Vehicle test control method and device, vehicle, electronic equipment and storage medium

Abstract

The invention provides a vehicle test control method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining the current actual speed and vehicle state parameters of a target vehicle, and determining the current expected speed according to the mapping relation between the expected speed and time; based on the vehicle configuration information, the expected speed and the actual speed, fusion processing is carried out through feedforward control and proportional integral differential control, and a first control torque for controlling the target vehicle is obtained; constructing a vehicle dynamics model to perform model prediction control, and solving the vehicle dynamics model to obtain a second control torque for controlling the target vehicle; and fusing the first control torque and the second control torque to obtain a target control torque, and controlling the target vehicle to test based on the target control torque. Compared with the prior art, personal errors can be reduced, the data accuracy is improved, and the test result is more reliable; especially in a repetitive test, consistency and stability can be maintained.

Description

Technical Field

[0001] The present disclosure relates to the technical field of vehicles, and particularly to a method and device for vehicle test control, an electronic device, and a storage medium. Background Art

[0002] Currently, for variable speed condition tests, especially standard condition (such as WLTC, CLTC, NEDC, etc.) tests, testers need to actively control the vehicle speed through the accelerator and brake pedals to meet the test condition standards. The entire condition test duration is relatively long, often reaching about 12 hours, which brings great challenges and burdens to the driver's body and mind. At the same time, due to the existence of human factors, there will be deviations in the test results. Such deviations may lead to inconsistent test results, thus affecting the efficiency and quality of subsequent data analysis and software optimization and improvement. Due to the inconsistency of human operations, the time and labor costs for data analysis and software optimization and improvement are relatively high. Therefore, how to automatically control a vehicle for testing under variable speed conditions to ensure the consistency of vehicle testing has become a problem to be solved. Summary of the Invention

[0003] The present disclosure provides a method and device for vehicle test control, an electronic device, and a storage medium. Its main purpose is to automatically control a vehicle for testing under variable speed condition tests.

[0004] According to a first aspect of the present disclosure, there is provided a method for vehicle test control, including:

[0005] Obtaining the current actual speed and vehicle state parameters of a target vehicle, and determining the current desired speed according to the mapping relationship between the desired speed and time, wherein the mapping relationship is generated according to different test conditions executed by the target vehicle;

[0006] Based on the vehicle configuration information, the desired speed, and the actual speed, performing fusion processing through feedforward control and proportional integral derivative control to obtain a first control torque for controlling the target vehicle;

[0007] According to the vehicle configuration information, the vehicle state parameters, and the actual speed, constructing a vehicle dynamics model for model predictive control, and solving the vehicle dynamics model to obtain a second control torque for controlling the target vehicle;

[0008] Performing fusion processing on the first control torque and the second control torque to obtain a target control torque, and controlling the target vehicle to perform a test based on the target control torque.

[0009] In some embodiments, the process of obtaining a first control torque for controlling the target vehicle, through fusing feedforward control with proportional-integral-derivative (PID) control based on the vehicle configuration information, the desired speed, and the actual speed, includes:

[0010] Construct a feedforward model based on the desired speed and the actual speed to perform feedforward control on the target vehicle, and solve the feedforward model to obtain a feedforward torque;

[0011] Calculate a resistance torque of the target vehicle based on the actual speed and the drag coefficient and / or friction coefficient in the vehicle configuration information;

[0012] Calculate the speed difference between the desired speed and the actual speed, and calculate the torque of each regulation term in the proportional-integral-derivative control based on the speed difference to obtain a regulation-term torque;

[0013] Fuse the feedforward torque, the resistance torque, and the regulation-term torque to obtain the first control torque.

[0014] In some embodiments, the process of constructing a vehicle dynamics model based on the vehicle configuration information, the vehicle state parameters, and the actual speed to perform model predictive control, and solving the vehicle dynamics model to obtain a second control torque for controlling the target vehicle, includes:

[0015] Establish a cost function of the vehicle dynamics model based on the desired speed;

[0016] Convert the vehicle dynamics model into a state-space equation, and discretize the state-space equation to obtain a discretized dynamics model;

[0017] Use the cost function to perform predictive optimization calculation on the discretized dynamics model to obtain the second control torque.

[0018] In some embodiments, the process of fusing the first control torque and the second control torque to obtain a target control torque includes:

[0019] Determine the test condition executed by the target vehicle, and determine the fusion weights corresponding to the first control torque and the second control torque respectively under the test condition;

[0020] Based on the fusion weights, fuse the first control torque and the second control torque to obtain the target control torque.

