Rotating hub testing method, device and equipment based on model predictive controller and medium

Abstract

The invention relates to the technical field of rotating hub testing, and discloses a rotating hub testing method, device and equipment based on a model prediction controller and a medium, and the method comprises the steps: operating a vehicle dynamics model in a simulation environment; processing the target speed sequence and the current speed value through a model prediction controller to obtain a control sequence at the next moment of the current moment, updating the control sequence by adopting a conjugate gradient method according to gradient information to obtain an updated control sequence, and selecting the updated control sequence as an optimal control sequence when the current value is the minimum value; and drawing a target speed curve according to the target speed sequence, obtaining an average error and a maximum error value between the current speed curve and the target speed curve, and when the average error is smaller than a first preset value and the maximum error value is smaller than a second preset value, selecting the endurance mileage corresponding to the current speed curve as a rotating hub test result of the target vehicle. The speed tracking precision is high, and the requirement of rotating hub testing is met.

Description

Technical Field

[0001] The present invention relates to the technical field of rotor hub testing, and in particular to a rotor hub testing method, device, equipment and medium based on a model predictive controller. Background Art

[0002] In the automotive industry, spin testing is an essential step in ensuring that vehicle performance and quality meet standards. The results of spin testing, often referred to as spin test results, can help automakers address existing issues.

[0003] However, the existing process for acquiring the target vehicle's hub test results is cumbersome, hindering efficiency. This is because existing technologies primarily rely on manual acquisition to obtain the target vehicle's hub test results. This manual acquisition consumes significant human and time resources, increases the time required to acquire the target vehicle's hub test results, and is susceptible to human intervention, hindering efficiency. Summary of the Invention

[0004] The present invention provides a hub test method, device, computer equipment and storage medium based on a model predictive controller to solve the technical problem that the acquisition process of the hub test results of the existing target vehicle is cumbersome and is not conducive to improving the efficiency of obtaining the hub test results.

[0005] In a first aspect, a hub test method based on a model predictive controller is provided, comprising: Obtain the road file corresponding to the test condition, import the road file into the simulation platform, and process the road file through the simulation platform to obtain the simulation environment; Import the vehicle dynamics model corresponding to the target vehicle into the simulation environment, and run the vehicle dynamics model in the simulation environment; Read the speed sensor data of the vehicle dynamics model at the current moment; Obtain the current speed value from the speed sensor data at the current moment, obtain the target speed sequence from the working condition file, and input the target speed sequence and the current speed value into the model predictive controller; The target speed sequence and the current speed value are processed by a model predictive controller to obtain a control sequence for the next moment from the current moment. The control sequence is updated according to the gradient information using a conjugate gradient method to obtain an updated control sequence. The updated control sequence is input into a cost function to obtain a cost value. When the cost value reaches a minimum value, the updated control sequence is selected as the optimal control sequence. In the optimal control sequence, the torque control value corresponding to each time point is read, and the torque control value corresponding to each time point is processed to obtain the vehicle speed corresponding to each time point. According to each time point and the vehicle speed corresponding to each time point, the current speed curve is drawn, and the target speed curve is drawn according to the target speed sequence. The average error and the maximum error value between the current speed curve and the target speed curve are obtained. When the average error is less than a first preset value and the maximum error value is less than a second preset value, the cruising range corresponding to the current speed curve is selected as the hub test result of the target vehicle.

[0006] Furthermore, the step of importing the vehicle dynamics model corresponding to the target vehicle into the simulation environment and running the vehicle dynamics model in the simulation environment includes: Obtain a model file, and in the model file, obtain a vehicle dynamics model corresponding to the target vehicle; Import the vehicle dynamics model corresponding to the target vehicle into the simulation environment, and run the vehicle dynamics model in the simulation environment.

[0007] Furthermore, the reading of the speed sensor data of the vehicle dynamics model at the current moment includes: Obtaining operational information of the vehicle dynamics model; A read instruction is obtained and executed, and speed sensor data of the vehicle dynamics model at the current moment is read from the operation information.

[0008] Furthermore, the method of obtaining a current speed value from the speed sensor data at the current moment, obtaining a target speed sequence from the operating condition file, and inputting the target speed sequence and the current speed value into the model predictive controller includes: Obtain the current speed value from the speed sensor data at the current moment, obtain the working condition file, and obtain the target speed sequence from the working condition file; Get input instructions, execute input instructions, and input the target speed sequence and current speed value into the model predictive controller.

[0009] Furthermore, the target speed sequence and the current speed value are processed by the model predictive controller to obtain a control sequence for the next moment of the current moment, the control sequence is updated according to the gradient information using the conjugate gradient method to obtain an updated control sequence, the updated control sequence is input into the cost function to obtain a cost value, and when the cost value is the minimum value, the updated control sequence is selected as the optimal control sequence, including: The target speed sequence and the current speed value are processed by the model predictive controller to obtain the control sequence of the next moment from the current moment, and the gradient information of the cost function with respect to the control sequence is obtained; The conjugate gradient method is used to update the control sequence according to the gradient information to obtain an updated control sequence, and the updated control sequence is input into the cost function to obtain a cost value. When the cost value is the minimum, the updated control sequence is selected as the optimal control sequence.

