveness of rollover testing.

[0038] For static rollover testing of new energy vehicles, factors related to static testing are selected from the knowledge base of rollover factor scenarios, such as the vehicle's center of gravity position and support point conditions when stationary. Different static rollover test scenarios are designed according to the requirements of the test objective. For example, if the static rollover test objective is to determine the rollover situation at different center of gravity heights, scenario factors related to center of gravity height are selected from the knowledge base, such as the center of gravity height corresponding to different cargo loading methods, to construct multiple static rollover test scenarios with different center of gravity heights. For dynamic rollover testing, factors related to dynamic driving are selected, such as different vehicle speeds, steering operations, and road conditions, and dynamic rollover test scenarios are constructed by combining them with scenario information from the knowledge base. Scenario construction software (such as dynamic simulation software like ADAMS) can be used to assist in constructing these scenarios.

[0039] By linking factors in the knowledge base with the test objectives to construct test scenarios, it is possible to ensure that the N static rollover test scenarios and M dynamic rollover test scenarios constructed not only conform to actual rollover situations but also meet the requirements of the test objectives. This improves the scientificity and rationality of rollover test scenario construction and provides a reliable scenario foundation for subsequent rollover tests.

[0040] Furthermore, such as Figure 2 As shown, step S3 includes: Step S31: Determine the test parameters for static rollover scenarios and dynamic rollover scenarios based on the knowledge base of new energy vehicle rollover factor scenarios.

[0041] Step S32: Construct an initial matrix for static rollover testing by using the N static rollover test scenarios as rows and the static rollover test parameters as columns.

[0042] Step S33: Construct an initial matrix for dynamic rollover testing by using the M dynamic rollover test scenarios as rows and the dynamic rollover test parameters as columns.

[0043] Step S34: Based on the new energy vehicle rollover accident dataset, analyze the parameter distribution intervals of the N static rollover test scenarios and the M dynamic rollover test scenarios to obtain the parameter intervals of the N static rollover scenarios and the parameter intervals of the M dynamic rollover scenarios.

[0044] Step S35: Using the N static rollover scenario parameter ranges and M dynamic rollover scenario parameter ranges as value constraints, assign parameter values ​​to the initial static rollover test matrix and the initial dynamic rollover test matrix to construct Q static scenario test matrices and Q dynamic scenario test matrices.

[0045] Specifically, this involves in-depth analysis of the knowledge base on rollover factors in new energy vehicles. For static rollover scenarios, based on the knowledge base's stored information about rollovers while the vehicle is stationary, test parameters such as tilt angle, load distribution, road conditions, and suspension settings are determined. For dynamic rollover scenarios, based on the knowledge base's information about rollovers during vehicle operation, test parameters such as vehicle stability factors at different speeds and vehicle dynamic responses to different steering operations are determined. For example, data on the center of gravity shift when the vehicle turns at different speeds is obtained from the knowledge base to determine dynamic rollover scenario test parameters such as vehicle speed and steering input.

[0046] Using N static rollover test scenarios as row elements and previously determined static rollover scenario test parameters as column elements, an initial static rollover test matrix is ​​constructed using spreadsheet software (such as Excel) or a dedicated matrix construction tool (such as matrix construction functions in Matlab). For example, if there are 3 static rollover test scenarios (N=3) and 4 static rollover scenario test parameters are determined (such as tilt angle, load distribution, road surface conditions, and suspension settings), a 3x4 initial matrix is ​​constructed, where each row corresponds to one test scenario and each column corresponds to one test parameter. This initial matrix is ​​an empty matrix, which initially organizes the relationship between scenarios and related parameters, but does not contain specific element values.

[0047] A method similar to that used for constructing the initial matrix for static rollover testing is employed. The M dynamic rollover test scenarios are used as rows of a matrix, and the determined dynamic rollover scenario test parameters are used as columns to construct the initial matrix for dynamic rollover testing. For example, if there are 5 dynamic rollover test scenarios (M=5) and 5 determined dynamic rollover scenario test parameters (such as vehicle speed, steering input, road conditions, crosswind, and vehicle state), a 5x5 matrix for dynamic rollover testing can be constructed using a matrix building tool. This initial matrix for dynamic rollover testing organizes the relationship between the dynamic rollover test scenarios and their corresponding test parameters, but does not contain specific element values.