[0021] In some embodiments, the method includes:

[0022] Calculate the throttle opening and braking opening corresponding to the target control torque to ensure the normal progress of the test items for retrieving the throttle opening and / or the braking opening.

[0023] In some embodiments, before obtaining the current actual speed and vehicle state parameters of the target vehicle and determining the current desired speed according to the mapping relationship between the desired speed and time, the method further includes:

[0024] Obtain the working condition information of the vehicle test, and establish the mapping relationship between the desired speed and time during the vehicle test according to the working condition information.

[0025] According to a second aspect of the present disclosure, there is provided a text generation device, including:

[0026] An acquisition unit, configured to obtain the current actual speed and vehicle state parameters of the target vehicle, and determine the current desired speed according to the mapping relationship between the desired speed and time, where the mapping relationship is generated according to different test working conditions executed by the target vehicle;

[0027] A processing unit, configured to perform fusion processing on the vehicle configuration information, the desired speed, and the actual speed through feedforward control and proportional-integral-derivative control to obtain a first control torque for controlling the target vehicle;

[0028] A solving unit, configured to construct a vehicle dynamics model based on the vehicle configuration information, the vehicle state parameters, and the actual speed to perform model predictive control, and solve the vehicle dynamics model to obtain a second control torque for controlling the target vehicle;

[0029] A fusion unit, configured to perform fusion processing on the first control torque and the second control torque to obtain a target control torque, and control the target vehicle to perform a test based on the target control torque.

[0030] In some embodiments, the processing unit includes:

[0031] A construction module, configured to construct a feedforward model based on the desired speed and the actual speed to perform feedforward control on the target vehicle, and solve the feedforward model to obtain a feedforward torque;

[0032] A first calculation module, configured to calculate the resistance torque of the target vehicle according to the actual speed and the air resistance coefficient and / or friction coefficient in the vehicle configuration information;

[0033] A second calculation module, configured to calculate the speed difference between the desired speed and the actual speed, and calculate the torque of each regulation item in the proportional-integral-derivative control according to the speed difference to obtain a regulation item torque;

[0034] The first fusion module is configured to fuse the feedforward torque, the resistance torque, and the regulation item torque to obtain the first control torque.

[0035] In some embodiments, the solving unit includes:

[0036] The establishment module is configured to establish a cost function of the vehicle dynamics model based on the desired speed.

[0037] The transformation module is configured to transform the vehicle dynamics model into a state space equation and discretize the state space equation to obtain a discretized dynamics model.

[0038] The third calculation module is configured to perform predictive optimization calculation on the discretized dynamics model by using the cost function to obtain the second control torque.

[0039] In some embodiments, the fusion unit includes:

[0040] The determination module is configured to determine the test condition executed by the target vehicle and determine the fusion weights corresponding to the first control torque and the second control torque under the test condition.

[0041] The second fusion module is configured to fuse the first control torque and the second control torque based on the fusion weights to obtain a target control torque.

[0042] In some embodiments, the device includes:

[0043] The calculation unit is configured to calculate the throttle opening and the braking opening corresponding to the target control torque to ensure the normal progress of the test items for retrieving the throttle opening and / or the braking opening.

[0044] In some embodiments, the device further includes:

[0045] The establishment unit is configured to obtain the working condition information of the vehicle test and establish the mapping relationship between the desired speed and time during the vehicle test according to the working condition information before obtaining the current actual speed and vehicle state parameters of the target vehicle and determining the current desired speed according to the mapping relationship between the desired speed and time.

[0046] According to a third aspect of the present disclosure, there is provided an electronic device, including:

[0047] At least one processor; and

[0048] A memory communicatively connected to the at least one processor; wherein,

[0049] The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method described in the foregoing first aspect.

[0050] According to a fourth aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause the computer to execute the method described in the foregoing first aspect.

[0051] According to a fifth aspect of the present disclosure, there is provided a computer program product including a computer program which, when executed by a processor, implements the method described in the foregoing first aspect.

[0052] According to a sixth aspect of the present disclosure, there is provided a vehicle including the device described in the foregoing second aspect, or the electronic device described in the third aspect, or the non-transitory computer-readable storage medium described in the fourth aspect, or the computer program product described in the fifth aspect.

