Offline testing method and offline testing device for vehicle steering system

Abstract

The application provides a vehicle steering system offline testing method and an offline testing device. The method comprises the following steps: activating an automatic driving mode of a vehicle, and controlling the vehicle to run along a preset test track; collecting running state data of the vehicle in real time during the running of the vehicle; comparing the running state data with a preset quality standard, and generating an offline testing result of the vehicle steering system based on a comparison result. In this way, the vehicle is controlled to run along the preset test track by using the automatic driving, which can improve the testing control accuracy of the vehicle steering system, and further improve the accuracy and consistency of the offline testing.

Description

Technical Field

[0001] This application relates to the field of off-line testing technology, and in particular to an off-line testing method and off-line testing device for a vehicle steering system. Background Technology

[0002] In the EOL (End of Line) stage of vehicle manufacturing, a comprehensive and accurate quality inspection of the steering system's performance is a crucial step in ensuring driving safety. This testing scenario requires the ability to effectively reproduce the vehicle's performance under real dynamic loads and accurately identify various potential faults, including steering symmetry, self-centering performance, and abnormal noises.

[0003] Existing technologies typically employ static bench testing or manual driving road tests for inspection. Static bench testing can only simulate limited road loads and cannot reproduce the real-world operating conditions of a vehicle under the coupled effects of centrifugal force and lateral acceleration. Manual driving road tests, on the other hand, heavily rely on the driver's experience, resulting in significant errors in steering wheel angle control. This leads to poor repeatability and large subjective errors in the testing process, making it difficult to reliably test indicators requiring precise control, such as steering symmetry. Summary of the Invention

[0004] In view of this, the purpose of this application is to provide a method and apparatus for off-line testing of a vehicle steering system. By using autonomous driving control to run the vehicle along a preset test trajectory, the test control accuracy of the vehicle steering system can be improved, thereby improving the accuracy and consistency of off-line testing.

[0005] In a first aspect, the present invention provides a method for off-line testing of a vehicle steering system, comprising: By activating the vehicle's autonomous driving mode, the vehicle is controlled to run along a preset test trajectory.

[0006] During vehicle operation, real-time data on the vehicle's operating status is collected.

[0007] The operational status data is compared with preset quality standards, and the offline test results of the vehicle steering system are generated based on the comparison results.

[0008] In an optional implementation, the step of controlling the vehicle to run according to a preset test trajectory by activating the vehicle's autonomous driving mode includes: Step S1: Based on the vehicle's current position, current speed, and preset test trajectory within the current control cycle, determine the target trajectory for the next control cycle.

[0009] Step S2: Determine the vehicle's target attitude data based on the target trajectory; the target attitude data includes the target sideslip angle and the target yaw rate.

[0010] Step S3: Based on the vehicle's current steering wheel angle, target attitude, and vehicle dynamics model, determine the steering angle reference value corresponding to the next control cycle; wherein, the vehicle dynamics model is used to characterize the correspondence between the vehicle's steering wheel angle and attitude data.

[0011] Step S4: Based on the current vehicle speed, preset vehicle parameters, and the turning radius corresponding to the target trajectory, determine the feedforward steering angle compensation amount for the next control cycle according to the preset feedforward calculation model.

[0012] Step S5: Based on the steering angle reference value and the feedforward steering angle compensation, generate the steering angle command corresponding to the next control cycle.

[0013] Step S6: Drive the vehicle's steering actuator according to the steering angle command, and repeat steps S1-S5 until the preset test trajectory is completed.

[0014] In an optional implementation, after the step of driving the vehicle's steering actuator according to the steering angle command, the method further includes: Get the real-time steering wheel angle.

[0015] Determine whether the real-time steering wheel angle matches the steering angle command.

[0016] If there is a discrepancy, the feedback correction amount is determined based on the deviation between the real-time steering wheel angle and the steering angle command, and the steering actuator is compensated based on the feedback correction amount.

[0017] In an optional implementation, the step of comparing the operating status data with a preset quality standard and generating the offline test results of the vehicle steering system based on the comparison results includes: After the vehicle completes the preset test trajectory, extract the key performance indicators from the running status data; there must be at least one key performance indicator.

[0018] Determine whether any key performance indicator exceeds its corresponding preset fault threshold.

[0019] If any key performance indicator exceeds its corresponding preset fault threshold, an anomaly is determined in the vehicle steering system, and the anomaly is recorded.

[0020] In optional implementations, key performance indicators include the difference between left and right turning angles, the integral difference between left and right turning torques, the return-to-center residual angle, the fluctuation of steering torque, and the sudden change in sound pressure level within a preset frequency band.

[0021] If any key performance indicator exceeds its corresponding preset fault threshold, the steps to determine that the offline test result is abnormal include: If the difference between the left and right turning angles corresponding to the intersection points of the preset test trajectory is greater than the preset turning angle difference threshold, or if the integral difference between any left and right turning torques during operation is greater than the preset integral difference threshold, it is determined that there is an asymmetry anomaly in the vehicle steering system.

[0022] If the residual angle at the test termination point exceeds the preset residual angle threshold, it is determined that the vehicle steering system has abnormal return-to-center performance.

[0023] If any steering torque fluctuation exceeds the preset torque fluctuation threshold during operation, it is determined that there is an abnormal torque fluctuation in the vehicle steering system.

[0024] If the sudden change in sound pressure level exceeds the preset change threshold, it is determined that there is an abnormal noise in the vehicle steering system.

[0025] In an optional implementation, after determining whether any key performance indicator exceeds its corresponding preset fault threshold, the method further includes: If each key performance indicator does not exceed its corresponding preset fault threshold, a comprehensive evaluation function is constructed based on the key performance indicators; the comprehensive evaluation function is used to perform weighted combination of each key performance indicator.

