A vehicle handling stability simulation analysis and post-processing method and system

Abstract

The present application belongs to the technical field of automobile engineering, and specifically relates to a simulation analysis and post-processing method and system for automobile handling stability. The method comprises: establishing a basic information database containing four types of tables, reading data thereof, performing fixed circle simulation, initially selecting steering wheel angle, performing multi-group working condition simulation and polynomial fitting, accurately solving the steering wheel angle corresponding to the target lateral acceleration, creating a final working condition file to complete batch simulation; extracting data from the simulation results, calculating handling stability performance indicators and summarizing them to Excel, drawing according to preset parameters, and automatically inserting the indicators and drawing results into PPT to generate a report. The system includes a basic information database, a steering wheel angle solving module, a simulation execution module, a result processing module, a drawing and report generation module, and a GUI interaction module. The present application realizes automatic creation of working condition files, accurate solution of steering angles and full-process automation of simulation post-processing, improves simulation efficiency and accuracy, reduces operation difficulty, and supports visual comparison of performance of multiple vehicle models.

Description

Technical Field

[0001] This invention belongs to the field of automotive engineering technology, specifically relating to a method and system for simulation analysis and post-processing of vehicle handling stability. Background Technology

[0002] During the development of vehicle handling and stability performance, multibody dynamics software is used to conduct simulation analysis of vehicle handling and steering performance to evaluate the vehicle's handling stability and steering performance. The simulation analysis phase requires designing simulations for multiple operating conditions, including steady-state turning, steering angle pulse, steering angle step, center zone steering, sinusoidal sweep frequency test, slalom test, straight-line acceleration, and straight-line braking. The simulation results are then post-processed to obtain evaluation index values, thereby completing the vehicle performance assessment.

[0003] In existing technologies, simulations of steering angle pulse, steering angle step, center zone steering, and sinusoidal sweep frequency tests require controlling the steering wheel angle to achieve specific lateral accelerations. For example, the steering angle pulse condition requires a maximum lateral acceleration of 0.4g, while the center zone steering, sinusoidal sweep frequency test, and steering angle step condition require a maximum lateral acceleration of 0.2g. However, due to differences in vehicle parameters, the steering wheel angle required to achieve the same lateral acceleration varies, often necessitating repeated adjustments to the steering wheel angle during simulation.

[0004] Chinese patent document CNCN117272508A discloses a "Simulation Analysis Method, System, and Terminal for Iterative Steering Wheel Angle of a Vehicle for Handling Stability." This method requires pre-filling the minimum and maximum values ​​of the steering wheel angle variation range and continuously trying within that range. While it improves efficiency compared to manual trial-and-error, it has significant drawbacks: First, the steering wheel angle range is large, and the iteration interval is limited. If the interval is too large, it is difficult to iterate to find a suitable steering angle; if the interval is too small, the iteration is time-consuming and inefficient. Second, when using the Compose software, there is no GUI interface, making operation complex and visually poor, causing inconvenience to operators. Furthermore, in the existing simulation analysis and post-processing workflow, the creation of working condition files, data extraction, index calculation, result summarization, and report generation are all independent processes, cumbersome to operate, and the overall simulation analysis efficiency is low, which is not conducive to the rapid optimization of vehicle handling stability and steering performance. Summary of the Invention

[0005] Technical Objective: To address the aforementioned problems in existing technologies, this invention provides a method and system for vehicle handling stability simulation analysis and post-processing. It aims to solve the issues of low efficiency and complex operation of steering wheel angle iteration in existing handling stability simulations, as well as the cumbersome simulation analysis process and poor visibility. The invention achieves automated creation of simulation condition files, accurate and rapid solution of steering wheel angle, automated post-processing of simulation results, visualization, and automatic report generation, thereby improving the overall efficiency and accuracy of vehicle handling stability simulation analysis.

