HIL test method, system and device for multi-target torque control algorithm and medium

Abstract

According to the HIL test method, system, equipment and medium of the multi-target torque control algorithm, actual vehicle dynamic characteristics are simulated by means of a distributed driving vehicle model constructed by vehicle dynamic simulation software, and signal interaction between the vehicle model and a controller is achieved through a signal analysis module. Closed-loop interaction of vehicle dynamic characteristics and algorithm control logic in a simulation process is constructed, a virtual test environment close to an actual driving scene is provided for an algorithm, independent signal transmission between a test end and a controller can be realized by using special communication equipment, signal interference between a simulation line and a test line is avoided, and the test efficiency is improved. The accuracy of monitoring quantity data acquisition and the reliability of algorithm parameter debugging are ensured; aiming at multiple control targets such as longitudinal driving skid resistance, lateral control stability and working condition self-adaptive switching which need to be realized by a multi-target torque control algorithm, the control logic and parameter adaptability of the algorithm under different driving working conditions is verified respectively, and the multi-target control requirement can be met under longitudinal, lateral and working condition switching scenes.

Description

Technical Field

[0001] This application relates to the interdisciplinary field of vehicle engineering and hardware-in-the-loop testing, specifically to a HIL testing method, system, equipment, and medium for a multi-objective torque control algorithm. Background Technology

[0002] With the development of distributed drive vehicle technology, multi-objective optimization torque control algorithms (including upper-level yaw rate tracking and center of gravity sideslip angle suppression, and lower-level expected additional yaw moment and wheel speed tracking, and subject to multiple constraints) have become the core of improving vehicle performance. HIL testing, as a key means of safely verifying this complex algorithm, has become a core requirement for the implementation of algorithm research and development by constructing a comprehensive and highly accurate HIL testing system.

[0003] In related technologies, HIL (Hyper-Induced Leverage) testing schemes mostly focus on traditional power vehicles or single-function verification scenarios. For example, a torque safety diagnostic scheme for hybrid vehicles monitors the phase current signal of the high-voltage circuit and uses a "Y-within-X" de-jittering method to process the error signal, thereby enhancing the robustness of the current sensor's rationality diagnosis. Its core objective is to troubleshoot electrical system faults, rather than verifying multi-objective torque control logic. Another example is a transmission torque control test scheme for engineering vehicles, which determines the gearbox gear and limits the engine output torque to protect the transmission components. This scheme only focuses on the torque-bearing safety of the mechanical structure and does not involve the interactive verification of vehicle dynamics and control algorithms.

[0004] However, the above solutions focus on fault diagnosis (such as current sensor error detection) or mechanical protection (such as torque limit of transmission components), only addressing a single safety indicator, and do not involve sufficient HIL testing of multi-objective control algorithms. Summary of the Invention

[0005] This application provides a method, system, device, and medium for HIL testing of a multi-objective torque control algorithm, which can solve the technical problem in related technologies that only focus on a single safety indicator and do not involve sufficient HIL testing of multi-objective control algorithms.

[0006] In a first aspect, embodiments of this application provide a method for testing the High Intensity Level (HIL) of a multi-objective torque control algorithm, the method comprising: Connect the industrial control computer to the controller that has recorded the multi-objective torque control algorithm, and connect the distributed drive vehicle model in the vehicle dynamics simulation software on the industrial control computer to the signal analysis module in the mathematical modeling and simulation software on the industrial control computer to complete the simulation circuit construction. The computer and controller, equipped with calibration and measurement software, are connected through a dedicated communication device for hardware-in-the-loop testing to complete the test circuit setup. Based on the completed simulation and test circuits, longitudinal operating condition algorithm tests, lateral operating condition algorithm tests, and adaptive operating condition strategy algorithm tests were conducted.

[0007] In conjunction with the first aspect, in one implementation, before connecting the industrial control computer to a controller that records a multi-objective torque control algorithm, and connecting the distributed drive vehicle model in the vehicle dynamics simulation software on the industrial control computer to the signal analysis module in the mathematical modeling and simulation software on the industrial control computer, and completing the simulation circuit setup, the following steps are also included: The multi-objective torque control algorithm is programmed into the controller, and test and monitoring quantities are preset in the algorithm. The test quantities include weight parameters and prediction time domain, and the monitoring quantities include wheel speed and tire slip ratio.

[0008] In conjunction with the first aspect, in one embodiment, connecting the distributed drive vehicle model in the vehicle dynamics simulation software on the industrial control computer with the signal analysis module in the mathematical modeling and simulation software on the industrial control computer includes: The signal parsing module performs bidirectional signal parsing operations, parsing the vehicle information and road surface adhesion information output by the vehicle dynamics simulation software into controller area network signals and sending them to the controller. At the same time, it receives the four-wheel torque control signals output by the controller, parses them into input signals that can be recognized by the vehicle dynamics simulation software, and then sends them to the distributed drive vehicle model.

[0009] In conjunction with the first aspect, in one implementation, after connecting the industrial control computer to a controller that records a multi-objective torque control algorithm, connecting the distributed drive vehicle model in the vehicle dynamics simulation software on the industrial control computer to the signal analysis module in the mathematical modeling and simulation software on the industrial control computer, and completing the simulation circuit construction, the method further includes: Load the hardware support package that adapts to the industrial control computer and controller into the signal analysis module of the mathematical modeling and simulation software, run the signal analysis module, and if the four-wheel torque control signal fed back by the controller and the vehicle signal output by the vehicle dynamics simulation software are observed, then the simulation circuit is deemed to be successfully built.

[0010] In conjunction with the first aspect, in one embodiment, before connecting the computer carrying calibration and measurement software to the controller via a dedicated hardware-in-the-loop test communication device to complete the test circuit setup, the method further includes: Install the driver for the hardware-in-the-loop testing dedicated communication device on the computer equipped with the calibration and measurement software to ensure normal communication between the computer and the dedicated communication device. At the same time, verify the operating status of the calibration and measurement software to ensure that it can read the controller signals normally.

[0011] In conjunction with the first aspect, in one implementation, the testing of longitudinal operating condition algorithms, lateral operating condition algorithms, and adaptive operating condition strategy algorithms based on the constructed simulation and test circuits includes: The controller's monitoring data is collected in real time using calibration and measurement software on a computer. The preset test parameters are manually modified, and the responsiveness of the multi-objective torque control algorithm to parameter adjustments is verified based on the changing trends of the monitoring data.

[0012] In conjunction with the first aspect, in one implementation, the testing of longitudinal operating condition algorithms, lateral operating condition algorithms, and adaptive operating condition strategy algorithms based on the constructed simulation and test circuits includes: When conducting longitudinal working condition algorithm tests, the weight parameters and prediction time domain of the multi-objective torque control algorithm are adjusted. The weight parameters include the expected additional yaw moment tracking weight and the expected wheel speed tracking weight. By adjusting the above parameters, the multi-objective torque control algorithm can achieve the driving anti-slip function under longitudinal working conditions, so that the tire slip ratio is maintained within a preset reasonable range.

[0013] In conjunction with the first aspect, in one embodiment, after adjusting the aforementioned parameters to enable the multi-objective torque control algorithm to achieve the drive anti-slip function under longitudinal operating conditions and maintain the tire slip ratio within a preset reasonable range, the method further includes: Adjust the lower limit of the sum of additional torques in the multi-objective torque control algorithm. Set the lower limit of the sum of additional torques to be equal to or lower than the threshold value set by the driver's analytical torque value to avoid excessive tire slippage due to excessive torque.

[0014] In conjunction with the first aspect, in one implementation, the testing of longitudinal operating condition algorithms, lateral operating condition algorithms, and adaptive operating condition strategy algorithms based on the constructed simulation and test circuits includes: When testing the lateral condition algorithm, adjust the yaw rate tracking weight and the center of mass sideslip angle suppression weight of the upper controller of the multi-objective torque control algorithm. The larger the yaw rate tracking weight, the larger the proportion of the yaw motion tracking error in the objective function, and the higher the yaw tracking accuracy. The larger the center of mass sideslip angle suppression weight, the larger the proportion of the lateral deviation suppression error in the objective function, and the better the lateral deviation suppression effect.

[0015] In conjunction with the first aspect, in one implementation, the testing of longitudinal operating condition algorithms, lateral operating condition algorithms, and adaptive operating condition strategy algorithms based on the constructed simulation and test circuits includes: Adjust the expected additional yaw moment tracking weight and expected wheel speed tracking weight of the lower-level controller of the multi-objective torque control algorithm. Prioritize adjusting the expected additional yaw moment tracking weight to ensure the generation of the expected additional yaw moment, and then adjust the expected wheel speed tracking weight to keep the tire slip ratio at a relatively reasonable level throughout the entire lateral working condition.