[0048] For N static rollover test scenarios, a dataset of new energy vehicle rollover accidents is analyzed to examine the actual values ​​of various parameters under similar static scenarios. The minimum and maximum values ​​of each parameter are statistically analyzed to determine the value ranges for the parameters in the N static rollover scenarios, i.e., the N static rollover scenario parameter ranges. For M dynamic rollover test scenarios, the same accident dataset is used to analyze the value ranges of parameters under different driving conditions to obtain the M dynamic rollover scenario parameter ranges. By analyzing the parameter distribution ranges, a reasonable range of parameter values ​​for each test scenario can be determined, avoiding the blind selection of parameter values. This provides constraints for subsequent matrix parameter assignment, ensuring that the parameter combinations in the constructed test matrix better reflect actual rollover situations and improving the scientific rigor and practicality of the test matrix.

[0049] For the initial matrix of static rollover tests, N static rollover scenario parameter ranges are used as constraints for parameter assignment. Values ​​are assigned to each parameter for each scenario, resulting in Q static scenario test matrices. Similarly, for the initial matrix of dynamic rollover tests, M dynamic rollover scenario parameter ranges are used as constraints for parameter assignment. Values ​​are assigned to each parameter for each dynamic scenario, resulting in Q dynamic scenario test matrices. These static and dynamic scenario test matrices are matrices after parameter assignment, with each element representing the test conditions under a specific static or dynamic scenario. Constructing Q static and Q dynamic scenario test matrices using parameter ranges as constraints ensures that the parameter combinations for each scenario are unique and consistent with actual conditions, comprehensively covering possible rollover situations during actual driving. This improves the accuracy and effectiveness of the test matrices, providing a reliable data foundation for subsequent accurate evaluation of the rollover stability of new energy vehicles.

[0050] Furthermore, step S35 includes: Step S351: Using the N static rollover scenario parameter ranges as value constraints, randomly assign values ​​to the initial static rollover test matrix Q times to obtain Q static rollover test matrices.

[0051] Step S352: Using the M dynamic rollover scenario parameter ranges as value constraints, randomly assign values ​​to the initial dynamic rollover test matrix Q times to obtain Q dynamic rollover test matrices.

[0052] Step S353: Perform significance tests and corrections on the Q static rollover test matrices and the Q dynamic rollover test matrices respectively to obtain Q static scene test matrices and Q dynamic scene test matrices.

[0053] Specifically, during the matrix assignment process, for each element in the initial static rollover test matrix (corresponding to each test parameter in each scenario), a random number is generated within the specified range using a random number generation algorithm, with the parameter range corresponding to one of the N static rollover scenario parameter ranges as the value constraint. This process is repeated for each static rollover test scenario, performing one assignment before proceeding to the next, for a total of Q assignments, resulting in Q static rollover test matrices. Q is a user-defined positive integer representing the number of assignments; the larger Q is, the more parameter combinations are generated, and the more rollover scenarios are covered.

[0054] Similarly, based on the parameter range of the dynamic rollover scenario, a random number generation algorithm is used to randomly assign values ​​to each element in the initial dynamic rollover test matrix. For example, for the dynamic rollover scenario test parameter of vehicle speed, if its value range is (10km / h, 50km / h), a speed value within this range is randomly generated and assigned to the corresponding matrix element. After Q assignment processes, Q dynamic rollover test matrices are obtained. By randomly assigning values, multiple different static and dynamic rollover test matrices can be quickly generated while satisfying the value constraints. This randomness helps to cover more possible parameter combinations, thereby more comprehensively simulating different static rollover scenarios and providing a diverse data foundation for subsequent tests.

[0055] Significance tests were performed on the Q static rollover test matrices and the Q dynamic rollover test matrices respectively. This involved determining whether the parameter values ​​in the matrices significantly affected the rollover results. If the effect of certain parameter combinations was insignificant or did not reflect reality, corrections were made, ultimately resulting in the Q static scenario test matrices and the Q dynamic scenario test matrices. These significance tests ensured that the parameter values ​​in the Q static and Q dynamic scenario test matrices were more reasonable and effective, avoiding interference from unreasonable parameter combinations on the rollover test results. This improved the accuracy of the test matrices in simulating actual rollover situations, thus providing more reliable data for subsequent accurate assessments of the rollover stability of new energy vehicles.

[0056] Furthermore, step S353 includes: Step S353-1: Perform significance tests on the Q static rollover test matrices and the Q dynamic rollover test matrices respectively to obtain the significance coefficients of the Q static matrices and the significance coefficients of the Q dynamic matrices.