[0006] Technical Solution: To achieve the above objectives, one of the objectives of this invention is to disclose a method for simulation analysis and post-processing of vehicle handling stability, comprising the following steps: S1. Establish a basic information database: The basic information database includes a vehicle parameter and basic information table, a vehicle load information table, a working condition output channel name table, and a working condition simulation index processing information table. S2. Calculate the steering wheel angle required for the target's lateral acceleration: Obtain the steering wheel angle required for the target's lateral acceleration under the test conditions of steering angle pulse, steering angle step, center zone steering, and sinusoidal frequency sweep. Specifically, this includes: S2.1 Obtaining data through fixed-circle simulation: Read the basic information database recorded in step S1 and define the fixed-circle simulation working condition file; use the driver model of Adams car to control the steering wheel angle through PID control, so that the vehicle accelerates from 10Km / h to 120Km / h around a fixed radius circle of 60m, generate XML working condition files of different load states and submit them to Adams car for the first round of simulation to obtain a res type simulation result file; S2.2 Initial selection of steering wheel angle: Using Python, read the steering wheel angle and lateral acceleration data from the res result file described in step S2.1, search for the value with the smallest difference from the target lateral acceleration, obtain the corresponding steering wheel angle, and write it into the vehicle parameter and basic information table described in step S1; S2.3. Generate multiple sets of working condition files and perform batch simulation: Based on the initial selection of steering wheel angle described in step S2.2, generate n sets of target working condition XML files with different steering wheel angles, automatically create batch simulation BAT files, run the BAT files to complete the simulation and obtain multiple sets of res result files; the value range of n is n≥2. S2.4. Fitting to obtain accurate steering wheel angle: Read the steering wheel angle and lateral acceleration data from the multiple sets of res result files in step S2.3, perform 1 to n-1 order polynomial fitting using NumPy, select the fitting equation with the R² value closest to 1, solve to obtain the accurate steering wheel angle corresponding to the target lateral acceleration, and write it into the vehicle parameter and basic information table in step S1. S2.5. Create the final simulation condition file: Based on the precise steering wheel angle described in step S2.5, recreate the final XML simulation condition file for each target condition; S3. Calculate the final res simulation results: Submit the final xml simulation condition file described in step S2.5 for the second round of batch simulation to obtain the final res simulation result file; S4. Save the summary file of index results: Perform performance index processing on the final res simulation result file described in step S3, extract data and calculate the handling and stability performance index, and save the summary index results to an Excel file. S5. Graphing and Report Generation: Graph the index results and save the graphing results as images to the corresponding working condition folder; insert the index results and graphing results from step S4 into a PowerPoint presentation to generate a vehicle handling stability simulation analysis report.

[0007] Further, in step S1, the vehicle parameters and basic information table presets the vehicle wheelbase, the name of the multibody dynamics simulation model under different load states, vehicle speed, sampling rate, signal interception time, maximum simulation time, initial steering wheel angle, and target lateral acceleration information; the vehicle load information table presets the vehicle axle load and simulation model name information; the working condition output channel name table presets the channel name for reading data from the res result file; and the working condition simulation index processing information table presets the working condition index processing method, index results, and plotting related parameter information.

[0008] Furthermore, the target lateral acceleration includes: 0.2g corresponding to the steering angle step, center zone steering, and sinusoidal sweep frequency test conditions, and 0.4g corresponding to the steering angle pulse condition; the different load states include no load, half load, and full load.

[0009] Furthermore, the incremental change gradient of the n groups of different steering wheel angles in step S2.3 is ±20%, or the incremental change can be customized to ±100%.

[0010] Further, the method for calculating the R² value of the polynomial fitting in step S2.4 is as follows: first calculate the average lateral acceleration y_mean, then calculate the total sum of squares SST and the regression sum of squares SSR, and finally obtain the goodness of fit value through R²=SSR / SST.