[0053] The present disclosure provides a method and apparatus for vehicle test control, an electronic device, and a storage medium. The actual speed and vehicle state parameters of a target vehicle are obtained, and the current desired speed is determined according to the mapping relationship between the desired speed and time, where the mapping relationship is generated according to different test conditions executed by the target vehicle; based on the vehicle configuration information, the desired speed, and the actual speed, fusion processing is performed through feedforward control and proportional-integral-derivative control to obtain a first control torque for controlling the target vehicle; according to the vehicle configuration information, the vehicle state parameters, and the actual speed, a vehicle dynamics model is constructed for model predictive control, and the vehicle dynamics model is solved to obtain a second control torque for controlling the target vehicle; the first control torque and the second control torque are fused to obtain a target control torque, and the target vehicle is controlled to perform a test based on the target control torque. Compared with the related art, the present disclosure realizes the automation and processing of data acquisition by obtaining information such as the actual speed and vehicle state parameters of the target vehicle, can reduce human errors, improve the accuracy of data, and make the test results more reliable; through the fusion processing of methods such as feedforward control, proportional-integral-derivative control, and model predictive control of the target vehicle, the precise control ability of the vehicle can be enhanced, the controllability of the test can be improved, and especially in repetitive tests, consistency and stability can be maintained.

[0054] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become easily understood through the following description. Description of the Drawings

[0055] The accompanying drawings are used to better understand the solution and do not limit the disclosure. Among them:

[0056] Figure 1 It is a schematic flowchart of a method for vehicle test control provided by an embodiment of the present disclosure;

[0057] Figure 2 It is a schematic flowchart of another method for vehicle test control provided by an embodiment of the present disclosure;

[0058] Figure 3 It is a schematic flowchart of a method for testing vehicle control;

[0059] Figure 4 It is a schematic structural diagram of a device for vehicle test control provided by an embodiment of the present disclosure;

[0060] Figure 5 It is a schematic structural diagram of another device for vehicle test control provided by an embodiment of the present disclosure;

[0061] Figure 6 It is a schematic block diagram of an exemplary electronic device provided by an embodiment of the present disclosure. Detailed implementation manners

[0062] The following describes exemplary embodiments of the present disclosure with reference to the accompanying drawings. Various details of the embodiments of the present disclosure are included to assist understanding, and they should be considered merely exemplary. Therefore, those of ordinary skill in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted below.

[0063] The following describes the method and device for vehicle test control, electronic device, and storage medium of embodiments of the present disclosure with reference to the accompanying drawings.

[0064] Figure 1 It is a schematic flowchart of a method for vehicle test control provided by an embodiment of the present disclosure.

[0065] As Figure 1 shown, the method includes the following steps:

[0066] Step 101, obtain the current actual speed and vehicle state parameters of the target vehicle, and determine the current desired speed according to the mapping relationship between the desired speed and time, where the mapping relationship is generated according to different test conditions executed by the target vehicle.

[0067] In an embodiment of the present disclosure, the target vehicle is the vehicle being tested. The actual speed of the target vehicle can be calculated by collecting the wheel speed of the target vehicle using a sensor, or can be measured using a satellite positioning system. The present disclosure does not limit this. The vehicle state parameters include but are not limited to the current torque, acceleration, engine and / or motor speed, etc. of the vehicle. The vehicle state parameters reflect the operating state of the target vehicle. The desired speed is the speed that the target vehicle should reach at a certain moment during the test when the target vehicle is being tested.

[0068] During the test, to achieve the consistency of the test; by generating the mapping relationship between the desired speed and time from the working condition information when the vehicle performs different tests. By controlling the target vehicle to perform the test according to the mapping relationship, the purpose of consistent test is achieved. Generating the mapping relationship according to different test working conditions enables the control system to adapt to different test environments and requirements.

[0069] Step 102, based on the vehicle configuration information, the desired speed, and the actual speed, perform fusion processing through feedforward control and proportional-integral-derivative control to obtain the first control torque for controlling the target vehicle.

[0070] In an embodiment of the present disclosure, the vehicle configuration information may include but is not limited to the weight of the vehicle, the friction coefficient of the vehicle tires, the vehicle wind resistance coefficient, etc. Feedforward control calculates in advance the control amount to be applied according to the current working condition and the expected target, in order to expect to reach the predetermined target. Feedforward control usually needs to be used in combination with feedback control to form a feedforward-feedback control system to cope with the influence of model uncertainty and other unmodeled dynamics. In such a system, feedforward control is responsible for predicting and compensating for disturbances, while feedback control is responsible for correcting the prediction error to improve the robustness and tracking performance of the system. In the present disclosure, proportional-integral-derivative control (abbreviated as PID control) is used as the feedback control method combined with feedforward control. By performing fusion processing of feedforward control and PID control, the first control torque is obtained. Since there are configuration differences between different vehicle models, the control system models constructed during feedforward control are different, and need to be constructed according to the vehicle configuration information of different vehicles. Feedforward control can respond quickly, but it does not consider the current state of the system and only calculates based on the expected value. The characteristic that feedforward control can respond quickly can well meet the control needs for working conditions that require quick response such as rapid acceleration.