[0057] Step S353-2: Set a test scenario significance threshold, and filter the significance coefficients of the Q static matrices and the Q dynamic matrices based on the test scenario significance threshold to obtain a set of static matrices to be corrected and a set of dynamic matrices to be corrected whose matrix significance coefficients are less than the test scenario significance threshold.

[0058] Step S353-3: Based on the value constraints, iteratively optimize and assign values ​​to the static matrix set and the dynamic matrix set to be corrected to obtain the static optimization matrix set and the dynamic optimization matrix set.

[0059] Step S353-4: Based on the static optimization matrix set and the dynamic optimization matrix set, correct and update the Q static rollover test matrices and the Q dynamic rollover test matrices to obtain the Q static scene test matrices and the Q dynamic scene test matrices.

[0060] Specifically, for each static rollover test matrix, the dynamic rollover scenario parameters in the matrix are treated as independent variables, and the rollover result as the dependent variable. Statistical methods are used for significance testing, such as multiple linear regression analysis or the method of variance. For example, in Python, the correlation functions in the statsmodels library can be used to perform multiple linear regression analysis and calculate the static matrix significance coefficient for each static rollover test matrix. For the dynamic rollover test matrix, the dynamic rollover scenario parameters (such as vehicle speed, steering input, etc.) are also treated as independent variables, and the rollover result as the dependent variable. Statistical analysis methods are used to calculate the dynamic matrix significance coefficient. These significance coefficients reflect the degree of significance of the parameters in each test matrix on the rollover result, providing a basis for subsequent matrix selection and optimization, thereby improving the effectiveness and accuracy of the test matrix and making the test results more reflective of actual rollover situations.

[0061] A significance threshold for the test scenario is set. This threshold is a pre-defined standard value used to determine whether the significance coefficients of the matrices meet the requirements. Then, the significance coefficients of each static and dynamic matrix are compared with this threshold. If a matrix significance coefficient is less than the threshold, it indicates that the parameters in the matrix do not significantly affect the rollover results, meaning the test scenario represented by the matrix is ​​not significantly different from other scenarios and needs correction. In this case, static rollover test matrices with significance coefficients less than the threshold are added to the set of static matrices to be corrected; dynamic rollover test matrices with significance coefficients less than the threshold are added to the set of dynamic matrices to be corrected. These sets of static and dynamic matrices to be corrected require further optimization to improve their significance in affecting the rollover results. By setting thresholds to filter out the sets of matrices that need correction, we can focus on test matrices with insufficient representativeness of the test scenarios, improving the targeting of subsequent optimization processes. This ensures that the final test matrices can more effectively simulate different rollover scenarios and cover as many test scenarios as possible, improving the universality and reliability of the test results.

[0062] For each matrix in the set of static matrices to be corrected, the parameters in the matrix are reassigned according to the parameter range (value constraints) of the static rollover scenario. For example, for the load distribution parameter in a certain matrix, if the previous assignment resulted in its insignificant impact on the rollover result, a more reasonable value is determined based on the actual situation and the value range. This reassignment process requires multiple iterations, and after each assignment, a significance test is performed again until the matrix significance coefficient is greater than or equal to the significance threshold of the test scenario. For the set of dynamic matrices to be corrected, iterative optimization assignment is also performed according to the parameter range of the dynamic rollover scenario. The optimized static and dynamic matrix sets replace the corresponding parts of the original Q static and Q dynamic rollover test matrices to obtain more accurate Q static and Q dynamic scenario test matrices. The parameters in these matrices have a higher significance impact on the rollover result and can more comprehensively and accurately simulate various static and dynamic rollover scenarios.

[0063] Furthermore, such as Figure 3 As shown, step S5 includes: Step S51: Extract static correlation indicators and dynamic correlation indicators from the rollover stability assessment indicator set to obtain the static stability assessment indicator set and the dynamic stability assessment indicator set.

[0064] Step S52: Select the set of static stability index calculation formulas and the set of dynamic stability index calculation formulas based on the set of static stability evaluation indexes and the set of dynamic stability evaluation indexes.

[0065] Step S53: Use the set of static stability index calculation formulas and the set of dynamic stability index calculation formulas to perform multi-dimensional index calculations on the Q static scene test parameters and the Q dynamic scene test parameters to obtain the set of static scene stability index parameters and the set of dynamic scene stability index parameters.

[0066] Step S54: Analyze and evaluate the static scene stability index parameter set and the dynamic scene stability index parameter set to determine the rollover stability test results of the new energy vehicle.