[0010] Furthermore, in the optimal control sequence, the torque control value corresponding to each time point is read, the torque control value corresponding to each time point is processed to obtain the vehicle speed corresponding to each time point, a current speed curve is drawn according to each time point and the vehicle speed corresponding to each time point, a target speed curve is drawn according to the target speed sequence, an average error and a maximum error value between the current speed curve and the target speed curve are obtained, and when the average error is less than a first preset value and the maximum error value is less than a second preset value, the cruising range corresponding to the current speed curve is selected as the hub test result of the target vehicle, including: In the optimal control sequence, the torque control value corresponding to each time point is read and transmitted to the control system of the vehicle dynamics model. The torque control value corresponding to each time point is processed by the control system of the vehicle dynamics model to obtain the vehicle speed corresponding to each time point. The current speed curve is drawn based on each time point and the vehicle speed corresponding to each time point. Draw a target speed curve according to the target speed sequence, obtain the error value between the current speed curve and the target speed curve at each sampling point, add the absolute value of the error value at each sampling point to obtain the total error, divide the total error by the number of sampling points to obtain the average error between the current speed curve and the target speed curve, obtain the maximum value among the absolute values of the error values at each sampling point, select the maximum value as the maximum error value between the current speed curve and the target speed curve, and when the average error is less than a first preset value and the maximum error value is less than a second preset value, select the cruising range corresponding to the current speed curve as the hub test result of the target vehicle.

[0011] Furthermore, in the optimal control sequence, the torque control value corresponding to each time point is read, the torque control value corresponding to each time point is processed to obtain the vehicle speed corresponding to each time point, a current speed curve is drawn according to each time point and the vehicle speed corresponding to each time point, a target speed curve is drawn according to the target speed sequence, an average error and a maximum error value between the current speed curve and the target speed curve are obtained, and when the average error is less than a first preset value and the maximum error value is less than a second preset value, the cruising range corresponding to the current speed curve is selected as the hub test result of the target vehicle. The hub test method includes: A display window is created to display the hub test result.

[0012] In a second aspect, a hub test device based on a model predictive controller is provided, comprising: An acquisition module is used to obtain the road file corresponding to the test condition, import the road file into the simulation platform, and process the road file through the simulation platform to obtain the simulation environment; An operation module is used to import the vehicle dynamics model corresponding to the target vehicle into the simulation environment and operate the vehicle dynamics model in the simulation environment; A reading module is used to read the speed sensor data of the vehicle dynamics model at the current moment; A component module is used to obtain the current speed value from the speed sensor data at the current moment, obtain the target speed sequence from the working condition file, and input the target speed sequence and the current speed value into the model predictive controller; a selection module for processing the target speed sequence and the current speed value through a model predictive controller to obtain a control sequence for the next moment of the current moment, updating the control sequence based on the gradient information using a conjugate gradient method to obtain an updated control sequence, inputting the updated control sequence into a cost function to obtain a cost value, and selecting the updated control sequence as the optimal control sequence when the cost value is the minimum; The test module is used to read the torque control value corresponding to each time point in the optimal control sequence, process the torque control value corresponding to each time point, obtain the vehicle speed corresponding to each time point, draw the current speed curve according to each time point and the vehicle speed corresponding to each time point, draw the target speed curve according to the target speed sequence, obtain the average error and maximum error value between the current speed curve and the target speed curve, and when the average error is less than a first preset value and the maximum error value is less than a second preset value, select the cruising range corresponding to the current speed curve as the hub test result of the target vehicle.

[0013] In a third aspect, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned hub testing method when executing the computer program.

[0014] In a fourth aspect, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned hub testing method are implemented.

[0015] The present application provides a hub test method, device, computer equipment and storage medium based on a model predictive controller, which has beneficial effects in two aspects. On the one hand, in the optimal control sequence, the torque control value corresponding to each time point is read, and the torque control value corresponding to each time point is processed to obtain the vehicle speed corresponding to each time point. According to each time point and the vehicle speed corresponding to each time point, the current speed curve is drawn, and the target speed curve is drawn according to the target speed sequence. The average error and maximum error value between the current speed curve and the target speed curve are obtained. When the average error is less than a first preset value and the maximum error value is less than a second preset value, the cruising range corresponding to the current speed curve is selected as the hub test result of the target vehicle. Since manual acquisition is not required, the acquisition time of the hub test result of the target vehicle is reduced, which is conducive to improving the acquisition efficiency of the hub test result of the target vehicle. On the other hand, since the hub test result is automatically acquired, it will not be affected by manual intervention, which is conducive to improving the reliability of the hub test result of the target vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments of the present invention. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0017] Figure 1 2. It is a schematic diagram of an application environment of a hub testing method according to an embodiment of the present invention; Figure 2 A schematic flow chart of a hub testing method provided in one embodiment of the present invention; Figure 3 yes Figure 2 A schematic flow chart of a specific implementation of step S23; Figure 4 yes Figure 2 A schematic flow chart of a specific implementation of step S25; Figure 5 yes Figure 2 A schematic flow chart of a specific implementation of step S26; Figure 6 2 is a schematic structural diagram of a rotary hub testing device according to an embodiment of the present invention; Figure 7 is a schematic structural diagram of a computer device according to an embodiment of the present invention; Figure 8 is a sample diagram of a target vehicle provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0019] See also Figure 1 , Figure 1 This is a schematic diagram of an application environment of a rotary hub test method according to an embodiment of the present invention. The rotary hub test method provided by the embodiment of the present invention can be applied in the following situations: Figure 1 In an application environment, a client communicates with a server through a network.