[0071] Step 103, based on the vehicle configuration information, the vehicle state parameters, and the actual speed, construct a vehicle dynamics model for model predictive control, and solve the vehicle dynamics model to obtain the second control torque for controlling the target vehicle.

[0072] In an embodiment of the present disclosure, Model Predictive Control (MPC) controls the behavior of a system based on a predictive model. The core idea of MPC is to adopt a predictive model and an optimal strategy in the design of a control law, so as to achieve the prediction of the system state and the optimization of the control input. By establishing a mathematical model of the system to predict future states, this model usually includes the dynamic equations of the system, constraint conditions, performance indicators, etc. Then, based on the current state and the model, the future state is predicted, and further the optimal control input is determined to make the output of the system as close as possible to the desired value. The computational complexity of the MPC algorithm is relatively high, and the control accuracy is high.

[0073] In the present disclosure, a vehicle dynamics model is established according to vehicle configuration information, vehicle state parameters, and actual speed, and then the MPC algorithm is used for solution calculation to determine the torque required for the target vehicle to reach the desired speed.

[0074] Step 104, fuse the first control torque and the second control torque to obtain a target control torque, and control the target vehicle to perform a test based on the target control torque.

[0075] In an embodiment of the present disclosure, due to the characteristic that feedforward control + PID control can respond quickly, it can well meet the control requirements for working conditions that require quick response, such as rapid acceleration. However, for working conditions that require precise calculation and have low requirements for response speed, such as fuel-saving working conditions, feedforward control + PID control cannot meet the corresponding control requirements. Similarly, due to the relatively high computational complexity and high control accuracy of the MPC algorithm, it cannot well meet the control requirements for working conditions that require quick response, such as rapid acceleration. Therefore, for different test working conditions, the control method to be used needs to be determined according to the situation of the test working condition. By fusing the control torques obtained by different control methods, the advantages of different control methods can be taken into account, and thus a more comprehensive and effective control method can be provided.

[0076] The present disclosure provides a method for vehicle test control, which obtains the current actual speed and vehicle state parameters of a target vehicle, and determines the current desired speed according to the mapping relationship between the desired speed and time, wherein the mapping relationship is generated according to different test conditions executed by the target vehicle; based on the vehicle configuration information, the desired speed and the actual speed, through the fusion of feedforward control and proportional integral derivative control, a first control torque for controlling the target vehicle is obtained; according to the vehicle configuration information, the vehicle state parameters and the actual speed, a vehicle dynamics model is constructed for model predictive control, and the vehicle dynamics model is solved to obtain a second control torque for controlling the target vehicle; the first control torque and the second control torque are fused to obtain a target control torque, and the target vehicle is controlled to perform a test based on the target control torque. Compared with the related art, the present disclosure realizes the automation and processing of data acquisition by obtaining information such as the current actual speed and vehicle state parameters of the target vehicle, which can reduce human errors, improve the accuracy of data, and make the test results more reliable; through the fusion of methods such as feedforward control, proportional integral derivative control and model predictive control of the target vehicle, the precise control ability of the vehicle can be enhanced, the controllability of the test can be improved, especially in repeated tests, consistency and stability can be maintained.

[0077] To clearly illustrate the embodiments of the present disclosure, this embodiment provides a schematic flowchart of another method for vehicle test control.

[0078] As Figure 2 shown, the method includes the following steps:

[0079] Step 201, obtain the working condition information of the vehicle test, and establish the mapping relationship between the desired speed and time during the vehicle test according to the working condition information.

[0080] Specifically, in step 201, under different test conditions, such as standard conditions such as WLTC, CLTC, NEDC, or test conditions independently created according to specific requirements, we can import relevant data by means of looking up a table of time-desired vehicle speed. This process of looking up the table is based on the mapping relationship between time and the desired vehicle speed. First, a mapping table of time-desired vehicle speed needs to be established, which contains the vehicle speed information corresponding to different time points.