[0011] Furthermore, the handling and stability performance indicators mentioned in step S4 include at least one of the following: understeer, roll gradient, maximum lateral acceleration, yaw rate response time, overshoot, resonance peak level, steering stiffness, and steering wheel torque gradient.

[0012] Furthermore, the vehicle handling stability simulation conditions include steady-state rotation, steering angle pulse, steering angle step, center zone steering, sinusoidal frequency sweep, serpentine test, linear acceleration, and linear braking conditions.

[0013] Furthermore, the operations in steps S1 to S6 are all implemented through a GUI interface written in Python. The GUI interface supports visualization operations such as selecting vehicle information files, pre-selecting steering wheel angles, creating operating condition files, processing simulation results, fitting steering wheel angles, drawing, and generating reports.

[0014] Furthermore, it also includes a multi-model comparative analysis step: plotting the simulation index curves of different models on the same image to achieve a visual comparison of the handling stability and steering performance of multiple models.

[0015] The second objective of this invention is to disclose a vehicle handling stability simulation analysis and post-processing system for implementing the method described in any one of claims 1-9, wherein the system comprises: The basic information database module is used to store vehicle parameters and basic information tables, vehicle load information tables, working condition output channel name tables, and working condition simulation index processing information tables, and supports data presetting, reading, and modification. The steering wheel angle calculation module includes a fixed circle simulation unit, a preliminary angle selection unit, multiple simulation units, a fitting and solution unit, and a final working condition creation unit. These components sequentially complete the acquisition of fixed circle simulation data, calculation of the preliminary steering wheel angle, simulation of multiple working conditions, precise steering wheel angle fitting, and creation of the final working condition file. The fitting and solution unit incorporates a NumPy polynomial fitting algorithm, which can automatically complete fitting from order 1 to n-1 and select the optimal fitting equation. The simulation execution module receives XML working condition files, submits them to Adams CAR software to complete batch simulation calculations, and outputs simulation result files of type .res; it automatically generates cmd and bat files for batch simulations to achieve automated simulation of multiple working conditions. The results processing module is used to read the res simulation results file, extract data and calculate the handling and stability performance indicators, and summarize the indicator results to an Excel file; it supports the calculation of pitch gradient indicators under linear acceleration and linear braking conditions, and is adapted to the handling and stability analysis needs of new energy vehicles with large axle loads. The plotting and report generation module is used to plot performance curves based on indicator results and save the images according to working conditions; it supports plotting indicator curves of multiple vehicle models on the same graph to achieve performance comparison of multiple vehicle models; it automatically inserts indicator results and plotting results into PPT to generate standardized simulation analysis reports. The GUI interaction module serves as the entry point for the above modules, enabling visual operations for each step and supporting file selection, parameter setting, command triggering, and result preview.

[0016] The beneficial effects of this invention are: 1. The vehicle handling stability simulation analysis and post-processing method provided by this invention initially selects the steering wheel angle through fixed circle simulation, and then obtains the accurate steering wheel angle through multiple sets of working condition simulation fitting. Compared with the existing fixed range iteration method, it greatly reduces the trial range of the steering wheel angle. Combined with the polynomial fitting algorithm to select the optimal fitting equation for solution, it significantly improves the solution efficiency and accuracy of the steering wheel angle corresponding to a specific lateral acceleration, and avoids the problems of low efficiency and poor accuracy caused by improper iteration interval setting.

[0017] 2. The vehicle handling stability simulation analysis and post-processing method provided by this invention establishes a standardized basic information database, integrates all the basic data required for simulation such as vehicle parameters, load information, data channels, and index processing methods, realizes unified management and access of data, provides standardized data support for the entire simulation process, and avoids the chaos caused by independent data in each stage.

[0018] 3. The vehicle handling stability simulation analysis and post-processing method provided by this invention realizes the automated creation of simulation condition files (xml, bat, cmd) through Python, eliminating the need for manual writing and modification. It also supports batch simulation of multiple conditions and load states, greatly reducing manual operation steps and improving simulation execution efficiency.