[0067] Specifically, indicators related to static rollover scenarios are selected from the rollover stability assessment indicator set to form a static stability assessment indicator set. Examples include vehicle weight, wheel load distribution, and center of gravity height. Simultaneously, indicators related to dynamic rollover scenarios are selected to form a dynamic stability assessment indicator set. Examples include vehicle speed, steering angular velocity, and lateral acceleration. By extracting static and dynamic correlation indicators separately, the rollover stability assessment indicator set can be refined into indicator sets specifically for static and dynamic conditions. This makes subsequent calculations and assessments more targeted, improves the accuracy of assessment results, and more accurately reflects the rollover stability of new energy vehicles under different conditions.

[0068] The static stability index calculation formula set is a collection of formulas used to calculate various indicators in the static stability assessment index set. These formulas are based on physical principles and mathematical models and are used to quantify each static stability index. The dynamic stability index calculation formula set is a collection of formulas used to calculate various indicators in the dynamic stability assessment index set, established based on principles such as vehicle dynamics. The static stability index calculation formula set is determined based on the content of the static stability assessment index set. For example, if the static stability assessment index set includes the center of gravity height indicator, based on static principles, a formula related to the center of gravity height for calculating vehicle static stability is selected (such as the formula based on torque balance: S=m*g*h). cg / L, where m is the vehicle weight, g is the acceleration due to gravity, and h cg (where L is the center of gravity height and L is the wheelbase). For the dynamic stability assessment index set, the corresponding calculation formulas are determined based on the dynamic stability indices, and these formulas are combined into a dynamic stability index calculation formula set. For example, for lateral acceleration in the dynamic index, according to vehicle dynamics formulas, lateral acceleration is related to factors such as vehicle speed and turning radius (e.g., a=v). 2 / r, where a is the lateral acceleration, v is the vehicle speed, and r is the turning radius. The formula determination process can refer to relevant engineering mechanics books, vehicle engineering handbooks, and other materials, or it can be derived and verified using professional engineering analysis software. Selecting an appropriate set of calculation formulas can quantify static and dynamic stability assessment indicators, providing accurate calculation methods for subsequent multi-dimensional indicator calculations, ensuring that the calculation results truly reflect the rollover stability of new energy vehicles, and improving the scientific rigor and reliability of the assessment.

[0069] For Q static scenario test parameters, each parameter is substituted into the corresponding formula in the static stability index calculation formula set to calculate static scenario stability index parameters such as the stability coefficient, forming a static scenario stability index parameter set. The parameters in this static scenario stability index parameter set reflect the static rollover stability of new energy vehicles from different dimensions. For Q dynamic scenario test parameters, dynamic scenario stability index parameters such as the dynamic stability margin are calculated according to the dynamic stability index calculation formula set, resulting in a dynamic scenario stability index parameter set. This dynamic scenario stability index parameter set is used to describe the dynamic rollover stability of new energy vehicles. Through multi-dimensional index calculation, the rollover stability of new energy vehicles in both static and dynamic scenarios can be comprehensively evaluated. The index parameter set obtained from multiple dimensions can describe the stability characteristics of the vehicle in more detail, providing rich data support for accurately judging the rollover risk of new energy vehicles.

[0070] The stability index parameter sets for static and dynamic scenarios are analyzed and evaluated. The stability indices calculated under static and dynamic scenarios are compared to assess rollover stability in each scenario and determine the rollover stability test results for new energy vehicles. For example, comparing the stability coefficients under different static scenarios, a lower stability coefficient in a certain scenario indicates a higher static rollover risk for the vehicle in that scenario. The dynamic scenario stability index parameter set is analyzed similarly. For instance, observing the changes in dynamic stability margin under different dynamic scenarios, if the dynamic stability margin approaches a critical value in certain scenarios, then the dynamic rollover risk of the vehicle is greater in these scenarios.

[0071] By comprehensively evaluating stability indicators in both static and dynamic scenarios, the rollover stability of new energy vehicles under different conditions can be accurately determined, their compliance with safety standards can be assessed, and the comprehensiveness, reliability, and accuracy of test results can be ensured.

[0072] Furthermore, step S54 includes: Step S541: Establish a rollover stability level assessment system, and evaluate the static scenario stability index parameter set and the dynamic scenario stability index parameter set based on the rollover stability level assessment system to obtain the rollover stability level of the new energy vehicle.