[0026] Adjust the weight coefficient of each key performance indicator according to the preset weight adjustment rules.

[0027] A comprehensive evaluation function is constructed based on weighting coefficients and key performance indicators; the comprehensive evaluation function is used to perform a weighted summation of each key performance indicator.

[0028] The comprehensive evaluation function is calculated based on the adjusted weighting coefficients to obtain the comprehensive quality assessment score of the vehicle steering system, and the comprehensive quality assessment score is recorded in the offline test results.

[0029] In an optional implementation, a preset weight adjustment rule is included, including: When the vehicle speed is within the preset high-speed range, the weight coefficient corresponding to the first key performance indicator is increased based on the preset step size; the first key performance indicator is data related to steering torque stability.

[0030] When the vehicle speed is in the preset low speed range, the weight coefficient corresponding to the second key performance indicator is increased based on the preset step size; the second key performance indicator is data related to acoustic signal anomaly detection.

[0031] In an optional implementation, the preset test trajectory is a figure-eight trajectory; the intersection point of the figure-eight trajectory is the position with the minimum radius of curvature.

[0032] In a second aspect, the present invention provides a vehicle steering system off-line testing device, comprising: a control module, and an on-board controller and a ground detection module respectively communicatively connected to the control module; the control module is used to execute the vehicle steering system off-line testing method of any of the foregoing embodiments.

[0033] In an optional implementation, the vehicle controller includes an autonomous driving control unit, a steering actuator, and an onboard sensor group; the onboard sensor group includes at least one of a torque sensor, an angle encoder, a microphone array, and a vehicle attitude monitoring device.

[0034] This application provides a method and apparatus for off-line testing of a vehicle steering system. By executing a preset trajectory in autonomous driving mode and collecting operational status data, the efficiency and objectivity of off-line testing can be improved, while reducing labor costs and error rates. Through the determination of key performance indicators and the calculation of a comprehensive evaluation function, anomalies can be fully identified and quality assessment results can be generated, thereby improving the intelligence level and overall reliability of off-line testing of the vehicle steering system.

[0035] Other features and advantages of this application will be set forth in the following description and will be apparent in part from the description or may be learned by practicing the application.

[0036] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0038] Figure 1 A schematic diagram of a vehicle steering system off-line testing device provided in an embodiment of this application; Figure 2 A flowchart of the offline testing method for a vehicle steering system provided in this application embodiment; Figure 3 A flowchart illustrating the autonomous driving mode control of vehicle operation provided in this application embodiment; Figure 4 This is a schematic diagram of an 8-shaped trajectory provided in an embodiment of this application.

[0039] Reference numerals: 1-Control module; 2-On-board controller; 3-Ground detection module; 21-Autonomous driving control unit; 22-Steering actuator; 23-On-board sensor group. Detailed Implementation

[0040] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0041] To facilitate understanding of this embodiment, the application scenarios and design concepts of this application will be briefly introduced.

[0042] There are many problems with the current offline testing process for vehicle steering system functions.

[0043] First, traditional testing methods struggle to simulate real-world dynamic driving conditions. For example, static bench tests can only simulate no more than 30% of the road load spectrum, failing to reproduce the actual working state of the steering system under the coupled effects of centrifugal force and lateral acceleration. Simple straight-line or single-curve tests can only trigger no more than 40% of steering system failure modes, rendering them ineffective for faults such as localized wear and abnormal noises that require bidirectional load variations to be detected.

[0044] Secondly, manual driving road tests suffer from serious accuracy and consistency issues. Due to the driver's manual operation, the steering wheel angle control error can be as high as ±5° to ±8°, resulting in a confidence level of less than 85% for detecting precise indicators such as left and right steering symmetry. This reliance on human subjectivity leads to poor repeatability of test results and makes it difficult to guarantee stable test quality.

[0045] Finally, existing technologies also have shortcomings in terms of inspection efficiency and completeness. Traditional methods typically cannot simultaneously inspect multiple key indicators such as steering symmetry, self-centering performance, and abnormal noises, and the inspection time for a single vehicle generally exceeds 15 minutes, which is difficult to meet the cycle time requirements of high-efficiency production.

[0046] To address this, this application proposes a method and apparatus for offline testing of vehicle steering systems. By activating the vehicle's autonomous driving mode, the vehicle is controlled to run along a preset test trajectory, and operational status data is collected in real time during operation. This allows for offline testing of the vehicle steering system without the need for manual driving or subjective judgment, thus ensuring the repeatability and data consistency of the testing process. By generating steering angle commands based on vehicle dynamics models, feedforward calculation models, and feedback correction mechanisms, the automatic completion of the test trajectory can be precisely controlled, thereby improving testing accuracy and stability. By extracting key performance indicators from operational status data and comparing them with preset thresholds, the types of anomalies in the vehicle steering system can be comprehensively identified. By constructing a comprehensive evaluation function when key performance indicators are qualified and combining it with adaptive weight adjustment based on vehicle speed, a comprehensive quality assessment score reflecting overall performance can be obtained, thus achieving intelligent comprehensive evaluation while meeting traditional offline testing criteria. Furthermore, this application can improve the efficiency and objectivity of offline testing, reduce labor costs and misjudgment rates, and enhance the intelligence level and overall reliability of offline testing of vehicle steering systems.

[0047] After introducing the application scenarios and design concepts of this application, the technical solutions provided by this application will be described in detail below.

[0048] This application provides an off-line testing device for a vehicle steering system, referring to... Figure 1 The vehicle steering system offline testing device provided in this application includes: a control module 1, and an on-board controller 2 and a ground detection module 3, which are respectively communicatively connected to the control module 1; the control module 1 is used to execute the vehicle steering system offline testing method.