[0020] The server obtains the road file corresponding to the test condition through the client, imports the road file into the simulation platform, and processes the road file through the simulation platform to obtain the simulation environment; Import the vehicle dynamics model corresponding to the target vehicle into the simulation environment, and run the vehicle dynamics model in the simulation environment; Read the speed sensor data of the vehicle dynamics model at the current moment; Obtain the current speed value from the speed sensor data at the current moment, obtain the target speed sequence from the working condition file, and input the target speed sequence and the current speed value into the model predictive controller; The target speed sequence and the current speed value are processed by a model predictive controller to obtain a control sequence for the next moment from the current moment. The control sequence is updated according to the gradient information using a conjugate gradient method to obtain an updated control sequence. The updated control sequence is input into a cost function to obtain a cost value. When the cost value reaches a minimum value, the updated control sequence is selected as the optimal control sequence. In the optimal control sequence, the torque control value corresponding to each time point is read, and the torque control value corresponding to each time point is processed to obtain the vehicle speed corresponding to each time point. According to each time point and the vehicle speed corresponding to each time point, the current speed curve is drawn, and the target speed curve is drawn according to the target speed sequence. The average error and the maximum error value between the current speed curve and the target speed curve are obtained. When the average error is less than a first preset value and the maximum error value is less than a second preset value, the cruising range corresponding to the current speed curve is selected as the hub test result of the target vehicle.

[0021] In the scheme implemented by the above-mentioned hub test method, device, equipment and medium, the beneficial effects are in two aspects. On the one hand, in the optimal control sequence, the torque control value corresponding to each time point is read, and the torque control value corresponding to each time point is processed to obtain the vehicle speed corresponding to each time point. According to each time point and the vehicle speed corresponding to each time point, the current speed curve is drawn, and the target speed curve is drawn according to the target speed sequence. The average error and the maximum error value between the current speed curve and the target speed curve are obtained. When the average error is less than the first preset value and the maximum error value is less than the second preset value, the cruising range corresponding to the current speed curve is selected as the hub test result of the target vehicle. Since no manual acquisition is required, the acquisition time of the hub test result of the target vehicle is reduced, which is conducive to improving the acquisition efficiency of the hub test result of the target vehicle. On the other hand, since the hub test result is automatically acquired, it will not be affected by manual intervention, which is conducive to improving the reliability of the hub test result of the target vehicle.

[0022] The device running the client is referred to as a client device.

[0023] The device running the server is referred to as the server device.

[0024] Among them, client devices include but are not limited to smartphones, personal computers, Internet of Vehicles terminals, tablets and portable wearable devices.

[0025] The server device can be implemented as an independent server or a server cluster composed of multiple servers. The present invention will be described in detail below through specific embodiments.

[0026] See also Figure 2 , Figure 2 A schematic flow chart of a hub testing method provided in one embodiment of the present invention includes the following steps: S21, obtaining a road file corresponding to the test condition, importing the road file into the simulation platform, and processing the road file through the simulation platform to obtain a simulation environment; Exemplarily, obtaining a road file corresponding to a test condition, importing the road file into a simulation platform, and processing the road file through the simulation platform to obtain a simulation environment includes: Select the Chinese light vehicle driving conditions or the world light vehicle test cycle conditions as the test conditions; Obtain the road file corresponding to the test condition, import the road file into the simulation platform, process the road file through the simulation platform, and obtain the simulation environment.

[0027] The China Light Vehicle Test Cycle is a test standard developed by China for light vehicles. The China Light Vehicle Test Cycle mainly includes the following parts: Urban conditions: simulates driving conditions on urban roads with a speed limit of 50km / h, covering frequent acceleration and deceleration processes to reflect the characteristics of urban congestion and frequent starts and stops.

[0028] Suburban conditions: Simulates driving conditions on suburban roads with a speed limit of 80km / h to test the performance of the target vehicle under suburban conditions.

[0029] High-speed conditions: simulate driving conditions on a highway with a speed limit of 100km / h, and test the target vehicle's endurance performance under high-speed conditions.