[0019] 4. The vehicle handling stability simulation analysis and post-processing method provided by this invention realizes automated post-processing of simulation results. It automatically extracts data from the res file, calculates handling stability performance indicators, and summarizes the indicator results into an Excel file. At the same time, it automatically plots according to preset parameters and automatically inserts the indicators and plot results into a PPT to generate an analysis report. It realizes full-process automation from simulation to report and solves the problems of independent process links and cumbersome operation in the existing process.

[0020] 5. The vehicle handling stability simulation analysis and post-processing system provided by this invention integrates all operation steps into a visual interface by writing a dedicated GUI interface based on Python. It supports visual operations such as file selection, parameter setting, and command triggering, which solves the problems of some existing software having no GUI interface, complex operation, and poor visibility. It reduces the skill requirements of operators and improves the convenience of operation. Attached Figure Description

[0021] Figure 1 This is a flowchart of the simulation analysis and post-processing method for vehicle handling stability; Figure 2 It is a screenshot of the vehicle parameters and basic information table for vehicle handling stability simulation analysis and post-processing methods. Figure 3 This is a screenshot of the vehicle load information table for automobile handling stability simulation analysis and post-processing methods; Figure 4 This is a screenshot of the output channel name table for vehicle handling stability simulation analysis and post-processing methods. Figure 5 This is a screenshot of the vehicle handling stability simulation analysis and post-processing method working condition simulation index processing information table. Figure 6 It is a graph showing the relationship between steering wheel angle and lateral acceleration in the simulation analysis and post-processing method of vehicle handling stability. Figure 7 This is a summary chart of the results of vehicle handling stability simulation analysis and post-processing methods. Figure 8 This is a simulation analysis and post-processing method for vehicle handling stability. (Image from a vehicle handling stability simulation analysis report.) Detailed Implementation

[0022] The following is in conjunction with the appendix Figure 1 To be continued Figure 8 The principles and features of the present invention are described, and the examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0023] Table 1: English-Chinese Glossary of Proper Nouns , like Figure 1 As shown, a simulation analysis and post-processing method for vehicle handling stability is presented, taking vehicle model code WB1000 as an example. The specific steps are as follows: S1. Establish a basic information database, including a vehicle parameter and basic information table, a vehicle load information table, a working condition output channel name table, and a working condition simulation index processing information table. Each table is created in Excel format, and the specific settings are as follows: 1.1 Vehicle Parameters and Basic Information Table: Named WB1000_Vehicle_informationHandling.xlsx, pre-sets the vehicle wheelbase to 2950mm, the multibody dynamics simulation model name for three load states (unloaded / half-loaded / fully loaded), simulation vehicle speed, sampling rate 0.01s, signal interception initial / termination time, maximum simulation time, initial steering wheel angle, target lateral acceleration (steering angle step / center zone steering / sine sweep frequency of 0.2g, steering angle pulse of 0.4g), etc. Figure 2 As shown; 1.2 Vehicle Load Information Table: Presets the axle load of the whole vehicle, and the names of the unloaded model (MDI_Demo_Vehicle_It), the half-loaded model (MDI_Demo_Vehicle_It_Half), and the fully loaded model (MDI_Demo_Vehicle_It_GW), such as... Figure 3 As shown; 1.3 Output Channel Name Table for Working Conditions: Named Xingneng_Full_channel_name.xlsx, this table presets the channel names for reading data from the res result file in the corresponding working condition's worksheet, such as lateral_acceleration, steering_wheel_angle, roll_angle, etc. Figure 4 As shown; 1.4 Working Condition Simulation Index Processing Information Table: In the working table corresponding to the working condition in Xingneng_Full_channal_name.xlsx, preset information such as horizontal axis, vertical axis, data scaling ratio, index processing method (e.g., slope, slope+Twochannel), whether to plot, horizontal / vertical axis units, index description and units, etc., are provided. Figure 5 As shown; S2. Calculate the steering wheel angle required to achieve the target's lateral acceleration: S2.1 Obtaining data through fixed-circle simulation: Read the vehicle parameters, load status, and speed range data from the basic information database in step S1, and define the fixed-circle simulation condition file; using the driver model of Adams car, control the steering wheel angle through PID control to make the vehicle accelerate from 10 km / h to 120 km / h around a 60m fixed radius circle, and generate XML condition files (such as GVW_CRC_Left_StraightCheck_0.xml, Curb_CRC_Left_StraightCheck_0.xml) for three load states: no load, half load, and full load. Submit the XML file to Adams car for the first round of simulation, name the vehicle model WB1000, and obtain the res type simulation result file.