[0016] In conjunction with the first aspect, in one implementation, the testing of longitudinal operating condition algorithms, lateral operating condition algorithms, and adaptive operating condition strategy algorithms based on the constructed simulation and test circuits includes: When testing the adaptive operating condition strategy algorithm, speed threshold, yaw rate threshold, and straight-line driving time threshold are preset as judgment parameters. These three judgment parameters are used to determine the current operating state of the vehicle, thereby distinguishing whether the vehicle is in a longitudinal or lateral operating condition.

[0017] In conjunction with the first aspect, in one implementation, the determination of the vehicle's current operating state through these three judgment parameters, thereby distinguishing between the vehicle's longitudinal or lateral operating conditions, includes: When the speed is greater than the speed threshold and the yaw rate is greater than the yaw rate threshold, or when the vehicle speed is greater than the speed threshold and the straight-line travel time is less than the straight-line travel time threshold, the vehicle is determined to be in a lateral working condition. In other cases, the vehicle is determined to be in a longitudinal working condition.

[0018] In conjunction with the first aspect, in one implementation, the step of pre-setting speed threshold, yaw rate threshold, and straight-line travel time threshold judgment parameters when performing adaptive driving condition strategy algorithm testing includes: The speed threshold, yaw rate threshold, and straight-line travel time threshold were tested and calibrated. By adjusting the threshold values ​​multiple times and observing the matching degree between the working condition judgment results of the multi-objective torque control algorithm and the actual working conditions, the optimal threshold combination was determined, so that the multi-objective torque control algorithm could accurately switch the corresponding control parameter combination based on the working condition judgment results.

[0019] Secondly, embodiments of this application provide a HIL (High-Intensity Interval) testing system for a multi-objective optimized torque control algorithm, the HIL testing system comprising: An industrial control computer is installed with vehicle dynamics simulation software and mathematical modeling and simulation software. The vehicle dynamics simulation software contains a distributed drive vehicle model, and the mathematical modeling and simulation software contains a signal analysis module. The controller records a multi-objective optimized torque control algorithm; A computer equipped with calibration and measurement software; A dedicated communication device for hardware-in-the-loop testing is used to connect the computer and the controller; The industrial control computer and the controller construct a simulation circuit through a signal analysis module and a distributed drive vehicle model. The computer, the hardware-in-the-loop test dedicated communication equipment, and the controller construct a test circuit.

[0020] In conjunction with the second aspect, in one embodiment, the industrial control computer is further provided with a controller local area network interface, and the industrial control computer and the controller are connected through the controller local area network interface to construct a simulation circuit; The controller is also equipped with a non-simulation controller LAN interface. The hardware-in-the-loop test dedicated communication device is connected to the controller through the non-simulation controller LAN interface to construct a test circuit.

[0021] In conjunction with the second aspect, in one implementation, the controller is provided with a hardware interface or software module that supports the writing of multi-objective optimized torque control algorithms. Furthermore, the multi-objective optimized torque control algorithm recorded in the controller has preset test quantities and monitoring quantities. The test quantities include weight parameters and prediction time domain, and the monitoring quantities include wheel speed and tire slip ratio.

[0022] In conjunction with the second aspect, in one embodiment, the signal parsing module is further configured to perform bidirectional signal parsing operations, parsing the vehicle information and road surface adhesion information output by the vehicle dynamics simulation software into controller local area network signals and sending them to the controller, while simultaneously receiving the four-wheel torque control signals output by the controller, parsing them into input signals recognizable by the vehicle dynamics simulation software, and sending them to the distributed drive vehicle model in the vehicle dynamics simulation software.

[0023] In conjunction with the second aspect, in one embodiment, the signal parsing module is further provided with a controller local area network interface adapted to the industrial control computer and a hardware support package for the controller.

[0024] Thirdly, embodiments of this application provide a HIL test device for a multi-objective optimized torque control algorithm. The HIL test device for the multi-objective optimized torque control algorithm includes a processor, a memory, and a HIL test program for the multi-objective optimized torque control algorithm stored in the memory and executable by the processor. When the HIL test program for the multi-objective optimized torque control algorithm is executed by the processor, it implements the steps of the HIL test method for the multi-objective torque control algorithm as described in some of the above embodiments.

[0025] Fourthly, embodiments of this application provide a computer-readable storage medium storing a HIL test program for a multi-objective optimized torque control algorithm. When the HIL test program for the multi-objective optimized torque control algorithm is executed by a processor, it implements the steps of the HIL test method for the multi-objective torque control algorithm as described in some of the above embodiments.

[0026] The beneficial effects of the technical solutions provided in this application include: A distributed drive vehicle model built using vehicle dynamics simulation software simulates the dynamic characteristics of actual vehicles. Simultaneously, a signal analysis module enables signal interaction between the vehicle model and the controller, constructing a closed-loop interaction between vehicle dynamics characteristics and algorithm control logic during simulation. This provides a virtual testing environment for the algorithm that closely resembles real-world driving scenarios. Dedicated communication equipment allows for independent signal transmission between the test terminal and the controller, avoiding signal interference between the simulation and test circuits and ensuring the accuracy of monitoring data acquisition and the reliability of algorithm parameter debugging. The algorithm can verify the control logic and parameter adaptability under different driving conditions for multiple control objectives required by the multi-objective torque control algorithm, such as longitudinal drive anti-slip, lateral handling stability, and adaptive switching of operating conditions. This ensures that the algorithm meets multi-objective control requirements in longitudinal, lateral, and operating condition switching scenarios. Attached Figure Description

[0027] Figure 1 This is a flowchart illustrating an embodiment of the HIL test method for the multi-objective torque control algorithm of this application; Figure 2 This is a block diagram of the multi-objective torque control algorithm in the HIL test method of the multi-objective torque control algorithm of this application; Figure 3 This is a structural block diagram of an embodiment of the HIL test system for the multi-objective torque control algorithm of this application; Figure 4 This is a schematic diagram of the test signal transmission in an embodiment of the HIL test system for the multi-objective torque control algorithm of this application; Figure 5 This is a flowchart illustrating the adaptive working condition identification strategy in the HIL test method of the multi-objective torque control algorithm of this application. Figure 6 This is a schematic diagram of the hardware structure of the HIL test equipment for the multi-objective optimized torque control algorithm involved in the embodiments of this application. Detailed Implementation

[0028] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0029] With the rapid development of distributed drive vehicle technology, multi-objective optimization torque control algorithms have become one of the core technologies for improving vehicle driving stability, handling, and safety. These algorithms typically employ a two-tier control architecture: the upper-level controller focuses on yaw rate tracking and center-of-gravity sideslip angle suppression as core optimization objectives, constrained by upper and lower limits of additional yaw moment; the lower-level controller focuses on tracking the desired additional yaw moment and the desired wheel speed, needing to meet upper and lower limits of additional torque and slip ratio. Because the algorithm involves multi-objective optimization logic, coupling of multiple constraints, and adaptation to complex operating conditions, its functional reliability, parameter rationality, and adaptability to operating conditions must be fully verified through hardware-in-the-loop (HIL) testing. HIL testing, as a key technology for connecting real controllers to a virtual simulation environment, can safely and controllably verify the response logic, communication protocol compatibility, and fault handling capabilities of the control algorithm in a laboratory environment by constructing vehicle models and simulating complex operating conditions. Therefore, building a targeted HIL testing system with comprehensive scenario coverage has become a core requirement in the research and development and implementation of multi-objective optimization torque control algorithms.

[0030] Among them, HIL test schemes mostly focus on traditional power vehicles or single-function verification scenarios. For example, the torque safety diagnostic scheme for hybrid vehicles monitors the phase current signal of the high-voltage circuit and uses the "Y in X" de-jittering method to process the error signal to enhance the robustness of the current sensor's rationality diagnosis. Its core objective is to troubleshoot electrical system faults, rather than to verify multi-objective torque control logic. Another example is the transmission torque control test scheme for engineering vehicles, which determines the gearbox gear and limits the engine output torque to protect the transmission components. It only focuses on the torque bearing safety of the mechanical structure and does not involve the interactive verification of vehicle dynamics and control algorithms.

[0031] However, the above solutions focus on fault diagnosis (such as current sensor error detection) or mechanical protection (such as torque limit of transmission components), only addressing a single safety indicator, and do not involve sufficient HIL testing of multi-objective control algorithms.

[0032] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0033] In a first aspect, embodiments of this application provide a HIL test method for a multi-objective torque control algorithm.

[0034] In one embodiment, reference is made to Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the HIL testing method for the multi-objective torque control algorithm of this application. Figure 1 As shown, the HIL test method for the multi-objective torque control algorithm includes: S100: Connect the industrial control computer to the controller that has recorded a multi-objective torque control algorithm, and connect the distributed drive vehicle model in the vehicle dynamics simulation software on the industrial control computer to the signal analysis module in the mathematical modeling and simulation software on the industrial control computer to complete the simulation circuit construction. S200: Connects the computer and controller, which are equipped with calibration and measurement software, through a dedicated communication device for hardware-in-the-loop testing to complete the test circuit setup; S300: Based on the completed simulation and test circuits, longitudinal operating condition algorithm testing, lateral operating condition algorithm testing, and adaptive operating condition strategy algorithm testing are conducted.