[0030] The World Light Vehicle Test Cycle (WLTC) is an international automotive evaluation standard. It aims to provide a unified test benchmark for light vehicles worldwide to measure their fuel consumption, emissions, and all-electric range. The WLTC consists of the following sections: Low-speed operating conditions: simulate urban congestion and low-speed driving conditions, covering frequent start-stop and acceleration and deceleration processes.

[0031] Medium-speed operating conditions: simulate driving conditions on suburban or rural roads, with a moderate speed and covering certain acceleration, deceleration and turning processes.

[0032] High-speed conditions: simulate driving conditions on a highway with a relatively high speed, including stable cruising and a small amount of acceleration and deceleration.

[0033] Ultra-high-speed condition: simulates the extreme driving state of the target vehicle on the highway.

[0034] S22, importing the vehicle dynamics model corresponding to the target vehicle into the simulation environment, and running the vehicle dynamics model in the simulation environment; The step of importing the vehicle dynamics model corresponding to the target vehicle into the simulation environment and running the vehicle dynamics model in the simulation environment includes: Obtain a model file, and in the model file, obtain a vehicle dynamics model corresponding to the target vehicle; Import the vehicle dynamics model corresponding to the target vehicle into the simulation environment, and run the vehicle dynamics model in the simulation environment.

[0035] Exemplarily, the vehicle dynamics model is: in, For traction; is the air resistance, is the inertial force; is the slope force, is the rolling resistance; in, is the air density; is the drag coefficient; is the projected area of the vehicle in the direction of motion; is the real-time vehicle speed; is the acceleration; is the vehicle mass; is the gravity coefficient; is the slope; is the rolling resistance coefficient.

[0036] Among them, aero is the identifier of air, and grade is the identifier of slope, that is, the degree of inclination of the terrain.

[0037] Among them, tr is the logo of the traction control system. The English name of the traction control system is Traction Control, and tr is the abbreviation of Traction Control.

[0038] Where rr is the roll indicator and i is the inertia indicator.

[0039] Here, sin represents the sine function.

[0040] refer to Figure 8 , Figure 8 is a sample diagram of a target vehicle provided in one embodiment of the present invention.

[0041] Figure 8 The vehicle dynamics model is applied to a target vehicle, and the force conditions of the target vehicle are shown.

[0042] exist Figure 8 middle, is the slope, For traction, is the slope force, is the rolling resistance, is the inertial force, is the air resistance.

[0043] The grade force is the force acting on the target vehicle when it is traveling on a sloped road. When the target vehicle is climbing a slope, the grade force acts as a resistance to its motion, slowing it down. When the target vehicle is descending a slope, the grade force accelerates it.

[0044] S23, reading the speed sensor data of the vehicle dynamics model at the current moment; S24, obtaining a current speed value from the speed sensor data at the current moment, obtaining a target speed sequence from the operating condition file, and inputting the target speed sequence and the current speed value into a model predictive controller; The method of obtaining the current speed value from the speed sensor data at the current moment, obtaining the target speed sequence from the working condition file, and inputting the target speed sequence and the current speed value into the model predictive controller includes: Obtain the current speed value from the speed sensor data at the current moment, obtain the working condition file, and obtain the target speed sequence from the working condition file; Get input instructions, execute input instructions, and input the target speed sequence and current speed value into the model predictive controller.

[0045] S25, processing the target speed sequence and the current speed value through a model predictive controller to obtain a control sequence for the next moment after the current moment, updating the control sequence based on the gradient information using a conjugate gradient method to obtain an updated control sequence, inputting the updated control sequence into a cost function to obtain a cost value, and selecting the updated control sequence as the optimal control sequence when the cost value is the minimum value; The model predictive controller processes the target speed sequence and the current speed value, and can predict the control sequence at the next moment after the current moment. The next moment after the current moment refers to the time point immediately after the current moment on the timeline.

[0046] For ease of explanation, let's take the example of a one-minute interval between the current moment and the next moment. The following example shows: For example, the current time is 9:00 AM, and the next time after the current time is 9:01 AM.

[0047] For example, the current time is 3:00 p.m., and the next time after the current time is 3:01 p.m.

[0048] For ease of explanation, let's take the example of a 30-second interval between the current moment and the next moment. For example, if the current time is 5:00 PM, the next time after the current time is 5:30 PM.

[0049] For example, if the current time is 6:00 PM, the next time after the current time is 6:30 PM.

[0050] The conjugate gradient method is used to update the control sequence according to the gradient information to obtain the updated control sequence, including: The Newton method is combined with the conjugate gradient method to update the control sequence according to the gradient information to obtain the updated control sequence.

[0051] The conjugate gradient method is an iterative algorithm for solving linear equations or optimization problems. Its core idea is to use the gradient information of the cost function to gradually generate a set of conjugate directions and search along these directions to find the optimal control sequence.