[0024] S2.2 Initial Steering Wheel Angle Selection: A Python program reads the `res` result file from step S2.1, extracts the `x_data` corresponding to the steering wheel angle "steering_wheel_angle" and the `y_data` corresponding to the lateral acceleration "lateral_acceleration", and plots the relationship between the steering wheel angle and lateral acceleration. Figure 6 As shown; The relevant values ​​are calculated using the NumPy data analysis library. The difference between the lateral acceleration target and the target is calculated using `differences = numpy.abs(y_data -lat_acc_target)`. The minimum difference is calculated using `min_diff = numpy.min(differences)`. The steering wheel angles corresponding to the lateral acceleration targets of 0.2g and 0.4g are obtained using `steer_angle = x_data[index]`. Specifically, the difference between y_data and the target lateral acceleration (0.2g=1960mm / s², 0.4g=3920mm / s²) is calculated using the NumPy data analysis library. The value with the smallest difference is found and its index is obtained. The corresponding steering wheel angle is extracted from x_data to obtain the initial steering wheel angle (e.g., 90.36deg for the steering angle pulse condition, and 79.85deg for the steering angle step / center zone steering / sine sweep frequency condition). The initial steering wheel angle is written into the specified column of the vehicle parameters and basic information table in step S1.

[0025] S2.3. Generate multiple sets of working condition files and perform batch simulation: Based on the initial steering wheel angle selected in step S2.2, set n=5, and generate 5 sets of target working condition XML files for different steering wheel angles (such as 54.2deg, 72.3deg, 90.36deg, 108.4deg, and 126.5deg for steering angle pulse working conditions) with incremental changes of ±20%. Automatically create a batch simulation cmd file and bat file (such as Half_PR_BAT_all_sim.bat) using Python. The bat file contains the Adams solver path and all XML working condition files to be called. Run the bat file, submit Adams car to complete the batch simulation of 5 working conditions, and obtain 5 sets of res result files.

[0026] S2.4. Fitting and Obtaining the Precise Steering Wheel Angle: Using Python, read the five sets of res result files from step S2.3, extract the maximum steering wheel angle and maximum lateral acceleration for each set, obtaining steering wheel angle data [54.2, 72.3, 90.36, 108.4, 126.5] and corresponding lateral acceleration data [2685.19258311, 3631.73092102, 4578.26925894, 5524.80759161, 6471.34592953] mm / s²; plot the steering wheel angle as the x-axis and the lateral acceleration as the y-axis, and perform multi-stage fitting from order 1 to 4 using NumPy. For each order of fitting equation, the R² value was calculated, resulting in R²=0.9935 for the first order, R²=0.999 for the second order, R²=0.999 for the third order, and R²=1.0 for the fourth order. The fourth order fitting equation was selected as the optimal equation. The target lateral acceleration of 3920 mm / s² was substituted into the fourth order fitting equation to obtain the precise steering wheel angle of 76.91 degrees for the steering angle pulse condition. The precise steering wheel angle of 0.2g corresponding to the steering angle step / center zone steering / sine sweep frequency condition was obtained in the same way. All precise steering wheel angles were written into the corresponding columns (U, V, W) of the vehicle parameters and basic information table in step S1 for unloaded / half-loaded / full-loaded.