[0035] In this embodiment, a simulation circuit is constructed by connecting an industrial control computer (ICC) to a controller that has recorded a multi-target torque control algorithm, and connecting the distributed drive vehicle model in the vehicle dynamics simulation software on the ICC to the signal analysis module in the mathematical modeling and simulation software on the ICC. This allows the distributed drive vehicle model built using the vehicle dynamics simulation software to simulate the actual vehicle dynamics characteristics. Simultaneously, the signal analysis module enables signal interaction between the vehicle model and the controller, constructing a closed-loop interaction between the vehicle dynamics characteristics and the algorithm control logic during the simulation process, providing a virtual test environment that closely resembles real-world driving scenarios. Finally, a dedicated hardware-in-the-loop testing communication device connects the computer carrying calibration and measurement software to the controller to complete the test circuit setup. The system can utilize dedicated communication equipment to achieve independent signal transmission between the test end and the controller, avoiding signal interference between the simulation circuit and the test circuit, and ensuring the accuracy of monitoring data acquisition and the reliability of algorithm parameter debugging. Based on the constructed simulation circuit and test circuit, longitudinal working condition algorithm testing, lateral working condition algorithm testing, and adaptive working condition strategy algorithm testing can be carried out. It can verify the control logic and parameter adaptability of the algorithm under different driving conditions for multiple control objectives that the multi-objective torque control algorithm needs to achieve, such as longitudinal drive anti-slip, lateral handling stability, and adaptive switching of working conditions. It ensures that the algorithm can meet the multi-objective control requirements in longitudinal, lateral, and working condition switching scenarios, and provides comprehensive and accurate HIL testing support for the safe research and development and application of the algorithm.

[0036] Furthermore, in one embodiment, prior to S100, the following step is also included: S000: The multi-objective torque control algorithm is written into the controller, and test quantities and monitoring quantities are preset in the algorithm. The test quantities include weight parameters and prediction time domain, and the monitoring quantities include wheel speed and tire slip ratio.

[0037] In this embodiment, by writing the multi-objective torque control algorithm into the controller, the controller acquires the basic capability to execute the algorithm, providing a core control entity for algorithm testing after the simulation and test circuits are built. Simultaneously, the algorithm includes preset test quantities such as weight parameters and prediction time domain parameters, providing clear adjustment targets for adjusting algorithm control parameters and verifying algorithm performance under different parameter configurations during subsequent testing. Preset monitoring quantities include wheel speed and tire slip ratio, providing key monitoring indicators for real-time acquisition of algorithm control effects and determination of whether the algorithm meets multi-objective control requirements during subsequent testing. This ensures that subsequent simulation and testing processes can be carried out systematically around clear control parameters and monitoring data, laying the foundation for accurate testing of the multi-objective torque control algorithm.

[0038] Furthermore, in one embodiment, S100 further includes the following step: S101: Utilizes the signal parsing module to perform bidirectional signal parsing operations, parsing the vehicle information and road surface adhesion information output by the vehicle dynamics simulation software into controller area network signals and sending them to the controller. Simultaneously, it receives the four-wheel torque control signals output by the controller, parses them into input signals recognizable by the vehicle dynamics simulation software, and then sends them to the distributed drive vehicle model.

[0039] In this embodiment, during the process of connecting the industrial control computer to a controller that records a multi-objective torque control algorithm, and connecting the distributed drive vehicle model in the vehicle dynamics simulation software on the industrial control computer to the signal parsing module in the mathematical modeling and simulation software on the industrial control computer to complete the simulation circuit construction, the signal parsing module performs bidirectional signal parsing operations. On the one hand, it parses the vehicle information and road surface adhesion information output by the vehicle dynamics simulation software into controller local area network signals and sends them to the controller, enabling the controller to generate corresponding four-wheel torque control signals based on the vehicle dynamics and road surface conditions in the simulation scenario. On the other hand, it receives the four-wheel torque control signals output by the controller, parses them into input signals that the vehicle dynamics simulation software can recognize, and sends them to the distributed drive vehicle model, enabling the vehicle model to update its own dynamic state according to the controller's control instructions. This forms a closed-loop signal interaction between the vehicle model and the controller, ensuring that the simulation circuit can realistically simulate the algorithm's control process of vehicle dynamics, and providing a realistic signal interaction environment for testing the multi-objective torque control algorithm.

[0040] Furthermore, in one embodiment, after S100, the following step is also included: S102: Load the hardware support package that adapts to the industrial control computer and controller into the signal analysis module of the mathematical modeling and simulation software, run the signal analysis module, and if the four-wheel torque control signal fed back by the controller and the vehicle signal output by the vehicle dynamics simulation software are observed, then the simulation circuit is deemed to be qualified.

[0041] In this embodiment, by loading a hardware support package adapted to the industrial control computer and controller into the signal analysis module of the mathematical modeling and simulation software, the signal analysis module is equipped with signal interaction capabilities compatible with the industrial control computer and controller, ensuring the compatibility of signal analysis and transmission. After running the signal analysis module, the signal interaction between the distributed drive vehicle model, the signal analysis module, and the controller in the simulation circuit is judged by observing whether the four-wheel torque control signal fed back by the controller and the vehicle signal output by the vehicle dynamics simulation software can be obtained. If both types of signals can be observed normally, it indicates that the signal closed loop of the simulation circuit has been effectively constructed, thus determining that the simulation circuit is qualified. This ensures that subsequent multi-objective torque control algorithm tests based on this simulation circuit can be carried out on a stable and effective signal interaction environment, avoiding test data deviations or test process interruptions due to circuit construction problems.

[0042] Furthermore, in one embodiment, prior to S100, the following step is also included: S001: Install the driver for the hardware-in-the-loop testing dedicated communication device on the computer equipped with the calibration and measurement software to ensure normal communication between the computer and the dedicated communication device. At the same time, verify the operating status of the calibration and measurement software to ensure that it can read the controller signals normally.

[0043] In this embodiment, by installing the driver for the dedicated communication device for hardware-in-the-loop testing on the computer equipped with the calibration and measurement software, communication compatibility barriers between the computer and the dedicated communication device are eliminated, ensuring stable data transmission between the two. This provides the hardware communication foundation for subsequently building a test circuit between the computer and the controller using the dedicated communication device. Simultaneously, by verifying the operating status of the calibration and measurement software, it is confirmed that the software has the function of normally reading controller signals, avoiding problems such as the inability to collect monitoring data or effectively issue parameter adjustment commands due to software malfunctions during subsequent testing. This provides reliable software support for real-time acquisition of monitoring data and manual modification of test parameters after the test circuit is built, ensuring the stability of the interaction between the test terminal and the controller throughout the entire HIL testing process.

[0044] Furthermore, in one embodiment, step S300 further includes the following step: S301: The controller's monitoring data is collected in real time through calibration and measurement software on the computer. The preset test parameters are manually modified. Based on the changing trend of the monitoring data, the timeliness of the multi-objective torque control algorithm in response to parameter adjustments is verified.

[0045] In this embodiment, during the longitudinal working condition algorithm test, lateral working condition algorithm test, and adaptive working condition strategy algorithm test based on the constructed simulation and test lines, relying on the hardware and software foundation of the constructed test lines, the monitoring data of the controller is collected in real time through calibration and measurement software on the computer. Key feedback information (such as wheel speed and tire slip ratio) under the current control state of the multi-objective torque control algorithm can be dynamically obtained. By manually modifying the preset test parameters (such as weight parameters and prediction time domain), algorithm operation scenarios under different parameter configurations can be artificially constructed. Based on the changing trend of the monitoring data with the adjustment of the test parameters, it is possible to determine whether the multi-objective torque control algorithm can quickly adjust the control logic and reflect the adjustment effect through the monitoring data when the parameters change. This verifies the timeliness of the algorithm's response to parameter adjustments and ensures that the algorithm can quickly adapt to different working condition requirements based on parameter optimization in subsequent more complex working condition tests. This provides reliable support at the parameter response level for further verification of the algorithm's control accuracy and dynamic adaptation capability.

[0046] Furthermore, in one embodiment, step S300 further includes the following step: S302: When performing longitudinal working condition algorithm testing, adjust the weight parameters and prediction time domain of the multi-objective torque control algorithm. The weight parameters include the expected additional yaw moment tracking weight and the expected wheel speed tracking weight. By adjusting the above parameters, the multi-objective torque control algorithm can achieve the driving anti-slip function under longitudinal working conditions, so that the tire slip ratio is maintained within a preset reasonable range.