[0081] During the automatic control process, the preprocessing software will look up in the mapping table according to the current time information to determine the target vehicle speed. This method of looking up tables can quickly and accurately obtain the corresponding vehicle speed information according to the time, providing accurate data support for subsequent control strategies. At the same time, the system supports importing multiple different working condition data simultaneously. This means that during the same test process, data can be imported and adjusted according to different working conditions. This provides great convenience for multi-condition testing. According to the time state scheduling, we can obtain the target vehicle speed at different time points. This scheduling method ensures that we can obtain accurate target vehicle speed information at any time, providing an important reference basis for subsequent vehicle control.

[0082] In some embodiments of the present disclosure, a parameter calibration switch is also provided, and the user can select the working condition to be output through this switch. In this way, in the case where multiple working conditions exist simultaneously, the user can select a specific working condition for output according to actual needs.

[0083] Step 202: Obtain the current actual speed and vehicle state parameters of the target vehicle, and determine the current desired speed according to the mapping relationship between the desired speed and time.

[0084] Specifically in step 202, please refer to Figure 3 , Figure 3 It is a schematic flow diagram of a method for testing vehicle control. The speed sensor collects the vehicle speed information of the target vehicle, including the wheel speeds of each wheel, longitudinal acceleration, motor speed or engine speed. By coupling the signals collected by the sensor, the actual speed of the target vehicle is calculated.

[0085] Step 203: According to the desired speed and the actual speed, construct a feedforward model to perform feedforward control on the target vehicle, and solve the feedforward model to obtain the feedforward torque.

[0086] Specifically in step 203, in actual operation, if the difference between the desired speed and the actual speed is large, then a larger torque may be required to accelerate or decelerate the vehicle. Conversely, if the difference is small, the torque can be correspondingly reduced. Feedforward control is a control strategy based on the difference between the desired speed and the actual speed. By constructing a feedforward model, we can predict the future behavior of the vehicle and take corresponding control measures in advance to adjust the state of the vehicle.

[0087] When constructing the feedforward model, we need to consider the dynamic characteristics of the vehicle, road conditions, and other relevant factors. For example, if the vehicle is climbing or descending a slope, the demand for feedforward torque may be different. Similarly, if the road conditions are poor (such as wet, slippery, uneven, etc.), then a larger feedforward torque may be required to maintain stable driving.

[0088] Step 204: Calculate the resistance torque of the target vehicle based on the actual speed, the drag coefficient, and / or the friction coefficient in the vehicle configuration information.

[0089] Specifically in Step 204, the resistance torque is the torque generated by the resistance suffered by the vehicle during movement. This torque is related to the vehicle's speed, drag coefficient (or friction coefficient), and other factors. By measuring the actual speed and obtaining the drag coefficient (or friction coefficient), we can calculate the resistance torque of the target vehicle under specific conditions.

[0090] When calculating the resistance torque, some details need to be noted. For example, the drag coefficient and friction coefficient may be affected by various factors such as vehicle speed, road conditions, and vehicle design. Therefore, during the calculation process, the influence of these factors on the resistance needs to be considered.

[0091] In addition, the calculation of the resistance torque also needs to consider the vehicle's dynamic characteristics and stability requirements. If the resistance torque is too large, it may cause the vehicle to stall or become unstable. Therefore, appropriate adjustments and controls need to be made according to the actual situation.

[0092] Step 205: Calculate the speed difference between the desired speed and the actual speed, and calculate the torque of each regulation item in the proportional-integral-derivative control according to the speed difference to obtain the regulation item torque.

[0093] Specifically in Step 205, the proportional-integral-derivative control is a commonly used control strategy applicable to many systems. For a vehicle control system, the proportional term torque is mainly used to adjust the error between the current speed and the desired speed of the vehicle, the integral term torque is mainly used to reduce the long-term error, and the derivative term torque is mainly used to reduce the overshoot during the speed change process. When calculating the torque of each regulation item, some factors need to be considered. For example, the magnitude of the proportional term torque depends on the error between the desired speed and the actual speed, while the magnitude of the integral term torque depends on the cumulative error at all past moments. The magnitude of the derivative term torque depends on the error change rate at the current moment and the past moment.

[0094] The unstable output of the I term can be controlled by the timed output method, and the continuous output of the I term, or the function of outputting once every several cycles, can be achieved by calibrating parameters.

[0095] In addition, to obtain better control effects, some other factors such as saturation limits and filters can also be introduced. These factors can limit the maximum value of the control output, avoid system overload, and at the same time improve the stability of the control output.

[0096] Step 206: Perform a fusion process on the feedforward torque, the resistance torque, and the regulation item torque to obtain the first control torque.