[0073] Step S542: Based on the static scene stability index parameter set and the dynamic scene stability index parameter set, perform weakness identification analysis on the new energy vehicle under test to obtain multiple stability weakness identification areas.

[0074] Step S543: Based on the rollover stability level of the new energy vehicle and the multiple stability weakness identification areas, determine the rollover stability test result of the new energy vehicle.

[0075] Specifically, the rollover stability rating system is a pre-constructed standard system for measuring the rollover stability of new energy vehicles. It categorizes rollover stability into different levels based on static and dynamic stability parameters, such as high, medium, and low stability, each corresponding to a specific range of parameter values. Several factors need to be considered when building this rating system. First, the range of values ​​for each parameter under different stability levels is determined based on industry experience, relevant standards, and extensive experimental data. Then, the parameters in the static and dynamic stability parameter sets are compared with this rating system to determine the range of each parameter's level. Finally, the rollover stability rating of the new energy vehicle is determined by combining the ratings of all parameters (including static and dynamic stability parameters) using threshold judgment, weighted average methods, etc. This rollover stability rating represents the approximate level of rollover stability of the new energy vehicle. By establishing a rollover stability rating system to evaluate new energy vehicles, the complex set of parameter values ​​can be transformed into an intuitive stability rating, facilitating the understanding and comparison of rollover stability under different scenarios.

[0076] Then, based on the static and dynamic stability index parameter sets, data analysis software (such as SPSS) is used to conduct detailed statistical analysis of the parameter sets to identify areas corresponding to parameters that deviate significantly from the overall stability index. These areas represent unstable factors or weak points in the new energy vehicle's test scenarios, i.e., weak point areas. These identified weak point areas are marked to determine stability weakness marker areas. These weaknesses involve factors such as center of gravity distribution, speed limits, and steering angles. For example, if the force distribution at a support point of the vehicle deviates significantly from the theoretically optimal force distribution under static conditions, then the area where this support point is located may be a stability weakness marker area. During dynamic driving, if an abnormally large lateral acceleration is found at a certain steering angle, it means that the vehicle has a stability weakness in the area related to this steering operation (such as the suspension system, tire-ground contact, etc.). These areas provide specific target areas for vehicle design improvement, manufacturing process optimization, or the development of targeted safety measures, helping to improve the overall rollover stability of the vehicle.

[0077] The overall rollover stability of a new energy vehicle is determined by its rollover stability rating. Then, by combining information from multiple stability weakness marker areas, a detailed description is provided of the areas where the vehicle is prone to rollover problems, resulting in the final rollover stability test results. For example, a low rollover stability rating indicates a high risk of rollover. Since the stability weakness marker areas include the rear suspension system and high-speed cornering scenarios, the test result can be stated as: the new energy vehicle has low rollover stability, with the main risk area being the rear suspension system, and the risk of rollover increases during high-speed cornering. Report templates can be used to compile this information into a standardized test result report, facilitating the accurate communication of test information to relevant personnel (such as vehicle manufacturers and regulatory authorities). By combining the rollover stability rating and stability weakness marker areas to determine the test results, comprehensive and targeted rollover stability assessment information for new energy vehicles can be provided.

[0078] In summary, the rollover stability test method for new energy vehicles provided in this application has the following technical effects: This application's embodiments combine backtracking learning from rollover scenarios with multi-scenario testing to establish a more comprehensive and representative rollover test model and conduct multi-dimensional evaluation, comprehensively and systematically testing the rollover stability of new energy vehicles. By constructing static and dynamic test scenarios, the rollover risks of new energy vehicles under various real-world driving conditions can be fully simulated. Finite element analysis optimizes the structure of new energy vehicles, providing an accurate physical model for simulation testing and ensuring more realistic and reliable test results. Through simulation and data analysis of numerous static and dynamic test scenarios, multi-dimensional calculations of rollover stability assessment are achieved, comprehensively improving the accuracy, comprehensiveness, and practical applicability of the test, and providing a more scientific and accurate basis for the safety design and risk assessment of new energy vehicles.

[0079] Example 2, as Figure 4 As shown, based on the same inventive concept as in Embodiment 1 above, this application provides a rollover stability testing system for new energy vehicles, the system comprising: The rollover test scenario construction module 10 is used to select the new energy vehicle to be tested, perform rollover scenario backtracking learning based on the new energy vehicle to be tested, and construct N static rollover test scenarios and M dynamic rollover test scenarios.