[0027] The R² value is calculated as follows: first, calculate the average lateral acceleration y_mean = np.mean(y), then calculate the total sum of squares SST = np.sum((y - y_mean) ** 2) and the regression sum of squares SSR = np.sum((y_fit - y_mean) ** 2), and finally obtain the goodness of fit value through R² = SSR / SST.

[0028] S2.5. Create the final simulation condition file: Read the precise steering wheel angle written into the vehicle parameters and basic information table in step S2.4, and recreate the final XML simulation condition files for each condition, including steering angle pulse, steering angle step, center zone steering, and sine sweep frequency, according to the parameters preset in the basic information database in step S1.

[0029] S3. Calculate the final res simulation results: Submit the final XML simulation condition file from step S2.5 to Adams car for the second round of batch simulation to obtain the final res simulation result file containing all handling and stability conditions.

[0030] S4. Save the summary file of index results: Read the preset channel names from the working condition output channel name table in step S1, and extract the corresponding data from the final res simulation result file in step S3; calculate the handling and stability performance indices according to the preset method in the working condition simulation index processing information table in step S1, including understeer, roll gradient, maximum lateral acceleration, yaw rate response time, overshoot, resonance peak level, steering stiffness, steering wheel torque gradient, etc.; summarize all index results and save them to a separate Excel file (Summy_all.xlsx), such as... Figure 7 As shown.

[0031] S5. Plotting and Report Generation: Following the preset plotting parameters in the simulation index processing information table of step S1, plot the index results from step S4 to generate performance curves such as time-roll angle, time-lateral acceleration, and time-yaw rate. Save the plotting results as images to the corresponding working condition folder. Use a Python program to automatically insert the index results from the Excel file and the plotted images of each working condition into a PPT template to generate a standardized vehicle handling stability simulation analysis report, such as... Figure 8 As shown.

[0032] In this embodiment, the vehicle handling stability simulation conditions also include steady-state turning, serpentine test, linear acceleration, and linear braking conditions. Each condition is simulated and post-processed according to the process of steps S1-S5. Among them, the pitch gradient index is calculated for the linear acceleration and linear braking conditions to meet the handling stability analysis requirements of new energy vehicles with large axle loads.

[0033] In this embodiment, all operations from steps S1 to S5 are implemented through a GUI interface written in Python. Operators can complete operations such as selecting vehicle information files, pre-selecting steering wheel angles, creating working condition files, processing simulation results, fitting steering wheel angles, drawing and generating reports in the GUI interface. The entire process is visualized and easy to operate.

[0034] This embodiment also includes a multi-model comparison analysis step: select another model and complete the simulation and post-processing according to the above steps, and plot the simulation index curves (such as lateral acceleration-steering wheel angle curve, roll angle-lateral acceleration curve) of the WB1000 model and the model in the same picture to intuitively show the difference in handling and stability performance between the two models.

[0035] A vehicle handling stability simulation analysis and post-processing system, used to implement the method described in Example 1, includes: The basic information database module is used to store vehicle parameters and basic information tables, vehicle load information tables, working condition output channel name tables, and working condition simulation index processing information tables, and supports data presetting, reading, and modification. The steering wheel angle calculation module includes a fixed circle simulation unit, a preliminary angle selection unit, multiple simulation units, a fitting and solution unit, and a final working condition creation unit. These components sequentially complete the acquisition of fixed circle simulation data, calculation of the preliminary steering wheel angle, simulation of multiple working conditions, precise steering wheel angle fitting, and creation of the final working condition file. The fitting and solution unit incorporates a NumPy polynomial fitting algorithm, which can automatically complete fitting from order 1 to n-1 and select the optimal fitting equation. The simulation execution module receives XML working condition files, submits them to Adams CAR software to complete batch simulation calculations, and outputs simulation result files of type .res; it automatically generates cmd and bat files for batch simulations to achieve automated simulation of multiple working conditions. The results processing module is used to read the res simulation results file, extract data and calculate the handling and stability performance indicators, and summarize the indicator results to an Excel file; it supports the calculation of pitch gradient indicators under linear acceleration and linear braking conditions, and is adapted to the handling and stability analysis needs of new energy vehicles with large axle loads. The plotting and report generation module is used to plot performance curves based on indicator results and save the images according to working conditions; it supports plotting indicator curves of multiple vehicle models on the same graph to achieve performance comparison of multiple vehicle models; it automatically inserts indicator results and plotting results into PPT to generate standardized simulation analysis reports. The GUI interaction module serves as the entry point for the above modules, enabling visual operations for each step and supporting file selection, parameter setting, command triggering, and result preview.