[0097] Specifically in step 206, during the fusion process, we need to perform weighted or linear combination on the feedforward torque, resistance torque, and regulation item torque to obtain the final control torque. This weighted or linear combination process needs to be designed according to specific control strategies and vehicle characteristics. To ensure the stability and robustness of the control system, we also need to consider the mutual influence and limitation between different torques. For example, the mutual influence between the feedforward torque and the resistance torque may lead to system instability, so appropriate adjustment and optimization are required during the fusion process.

[0098] In addition, to improve the performance and accuracy of the control system, we can also introduce some other factors, such as sensor noise, model error, etc., for additional processing and compensation.

[0099] Step 207, based on the desired speed, establish the cost function of the vehicle dynamics model.

[0100] Step 208, transform the vehicle dynamics model into a state space equation, and discretize the state space equation to obtain a discretized dynamics model.

[0101] Step 209, use the cost function to perform predictive optimization calculation on the discretized dynamics model to obtain the second control torque.

[0102] Specifically in steps 207 to 209, transforming the vehicle dynamics model into a state space equation is a key step in modeling, which can describe the state and dynamic behavior of the vehicle at different moments. Discretization is to convert the continuous state space equation into a discrete form suitable for actual control systems for numerical calculation and optimization on a computer.

[0103] In the predictive optimization calculation, we use the cost function to evaluate the performance of different control strategies. The design of the cost function needs to consider the requirements and constraints of actual applications, such as ensuring the safety and stability of the vehicle, minimizing control energy, etc. Through continuous iteration of the optimization algorithm, we can find the control strategy that minimizes the cost function, thereby obtaining the optimized second control torque. The second control torque is obtained based on the discretized dynamics model and the optimized control input, which can guide the actual control of the vehicle. By applying the second control torque, we can achieve precise and stable control of the vehicle.

[0104] Step 210, determine the test conditions executed by the target vehicle, and determine the fusion weights corresponding to the first control torque and the second control torque respectively under the test conditions.

[0105] Step 211: Based on the fusion weights, fuse the first control torque and the second control torque to obtain the target control torque.

[0106] Specifically, in Steps 210 to 211, due to the characteristic that the feedforward control + PID control can respond quickly, it can well meet the control requirements for working conditions that require quick response, such as sudden acceleration. However, for working conditions that require precise calculation and have low requirements for response speed, such as fuel-saving working conditions, the feedforward control + PID control cannot meet the corresponding control requirements. Similarly, due to the high computational complexity and high control precision of the MPC algorithm, it cannot well meet the control requirements for working conditions that require quick response, such as sudden acceleration. Therefore, for different test working conditions, the control method used needs to be determined according to the situation of the test working conditions. By determining the fusion weights corresponding to the first control torque and the second control torque under different test working conditions, different test working conditions can be adapted. For example: To achieve the fastest and optimal working condition goal, the weight of the feedforward + PID control can be increased and the MPC function can be weakened. To achieve the lowest energy consumption and the most economical output, the weight of the MPC control can be increased and it can be achieved by adjusting the calculation method of the cost function. Finally, the automatic control torque is coupled.

[0107] Step 212: Calculate the throttle opening and the brake opening corresponding to the target control torque to ensure the normal progress of the test items for retrieving the throttle opening and / or the brake opening.

[0108] Specifically, in Step 212, after the automatic test of the shifting working condition is started, the vehicle system should respond to the automatic control torque, disable the creep function, replace the driver torque output, back-calculate the throttle and brake openings and the depressed states, and provide them for other test items that need to call the throttle opening and / or the brake opening to ensure the normal execution of other vehicle functions tests.

[0109] It should be noted that there may be multiple steps in the embodiments of the present disclosure. For the convenience of description, these steps are numbered, but these numbers are not intended to limit the execution time slots and execution orders between the steps; these steps can be implemented in any order, and the embodiments of the present disclosure do not make any limitations in this regard.

[0110] Corresponding to the above vehicle test control method, the present invention also proposes a vehicle test control device. Since the device embodiment of the present invention corresponds to the above method embodiment, the details not disclosed in the device embodiment can be referred to the above method embodiment, and will not be elaborated in the present invention.