[0052] The Model Predictive Controller (MPC) is a control algorithm. When tracking a target velocity sequence, the MPC predicts future system states and outputs in real time and optimizes control inputs to achieve optimal trajectory tracking. However, the computational complexity of the MPC is high. The Newton method combined with the conjugate gradient method can significantly improve the accuracy of the MPC in tracking the target velocity sequence. The Newton method utilizes the second-order derivative information of the objective function to construct search directions, which has the advantage of fast convergence, especially when approaching the optimal solution. The conjugate gradient method combines the simplicity of the steepest descent method with the fast convergence of the Newton method. By utilizing the first-order derivative information to construct conjugate directions, it avoids repeated searches in the same direction. Furthermore, it does not require the direct calculation and storage of the Hessian matrix, reducing the computational effort. Therefore, the Newton method combined with the conjugate gradient method maintains computational efficiency and quickly finds the optimal control sequence. This optimal control sequence makes the vehicle's control inputs more accurate, improving the accuracy of tracking the target velocity sequence and thus contributing to improved velocity tracking accuracy.

[0053] The optimization algorithm used in the model predictive controller is the Newton method combined with the conjugate gradient method. The Newton method utilizes second-order derivative information to converge to the optimal solution more quickly. The conjugate gradient method also demonstrates excellent performance when solving large-scale linear systems. Compared to the traditional quadratic programming optimization algorithm of the model predictive controller, which suffers from a heavy computational burden within each control cycle, the model predictive controller's combination of the Newton method and the conjugate gradient method significantly reduces the amount of computation and increases computational speed, thereby better meeting the needs of real-time control.

[0054] Furthermore, the model predictive controller uses a traditional quadratic programming optimization algorithm, primarily for use on processor platforms and not in embedded systems. However, the model predictive controller utilizes the Newton method combined with the conjugate gradient method, making it suitable for embedded systems. The Newton method improves the efficiency of the model predictive controller with its rapid convergence rate, while the conjugate gradient method significantly reduces the computational complexity and memory usage of the model predictive controller by avoiding the direct calculation and storage of large matrices. This combination enables the model predictive controller to run more efficiently and in real time on resource-constrained embedded systems while maintaining its performance and robustness.

[0055] The cost function serves as an indicator for evaluating the quality of a control strategy. It guides the conjugate gradient method in finding the optimal control sequence. The cost function includes a state error term and a control input term. The state error term is the error between the target value and the actual value, while the control input term is the cost of executing the control sequence.

[0056] The specific form of the cost function is usually designed according to the characteristics of the system and the control objectives. The specific form of the cost function can be adjusted by itself, so the specific form of the cost function is not limited here.

[0057] S26. In the optimal control sequence, read the torque control value corresponding to each time point, process the torque control value corresponding to each time point, obtain the vehicle speed corresponding to each time point, draw the current speed curve according to each time point and the vehicle speed corresponding to each time point, draw the target speed curve according to the target speed sequence, obtain the average error and maximum error value between the current speed curve and the target speed curve, and when the average error is less than the first preset value and the maximum error value is less than the second preset value, select the cruising range corresponding to the current speed curve as the hub test result of the target vehicle.

[0058] Among them, when the average error is less than the first preset value and the maximum error value is less than the second preset value, it means that the speed tracking error is effectively controlled within the preset range, the speed tracking accuracy is high, and the requirements of the rotating hub test are met. At the same time, it means that the target vehicle can very accurately follow the target speed curve during driving, reducing the additional energy consumption that may be caused by speed fluctuations. This precise speed control makes the energy consumption of the target vehicle in the rotating hub test closer to the actual driving state. Therefore, the cruising range obtained from the test is more accurate and reliable, and can more truly reflect the cruising performance of the target vehicle in actual use. Selecting the cruising range corresponding to the current speed curve as the rotating hub test result of the target vehicle can intuitively reflect the cruising ability of the target vehicle under simulated real road conditions. Among them, in the optimal control sequence, the torque control value corresponding to each time point is read to improve the operating efficiency and stability of the target vehicle. By precisely controlling the torque, it can be ensured that the target vehicle maintains the best working state during operation, reducing unnecessary energy loss, thereby improving operating efficiency.

[0059] Among them, through the current speed curve, the acceleration, deceleration and constant speed driving capabilities of the target vehicle under different working conditions can be clearly observed, which is crucial for evaluating and optimizing the target vehicle's power performance, fuel economy and driving comfort.

[0060] Wherein, in the optimal control sequence, the torque control value corresponding to each time point is read, the torque control value corresponding to each time point is processed to obtain the vehicle speed corresponding to each time point, a current speed curve is drawn according to each time point and the vehicle speed corresponding to each time point, a target speed curve is drawn according to the target speed sequence, an average error and a maximum error value between the current speed curve and the target speed curve are obtained, and when the average error is less than a first preset value and the maximum error value is less than a second preset value, the cruising range corresponding to the current speed curve is selected as the hub test result of the target vehicle. The hub test method includes: A display window is created to display the hub test result.

[0061] Among them, the hub test results are displayed, and R&D personnel can identify potential performance bottlenecks, thereby making targeted optimization designs to improve the driving experience and safety of the target vehicle.