[0036] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for simulation analysis and post-processing of vehicle handling stability, characterized in that, Includes the following steps: S1. Establish a basic information database: The basic information database includes a vehicle parameter and basic information table, a vehicle load information table, a working condition output channel name table, and a working condition simulation index processing information table. S2. Calculate the steering wheel angle required for the target's lateral acceleration: Obtain the steering wheel angle required for the target's lateral acceleration under the test conditions of steering angle pulse, steering angle step, center zone steering, and sinusoidal frequency sweep. Specifically, this includes: S2.1 Obtaining data through fixed-circle simulation: Read the basic information database recorded in step S1 and define the fixed-circle simulation working condition file; use the driver model of Adams car to control the steering wheel angle through PID control, so that the vehicle accelerates from 10Km / h to 120Km / h around a fixed radius circle of 60m, generate XML working condition files of different load states and submit them to Adams car for the first round of simulation to obtain a res type simulation result file; S2.2 Initial selection of steering wheel angle: Using Python, read the steering wheel angle and lateral acceleration data from the res result file described in step S2.1, search for the value with the smallest difference from the target lateral acceleration, obtain the corresponding steering wheel angle, and write it into the vehicle parameter and basic information table described in step S1; S2.

3. Generate multiple sets of working condition files and perform batch simulation: Based on the initial selection of steering wheel angle described in step S2.2, generate n sets of target working condition XML files with different steering wheel angles, automatically create batch simulation BAT files, run the BAT files to complete the simulation and obtain multiple sets of res result files; the value range of n is n≥2. S2.

4. Fitting to obtain accurate steering wheel angle: Read the steering wheel angle and lateral acceleration data from the multiple sets of res result files in step S2.3, perform 1 to n-1 order polynomial fitting using NumPy, select the fitting equation with the R² value closest to 1, solve to obtain the accurate steering wheel angle corresponding to the target lateral acceleration, and write it into the vehicle parameter and basic information table in step S1. S2.

5. Create the final simulation condition file: Based on the precise steering wheel angle described in step S2.5, recreate the final XML simulation condition file for each target condition; S3. Calculate the final res simulation results: Submit the final xml simulation condition file described in step S2.5 for the second round of batch simulation to obtain the final res simulation result file; S4. Save the summary file of index results: Perform performance index processing on the final res simulation result file described in step S3, extract data and calculate the handling and stability performance index, and save the summary index results to an Excel file. S5. Graphing and Report Generation: Graph the index results and save the graphing results as images to the corresponding working condition folder; insert the index results and graphing results from step S4 into a PowerPoint presentation to generate a vehicle handling stability simulation analysis report.

2. The vehicle handling stability simulation analysis and post-processing method according to claim 1, characterized in that, In step S1, the vehicle parameters and basic information table includes preset information such as vehicle wheelbase, name of multibody dynamics simulation model under different load conditions, vehicle speed, sampling rate, signal interception time, maximum simulation time, initial steering wheel angle, and target lateral acceleration information. The vehicle load information table presets the vehicle axle load and simulation model name information; the working condition output channel name table presets the channel names for reading data from the res result file; The working condition simulation index processing information table includes preset working condition index processing methods, index results, and plotting-related parameter information.