[0080] The finite element analysis module 20 is used to perform finite element analysis optimization based on the structural parameter information and material property information of the new energy vehicle to be tested, and generate a finite element simulation model of the new energy vehicle.

[0081] The test parameter parsing module 30 is used to parse the test parameters of the N static rollover test scenarios and the M dynamic rollover test scenarios, and construct Q static scenario test matrices and Q dynamic scenario test matrices.

[0082] The rollover test module 40 is used to load the new energy vehicle finite element simulation model and perform rollover tests on the new energy vehicle sequentially using the Q static scene test matrices and the Q dynamic scene test matrices to obtain Q static scene test parameters and Q dynamic scene test parameters.

[0083] The test result calculation module 50 is used to obtain the rollover stability evaluation index set, and to perform multi-dimensional calculation and evaluation on the Q static scenario test parameters and Q dynamic scenario test parameters based on the rollover stability evaluation index set to determine the rollover stability test result of the new energy vehicle.

[0084] Furthermore, the rollover test scenario construction module 10 in this embodiment is also used to perform the following steps: A dataset of new energy vehicle rollover accidents is obtained through mining. Rollover factors are extracted from the dataset to obtain a set of key rollover factors. Based on the set of key rollover factors, a scenario backtracking process is performed on the new energy vehicle rollover accident dataset to obtain a knowledge base of new energy vehicle rollover factor scenarios. Static rollover test targets and dynamic rollover test targets for new energy vehicles are determined. Based on the knowledge base of new energy vehicle rollover factor scenarios, factor scenario association design is performed on the static rollover test targets and dynamic rollover test targets respectively to construct the N static rollover test scenarios and M dynamic rollover test scenarios.

[0085] Furthermore, in this embodiment of the application, the test parameter parsing module 30 is also used to perform the following steps: Based on the knowledge base of new energy vehicle rollover factors and scenarios, static rollover scenario test parameters and dynamic rollover scenario test parameters are determined. An initial static rollover test matrix is ​​constructed by using the N static rollover test scenarios as rows and the static rollover scenario test parameters as columns. Similarly, an initial dynamic rollover test matrix is ​​constructed by using the M dynamic rollover test scenarios as rows and the dynamic rollover scenario test parameters as columns. Based on the new energy vehicle rollover accident dataset, parameter distribution intervals are analyzed for the N static rollover test scenarios and the M dynamic rollover test scenarios to obtain N static rollover scenario parameter intervals and M dynamic rollover scenario parameter intervals. Using the N static rollover scenario parameter intervals and the M dynamic rollover scenario parameter intervals as value constraints, parameter values ​​are assigned to the initial static rollover test matrix and the initial dynamic rollover test matrix to construct Q static scenario test matrices and Q dynamic scenario test matrices.

[0086] Furthermore, in this embodiment of the application, the test parameter parsing module 30 is also used to perform the following steps: Using the N static rollover scenario parameter ranges as value constraints, the initial static rollover test matrix is ​​randomly assigned Q times to obtain Q static rollover test matrices; using the M dynamic rollover scenario parameter ranges as value constraints, the initial dynamic rollover test matrix is ​​randomly assigned Q times to obtain Q dynamic rollover test matrices; significance tests are performed on the Q static rollover test matrices and the Q dynamic rollover test matrices respectively to obtain Q static scenario test matrices and Q dynamic scenario test matrices.

[0087] Furthermore, in this embodiment of the application, the test parameter parsing module 30 is also used to perform the following steps: Significance tests are performed on the Q static rollover test matrices and the Q dynamic rollover test matrices respectively to obtain the significance coefficients of the Q static matrices and the Q dynamic matrices. A test scenario significance threshold is set, and the significance coefficients of the Q static matrices and the Q dynamic matrices are filtered based on the test scenario significance threshold to obtain a set of static matrices and a set of dynamic matrices to be corrected whose matrix significance coefficients are less than the test scenario significance threshold. The set of static matrices and the set of dynamic matrices to be corrected are iteratively optimized and assigned values ​​based on the value constraints to obtain a set of static optimized matrices and a set of dynamic optimized matrices. The Q static rollover test matrices and the Q dynamic rollover test matrices are corrected and updated based on the set of static optimized matrices and the set of dynamic optimized matrices to obtain the Q static scenario test matrices and the Q dynamic scenario test matrices.