[0049] The vehicle controller 2 includes an autonomous driving control unit 21, a steering actuator 22, and an on-board sensor group 23; the on-board sensor group 23 includes at least one of a torque sensor, an angle encoder, a microphone array, and a vehicle attitude monitoring device.

[0050] In one embodiment, the vehicle controller 2 is integrated inside the vehicle under test and is responsible for specific autonomous driving control, command execution and data acquisition. It mainly includes an autonomous driving control unit 21, a steering execution unit 22 and a vehicle sensor group 23.

[0051] The autonomous driving control unit 21 integrates a high-precision real-time vehicle dynamics model. It is responsible for the autonomous driving mode, employing model predictive control algorithms for roll optimization to generate high-precision steering angle commands. Simultaneously, it also incorporates a feedforward-feedback composite controller to compensate for and correct the commands, addressing the physical delays in the steering system.

[0052] The steering actuator 22 employs a steer-by-wire actuator. It is responsible for receiving the final steering angle command from the automatic driving control unit 21 and driving the vehicle's steering mechanism with a control accuracy of no less than ±0.5°, ensuring that the vehicle can strictly follow the preset test trajectory.

[0053] The onboard sensor group 23 is responsible for comprehensively collecting the vehicle's status data during operation. This sensor group includes at least one or more of the following: A torque sensor, mounted on the steering wheel input shaft, is used to measure the magnitude and fluctuation of steering torque.

[0054] An angle encoder, mounted on the steering column, is used to accurately measure the real-time steering wheel angle and to perform feedback control and performance calculations.

[0055] A microphone array, deployed inside the steering cabin, is used to collect acoustic signals in specific frequency bands (such as 200-4000Hz) for abnormal noise detection and location.

[0056] The vehicle attitude monitoring device is an IMU (Inertial Measurement Unit). It is used to monitor the vehicle's attitude in real time, providing core dynamic parameters such as yaw rate and lateral acceleration, and providing data support for MPC (Model Predictive Control) algorithms and multi-parameter coupling analysis.

[0057] In addition, the sensor array may include a Hall effect current sensor to monitor phase current harmonics of the steering motor and assist in the diagnosis of latent electrical faults.

[0058] In this embodiment, the ground detection module 3 is deployed at the test site and is responsible for providing high-precision positioning, data analysis and communication assurance. It mainly includes a high-precision positioning system, a central analysis platform and a V2X communication base station (Vehicle-to-Everything Communication Base Station).

[0059] To achieve high-precision tracking control, the high-precision positioning system employs a fusion of UWB (Ultra-Wideband) wireless communication technology, RTK-GNSS (Real-Time Kinematic Global Navigation Satellite System), and laser-based SLAM (Laser-based Simultaneous Localization and Mapping). This high-precision positioning system can provide the vehicle under test with real-time location information at the centimeter level, or even better than 1 cm, enabling accurate trajectory tracking.

[0060] The central analysis platform is the physical carrier of control module 1. It is responsible for receiving all operational status data transmitted in real time by the vehicle controller 2 via V2X. After the vehicle completes testing, the central analysis platform diagnoses the operational data obtained from the test, including extracting key performance indicators, performing specific fault diagnosis, constructing a comprehensive evaluation function under applicable conditions for overall quality assessment, and finally generating a test report that meets preset standards.

[0061] The V2X communication base station is used to ensure low-latency, high-bandwidth wireless communication between the vehicle controller 2 and the ground detection module 3, ensuring the real-time and complete transmission of operational data.

[0062] In a typical test, the workflow of the offline testing device is as follows: First, the vehicle under test enters the test area covered by the high-precision positioning system. After receiving the start command, the onboard controller 2's autonomous driving control unit 21 begins to execute the high-precision tracking control method, driving the steering execution unit 22 to control the vehicle to run along a preset figure-eight trajectory. Simultaneously, the onboard sensor group 23 collects the entire running status data in real time. During and after the operation, the data is transmitted to the ground detection module 3 in real time via the V2X communication base station. Finally, the central analysis platform processes and analyzes all the data, executes hierarchical diagnostic logic, and generates the final offline test report.

[0063] The vehicle steering system offline testing method provided in this application integrates a high-precision positioning system, an autonomous driving control unit 21, and a multi-channel sensor group to achieve high-precision automated control of the vehicle under dynamic trajectories. It not only realistically simulates dynamic load conditions that traditional static test benches cannot reproduce, but also enables objective and in-depth fault diagnosis through a collaborative analysis platform between the vehicle and the ground. This significantly improves steering angle control accuracy while expanding the fault coverage to include many hidden faults that are difficult to detect using traditional methods. Ultimately, it shortens the single-vehicle testing time and improves the automation level and diagnostic reliability of offline testing.

[0064] Based on the above embodiments, this application provides a method for off-line testing of a vehicle steering system, referring to... Figure 2 The general flow of the vehicle steering system off-line testing method provided in this application embodiment is as follows: Step S101: Activate the vehicle's autonomous driving mode and control the vehicle to run according to the preset test trajectory.

[0065] Activating the vehicle's autonomous driving mode can be achieved in several ways. In one embodiment, the tester can manually send a start command to activate the autonomous driving mode of the vehicle under test through the human-machine interface of the ground detection module. In another embodiment, the activation process can also be fully automatic. For example, when the vehicle under test recognizes that it has entered a preset test area through its own sensors (such as an RFID reader, UWB tag, or vision camera), its autonomous driving mode is automatically triggered.

[0066] Controlling a vehicle to run along a preset test trajectory means using the onboard autonomous driving control unit to precisely drive the vehicle's steering, acceleration, and braking mechanisms, making it strictly follow a pre-set path.