[0047] In this embodiment, during the testing of longitudinal driving condition algorithms, lateral driving condition algorithms, and adaptive driving condition strategy algorithms based on the constructed simulation and test lines, when conducting longitudinal driving condition algorithm testing, the weight parameters (including the expected additional yaw moment tracking weight and the expected wheel speed tracking weight) and prediction time domain of the multi-objective torque control algorithm are adjusted to specifically adapt to the control requirements under longitudinal driving scenarios. Specifically, adjusting the expected additional yaw moment tracking weight and the expected wheel speed tracking weight optimizes the algorithm's emphasis on longitudinal torque distribution and wheel speed control, while adjusting the prediction time domain adapts to different longitudinal dynamic response requirements. Through these parameter adjustments, the multi-objective torque control algorithm can effectively achieve drive anti-slip function under longitudinal driving conditions, stably controlling the tire slip ratio within a preset reasonable range. This verifies the algorithm's control capability for drive safety and stability under longitudinal driving scenarios, ensuring that the algorithm adapts to the core control objectives of longitudinal driving conditions.

[0048] Furthermore, in one embodiment, after S302, the following step is also included: S303: Adjust the lower limit of the sum of additional torques in the multi-objective torque control algorithm. Set the lower limit of the sum of additional torques to be equal to or lower than the threshold value set by the driver's analytical torque value to avoid excessive tire slippage due to excessive torque.

[0049] In this embodiment, after adjusting the weight parameters and prediction time domain of the multi-objective torque control algorithm to enable the algorithm to achieve the driving anti-slip function under longitudinal conditions and maintain the tire slip ratio within a preset reasonable range, the lower limit of the sum of additional torques of the algorithm is further adjusted to be equal to or slightly lower than the threshold value set by the driver's analytical torque value. By constraining the total torque output, the excessive output of driving torque is limited as a whole, avoiding excessive slippage caused by the excessive superposition of torques from a single wheel or multiple wheels, which would cause the tire to exceed the reasonable slip ratio range. This supplements and improves the driving anti-slip control logic under longitudinal conditions, ensuring that the algorithm can not only achieve basic anti-slip through the adjustment of weight parameters and prediction time domain in longitudinal driving scenarios, but also enhance the anti-slip effect through the constraint of the sum of torques, thus fully verifying the algorithm's ability to guarantee longitudinal driving safety.

[0050] Furthermore, in one embodiment, step S300 further includes the following step: S304: When performing lateral condition algorithm testing, adjust the yaw rate tracking weight and the center of mass sideslip angle suppression weight of the upper controller of the multi-objective torque control algorithm; among them, the larger the yaw rate tracking weight, the larger the proportion of the yaw motion tracking error in the objective function, and the higher the yaw tracking accuracy; the larger the center of mass sideslip angle suppression weight, the larger the proportion of the lateral deviation suppression error in the objective function, and the better the lateral deviation suppression effect.

[0051] In this embodiment, during the longitudinal driving condition algorithm test, lateral driving condition algorithm test, and adaptive driving condition strategy algorithm test based on the constructed simulation and test lines, when conducting the lateral driving condition algorithm test, the yaw rate tracking weight and the center of gravity sideslip angle suppression weight of the upper controller of the multi-objective torque control algorithm are adjusted to adapt to the handling stability requirements under lateral driving scenarios. Specifically, a larger yaw rate tracking weight indicates a larger proportion of the yaw motion tracking error in the objective function, corresponding to higher yaw tracking accuracy; a larger center of gravity sideslip angle suppression weight indicates a larger proportion of the sideslip suppression error in the objective function, corresponding to better sideslip suppression effect. Through the above parameter adjustments, the algorithm's control emphasis on lateral yaw motion and sideslip state can be specifically optimized, thereby verifying the multi-objective torque control algorithm's control capability for vehicle handling stability under lateral driving conditions and ensuring that the algorithm adapts to the core control objectives of lateral driving conditions.

[0052] Furthermore, in one embodiment, step S300 further includes the following step: S305: Adjust the expected additional yaw moment tracking weight and expected wheel speed tracking weight of the lower controller of the multi-objective torque control algorithm. Prioritize adjusting the expected additional yaw moment tracking weight to ensure the generation of the expected additional yaw moment, and then adjust the expected wheel speed tracking weight to keep the tire slip ratio at a relatively reasonable level throughout the entire lateral working condition.

[0053] In this embodiment, the desired additional yaw moment tracking weight and desired wheel speed tracking weight of the lower-level controller of the multi-objective torque control algorithm are adjusted to adapt to the execution requirements of the upper-level control objective under lateral conditions. Prioritizing the adjustment of the desired additional yaw moment tracking weight ensures that the lower-level controller accurately generates the desired additional yaw moment output by the upper-level controller, providing a basic execution guarantee for lateral handling stability. Based on this, adjusting the desired wheel speed tracking weight optimizes the control accuracy of each wheel speed, maintaining the tire slip ratio at a relatively reasonable level throughout the lateral conditions and preventing abnormal slip ratios from affecting lateral force output. Through this layered adjustment, the matching between the lower-level execution logic of the multi-objective torque control algorithm and the upper-level control objective is verified, ensuring that the algorithm can achieve stable control of yaw and sideslip under lateral conditions while ensuring the reasonable working state of the tires.

[0054] Furthermore, in one embodiment, step S300 further includes the following step: S306: When testing the adaptive working condition strategy algorithm, preset the speed threshold, yaw rate threshold and straight-line driving time threshold judgment parameters. These three judgment parameters are used to determine the current operating state of the vehicle, thereby distinguishing whether the vehicle is in a longitudinal or lateral working condition.

[0055] In this embodiment, during the longitudinal working condition algorithm test, lateral working condition algorithm test, and adaptive working condition strategy algorithm test based on the constructed simulation and test lines, when conducting the adaptive working condition strategy algorithm test, speed threshold, yaw rate threshold, and straight-line driving time threshold are preset as judgment parameters. These three parameters are used to comprehensively determine the current operating state of the vehicle, thereby clearly distinguishing whether the vehicle is in the longitudinal or lateral working condition. This provides a basis for the multi-objective torque control algorithm to automatically switch control strategies according to different working conditions, ensuring that the algorithm can accurately identify the current driving state in scenarios with dynamic changes in working conditions, and then adapt to the corresponding control logic, thus verifying the algorithm's working condition adaptive capability.

[0056] Furthermore, in one embodiment, step S306 further includes the following step: S306-2: When the speed is greater than the speed threshold and the yaw rate is greater than the yaw rate threshold, or when the vehicle speed is greater than the speed threshold and the straight travel time is less than the straight travel time threshold, the vehicle is determined to be in a lateral working condition. In other cases, the vehicle is determined to be in a longitudinal working condition.

[0057] In this embodiment, by pre-setting speed threshold, yaw rate threshold, and straight-line travel time threshold as judgment parameters, and using these three parameters to determine the current operating state of the vehicle to distinguish between longitudinal and lateral operating conditions, specific judgment rules are set: when the vehicle speed is greater than the speed threshold and the yaw rate is greater than the yaw rate threshold, or when the vehicle speed is greater than the speed threshold and the straight-line travel time is less than the straight-line travel time threshold, the vehicle is determined to be in a lateral operating condition if either condition is met; otherwise, the vehicle is determined to be in a longitudinal operating condition. This specific judgment logic provides a clear basis for operating condition identification for the multi-objective torque control algorithm, ensuring that the algorithm can accurately distinguish between longitudinal and lateral driving states in adaptive operating condition strategy testing, thereby providing a reliable foundation for the automatic switching of subsequent control strategies and verifying the algorithm's accuracy in identifying dynamic changes in operating conditions.

[0058] Furthermore, in one embodiment, step S306 further includes the following step: S306-1: Test and calibrate the speed threshold, yaw rate threshold, and straight-line travel time threshold. By adjusting the threshold values ​​multiple times and observing the matching degree between the working condition judgment results of the multi-objective torque control algorithm and the actual working conditions, determine the optimal threshold combination so that the multi-objective torque control algorithm can accurately switch the corresponding control parameter combination based on the working condition judgment results.

[0059] In this embodiment, during the process of determining the current operating state of the vehicle by setting speed threshold, yaw rate threshold, and straight-line travel time threshold as pre-set judgment parameters and using these three parameters to distinguish between longitudinal and lateral operating conditions, the above three thresholds are tested and calibrated: by adjusting the threshold values ​​multiple times, the degree of matching between the operating condition judgment result output by the multi-objective torque control algorithm and the actual operating condition is observed simultaneously. Based on the matching degree, the optimal threshold combination is determined, so that the multi-objective torque control algorithm can accurately identify the operating condition of the vehicle based on the optimal threshold combination, and accurately switch the corresponding control parameter combination based on the operating condition judgment result. This ensures that the algorithm forms a closed loop in the identification of the operating condition and the adaptation of the control strategy in the adaptive operating condition strategy test, and guarantees the control effectiveness of the algorithm in the scenario of dynamic changes in operating conditions.