[0088] Furthermore, the test result calculation module 50 in this embodiment is also used to perform the following steps: Static and dynamic correlation indicators are extracted from the rollover stability assessment indicator set to obtain a static stability assessment indicator set and a dynamic stability assessment indicator set. Based on these sets, static and dynamic stability indicator calculation formulas are selected. Multi-dimensional indicator calculations are performed on the Q static and Q dynamic scenario test parameters using these formulas to obtain a static scenario stability indicator parameter set and a dynamic scenario stability indicator parameter set. These sets are then analyzed and evaluated to determine the rollover stability test results for the new energy vehicle.

[0089] Furthermore, the test result calculation module 50 in this embodiment is also used to perform the following steps: A rollover stability level assessment system is established. Based on this system, the static scenario stability index parameter set and the dynamic scenario stability index parameter set are evaluated to obtain the rollover stability level of the new energy vehicle. Based on the static scenario stability index parameter set and the dynamic scenario stability index parameter set, the new energy vehicle under test is subjected to weakness identification analysis to obtain multiple stability weakness identification areas. Based on the rollover stability level of the new energy vehicle and the multiple stability weakness identification areas, the rollover stability test result of the new energy vehicle is determined.

[0090] Through the foregoing detailed description of a rollover stability test method for a new energy vehicle, those skilled in the art can clearly understand that the rollover stability test system for a new energy vehicle in this embodiment corresponds to the system disclosed in Embodiment 2. As it is similar to the method disclosed in Embodiment 1, it has corresponding functional modules and beneficial effects. For relevant details, please refer to the method section.

[0091] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for testing the rollover stability of a new energy vehicle, characterized in that, The method includes: Select a new energy vehicle to be tested, and perform rollover scenario backtracking learning based on the new energy vehicle to be tested to construct N static rollover test scenarios and M dynamic rollover test scenarios; Based on the structural parameter information and material property information of the new energy vehicle to be tested, finite element analysis and optimization are performed to generate a finite element simulation model of the new energy vehicle. Test parameters are analyzed for the N static rollover test scenarios and M dynamic rollover test scenarios to construct Q static scenario test matrices and Q dynamic scenario test matrices; Using the Q static scene test matrices and Q dynamic scene test matrices, the new energy vehicle finite element simulation model is loaded and the new energy vehicle rollover test is executed sequentially to obtain Q static scene test parameters and Q dynamic scene test parameters; Obtain a rollover stability assessment index set, and perform multi-dimensional calculation and evaluation on the Q static scenario test parameters and Q dynamic scenario test parameters based on the rollover stability assessment index set to determine the rollover stability test results of the new energy vehicle.

2. The rollover stability test method for a new energy vehicle as described in claim 1, characterized in that, The construction of N static rollover test scenarios and M dynamic rollover test scenarios includes: A dataset of new energy vehicle rollover accidents was obtained by mining and extracting rollover factors from the dataset to obtain a set of key factors for new energy vehicle rollovers. Based on the set of key factors for new energy vehicle rollover, perform a backtracking of related scenarios on the dataset of new energy vehicle rollover accidents to obtain a knowledge base of new energy vehicle rollover factor scenarios. Determine the static rollover test objectives and dynamic rollover test objectives for new energy vehicles; Based on the knowledge base of new energy vehicle rollover factors and scenarios, factor scenario association design is carried out for the static rollover test target and the dynamic rollover test target of new energy vehicles, respectively, to construct the N static rollover test scenarios and M dynamic rollover test scenarios.

3. The rollover stability test method for a new energy vehicle as described in claim 2, characterized in that, The construction of Q static scene test matrices and Q dynamic scene test matrices includes: Based on the knowledge base of new energy vehicle rollover factors and scenarios, determine the test parameters for static rollover scenarios and dynamic rollover scenarios; Construct an initial matrix for static rollover testing by using the N static rollover test scenarios as rows and the test parameters of the static rollover scenarios as columns. Construct an initial matrix for dynamic rollover testing by using the M dynamic rollover test scenarios as rows and the test parameters of the dynamic rollover scenarios as columns. Based on the new energy vehicle rollover accident dataset, the parameter distribution intervals of the N static rollover test scenarios and the M dynamic rollover test scenarios are analyzed to obtain the parameter intervals of the N static rollover scenarios and the parameter intervals of the M dynamic rollover scenarios. Using the N static rollover scenario parameter ranges and M dynamic rollover scenario parameter ranges as value constraints, parameter values ​​are assigned to the initial static rollover test matrix and the initial dynamic rollover test matrix to construct Q static scenario test matrices and Q dynamic scenario test matrices.