[0067] The preset test trajectory is designed to fully stimulate the performance of the steering system under test under different dynamic loads. In a preferred embodiment, refer to... Figure 4 The preset test trajectory is designed as a figure-eight pattern. The intersection point of the figure-eight trajectory has the minimum radius of curvature, which generates the maximum lateral acceleration, making it an ideal condition for verifying steering symmetry. Its apex (the point on the same horizontal line as the intersection point) has an infinite radius of curvature, providing optimal conditions for verifying steering self-centering performance. When production line space is limited, the figure-eight trajectory can be decomposed into a combination of S-curves and circular trajectories. In a feasible embodiment, the test trajectory can also be other composite trajectory capable of effectively evaluating steering performance, such as continuous S-curves, slalom courses, or a custom combination trajectory composed of arcs and straight lines with different radii of curvature.

[0068] Vehicle control can be achieved using a variety of advanced control algorithms. In one feasible embodiment, a mature industrial proportional-integral-derivative controller, a linear quadratic regulator, or a controller based on artificial intelligence algorithms such as fuzzy logic and neural networks can be used to achieve high-precision trajectory tracking of the vehicle.

[0069] Step S102: During the operation of the vehicle, real-time data on the vehicle's operating status is collected.

[0070] Here, operational status data is collected through an onboard sensor array deployed on the vehicle. In a preferred embodiment, the operational status data includes steering wheel torque collected by a torque sensor, real-time steering wheel angle collected by an angle encoder, acoustic signals from within the steering gear compartment collected by a microphone array, and yaw rate and lateral acceleration collected by vehicle attitude monitoring equipment. In other embodiments, the operational status data may further include phase current data of the steering motor, vehicle speed sensor data, suspension travel sensor data, and high-precision GPS (Global Positioning System) / GNSS (Global Navigation Satellite System) data for trajectory verification, etc.

[0071] Throughout a complete test run, data from one or more sensors is continuously and uninterruptedly recorded. The data acquisition frequency should be high enough (e.g., no less than 1 kHz) to capture instantaneous torque fluctuations or abnormal noise signals. The acquired data can be cached in the vehicle-mounted controller and uploaded uniformly after the test. Alternatively, the data can be streamed in real time to the central analysis platform of the ground detection module via the vehicle's wireless communication module (such as a V2X or 5G module).

[0072] Step S103: Compare the operating status data with the preset quality standards, and generate the offline test results of the vehicle steering system based on the comparison results.

[0073] Here, the preset quality standard can be a combination of one or more judgment criteria.

[0074] In a preferred embodiment, the preset quality standard can be a hierarchical diagnostic system. The first level is explicit fault diagnosis based on fixed fault thresholds, which involves extracting a series of key performance indicators (such as left and right steering angle difference, return-to-center residual angle, torque fluctuation, etc.) from the operating status data and determining whether these indicators exceed their respective explicit numerical thresholds. The second level is a comprehensive quality assessment initiated after the first level diagnosis is passed, which involves constructing a multi-parameter coupled comprehensive evaluation function to provide a quantitative score for the overall quality of the steering system.

[0075] In other feasible embodiments, the preset quality standard can also be based on a comparison with a reference template. For example, the operating status data of one or more sample vehicles under the same test trajectory can be collected in advance and used as a standard template. During the test, the data curve of the vehicle under test is compared with the standard template by waveform comparison or correlation analysis, and the deviation is used as the basis for judgment.

[0076] For example, a preset quality standard can also be a pre-trained machine learning model (such as anomaly detection algorithms like Isolation Forest or Autoencoder). This model learns from a large amount of data on qualified and unqualified vehicles, thus forming its own quality standard. During testing, the operating status data of the vehicle under test is input into the model, which then directly outputs a judgment result of normal or abnormal.

[0077] Based on the comparison results, the offline test results are generated, i.e., a test report is output.

[0078] If the comparison results show an anomaly (e.g., any key performance indicator exceeds the fault threshold), the test result should be clearly marked as failing, and the specific anomalies should be listed in detail. Relevant data graphs can also be attached as evidence, and the subsequent maintenance and / or re-inspection process should be automatically triggered.

[0079] If the comparison results are normal, the test result is marked as passed. Furthermore, the report can include a comprehensive quality assessment score, used for quantitative management of overall vehicle quality, monitoring of production batch consistency, and early warning of potential quality risks.

[0080] In one embodiment, reference is made to Figure 3 Step S101 includes the following steps S1-S6.

[0081] Step S1: Based on the vehicle's current position, current speed, and preset test trajectory within the current control cycle, determine the target trajectory for the next control cycle.

[0082] Here, at the beginning of each control cycle, the onboard autonomous driving control unit first obtains the vehicle's precise current position in the test field through a high-precision positioning system, and combines this with the current vehicle speed obtained from the vehicle speed sensor. Based on real-time information and a pre-loaded, complete preset test trajectory, the autonomous driving control unit plans a short target trajectory that the vehicle should follow in the next very short time period (i.e., the next control cycle) starting from the current moment.

[0083] Step S2: Determine the vehicle's target attitude data based on the target trajectory; the target attitude data includes the target sideslip angle and the target yaw rate.

[0084] Here, the target trajectory not only defines the spatial path but also implicitly encapsulates the vehicle's expected dynamic performance along that path. The autonomous driving control unit calculates the ideal dynamic state the vehicle should achieve in the next control cycle—the target attitude data—based on the geometric characteristics of the target trajectory (such as curvature) and the desired vehicle speed. Specifically, the target attitude data may include the target sideslip angle β and the target yaw rate. .