[0060] Here, the complete technical solution provided by the embodiments of this application is summarized and described as follows: I. Overall Framework of the Technical Solution The HIL test technology solution for the multi-objective torque control algorithm provided in this application embodiment revolves around three core aspects: "hardware preparation - circuit construction - working condition testing". Through hardware collaboration of industrial control computer, controller, computer (Windows system) and dedicated communication equipment, combined with software support of vehicle dynamics simulation software, mathematical modeling and simulation software, calibration and measurement software, a dual-circuit test architecture of "simulation circuit + test circuit" is constructed to realize the comprehensive verification of the multi-objective torque control algorithm under longitudinal, lateral and adaptive working conditions.

[0061] II. Detailed Technical Solution Overview (I) Step 1: Hardware and software preparation and algorithm flashing 1. Hardware Preparation Industrial PC: Equipped with a Controller Area Network (CAN) interface for signal interaction with the controller. As the core hardware carrier of the simulation circuit, it needs to meet the real-time requirements of vehicle dynamics simulation and mathematical modeling. Controller: As the main body for executing the multi-objective torque control algorithm, it needs to support the algorithm writing function and have at least two CAN interfaces (one for simulation circuit and one for test circuit connection, to avoid signal interference). Computer (Windows system): Equipped with calibration and measurement software (INCA) for collecting monitoring data and adjusting test parameters of the test circuit; Hardware-in-the-loop test dedicated communication device (Kvaser): As a signal transmission bridge for the test circuit, it realizes CAN signal parsing and transmission between the computer and the controller, and needs to be adapted to the controller's non-simulation CAN interface.

[0062] 2. Software module deployment CarSim, a vehicle dynamics simulation software, is installed on an industrial computer and used to build distributed drive vehicle models. The model parameters are clearly defined: "four-wheel torque" is set as the algorithm control input, and "vehicle information (such as wheel speed, yaw rate, and center of gravity sideslip angle)" and "road adhesion information (such as road friction coefficient)" are set as simulation outputs to provide the algorithm with a vehicle dynamics environment that closely resembles reality. Mathematical modeling and simulation software (Matlab / Simulink): Installed on an industrial computer, used to build a signal analysis module, which has bidirectional signal analysis capabilities. Forward parsing: Converts the vehicle information and road surface adhesion information output by CarSim into CAN signals that the controller can recognize and sends them to the controller; Reverse analysis: Receive the four-wheel torque control signals output by the controller, convert them into input signals that CarSim can recognize, and feed them back to the distributed drive vehicle model to form a simulation closed loop; Calibration and Measurement Software (INCA): Installed on a Windows computer, it is used to collect real-time monitoring data of the controller (such as wheel speed and tire slip ratio) and supports manual modification of the algorithm's preset test parameters (such as weight parameters and prediction time domain).

[0063] 3. Multi-objective torque control algorithm programming and parameter preset Algorithm flashing: The multi-objective torque control algorithm is flashed into the controller. The algorithm structure is as follows: Figure 2 (The block diagram of the multi-objective torque control algorithm of this application is shown, which consists of two layers of controllers:) Upper-level controller: includes a yaw rate tracking module and a center of mass sideslip suppression module. The optimization objective is "yaw rate tracking + center of mass sideslip suppression", and the constraint condition is "additional upper and lower limits of yaw moment". The lower-level controller includes a desired additional yaw moment tracking module and a desired wheel speed tracking module. The optimization objective is "desired additional yaw moment tracking + desired wheel speed tracking", and the constraints are "additional torque upper and lower limits + tire slip ratio upper and lower limits". Parameter preset: The test quantity and monitoring quantity are preset in the algorithm. The test quantity includes weight parameters (such as expected additional yaw moment tracking weight, expected wheel speed tracking weight, and yaw rate tracking weight) and prediction time domain; the monitoring quantity includes wheel speed and tire slip ratio, providing clear adjustment objects and monitoring indicators for subsequent tests.

[0064] (II) Second step: Simulation circuit construction 1. Line connection logic based on Figure 3 In the "Simulation Circuit" section of the (HIL test system block diagram of the multi-objective torque control algorithm of this application), complete the following connections: Establish a data link between the CarSim distributed drive vehicle model on the industrial control computer and the Simulink signal parsing module to ensure that the signals of the two can interact in real time. The industrial control computer is connected to the controller's "simulation CAN interface" via the industrial control computer's CAN interface, thus forming a CAN communication link between the signal analysis module and the controller.

[0065] 2. Simulation Operating Conditions and Circuit Verification CarSim allows you to preset simulation conditions (such as straight-line acceleration and cornering) to simulate different driving scenarios. Load the hardware support package for "Adapting to CAN Interface and Controller of Industrial PC" into the Simulink signal parsing module and run the signal parsing module; Observe the signal interaction status: If the "four-wheel torque control signal fed back by the controller" and the "vehicle information output by CarSim" can be obtained in real time, the simulation circuit is deemed to be qualified, ensuring the simulation closed loop is effective.

[0066] (III) Step 3: Test circuit setup 1. Line connection logic based on Figure 3 In the "Test Circuit" section of the (System Structure Diagram), complete the following connections: By using a dedicated hardware-in-the-loop test communication device (Kvaser), the Windows computer is connected to the controller's "non-emulation CAN interface" to form a test communication link independent of the emulation circuit, thus avoiding interference between emulation signals and test signals. Install the Kvaser driver on your Windows computer to ensure proper communication between the computer and Kvaser. Start the INCA software and verify its running status: If INCA can read the controller's monitoring data (such as wheel speed and tire slip ratio) in real time, then the test circuit is considered to be qualified.

[0067] 2. Test interaction verification Manually modify the preset test parameters (such as weight parameters) of the algorithm using INCA software and observe the changing trend of the monitored data. If the monitoring data changes synchronously with the test quantity adjustment, it indicates that the "parameter distribution-data acquisition" link of the test line is smooth, laying the foundation for subsequent working condition tests.

[0068] (iv) Step 4: Longitudinal working condition algorithm test 1. Testing Principles Adhering to the principle of "implementing functionality first, then optimizing performance," the algorithm's anti-slip driving function was verified through parameter adjustment. Phase 1: Adjusting test parameters to achieve the anti-slip function of the drive; Phase 2: Adjust constraint parameters to optimize drive anti-slip performance.

[0069] 2. Specific parameter adjustment and function verification Phase 1: Function Implementation (Parameter Adjustment): Adjusting weight parameters: including "expected additional yaw moment tracking weight" and "expected wheel speed tracking weight"; Desired additional yaw moment tracking weight: used to coordinate longitudinal and lateral motion control conflicts. The larger the value, the closer the additional torque output by the controller is to the "desired additional yaw moment", thus avoiding longitudinal and lateral control imbalance. Expected wheel speed tracking weight: used to suppress tire slip ratio. The larger the value, the greater the proportion of the "difference between expected wheel speed and actual wheel speed" in the algorithm's objective function. The controller will prioritize reducing the wheel speed difference, thereby reducing tire slippage. Adjusting the prediction time domain: Adapting to different longitudinal dynamic response requirements (such as rapid acceleration and slow acceleration) to ensure that the algorithm control rhythm matches the working conditions; Functional verification: The tire slip ratio is observed through INCA. If the slip ratio is maintained within a preset reasonable range (such as 5%-15%), the anti-skid function is determined to be effective.

[0070] Phase Two: Performance Optimization (Constraint Adjustment) Adjust the "Lower limit of the sum of additional torques": Set it to "equal to the driver's analytical torque value" or "slightly lower than the driver's analytical torque value"; Optimization goal: To avoid excessive tire slippage due to excessive additional torque, and to further improve the stability of drive anti-skid.

[0071] (V) Step 5: Lateral load condition algorithm test 1. Testing Core In lateral conditions, the core focus is on "handling stability". The algorithm control focuses on the upper controller (yaw rate tracking and center of gravity sideslip angle suppression), while the lower controller mainly "tracks the upper command" (the tire slip ratio is usually small in lateral conditions).

[0072] 2. Specific parameter adjustment and function verification Upper-level controller parameter adjustment: Adjust the "yaw rate tracking weight": The larger the value, the greater the proportion of "yaw rate tracking error" in the algorithm's objective function, and the higher the tracking accuracy of yaw motion (e.g., when driving on a curve, the actual yaw rate is closer to the expected yaw rate). Adjust the "center of gravity sideslip angle suppression weight": the larger the value, the greater the proportion of "center of gravity sideslip angle suppression error" in the objective function, and the better the sideslip suppression effect during lateral movement (such as reducing the degree of vehicle tilt during emergency lane changes).