[0111] Figure 4 As shown in the structural schematic diagram of a vehicle test control device provided by the embodiments of the present disclosure, Figure 4 it includes:

[0112] An acquisition unit 31, configured to acquire the current actual speed and vehicle state parameters of a target vehicle, and determine the current desired speed according to the mapping relationship between the desired speed and time, where the mapping relationship is generated according to different test conditions executed by the target vehicle;

[0113] A processing unit 32, configured to perform fusion processing through feedforward control and proportional-integral-derivative control based on vehicle configuration information, the desired speed, and the actual speed to obtain a first control torque for controlling the target vehicle;

[0114] A solving unit 33, configured to construct a vehicle dynamics model for model predictive control according to the vehicle configuration information, the vehicle state parameters, and the actual speed, and solve the vehicle dynamics model to obtain a second control torque for controlling the target vehicle;

[0115] A fusion unit 34, configured to perform fusion processing on the first control torque and the second control torque to obtain a target control torque, and control the target vehicle to perform a test based on the target control torque.

[0116] The present disclosure provides a device for vehicle test control, which acquires the current actual speed and vehicle state parameters of a target vehicle, and determines the current desired speed according to the mapping relationship between the desired speed and time, where the mapping relationship is generated according to different test conditions executed by the target vehicle; performs fusion processing through feedforward control and proportional-integral-derivative control based on vehicle configuration information, the desired speed, and the actual speed to obtain a first control torque for controlling the target vehicle; constructs a vehicle dynamics model for model predictive control according to the vehicle configuration information, the vehicle state parameters, and the actual speed, and solves the vehicle dynamics model to obtain a second control torque for controlling the target vehicle; performs fusion processing on the first control torque and the second control torque to obtain a target control torque, and controls the target vehicle to perform a test based on the target control torque. Compared with the related art, the present disclosure realizes the automation and processing of data acquisition by acquiring information such as the current actual speed and vehicle state parameters of the target vehicle, can reduce human errors, improve the accuracy of data, and make the test results more reliable; through the fusion processing of methods such as feedforward control, proportional-integral-derivative control, and model predictive control of the target vehicle, the precise control ability of the vehicle can be enhanced, the controllability of the test can be improved, and particularly in repeated tests, consistency and stability can be maintained.

[0117] Further, in a possible implementation manner of this embodiment, as Figure 5 shown, the processing unit 32 includes:

[0118] A building module 321, configured to build a feedforward model according to the desired speed and the actual speed for feedforward control of the target vehicle, and solve the feedforward model to obtain a feedforward torque;

[0119] A first calculation module 322, configured to calculate a resistance torque of the target vehicle according to the actual speed and the air resistance coefficient and / or friction coefficient in the vehicle configuration information;

[0120] A second calculation module 323, configured to calculate a speed difference between the desired speed and the actual speed, and calculate torques of respective regulation items in proportional integral derivative control according to the speed difference to obtain regulation item torques;

[0121] A first fusion module 324, configured to perform a fusion process on the feedforward torque, the resistance torque, and the regulation item torques to obtain the first control torque.

[0122] Further, in a possible implementation manner of this embodiment, as Figure 5 shown, the solving unit 33 includes:

[0123] A building module 331, configured to build a cost function of the vehicle dynamics model based on the desired speed;

[0124] A conversion module 332, configured to convert the vehicle dynamics model into a state space equation, and discretize the state space equation to obtain a discretized dynamics model;

[0125] A third calculation module 333, configured to perform a predictive optimization calculation on the discretized dynamics model by using the cost function to obtain the second control torque.

[0126] Further, in a possible implementation manner of this embodiment, as Figure 5 shown, the fusion unit 34 includes:

[0127] A determination module 341, configured to determine a test condition executed by the target vehicle, and determine respective fusion weights corresponding to the first control torque and the second control torque under the test condition;

[0128] A second fusion module 342, configured to perform a fusion process on the first control torque and the second control torque based on the fusion weights to obtain a target control torque.

[0129] Further, in a possible implementation manner of this embodiment, as Figure 5 shown, the device includes:

[0130] A calculation unit 35, configured to calculate the throttle opening and the braking opening corresponding to the target control torque, so as to ensure the normal progress of the test item for retrieving the throttle opening and / or the braking opening.

[0131] Further, in a possible implementation manner of this embodiment, as Figure 5 shown, the device further includes:

[0132] A building unit 36, configured to obtain the working condition information of the vehicle test and build a mapping relationship between the desired speed and time during the vehicle test according to the working condition information before obtaining the current actual speed and vehicle state parameters of the target vehicle and determining the current desired speed according to the mapping relationship between the desired speed and time.

[0133] It should be noted that the foregoing explanations of the method embodiments are also applicable to the device in this embodiment. The principles are the same and will not be limited in this embodiment.