4. The rollover stability test method for a new energy vehicle as described in claim 3, characterized in that, The construction of Q static scene test matrices and Q dynamic scene test matrices includes: Using the N static rollover scenario parameter ranges as value constraints, the initial static rollover test matrix is ​​randomly assigned Q times to obtain Q static rollover test matrices. Using the range of parameters for the M dynamic rollover scenarios as a constraint, the initial matrix for dynamic rollover testing is randomly assigned Q times to obtain Q dynamic rollover test matrices. Significance tests are performed on the Q static rollover test matrices and the Q dynamic rollover test matrices respectively to obtain Q static scene test matrices and Q dynamic scene test matrices.

5. The rollover stability test method for a new energy vehicle as described in claim 4, characterized in that, Obtaining Q static scene test matrices and Q dynamic scene test matrices includes: Significance tests were performed on the Q static rollover test matrices and the Q dynamic rollover test matrices respectively to obtain the significance coefficients of the Q static matrices and the Q dynamic matrices. Set a test scenario significance threshold, and filter the significance coefficients of the Q static matrices and the Q dynamic matrices based on the test scenario significance threshold to obtain a set of static matrices to be corrected and a set of dynamic matrices to be corrected whose matrix significance coefficients are less than the test scenario significance threshold. Based on the value constraints, the static matrix set and the dynamic matrix set to be corrected are iteratively optimized and assigned values ​​to obtain the static optimized matrix set and the dynamic optimized matrix set; Based on the static optimization matrix set and the dynamic optimization matrix set, the Q static rollover test matrices and the Q dynamic rollover test matrices are corrected and updated to obtain the Q static scene test matrices and the Q dynamic scene test matrices.

6. The rollover stability test method for a new energy vehicle as described in claim 1, characterized in that, The determination of the rollover stability test results for new energy vehicles includes: Static correlation indicators and dynamic correlation indicators are extracted from the rollover stability assessment indicator set to obtain the static stability assessment indicator set and the dynamic stability assessment indicator set, respectively. Based on the static stability evaluation index set and the dynamic stability evaluation index set, select the static stability index calculation formula set and the dynamic stability index calculation formula set; The static stability index calculation formula set and the dynamic stability index calculation formula set are used to perform multi-dimensional index calculation on the Q static scene test parameters and the Q dynamic scene test parameters to obtain the static scene stability index parameter set and the dynamic scene stability index parameter set. The static scenario stability index parameter set and the dynamic scenario stability index parameter set are analyzed and evaluated to determine the rollover stability test results of the new energy vehicle.

7. The rollover stability test method for a new energy vehicle as described in claim 6, characterized in that, The determination of the rollover stability test results for new energy vehicles includes: A rollover stability level assessment system is established, and the static scenario stability index parameter set and the dynamic scenario stability index parameter set are evaluated based on the rollover stability level assessment system to obtain the rollover stability level of new energy vehicles. Based on the static scene stability index parameter set and the dynamic scene stability index parameter set, the new energy vehicle under test is subjected to weakness identification analysis to obtain multiple stability weakness identification areas. Based on the rollover stability level of the new energy vehicle and the multiple stability weakness identification areas, the rollover stability test results of the new energy vehicle are determined.

8. A rollover stability testing system for new energy vehicles, characterized in that, The system is used to perform a rollover stability test method for a new energy vehicle as described in any one of claims 1-7, including: The rollover test scenario construction module is used to select the new energy vehicle to be tested, perform rollover scenario backtracking learning based on the new energy vehicle to be tested, and construct N static rollover test scenarios and M dynamic rollover test scenarios. The finite element analysis module is used to perform finite element analysis optimization based on the structural parameter information and material property information of the new energy vehicle under test, and generate a finite element simulation model of the new energy vehicle. The test parameter parsing module is used to parse the test parameters of the N static rollover test scenarios and M dynamic rollover test scenarios, and construct Q static scenario test matrices and Q dynamic scenario test matrices. The rollover test module is used to load the new energy vehicle finite element simulation model and perform the new energy vehicle rollover test sequentially using the Q static scene test matrices and Q dynamic scene test matrices to obtain Q static scene test parameters and Q dynamic scene test parameters. The test result calculation module is used to obtain a rollover stability evaluation index set, and to perform multi-dimensional calculation and evaluation on the Q static scenario test parameters and Q dynamic scenario test parameters based on the rollover stability evaluation index set to determine the rollover stability test results of the new energy vehicle.

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