[0073] Lower-level controller parameter adjustment: Prioritize adjusting the "expected additional yaw moment tracking weight": ensure that the lower-level controller can accurately generate the "expected additional yaw moment" output by the upper level, and ensure the effective execution of lateral control commands; Readjust the "desired wheel speed tracking weight": keep the tire slip ratio at a relatively reasonable level (such as below 10%) to avoid excessive slip ratio affecting lateral force output and ensure lateral handling stability.

[0074] (vi) Step 6: Adaptive working condition strategy algorithm testing 1. Operating Condition Identification Logic based on Figure 5 (A schematic diagram of the adaptive working condition identification strategy in the HIL test method of the multi-objective torque control algorithm of this application) The longitudinal / lateral working conditions are distinguished by the "three-threshold judgment method". The specific process is as follows: Startup determination: The prerequisite is "vehicle speed ≥ speed threshold" (if the speed is lower than the threshold, it is directly determined to be a longitudinal working condition). Branch decision: If "vehicle speed ≥ speed threshold" and "yaw rate ≥ yaw rate threshold", then it is determined to be a lateral working condition; If "vehicle speed ≥ speed threshold" and "yaw rate < yaw rate threshold", then further determine "straight travel time ≤ straight travel time threshold": if satisfied, it is determined to be a lateral working condition; if not satisfied, it is determined to be a longitudinal working condition. All other situations (such as speed < speed threshold) are judged as longitudinal working conditions.

[0075] 2. Threshold calibration and strategy verification Threshold calibration: Multiple tests were conducted to calibrate the "speed threshold, yaw rate threshold, and straight-line travel time threshold". Each time the threshold value is adjusted, a combined driving condition (such as "straight-line acceleration → cornering → straight-line deceleration") is simulated in CarSim. By comparing the working condition judgment results of the INCA observation algorithm with the actual working conditions of CarSim, the threshold combination with the "highest working condition judgment matching degree" is selected as the optimal threshold. Strategy verification: Based on the optimal threshold, simulate compound working conditions and observe the switching state of the algorithm control parameters. If the algorithm can automatically switch the corresponding control parameter combination when switching between "longitudinal → lateral" and "lateral → longitudinal" operating conditions (e.g., using "high expected wheel speed tracking weight" for longitudinal operating conditions and "high yaw rate tracking weight" for lateral operating conditions), then the adaptive operating condition strategy is deemed effective.

[0076] (vii) Signal transmission logic (combined) Figure 4 ) based on Figure 4 (Schematic diagram of test signal transmission in the HIL test system for the multi-objective torque control algorithm of this application), clearly defining the signal flow of the entire system to ensure coordination among all components: Simulation signal flow: CarSim (vehicle / road information) → Simulink signal parsing module (converted to CAN signal) → industrial computer CAN interface → controller (algorithm calculation); Simulation feedback flow: Controller (four-wheel torque control signal) → Industrial computer CAN interface → Simulink signal parsing module (converts to CarSim recognizable signal) → CarSim (updates vehicle dynamics state); Test data flow: Controller (monitoring data) → Non-simulation CAN interface → Kvaser → Windows computer (INCA acquisition); Test command flow: Windows computer (INCA issues test quantity adjustment command) → Kvaser → Controller non-simulation CAN interface → Algorithm (parameter update).

[0077] Secondly, embodiments of this application also provide a HIL test system for a multi-objective optimized torque control algorithm. The HIL test system includes: an industrial control computer equipped with vehicle dynamics simulation software and mathematical modeling and simulation software; the vehicle dynamics simulation software contains a distributed drive vehicle model, and the mathematical modeling and simulation software contains a signal analysis module; a controller that records a multi-objective optimized torque control algorithm; a computer equipped with calibration and measurement software; and a dedicated hardware-in-the-loop (HIL) test communication device for connecting the computer and the controller. The industrial control computer and the controller construct a simulation circuit through the signal analysis module and the distributed drive vehicle model, and the computer, the dedicated HIL test communication device, and the controller construct a test circuit.

[0078] In this embodiment, a complete test architecture is constructed by setting up an industrial control computer, a controller, a computer, and a dedicated communication device for hardware-in-the-loop testing. The industrial control computer is equipped with vehicle dynamics simulation software and mathematical modeling and simulation software. The vehicle dynamics simulation software constructs a distributed drive vehicle model to simulate the dynamic characteristics of actual vehicles. The mathematical modeling and simulation software constructs a signal parsing module to realize signal format conversion and interaction. The controller, as the carrier of the multi-objective optimization torque control algorithm, records the algorithm to execute control logic operations. The computer is equipped with calibration and measurement software to realize test parameter adjustment and monitoring data acquisition. The dedicated communication device for hardware-in-the-loop testing is used to establish a communication link between the computer and the controller. A simulation circuit is constructed by the industrial control computer and the controller based on the signal parsing module and the distributed drive vehicle model. The vehicle information and road surface adhesion information output by the distributed drive vehicle model are processed by the signal parsing module... After the block is converted into a signal recognizable by the controller, it is transmitted to the controller. The four-wheel torque control signal output by the controller is converted into a signal recognizable by the vehicle dynamics simulation software by the signal analysis module and then fed back to the distributed drive vehicle model, forming a simulation closed loop to simulate the algorithm control process under actual driving scenarios. At the same time, a test circuit is constructed with the controller through a computer, a dedicated hardware-in-the-loop test communication device, and the controller. The computer can send test parameter adjustment commands to the controller through the dedicated hardware-in-the-loop test communication device and collect the monitoring data output by the controller in real time. This realizes the independent operation of simulation and testing without interference between them. In this way, a stable and reliable hardware carrier and software support are provided for the functional verification of the multi-objective optimized torque control algorithm under longitudinal, lateral and adaptive conditions. This ensures the real-time signal interaction and the accuracy of data acquisition during the algorithm testing process, and guarantees the effective verification of the algorithm control logic and parameter adaptability.

[0079] In conjunction with the second aspect, in one embodiment, the industrial control computer is further provided with a controller local area network (Controller Area Network) interface, and the industrial control computer and the controller are connected through the Controller Area Network interface to construct a simulation circuit; the controller is further provided with a non-simulation Controller Area Network interface, and the hardware-in-the-loop test dedicated communication device is connected to the controller through the non-simulation Controller Area Network interface to construct a test circuit.

[0080] In this embodiment, the industrial control computer is further configured with a controller local area network (Controller Area Network) interface. This Controller Area Network interface serves as a signal transmission carrier between the industrial control computer and the controller, enabling the industrial control computer to establish a stable communication link with the controller through this interface. Combined with the distributed drive vehicle model in the vehicle dynamics simulation software within the industrial control computer and the signal analysis module of the mathematical modeling and simulation software, a complete simulation circuit is constructed. This ensures that the vehicle information and road surface adhesion information output by the distributed drive vehicle model, after being converted by the signal analysis module, can be accurately transmitted to the controller through the Controller Area Network interface. Simultaneously, the four-wheel torque control signals output by the controller can also be fed back to the signal analysis module through this interface, ensuring the effectiveness of signal interaction in the simulation closed loop. A non-simulation controller LAN interface is synchronously configured. This non-simulation controller LAN interface is adapted to a dedicated communication device for hardware-in-the-loop testing. One end of the dedicated communication device is connected to the computer, and the other end is connected to the controller through this non-simulation controller LAN interface. This constructs a test circuit independent of the simulation circuit, enabling dedicated communication between the computer and the controller for issuing test parameter adjustment commands and collecting monitoring data. This avoids signal interference between the simulation circuit and the test circuit, ensuring the real-time transmission of vehicle dynamics signals during simulation and the accuracy of parameter adjustment and data acquisition during testing. It provides independent and stable communication support for the multi-objective optimized torque control algorithm under different working conditions.

[0081] In conjunction with the second aspect, in one embodiment, the controller is provided with a hardware interface or software module that supports the writing of multi-objective optimized torque control algorithms; and the multi-objective optimized torque control algorithm recorded in the controller has preset test quantities and monitoring quantities, wherein the test quantities include weight parameters and prediction time domain, and the monitoring quantities include wheel speed and tire slip ratio.

[0082] In this embodiment, the controller of the HIL test system for the multi-objective optimized torque control algorithm is equipped with a hardware interface or software module that supports the writing of the multi-objective optimized torque control algorithm. This hardware interface or software module serves as the loading carrier for the multi-objective optimized torque control algorithm, providing adaptation support for the writing and updating of the algorithm, ensuring that the controller can stably carry and execute the multi-objective optimized torque control algorithm. Furthermore, the multi-objective optimized torque control algorithm recorded in the controller has preset test quantities and monitoring quantities. The test quantities include weight parameters and prediction time domain, while the monitoring quantities include wheel speed and tire slip ratio. The preset test quantities provide clear parameter adjustment objects for subsequent algorithm testing based on simulation circuits and test circuits. The preset monitoring quantities provide key monitoring basis for real-time acquisition of algorithm control effects and judgment of whether the algorithm meets the multi-objective control requirements. In turn, it forms a synergy with the simulation function of the industrial control computer, the calibration and measurement function of the computer, and the communication function of the hardware-in-the-loop test dedicated communication equipment, ensuring that the multi-objective optimized torque control algorithm can be verified in an orderly manner around clear parameters and indicators during the sub-condition test process.