[0062] In an embodiment of the present invention, the beneficial effects are in two aspects. On the one hand, in the optimal control sequence, the torque control value corresponding to each time point is read, and the torque control value corresponding to each time point is processed to obtain the vehicle speed corresponding to each time point. According to each time point and the vehicle speed corresponding to each time point, the current speed curve is drawn, and the target speed curve is drawn according to the target speed sequence. The average error and the maximum error value between the current speed curve and the target speed curve are obtained. When the average error is less than the first preset value and the maximum error value is less than the second preset value, the cruising range corresponding to the current speed curve is selected as the hub test result of the target vehicle. Since manual acquisition is not required, the acquisition time of the hub test result of the target vehicle is reduced, which is conducive to improving the acquisition efficiency of the hub test result of the target vehicle. On the other hand, since the hub test result is automatically acquired, it will not be affected by manual intervention, which is conducive to improving the reliability of the hub test result of the target vehicle.

[0063] See also Figure 3 , Figure 3 yes Figure 2 A specific implementation flow diagram of step S23 is described in detail as follows: S31, obtaining operation information of a vehicle dynamics model; S32, obtaining a reading instruction, executing the reading instruction, and reading the speed sensor data of the vehicle dynamics model at the current moment in the operation information.

[0064] In the embodiment of the present invention, reading the speed sensor data of the vehicle dynamics model at the current moment is conducive to fully understanding the operating status of the vehicle dynamics model.

[0065] See also Figure 4 , Figure 4 yes Figure 2 A specific implementation flow diagram of step S25 is described in detail as follows: S41, processing the target speed sequence and the current speed value through the model predictive controller to obtain the control sequence of the next moment of the current moment, and obtaining the gradient information of the cost function with respect to the control sequence; S42, using the conjugate gradient method, based on the gradient information, updating the control sequence to obtain an updated control sequence, inputting the updated control sequence into the cost function to obtain a cost value, and when the cost value is the minimum value, selecting the updated control sequence as the optimal control sequence.

[0066] In the embodiment of the present invention, when the cost value is the minimum value, it indicates that the performance of the updated control sequence is the best. Selecting the updated control sequence as the optimal control sequence can ensure the stability and reliability of the optimal control sequence.

[0067] See also Figure 5 , Figure 5 yes Figure 2 A specific implementation flow diagram of step S26 is described in detail as follows: S51, in the optimal control sequence, reading the torque control value corresponding to each time point, transmitting the torque control value corresponding to each time point to the control system of the vehicle dynamics model, processing the torque control value corresponding to each time point by the control system of the vehicle dynamics model to obtain the vehicle speed corresponding to each time point, and drawing a current speed curve based on each time point and the vehicle speed corresponding to each time point; S52, draw a target speed curve according to the target speed sequence, obtain the error value between the current speed curve and the target speed curve at each sampling point, add the absolute value of the error value at each sampling point to obtain the total error, divide the total error by the number of sampling points to obtain the average error between the current speed curve and the target speed curve, obtain the maximum value among the absolute values of the error values at each sampling point, select the maximum value as the maximum error value between the current speed curve and the target speed curve, and when the average error is less than a first preset value and the maximum error value is less than a second preset value, select the cruising range corresponding to the current speed curve as the hub test result of the target vehicle.

[0068] For ease of explanation, the following examples are given: For example, there are multiple sampling points, and the multiple sampling points are sampling point 1, sampling point 2, and sampling point 3; The value of the current speed curve at sampling point 1 is A1, the value of the current speed curve at sampling point 2 is A2, and the value of the current speed curve at sampling point 2 is A3; The value of the target speed curve at sampling point 1 is B1, the value of the target speed curve at sampling point 2 is B2, and the value of the target speed curve at sampling point 3 is B3; The absolute value of the error between A1 and B1 is C1, the absolute value of the error between A2 and B2 is C2, and the absolute value of the error between A3 and B3 is C3. Add C1, C2, and C3 to get the total error. Divide the total error by the number of sampling points to get the average error between the current speed curve and the target speed curve. The maximum value among C1, C2, and C3 is selected as the maximum error between the current speed curve and the target speed curve.

[0069] In the embodiment of the present invention, since there is no need to manually obtain the hub test results, the time for obtaining the hub test results of the target vehicle is reduced, which is conducive to improving the efficiency of obtaining the hub test results of the target vehicle.