[0085] Step S3: Based on the vehicle's current steering wheel angle, target attitude, and vehicle dynamics model, determine the steering angle reference value corresponding to the next control cycle; wherein, the vehicle dynamics model is used to characterize the correspondence between the vehicle's steering wheel angle and attitude data.

[0086] Here, the MPC controller within the autonomous driving control unit performs rolling optimization. Specifically, the autonomous driving control unit uses a pre-established vehicle dynamics model for forward prediction. The vehicle dynamics model characterizes the mathematical relationship between steering wheel angle (input) and vehicle attitude data (output). The MPC algorithm virtually tries multiple different steering angle inputs and predicts, using the vehicle dynamics model, the expected deviation (i.e., tracking error) between the vehicle's actual attitude and the determined target attitude in the next control cycle under each virtual input. Through rapid iteration, an optimal steering angle that minimizes the expected tracking error is finally found; this optimal solution is determined as the steering angle reference value. .

[0087] Step S4: Based on the current vehicle speed, preset vehicle parameters, and the turning radius corresponding to the target trajectory, determine the feedforward steering angle compensation amount for the next control cycle according to the preset feedforward calculation model.

[0088] Here, to compensate for the inherent physical response delay of the steering system (including the steering gear, tires, etc.), this embodiment employs feedforward control for pre-compensation. The autonomous driving control unit will determine the pre-compensation based on the current vehicle speed and inherent vehicle parameters (such as wheelbase). Steering characteristics Based on the target trajectory's turning radius R, and a pre-defined feedforward calculation model, a feedforward steering angle compensation is calculated. .

[0089] The feedforward calculation model is as follows: As shown. Among them, This is the feedforward steering angle compensation amount; Wheelbase; The target turning radius; The understeer gradient of the vehicle; For vehicle speed; This is the correlation coefficient for vehicle speed.

[0090] The feedforward steering angle compensation is adjusted according to the square of the vehicle speed. The higher the vehicle speed, the greater the compensation, thereby achieving effective compensation for dynamic delay.

[0091] Step S5: Based on the steering angle reference value and the feedforward steering angle compensation, generate the steering angle command corresponding to the next control cycle.

[0092] Here, the steering angle command .

[0093] Step S6: Drive the vehicle's steering actuator according to the steering angle command, and repeat steps S1-S5 until the preset test trajectory is completed.

[0094] Here, the autonomous driving control unit sends steering angle commands to the vehicle's steering actuators, such as a high-precision steer-by-wire actuator. Based on the steering commands, the steer-by-wire actuator drives the wheels to rotate. Subsequently, the entire cycle of steps S1 to S5 is repeated, performing rolling optimization and closed-loop corrections at a high frequency (e.g., tens to hundreds of times per second) until the vehicle has completed all the preset test tracks.

[0095] In one embodiment, after step S6, in which the steering execution unit of the vehicle is driven according to the steering angle command, the method further includes the following steps S301-S303.

[0096] Step S301: Obtain the real-time steering wheel angle.

[0097] Here, during the control process, the real-time steering wheel angle is obtained by an angle encoder deployed on the steering column.

[0098] Step S302: Determine whether the real-time steering wheel angle is consistent with the steering angle command.

[0099] Here, the real-time steering angle is compared with the expected steering angle (steering angle reference value).

[0100] In step S303, if there is a discrepancy, a feedback correction amount is determined based on the deviation between the real-time steering wheel angle and the steering angle command, and the steering execution unit is compensated based on the feedback correction amount.

[0101] Here, the real-time steering angle is calculated and compared with the steering angle reference value to obtain the deviation between the two, i.e., the tracking error e. Based on the tracking error e, the feedback controller calculates a feedback correction amount.

[0102] The feedback correction amount is calculated using the following formula: As shown. Among them, This is the amount of feedback correction; The deviation between the target steering wheel angle and the actual steering wheel angle; For proportional gain; This is the integral gain.

[0103] Therefore, in a more complete embodiment, the steering angle command is generated by combining the steering angle reference value, the feedforward steering angle compensation amount, and the feedback correction amount, as in the steering angle command. .

[0104] In one embodiment, step S103 includes the following steps S401-S403.

[0105] Step S401: After the vehicle completes the operation of the preset test trajectory, extract the key performance indicators from the operation status data; there is at least one key performance indicator.

[0106] Here, after the vehicle completes its journey, the onboard controller collects real-time operational status data (including but not limited to torque, angle, acoustic signals, and attitude data) and transmits it to the central analysis platform. The platform first processes these raw data streams, extracting a series of values ​​that can quantify the steering system's performance in this dynamic test, namely key performance indicators.

[0107] Step S402: Determine whether any key performance indicator exceeds its corresponding preset fault threshold.

[0108] Here, after extracting the key performance indicators, a clear fault diagnosis is performed.

[0109] Step S403: If any key performance indicator exceeds its corresponding preset fault threshold, it is determined that there is an abnormality in the vehicle steering system, and the abnormality item is recorded.

[0110] Here, if the calculated value of any key performance indicator in any diagnostic project exceeds its corresponding preset fault threshold, the final offline test result will be determined to be abnormal (i.e., fail), and the specific abnormal items corresponding to the unqualified key performance indicators (such as symmetry abnormality, abnormal noise) will be recorded in detail in the generated report to facilitate subsequent quality traceability and maintenance.

[0111] In optional implementations, key performance indicators include the difference between left and right turning angles, the integral difference between left and right turning torques, the return-to-center residual angle, the fluctuation of steering torque, and the sudden change in sound pressure level within a preset frequency band.