[0083] In conjunction with the second aspect, in one embodiment, the signal parsing module is further configured to perform bidirectional signal parsing operations, parsing the vehicle information and road surface adhesion information output by the vehicle dynamics simulation software into controller local area network signals and sending them to the controller, while simultaneously receiving the four-wheel torque control signals output by the controller, parsing them into input signals recognizable by the vehicle dynamics simulation software, and sending them to the distributed drive vehicle model in the vehicle dynamics simulation software.

[0084] In this embodiment, the signal parsing module (built within the mathematical modeling and simulation software of the industrial control computer) is also used to perform bidirectional signal parsing operations. On the one hand, it parses the vehicle information (such as wheel speed, yaw rate, and center of gravity sideslip angle) and road surface adhesion information (such as road friction coefficient) output by the vehicle dynamics simulation software (installed on the industrial control computer) into controller local area network (LAN) signals that the controller can recognize. These signals are then sent to the controller through the LAN interface of the industrial control computer, enabling the controller to generate corresponding four-wheel torque control signals based on the vehicle dynamic characteristics and road conditions in the simulation scenario. On the other hand, it receives the four-wheel torque control signals output by the controller through the LAN interface, parses them into input signals that the vehicle dynamics simulation software can recognize, and feeds them back to the distributed drive vehicle model in the vehicle dynamics simulation software. This allows the model to update its own dynamic operating state according to the controller's control commands, thereby forming a closed loop of signal interaction between the vehicle dynamics simulation software, the signal parsing module, and the controller. This ensures that the simulation circuit can realistically simulate the multi-objective optimized torque control algorithm's control process of vehicle dynamics, providing a signal interaction environment close to the actual driving scenario for the algorithm in subsequent sub-condition tests.

[0085] In conjunction with the second aspect, in one embodiment, the signal parsing module is further provided with a controller local area network interface adapted to the industrial control computer and a hardware support package for the controller.

[0086] In this embodiment, a hardware support package is also provided to adapt the industrial control computer's controller LAN interface and the controller. This hardware support package can eliminate communication compatibility barriers between the signal parsing module and the industrial control computer's controller LAN interface and the controller. This enables the signal parsing module to have signal processing and transmission capabilities compatible with the industrial control computer's controller LAN interface and the controller. It ensures that when the signal parsing module performs bidirectional signal parsing operations (parsing the vehicle information and road surface adhesion information output by the vehicle dynamics simulation software into controller LAN signals and sending them to the controller, while simultaneously receiving the four-wheel torque control signals output by the controller and parsing them into input signals recognizable by the vehicle dynamics simulation software), the signal parsing format and transmission rate match the communication requirements of the industrial control computer's controller LAN interface and the controller. This avoids signal distortion, transmission interruption, or interaction delays caused by compatibility issues, thereby ensuring that the simulation circuit built by the industrial control computer and the controller based on the signal parsing module can achieve stable closed-loop signal interaction. This provides compatible and reliable signal processing support for the simulation testing of multi-objective optimized torque control algorithms.

[0087] The functional implementation of each module in the HIL test system of the above-mentioned multi-objective optimized torque control algorithm corresponds to each step in the HIL test method embodiment of the above-mentioned multi-objective torque control algorithm. Their functions and implementation processes will not be described in detail here.

[0088] Thirdly, embodiments of this application provide a HIL test device for a multi-objective optimized torque control algorithm. The HIL test device for the multi-objective optimized torque control algorithm can be a personal computer (PC), laptop computer, server, or other device with data processing capabilities.

[0089] Reference Figure 6 , Figure 6 This is a schematic diagram of the hardware structure of the HIL test device for the multi-objective optimized torque control algorithm involved in the embodiments of this application. In the embodiments of this application, the HIL test device for the multi-objective optimized torque control algorithm may include a processor, a memory, a communication interface, and a communication bus.

[0090] The communication bus can be of any type and is used to interconnect the processor, memory, and communication interface.

[0091] The communication interface includes input / output (I / O) interfaces, physical interfaces, and logical interfaces used for interconnecting devices within the HIL test equipment that implements the multi-objective optimized torque control algorithm, as well as interfaces for interconnecting the HIL test equipment with other devices (such as other computing devices or user equipment). Physical interfaces can be Ethernet interfaces, fiber optic interfaces, ATM interfaces, etc.; user equipment can be displays, keyboards, etc.

[0092] Memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0093] The processor can be a general-purpose processor, which can call the HIL test program of the multi-objective optimized torque control algorithm stored in memory and execute the HIL test method of the multi-objective torque control algorithm provided in the embodiments of this application. For example, the general-purpose processor can be a central processing unit (CPU). The method executed when the HIL test program of the multi-objective optimized torque control algorithm is called can refer to the various embodiments of the HIL test method of the multi-objective torque control algorithm of this application, and will not be repeated here.

[0094] Those skilled in the art will understand that Figure 6 The hardware structure shown does not constitute a limitation of this application and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0095] Fourthly, embodiments of this application also provide a readable storage medium.

[0096] The present application stores a HIL test program for a multi-objective optimized torque control algorithm on a readable storage medium, wherein when the HIL test program for the multi-objective optimized torque control algorithm is executed by a processor, the steps of the HIL test method for the multi-objective torque control algorithm as described above are implemented.

[0097] The method implemented when the HIL test program of the multi-objective optimized torque control algorithm is executed can refer to the various embodiments of the HIL test method of the multi-objective torque control algorithm of this application, and will not be repeated here.

[0098] It should be noted that the sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0099] The terms "comprising" and "having," and any variations thereof, in the specification, claims, and accompanying drawings of this application are intended to cover a non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus. The terms "first," "second," and "third," etc., are used to distinguish different objects, etc., and do not indicate a sequence, nor do they limit "first," "second," and "third" to different types.

[0100] In the description of the embodiments of this application, terms such as "exemplary," "for example," or "for instance" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as "exemplary," "for example," or "for instance" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary," "for example," or "for instance" is intended to present the relevant concepts in a concrete manner.

[0101] In the description of the embodiments of this application, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of this application, "multiple" means two or more.

[0102] In some processes described in the embodiments of this application, multiple operations or steps are included in a specific order. However, it should be understood that these operations or steps may not be executed in the order they appear in the embodiments of this application, or they may be executed in parallel. The sequence number of the operation is only used to distinguish different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed sequentially or in parallel, and these operations or steps may be combined.

[0103] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as described above, and includes several instructions to cause a terminal device to execute the methods described in the various embodiments of this application.

[0104] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A HIL test method for a multi-objective torque control algorithm, characterized in that, The HIL test method for the multi-objective torque control algorithm includes: Connect the industrial control computer to the controller that has recorded the multi-objective torque control algorithm, and connect the distributed drive vehicle model in the vehicle dynamics simulation software on the industrial control computer to the signal analysis module in the mathematical modeling and simulation software on the industrial control computer to complete the simulation circuit construction. The computer and controller, equipped with calibration and measurement software, are connected through a dedicated communication device for hardware-in-the-loop testing to complete the test circuit setup. Based on the completed simulation and test circuits, longitudinal operating condition algorithm tests, lateral operating condition algorithm tests, and adaptive operating condition strategy algorithm tests were conducted.

2. The HIL test method for the multi-objective torque control algorithm as described in claim 1, characterized in that, Before connecting the industrial control computer to the controller that records the multi-objective torque control algorithm, and connecting the distributed drive vehicle model in the vehicle dynamics simulation software on the industrial control computer to the signal analysis module in the mathematical modeling and simulation software on the industrial control computer, and before completing the simulation circuit construction, the following steps are also included: The multi-objective torque control algorithm is programmed into the controller, and test and monitoring quantities are preset in the algorithm. The test quantities include weight parameters and prediction time domain, and the monitoring quantities include wheel speed and tire slip ratio.

3. The HIL test method for the multi-objective torque control algorithm as described in claim 1, characterized in that, The connection between the distributed drive vehicle model in the vehicle dynamics simulation software on the industrial control computer and the signal analysis module in the mathematical modeling and simulation software on the industrial control computer includes: The signal parsing module performs bidirectional signal parsing operations, parsing the vehicle information and road surface adhesion information output by the vehicle dynamics simulation software into controller area network signals and sending them to the controller. At the same time, it receives the four-wheel torque control signals output by the controller, parses them into input signals that can be recognized by the vehicle dynamics simulation software, and then sends them to the distributed drive vehicle model.