[0070] See also Figure 6 , Figure 6 FIG. 1 is a schematic structural diagram of a rotary hub testing device according to an embodiment of the present invention. Figure 6 As shown, the hub testing device includes an acquisition module 101, an operation module 102, a reading module 103, a composition module 104, a selection module 105, and a testing module 106. The functional modules are described in detail as follows: An acquisition module 101 is used to acquire a road file corresponding to a test condition, import the road file into a simulation platform, and process the road file through the simulation platform to obtain a simulation environment; An operation module 102 is used to import the vehicle dynamics model corresponding to the target vehicle into the simulation environment and operate the vehicle dynamics model in the simulation environment; A reading module 103 is used to read the speed sensor data of the vehicle dynamics model at the current moment; The component 104 is used to obtain the current speed value from the speed sensor data at the current moment, obtain the target speed sequence from the working condition file, and input the target speed sequence and the current speed value into the model predictive controller; A selection module 105 is configured to process the target speed sequence and the current speed value through a model predictive controller to obtain a control sequence for the next moment after the current moment, update the control sequence based on the gradient information using a conjugate gradient method to obtain an updated control sequence, input the updated control sequence into a cost function to obtain a cost value, and select the updated control sequence as the optimal control sequence when the cost value is the minimum value; The test module 106 is used to read the torque control value corresponding to each time point in the optimal control sequence, process the torque control value corresponding to each time point, obtain the vehicle speed corresponding to each time point, draw the current speed curve according to each time point and the vehicle speed corresponding to each time point, draw the target speed curve according to the target speed sequence, obtain the average error and maximum error value between the current speed curve and the target speed curve, and when the average error is less than a first preset value and the maximum error value is less than a second preset value, select the cruising range corresponding to the current speed curve as the hub test result of the target vehicle.

[0071] In an embodiment of the present invention, the beneficial effects are in two aspects. On the one hand, in the optimal control sequence, the torque control value corresponding to each time point is read, and the torque control value corresponding to each time point is processed to obtain the vehicle speed corresponding to each time point. According to each time point and the vehicle speed corresponding to each time point, the current speed curve is drawn, and the target speed curve is drawn according to the target speed sequence. The average error and the maximum error value between the current speed curve and the target speed curve are obtained. When the average error is less than the first preset value and the maximum error value is less than the second preset value, the cruising range corresponding to the current speed curve is selected as the hub test result of the target vehicle. Since manual acquisition is not required, the acquisition time of the hub test result of the target vehicle is reduced, which is conducive to improving the acquisition efficiency of the hub test result of the target vehicle. On the other hand, since the hub test result is automatically acquired, it will not be affected by manual intervention, which is conducive to improving the reliability of the hub test result of the target vehicle.

[0072] For the specific limitations of the rotating hub test device, please refer to the limitations of the rotating hub test method above, which will not be repeated here.

[0073] Each module in the aforementioned hub testing device can be implemented in whole or in part through software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a computer device memory in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0074] See also Figure 7 , Figure 7 Schematic diagram of the structure of a computer device in one embodiment of the present invention. In one embodiment, a computer device is provided. The computer device is a server device or a client device. The internal structure diagram thereof can be as follows: Figure 7As shown. The computer device includes a processor, memory, network interface, display screen and input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with external devices. When the computer program is executed by the processor, it can realize the functions or steps of a hub testing method based on a model predictive controller.

[0075] In one embodiment, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor.

[0076] It should be noted that the above functions or steps that can be implemented by the computer-readable storage medium or computer device can refer to the relevant description of the aforementioned method embodiment. To avoid repetition, they will not be described one by one here.

[0077] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a graphics processing unit (GPU), and a network processor (NP); it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0078] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software may depend on the specific application and design constraints of the technical solution. Technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the embodiments of the present disclosure.

Claims

1. A hub test method based on a model predictive controller, characterized in that: include: Obtain the road file corresponding to the test condition, import the road file into the simulation platform, and process the road file through the simulation platform to obtain the simulation environment; Import the vehicle dynamics model corresponding to the target vehicle into the simulation environment, and run the vehicle dynamics model in the simulation environment; Read the speed sensor data of the vehicle dynamics model at the current moment; Obtain the current speed value from the speed sensor data at the current moment, obtain the target speed sequence from the working condition file, and input the target speed sequence and the current speed value into the model predictive controller; The target speed sequence and the current speed value are processed by a model predictive controller to obtain a control sequence for the next moment from the current moment. The control sequence is updated according to the gradient information using a conjugate gradient method to obtain an updated control sequence. The updated control sequence is input into a cost function to obtain a cost value. When the cost value reaches a minimum value, the updated control sequence is selected as the optimal control sequence. In the optimal control sequence, the torque control value corresponding to each time point is read, and the torque control value corresponding to each time point is processed to obtain the vehicle speed corresponding to each time point. According to each time point and the vehicle speed corresponding to each time point, the current speed curve is drawn, and the target speed curve is drawn according to the target speed sequence. The average error and the maximum error value between the current speed curve and the target speed curve are obtained. When the average error is less than a first preset value and the maximum error value is less than a second preset value, the cruising range corresponding to the current speed curve is selected as the hub test result of the target vehicle.

2. The hub testing method according to claim 1, characterized in that: The step of importing the vehicle dynamics model corresponding to the target vehicle into the simulation environment and running the vehicle dynamics model in the simulation environment includes: Obtain a model file, and in the model file, obtain a vehicle dynamics model corresponding to the target vehicle; Import the vehicle dynamics model corresponding to the target vehicle into the simulation environment, and run the vehicle dynamics model in the simulation environment.

3. The hub testing method according to claim 1, characterized in that: The reading of the speed sensor data of the vehicle dynamics model at the current moment includes: Obtaining operational information of the vehicle dynamics model; A read instruction is obtained and executed, and speed sensor data of the vehicle dynamics model at the current moment is read from the operation information.