[0112] Here, the difference in left and right steering angles reflects the symmetry of the response on both sides of the steering system. The integral difference in left and right steering torque reflects the balance of forces on both sides of the steering system. The self-centering residual angle is used to evaluate the steering system's ability to automatically return to center. Steering torque fluctuation is used to evaluate the smoothness and stability of the steering process. The sound pressure level abrupt change value is used to identify unsteady-state impacts or friction noises. The preset frequency band can be set to 200-4000Hz.

[0113] Step S403 includes: If the difference between the left and right turning angles corresponding to the intersection points of the preset test trajectory is greater than the preset turning angle difference threshold, or if the integral difference between any left and right turning torques during operation is greater than the preset integral difference threshold, it is determined that there is an asymmetry anomaly in the vehicle steering system.

[0114] Here, the angle encoder data is analyzed when the vehicle passes through the intersection of the figure-eight trajectory. If the absolute value of the calculated difference between the left and right turning angles is greater than the preset turning angle difference threshold (e.g., 1.5°), then a symmetry anomaly is determined to exist.

[0115] Calculate the cumulative torque values ​​(integrals) for all left and right turns during the entire test process, and compare the percentage difference between the two. If the difference exceeds a preset integral difference threshold (e.g., 5%), an asymmetry anomaly is determined to exist.

[0116] If the residual angle at the test termination point exceeds the preset residual angle threshold, it is determined that the vehicle steering system has abnormal return-to-center performance.

[0117] Here, at the test termination point, i.e., after the vehicle has come to a complete stop, the steering column angle is measured by the angle encoder. If the absolute value of this angle (i.e., the residual angle) is greater than a preset residual angle threshold (e.g., 2°), it is determined that there is an abnormality in the self-centering performance. In a more specific embodiment, it can also be determined at the same time whether the residual angular velocity at this time is greater than a preset angular velocity threshold (e.g., 0.5° / s) to avoid design defects of overdamping or underdamping.

[0118] If any steering torque fluctuation exceeds the preset torque fluctuation threshold during operation, it is determined that there is an abnormal torque fluctuation in the vehicle steering system.

[0119] Here, the torque data measured by the torque sensor during the entire operation is analyzed, and its fluctuation amplitude (ΔTorque) is calculated. If the fluctuation amplitude is greater than the preset torque fluctuation threshold (e.g., 3Nm), it is determined that there is an abnormal torque fluctuation, which may mean that there is jamming or abnormal resistance during steering.

[0120] If the sudden change in sound pressure level exceeds the preset change threshold, it is determined that there is an abnormal noise in the vehicle steering system.

[0121] Here, the acoustic signal collected by the microphone is analyzed. If the sound pressure level of the signal changes abruptly within a short period of time, and the change value exceeds a preset threshold (e.g., 6dB), then an abnormal noise is determined to exist.

[0122] In some embodiments, once an abnormal noise is detected, beamforming technology can be used to locate the sound source in three dimensions to assist in subsequent maintenance.

[0123] In one specific case, a characteristic peak of 92dB was detected in the 200Hz frequency band, while the background noise was only 86dB, thus triggering the localization process.

[0124] Once an abnormal noise is detected, the acoustic detection module in the ground detection system will initiate beamforming analysis. Beamforming technology, by processing the time difference of signals received by multiple microphones in the microphone array, can calculate the specific location of the sound source in space, achieving three-dimensional localization of the abnormal noise source. In this case, using this technology, the sound source coordinates were successfully located below the steering column (-0.35m, 1.2m).

[0125] After locating the physical position of the sound source, the acoustic event is further correlated and analyzed with other sensor data in order to diagnose the root cause of the fault.

[0126] In this case, the 200Hz abnormal noise located below the steering column was correlated with the 3.5Nm torque fluctuation spectrum detected by the torque sensor at that moment, occurring at a steering angle of ±120°. Through this data coupling, the abnormal noise was ultimately diagnosed as being caused by excessive universal joint clearance, and a repair work order was automatically triggered.

[0127] In one embodiment, after a clear fault diagnosis based on a fixed fault threshold, if all key performance indicators do not exceed their corresponding fault thresholds, i.e., the vehicle does not have a clear, single fault, a quality assessment is performed on the vehicle. The overall performance of the steering system is quantitatively scored to identify potential edge performance or quality risks that have not yet triggered a single fault threshold. Following step S401 are the following steps S501-S504.

[0128] Step S501: If each key performance indicator does not exceed its corresponding preset fault threshold, construct a comprehensive evaluation function based on the key performance indicators; the comprehensive evaluation function is used to weight and combine each key performance indicator.

[0129] Here, once the vehicle passes a clear fault diagnosis, the central analysis platform constructs a comprehensive evaluation function based on key performance indicators collected throughout the testing process. The core purpose of this comprehensive evaluation function is to couple multiple performance dimensions through a mathematical model into a single, quantitatively assessing comprehensive score that evaluates the overall health of the steering system.

[0130] In a preferred embodiment, the comprehensive evaluation function is a model that weights and sums multiple core performance parameters. These core performance parameters are closely related to key performance indicators and may include: steering symmetry deviation (e.g., the absolute value of the difference between left and right steering angles); torque tracking error integral (e.g., the cumulative value of the difference between the actual steering torque and the target steering torque throughout the testing process); and abnormal noise sound pressure level (e.g., the average or peak sound pressure level measured within a specific frequency band (200-400Hz).

[0131] The comprehensive evaluation function is shown in the following formula:

[0132] in, This indicates the steering wheel angle when the vehicle turns left or right; This indicates a difference in steering symmetry. If the difference is large, it means that the angles of turning the steering wheel on the left and right are not balanced, which may indicate a mechanical or power steering system malfunction.

[0133] This refers to the actual steering wheel turning torque. For the theoretical or target steering wheel torque, the integral term It reflects the cumulative amount of torque error during the test and can detect whether there is a continuous deviation or fluctuation during steering.