3. The vehicle handling stability simulation analysis and post-processing method according to claim 2, characterized in that, The target lateral acceleration includes: 0.2g corresponding to the steering angle step, center zone steering, and sinusoidal sweep frequency test conditions, and 0.4g corresponding to the steering angle pulse condition; the different load states include no load, half load, and full load.

4. The vehicle handling stability simulation analysis and post-processing method according to claim 1, characterized in that, The incremental change gradient of the n groups of different steering wheel angles in step S2.3 is ±20%.

5. The vehicle handling stability simulation analysis and post-processing method according to claim 1, characterized in that, The method for calculating the R² value of the polynomial fitting in step S2.4 is as follows: first calculate the average lateral acceleration y_mean, then calculate the total sum of squares SST and the regression sum of squares SSR, and finally obtain the goodness of fit value through R²=SSR / SST.

6. The vehicle handling stability simulation analysis and post-processing method according to claim 1, characterized in that, The handling and stability performance indicators mentioned in step S4 include at least one of the following: understeer, roll gradient, maximum lateral acceleration, yaw rate response time, overshoot, resonance peak level, steering stiffness, and steering wheel torque gradient.

7. The vehicle handling stability simulation analysis and post-processing method according to claim 1, characterized in that, The vehicle handling stability simulation conditions include steady-state rotation, steering angle pulse, steering angle step, center zone steering, sine sweep frequency, serpentine test, linear acceleration, and linear braking conditions.

8. The vehicle handling stability simulation analysis and post-processing method according to claim 1, characterized in that, The operations in steps S1 to S6 are all implemented through a GUI interface written in Python. The GUI interface supports visual operations such as selecting vehicle information files, pre-selecting steering wheel angles, creating working condition files, processing simulation results, fitting steering wheel angles, drawing, and generating reports.

9. The vehicle handling stability simulation analysis and post-processing method according to claim 1, characterized in that, It also includes a multi-model comparative analysis step: plotting the simulation index curves of different models on the same image to achieve a visual comparison of the handling stability and steering performance of multiple models.

10. A vehicle handling stability simulation analysis and post-processing system, characterized in that, The system for implementing the method according to any one of claims 1-9 comprises: The basic information database module is used to store vehicle parameters and basic information tables, vehicle load information tables, working condition output channel name tables, and working condition simulation index processing information tables, and supports data presetting, reading, and modification. The steering wheel angle calculation module includes a fixed circle simulation unit, a preliminary angle selection unit, multiple simulation units, a fitting and solution unit, and a final working condition creation unit. These components sequentially complete the acquisition of fixed circle simulation data, calculation of the preliminary steering wheel angle, simulation of multiple working conditions, precise steering wheel angle fitting, and creation of the final working condition file. The fitting and solution unit incorporates a NumPy polynomial fitting algorithm, which can automatically complete fitting from order 1 to n-1 and select the optimal fitting equation. The simulation execution module receives XML working condition files, submits them to Adams CAR software to complete batch simulation calculations, and outputs simulation result files of type .res; it automatically generates cmd and bat files for batch simulations to achieve automated simulation of multiple working conditions. The results processing module is used to read the res simulation results file, extract data and calculate the handling and stability performance indicators, and summarize the indicator results to an Excel file; it supports the calculation of pitch gradient indicators under linear acceleration and linear braking conditions, and is adapted to the handling and stability analysis needs of new energy vehicles with large axle loads. The plotting and report generation module is used to plot performance curves based on indicator results and save the images according to working conditions; it supports plotting indicator curves of multiple vehicle models on the same graph to achieve performance comparison of multiple vehicle models; it automatically inserts indicator results and plotting results into PPT to generate standardized simulation analysis reports. The GUI interaction module serves as the entry point for the above modules, enabling visual operations for each step and supporting file selection, parameter setting, command triggering, and result preview.

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