4. The HIL test method for the multi-objective torque control algorithm as described in claim 1, characterized in that, After connecting the industrial control computer to the controller with the recorded multi-objective torque control algorithm, and connecting the distributed drive vehicle model in the vehicle dynamics simulation software on the industrial control computer to the signal analysis module in the mathematical modeling and simulation software on the industrial control computer, and completing the simulation circuit construction, the following is also included: Load the hardware support package that adapts to the industrial control computer and controller into the signal analysis module of the mathematical modeling and simulation software, run the signal analysis module, and if the four-wheel torque control signal fed back by the controller and the vehicle signal output by the vehicle dynamics simulation software are observed, then the simulation circuit is deemed to be successfully built.

5. The HIL test method for the multi-objective torque control algorithm as described in claim 1, characterized in that, Before connecting the computer and controller, which carry calibration and measurement software, through a dedicated hardware-in-the-loop test communication device to complete the test circuit setup, the following steps are also included: Install the driver for the hardware-in-the-loop testing dedicated communication device on the computer equipped with the calibration and measurement software to ensure normal communication between the computer and the dedicated communication device. At the same time, verify the operating status of the calibration and measurement software to ensure that it can read the controller signals normally.

6. The HIL test method for the multi-objective torque control algorithm as described in claim 1, characterized in that, Based on the constructed simulation and test circuits, longitudinal operating condition algorithm testing, lateral operating condition algorithm testing, and adaptive operating condition strategy algorithm testing are conducted, including: The controller's monitoring data is collected in real time using calibration and measurement software on a computer. The preset test parameters are manually modified, and the responsiveness of the multi-objective torque control algorithm to parameter adjustments is verified based on the changing trends of the monitoring data.

7. The HIL test method for the multi-objective torque control algorithm as described in claim 1, characterized in that, Based on the constructed simulation and test circuits, longitudinal operating condition algorithm testing, lateral operating condition algorithm testing, and adaptive operating condition strategy algorithm testing are conducted, including: When conducting longitudinal working condition algorithm tests, the weight parameters and prediction time domain of the multi-objective torque control algorithm are adjusted. The weight parameters include the expected additional yaw moment tracking weight and the expected wheel speed tracking weight. By adjusting the above parameters, the multi-objective torque control algorithm can achieve the driving anti-slip function under longitudinal working conditions, so that the tire slip ratio is maintained within a preset reasonable range.

8. The HIL test method for the multi-objective torque control algorithm as described in claim 7, characterized in that, After adjusting the above parameters to enable the multi-objective torque control algorithm to achieve the drive anti-slip function under longitudinal working conditions and maintain the tire slip ratio within a preset reasonable range, the method further includes: Adjust the lower limit of the sum of additional torques in the multi-objective torque control algorithm. Set the lower limit of the sum of additional torques to be equal to or lower than the threshold value set by the driver's analytical torque value to avoid excessive tire slippage due to excessive torque.

9. The HIL test method for the multi-objective torque control algorithm as described in claim 1, characterized in that, Based on the constructed simulation and test circuits, longitudinal operating condition algorithm testing, lateral operating condition algorithm testing, and adaptive operating condition strategy algorithm testing are conducted, including: When testing the lateral condition algorithm, adjust the yaw rate tracking weight and the center of mass sideslip angle suppression weight of the upper controller of the multi-objective torque control algorithm. The larger the yaw rate tracking weight, the larger the proportion of the yaw motion tracking error in the objective function, and the higher the yaw tracking accuracy. The larger the center of mass sideslip angle suppression weight, the larger the proportion of the lateral deviation suppression error in the objective function, and the better the lateral deviation suppression effect.

10. The HIL test method for the multi-objective torque control algorithm as described in claim 9, characterized in that, Based on the constructed simulation and test circuits, longitudinal operating condition algorithm testing, lateral operating condition algorithm testing, and adaptive operating condition strategy algorithm testing are conducted, including: Adjust the expected additional yaw moment tracking weight and expected wheel speed tracking weight of the lower-level controller of the multi-objective torque control algorithm. Prioritize adjusting the expected additional yaw moment tracking weight to ensure the generation of the expected additional yaw moment, and then adjust the expected wheel speed tracking weight to keep the tire slip ratio at a relatively reasonable level throughout the entire lateral working condition.

11. The HIL test method for the multi-objective torque control algorithm as described in claim 1, characterized in that, Based on the constructed simulation and test circuits, longitudinal operating condition algorithm testing, lateral operating condition algorithm testing, and adaptive operating condition strategy algorithm testing are conducted, including: When testing the adaptive operating condition strategy algorithm, speed threshold, yaw rate threshold, and straight-line driving time threshold are preset as judgment parameters. These three judgment parameters are used to determine the current operating state of the vehicle, thereby distinguishing whether the vehicle is in a longitudinal or lateral operating condition.

12. The HIL test method for the multi-objective torque control algorithm as described in claim 11, characterized in that, The process of determining the vehicle's current operating state using these three parameters, thereby distinguishing between longitudinal and lateral operating conditions, includes: When the speed is greater than the speed threshold and the yaw rate is greater than the yaw rate threshold, or when the vehicle speed is greater than the speed threshold and the straight-line travel time is less than the straight-line travel time threshold, the vehicle is determined to be in a lateral working condition. In other cases, the vehicle is determined to be in a longitudinal working condition.

13. The HIL test method for the multi-objective torque control algorithm as described in claim 11, characterized in that, When testing the adaptive driving condition strategy algorithm, the preset judgment parameters for speed threshold, yaw rate threshold, and straight-line driving time threshold include: The speed threshold, yaw rate threshold, and straight-line travel time threshold were tested and calibrated. By adjusting the threshold values ​​multiple times and observing the matching degree between the working condition judgment results of the multi-objective torque control algorithm and the actual working conditions, the optimal threshold combination was determined, so that the multi-objective torque control algorithm could accurately switch the corresponding control parameter combination based on the working condition judgment results.

14. A HIL test system for a multi-objective optimized torque control algorithm, characterized in that, The HIL test system for the multi-objective optimized torque control algorithm includes: An industrial control computer is installed with vehicle dynamics simulation software and mathematical modeling and simulation software. The vehicle dynamics simulation software contains a distributed drive vehicle model, and the mathematical modeling and simulation software contains a signal analysis module. The controller records a multi-objective optimized torque control algorithm; A computer equipped with calibration and measurement software; A dedicated communication device for hardware-in-the-loop testing is used to connect the computer and the controller; The industrial control computer and the controller construct a simulation circuit through a signal analysis module and a distributed drive vehicle model. The computer, the hardware-in-the-loop test dedicated communication equipment, and the controller construct a test circuit.

15. The HIL test system for the multi-objective optimized torque control algorithm as described in claim 14, characterized in that, The industrial computer is also equipped with a controller local area network interface, and the industrial computer and the controller are connected through the controller local area network interface to construct a simulation circuit; The controller is also equipped with a non-simulation controller LAN interface. The hardware-in-the-loop test dedicated communication device is connected to the controller through the non-simulation controller LAN interface to construct a test circuit.

16. The HIL test system for the multi-objective optimized torque control algorithm as described in claim 14, characterized in that, The controller is equipped with a hardware interface or software module that supports the writing of multi-objective optimized torque control algorithms. Furthermore, the multi-objective optimized torque control algorithm recorded in the controller has preset test quantities and monitoring quantities. The test quantities include weight parameters and prediction time domain, and the monitoring quantities include wheel speed and tire slip ratio.

17. The HIL test system for the multi-objective optimized torque control algorithm as described in claim 14, characterized in that, The signal parsing module is also used to perform bidirectional signal parsing operations, parsing the vehicle information and road surface adhesion information output by the vehicle dynamics simulation software into controller local area network signals and sending them to the controller. At the same time, it receives the four-wheel torque control signals output by the controller, parses them into input signals that can be recognized by the vehicle dynamics simulation software, and then sends them to the distributed drive vehicle model in the vehicle dynamics simulation software.

18. The HIL test system for the multi-objective optimized torque control algorithm as described in claim 14, characterized in that, The signal parsing module is also equipped with a controller local area network interface adapted to the industrial control computer and a hardware support package for the controller.

19. A HIL test device for a multi-objective optimized torque control algorithm, characterized in that, The HIL test device for the multi-objective optimized torque control algorithm includes a processor, a memory, and a HIL test program for the multi-objective optimized torque control algorithm stored in the memory and executable by the processor. When the HIL test program for the multi-objective optimized torque control algorithm is executed by the processor, it implements the steps of the HIL test method for the multi-objective torque control algorithm as described in any one of claims 1 to 13.

20. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a HIL test program for a multi-objective optimized torque control algorithm, wherein when the HIL test program for the multi-objective optimized torque control algorithm is executed by a processor, it implements the steps of the HIL test method for the multi-objective torque control algorithm as described in any one of claims 1 to 13.

Images