[0134] It measures sound pressure level, with a specific frequency range of 200-4000Hz, and is used to detect abnormal noises during steering.

[0135] Weighting coefficient , and It is dynamically adjusted according to the preset weight adjustment rules.

[0136] Step S502: Adjust the weight coefficient of each key performance indicator according to the preset weight adjustment rules.

[0137] In one embodiment, a preset weight adjustment rule is included: When the vehicle speed is within the preset high-speed range, the weight coefficient corresponding to the first key performance indicator is increased based on the preset step size; the first key performance indicator is data related to steering torque stability.

[0138] When the vehicle speed is in the preset low speed range, the weight coefficient corresponding to the second key performance indicator is increased based on the preset step size; the second key performance indicator is data related to acoustic signal anomaly detection.

[0139] Here, the default weight adjustment rule is to adjust based on vehicle speed.

[0140] When the vehicle speed is within the preset high-speed range, the vehicle's handling stability is crucial, thus increasing the weighting coefficients of performance parameters related to steering torque stability (such as the integral of torque tracking error). .

[0141] When the vehicle speed is within a preset low-speed range, problems such as friction and clearance in mechanical components are more easily exposed through abnormal noises. Therefore, it is important to increase the weighting coefficients of performance parameters related to acoustic signal anomaly detection (such as abnormal noise sound pressure level). .

[0142] Step S503: Based on the weighting coefficients and key performance indicators, construct a comprehensive evaluation function; the comprehensive evaluation function is used to perform a weighted summation of each key performance indicator.

[0143] Here, after determining the weighting coefficients of each performance parameter based on the real-time vehicle speed, the final value of the comprehensive evaluation function is calculated based on the adjusted weighting coefficients, thereby obtaining a quantitative comprehensive quality assessment score.

[0144] Step S504: Calculate the comprehensive evaluation function based on the adjusted weighting coefficients to obtain the comprehensive quality evaluation score corresponding to the vehicle steering system, and record the comprehensive quality evaluation score in the offline test results.

[0145] Here, the overall quality assessment score is recorded in the final generated off-line test results. At this point, the off-line test result will be determined as passed because the vehicle did not trigger any explicit malfunctions. However, this overall quality assessment score attached to the report can provide the quality management department with richer information, such as for monitoring the consistency of production batches, identifying performance trends, and providing early warnings for products on the verge of failure.

[0146] In a specific embodiment of the off-line steering system test of a certain model of pure electric SUV, a pure electric SUV that has completed final assembly is used as the test vehicle and enters a dedicated test area where the off-line test device described in this application is deployed.

[0147] After the vehicle enters the testing area, the system activates its autonomous driving mode. The onboard controller establishes V2X communication with the ground testing module, and the high-precision positioning system begins to provide the vehicle with centimeter-level real-time location information.

[0148] Under autonomous driving control, the vehicle began to travel along a preset standard figure-eight trajectory, completing three laps and covering a total distance of approximately 150 meters. Throughout the test, the onboard sensor array collected and transmitted various operational status data to the central analysis platform in real time.

[0149] The central analysis platform performs real-time monitoring and post-event analysis on the received data, triggering the following two typical diagnostic cases: Case 1: Diagnosis and Location of Abnormal Noises During the test, the platform monitored the following acceptable data in real time: steering torque fluctuation was 3.2 Nm (acceptable threshold is ≤5 Nm), and the difference between left and right turning angles was 1.2° (acceptable threshold is ≤1.5°). However, the microphone array of the acoustic detection module detected an abnormal sound pressure peak of 92 dB in the 200 Hz frequency band, which was significantly higher than the background noise of 86 dB.

[0150] Subsequently, the system initiated beamforming analysis, successfully pinpointing the source coordinates of the abnormal noise to a location below the steering column (-0.35m, 1.2m). The platform further coupled this noise source information with the torque spectrum analysis, discovering that the abnormal noise occurred simultaneously with a 3.5Nm torque fluctuation at a steering angle of ±120°. Based on this chain of multiple data pieces of evidence, the system ultimately diagnosed the fault as an out-of-tolerance clearance in the steering column universal joint and automatically generated a report indicating a failed inspection and a repair work order.

[0151] Case 2: Diagnosis of Hidden Electrical Faults In another test, the vehicle's left and right turning angle difference was 1.3°, within the acceptable threshold of 1.5°. However, when the central analysis platform conducted a more in-depth analysis, it found that the integral difference of its left and right turning torque reached 7.2%, exceeding the preset fault threshold of 5%.

[0152] Based on the specific phenomenon of symmetrical steering angle but asymmetrical torque, the system's built-in fault tree analysis logic pointed to an imbalance in the three-phase current of the steering motor as the root cause of the fault. Subsequent disassembly and inspection revealed a partial short circuit in the steering motor windings. This case demonstrates that this application can effectively diagnose latent faults that cannot be detected by a single indicator through the correlation and comparison of multiple indicators.

[0153] This application provides a method for offline testing of a vehicle steering system. By activating the vehicle's autonomous driving mode, the vehicle is controlled to run along a preset test trajectory, and operational status data is collected in real time during the operation. This method can complete the offline testing of the vehicle steering system without the need for manual driving or subjective judgment, thereby ensuring the repeatability of the testing process and the consistency of the data. Furthermore, this application can improve the efficiency and objectivity of offline testing, reduce labor costs and error rates, and enhance the intelligence level and overall reliability of offline testing of vehicle steering systems.

[0154] The computer program product provided in this application includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the preceding method embodiments. For specific implementation details, please refer to the method embodiments, which will not be repeated here.