4. The hub testing method according to claim 1, characterized in that: The method of obtaining a current speed value from the speed sensor data at the current moment, obtaining a target speed sequence from the working condition file, and inputting the target speed sequence and the current speed value into the model predictive controller includes: Obtain the current speed value from the speed sensor data at the current moment, obtain the working condition file, and obtain the target speed sequence from the working condition file; Get input instructions, execute input instructions, and input the target speed sequence and current speed value into the model predictive controller.

5. The hub testing method according to claim 1, characterized in that: The target speed sequence and the current speed value are processed by the model predictive controller to obtain a control sequence for the next moment after the current moment; the control sequence is updated according to the gradient information using the conjugate gradient method to obtain an updated control sequence; the updated control sequence is input into the cost function to obtain a cost value; and when the cost value is the minimum value, the updated control sequence is selected as the optimal control sequence, including: The target speed sequence and the current speed value are processed by the model predictive controller to obtain the control sequence of the next moment from the current moment, and the gradient information of the cost function with respect to the control sequence is obtained; The conjugate gradient method is used to update the control sequence according to the gradient information to obtain an updated control sequence, and the updated control sequence is input into the cost function to obtain a cost value. When the cost value is the minimum, the updated control sequence is selected as the optimal control sequence.

6. The hub testing method according to claim 1, characterized in that: The method comprises: reading a torque control value corresponding to each time point in the optimal control sequence, processing the torque control value corresponding to each time point to obtain a vehicle speed corresponding to each time point, drawing a current speed curve according to each time point and the vehicle speed corresponding to each time point, drawing a target speed curve according to a target speed sequence, obtaining an average error and a maximum error value between the current speed curve and the target speed curve, and selecting a cruising range corresponding to the current speed curve as a hub test result of the target vehicle when the average error is less than a first preset value and the maximum error value is less than a second preset value. In the optimal control sequence, the torque control value corresponding to each time point is read and transmitted to the control system of the vehicle dynamics model. The torque control value corresponding to each time point is processed by the control system of the vehicle dynamics model to obtain the vehicle speed corresponding to each time point. The current speed curve is drawn based on each time point and the vehicle speed corresponding to each time point. Draw a target speed curve according to the target speed sequence, obtain the error value between the current speed curve and the target speed curve at each sampling point, add the absolute value of the error value at each sampling point to obtain the total error, divide the total error by the number of sampling points to obtain the average error between the current speed curve and the target speed curve, obtain the maximum value among the absolute values of the error values at each sampling point, select the maximum value as the maximum error value between the current speed curve and the target speed curve, and when the average error is less than a first preset value and the maximum error value is less than a second preset value, select the cruising range corresponding to the current speed curve as the hub test result of the target vehicle.

7. The hub testing method according to claim 1, characterized in that: In the optimal control sequence, the torque control value corresponding to each time point is read, the torque control value corresponding to each time point is processed to obtain the vehicle speed corresponding to each time point, a current speed curve is drawn according to each time point and the vehicle speed corresponding to each time point, a target speed curve is drawn according to the target speed sequence, an average error and a maximum error value between the current speed curve and the target speed curve are obtained, and when the average error is less than a first preset value and the maximum error value is less than a second preset value, the cruising range corresponding to the current speed curve is selected as the hub test result of the target vehicle. The hub test method includes: A display window is created to display the hub test result.

8. A hub test device based on a model predictive controller, characterized in that: include: An acquisition module is used to obtain the road file corresponding to the test condition, import the road file into the simulation platform, and process the road file through the simulation platform to obtain the simulation environment; An operation module is used to import the vehicle dynamics model corresponding to the target vehicle into the simulation environment and operate the vehicle dynamics model in the simulation environment; A reading module is used to read the speed sensor data of the vehicle dynamics model at the current moment; A component module is used to obtain the current speed value from the speed sensor data at the current moment, obtain the target speed sequence from the working condition file, and input the target speed sequence and the current speed value into the model predictive controller; a selection module for processing the target speed sequence and the current speed value through a model predictive controller to obtain a control sequence for the next moment of the current moment, updating the control sequence based on the gradient information using a conjugate gradient method to obtain an updated control sequence, inputting the updated control sequence into a cost function to obtain a cost value, and selecting the updated control sequence as the optimal control sequence when the cost value is the minimum; The test module is used to read the torque control value corresponding to each time point in the optimal control sequence, process the torque control value corresponding to each time point, obtain the vehicle speed corresponding to each time point, draw the current speed curve according to each time point and the vehicle speed corresponding to each time point, draw the target speed curve according to the target speed sequence, obtain the average error and maximum error value between the current speed curve and the target speed curve, and when the average error is less than a first preset value and the maximum error value is less than a second preset value, select the cruising range corresponding to the current speed curve as the hub test result of the target vehicle.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the hub testing method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the hub testing method according to any one of claims 1 to 7 are implemented.

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