[0155] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system and apparatus described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0156] Furthermore, in the description of the embodiments of this application, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0157] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0158] In the description of this application, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0159] Finally, it should be noted that the above-described embodiments are merely specific implementations of this application, used to illustrate the technical solutions of this application, and not to limit them. The protection scope of this application is not limited thereto. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the scope of the technology disclosed in this application, or make equivalent substitutions for some of the technical features. Such modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be covered within the protection scope of this application.

Claims

1. A method of off-line testing of a vehicle steering system, characterized by, include: Step S1: Based on the vehicle's current position, current speed, and preset test trajectory within the current control cycle, determine the target running trajectory for the next control cycle; Step S2: Determine the target attitude data of the vehicle based on the target trajectory; The target attitude data includes the target sideslip angle and the target yaw rate; Step S3: Based on the vehicle's current steering wheel angle, the target attitude, and the vehicle dynamics model, determine the steering angle reference value corresponding to the next control cycle; wherein, the vehicle dynamics model is used to characterize the correspondence between the vehicle's steering wheel angle and attitude data; Step S4: Based on the current vehicle speed, preset vehicle parameters, and the turning radius corresponding to the target trajectory, determine the feedforward steering angle compensation amount corresponding to the next control cycle according to the preset feedforward calculation model; Step S5: Based on the steering angle reference value and the feedforward steering angle compensation amount, generate the steering angle command corresponding to the next control cycle; Step S6: Drive the vehicle's steering execution unit according to the steering angle command, and repeat steps S1-S5 until the preset test trajectory is completed; During the operation of the vehicle, the vehicle's operating status data is collected in real time; The operational status data is compared with preset quality standards, and the offline test results of the vehicle steering system are generated based on the comparison results.

2. The method of claim 1, wherein After the step of driving the vehicle's steering actuator according to the steering angle command, the method further includes: Get the real-time steering wheel angle; Determine whether the real-time steering wheel angle is consistent with the steering angle command; If there is a discrepancy, a feedback correction amount is determined based on the deviation between the real-time steering wheel angle and the steering angle command, and the steering execution unit is compensated based on the feedback correction amount.

3. The method for offline testing of a vehicle steering system according to claim 1, characterized in that, The step of comparing the operational status data with preset quality standards and generating the offline test results of the vehicle steering system based on the comparison results includes: After the vehicle completes the preset test trajectory, key performance indicators are extracted from the running status data; there is at least one key performance indicator. Determine whether any of the key performance indicators exceeds its corresponding preset fault threshold; If any of the key performance indicators exceeds its corresponding preset fault threshold, it is determined that there is an abnormality in the vehicle steering system, and the abnormality is recorded.

4. The method for offline testing of a vehicle steering system according to claim 3, characterized in that, The key performance indicators include the difference between left and right turning angles, the integral difference between left and right turning torques, the return-to-center residual angle, the steering torque fluctuation, and the sudden change value of sound pressure level within the preset frequency band. If any of the key performance indicators exceeds its corresponding preset fault threshold, the step of determining that the offline test result is abnormal includes: If the difference between the left and right turning angles corresponding to the intersection of the preset test trajectory is greater than the preset turning angle difference threshold, or if the integral difference between the left and right turning torques during operation is greater than the preset integral difference threshold, it is determined that there is an asymmetry anomaly in the vehicle steering system. If the residual angle corresponding to the test termination point exceeds the preset residual angle threshold, it is determined that the vehicle steering system has abnormal return-to-center performance. If any of the steering torque fluctuations during the operation exceeds a preset torque fluctuation threshold, it is determined that there is an abnormal torque fluctuation in the vehicle steering system. If the sudden change value of the sound pressure level exceeds the preset change threshold, it is determined that there is an abnormal noise in the vehicle steering system.

5. The method for offline testing of a vehicle steering system according to claim 3, characterized in that, After determining whether any of the key performance indicators exceeds its corresponding preset fault threshold, the method further includes: If none of the key performance indicators exceed their corresponding preset fault thresholds, a comprehensive evaluation function is constructed based on the key performance indicators; the comprehensive evaluation function is used to perform a weighted combination of each key performance indicator. According to the preset weight adjustment rules, adjust the weight coefficient of each key performance indicator; Based on the weighting coefficients and the key performance indicators, a comprehensive evaluation function is constructed; the comprehensive evaluation function is used to perform a weighted summation on each of the key performance indicators. The comprehensive evaluation function is calculated based on the adjusted weighting coefficients to obtain the comprehensive quality assessment score corresponding to the vehicle steering system, and the comprehensive quality assessment score is recorded in the offline test results.

6. The method for offline testing of a vehicle steering system according to claim 5, characterized in that, The preset weight adjustment rules include: When the vehicle speed is within a preset high-speed range, the weighting coefficient of the first key performance indicator is increased based on a preset step size; the first key performance indicator is data related to steering torque stability. When the vehicle speed is in a preset low-speed range, the weighting coefficient corresponding to the second key performance indicator is increased based on the preset step size; the second key performance indicator is data related to acoustic signal anomaly detection.

7. The method for offline testing of a vehicle steering system according to claim 1, characterized in that, The preset test trajectory is a figure-eight shaped trajectory; the intersection point of the figure-eight shaped trajectory is the position with the minimum radius of curvature.

8. A vehicle steering system off-line testing device, characterized in that, include: The control module includes an on-board controller and a ground detection module, which are respectively communicatively connected to the control module; the control module is used to perform the offline testing method for the vehicle steering system according to any one of claims 1-7.

9. The off-line testing device for a vehicle steering system according to claim 8, characterized in that, The vehicle controller includes an autonomous driving control unit, a steering actuator, and an on-board sensor group; the on-board sensor group includes at least one of a torque sensor, an angle encoder, a microphone array, and a vehicle attitude monitoring device.

Images