[0134] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0135] Figure 6 FIG. shows a schematic block diagram of an exemplary electronic device 400 that can be used to implement the embodiments of the present disclosure. The electronic device is intended to represent various forms of digital computers, such as, a laptop computer, a desktop computer, a workbench, a personal digital assistant, a server, a blade server, a mainframe computer, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, a personal digital processor, a cellular phone, a smart phone, a wearable device, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0136] As Figure 6 shown, the device 400 includes a calculation unit 401, which can execute various appropriate actions and processes according to the computer program stored in the ROM (Read-Only Memory) 402 or the computer program loaded from the storage unit 408 into the RAM (Random Access Memory) 403. In the RAM 403, various programs and data required for the operation of the device 400 can also be stored. The calculation unit 401, the ROM 402, and the RAM 403 are connected to each other through a bus 404. The I / O (Input / Output) interface 405 is also connected to the bus 404.

[0137] Multiple components in device 400 are connected to I / O interface 405, including: an input unit 406, such as a keyboard, a mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a magnetic disk, an optical disc, etc.; and a communication unit 409, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 409 allows device 400 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0138] The computing unit 401 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 401 include but are not limited to a CPU (Central Processing Unit), a GPU (Graphic Processing Units), various dedicated AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, a DSP (Digital Signal Processor), and any suitable processor, controller, microcontroller, etc. The computing unit 401 executes the various methods and processes described above, such as the method for vehicle test control. For example, in some embodiments, the method for vehicle test control can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as the storage unit 408. In some embodiments, part or all of the computer program can be loaded and / or installed onto device 400 via the ROM 402 and / or the communication unit 409. When the computer program is loaded into the RAM 403 and executed by the computing unit 401, one or more steps of the method described above can be executed. Alternatively, in other embodiments, the computing unit 401 can be configured to execute the aforementioned method for vehicle test control in any other suitable manner (e.g., by means of firmware).

[0139] The various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuitry, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application Specific Standard Products), SOCs (System On Chip), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that receives data and instructions from a storage system, at least one input device, and at least one output device, and transmits the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0140] The program code for implementing the methods of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the program codes are executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0141] In the context of this disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a RAM, a ROM, an EPROM (Electrically Programmable Read-Only-Memory), or a flash memory, an optical fiber, a CD-ROM (Compact Disc Read-Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0142] To provide for interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (Cathode-Ray Tube) or an LCD (Liquid Crystal Display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can also be used to provide for interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0143] The systems and techniques described herein can be implemented in a computing system that includes backend components (such as, for example, a data server), or a computing system that includes middleware components (such as, for example, an application server), or a computing system that includes frontend components (such as, for example, a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (such as, for example, a communication network). Examples of a communication network include: a LAN (Local Area Network), a WAN (Wide Area Network), the Internet, and a blockchain network.

[0144] A computer system may include a client and a server. The client and the server are generally far from each other and usually interact through a communication network. The relationship between the client and the server is generated by computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, solving the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services ("Virtual Private Server", or simply "VPS" for short). The server may also be a server of a distributed system or a server combined with a blockchain.

[0145] Herein, it should be noted that artificial intelligence is a discipline that studies how to make a computer simulate certain thinking processes and intelligent behaviors of humans (such as learning, reasoning, thinking, planning, etc.), and it has both hardware-level technologies and software-level technologies. Artificial intelligence hardware technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, and big data processing; artificial intelligence software technologies mainly include several major directions such as computer vision technology, speech recognition technology, natural language processing technology, and machine learning / deep learning, big data processing technology, and knowledge graph technology.

[0146] The present disclosure also provides a vehicle. The vehicle in the embodiments of the present disclosure further includes the aforementioned electronic device, or a readable storage medium, or a computer program product. For specific descriptions, please refer to the above embodiments and will not be elaborated herein one by one.

[0147] The various numerical numbers such as the first, the second, etc. involved in the present disclosure are only for the convenience of description for distinction and do not limit the scope of the embodiments of the present disclosure, nor do they represent the sequence.

[0148] At least one in the present disclosure may also be described as one or more. The more may be two, three, four, or more, and the present disclosure does not make any limitations. In the embodiments of the present disclosure, for a technical feature, the technical features in this technical feature are distinguished by "the first", "the second", "the third", "A", "B", "C", and "D", etc. There is no sequence or size order among the technical features described by "the first", "the second", "the third", "A", "B", "C", and "D".

[0149] It should be understood that various forms of processes shown above can be used, reordering, adding, or deleting steps. For example, the steps recorded in the present disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved, and no limitations are made herein.

[0150] The above specific embodiments do not constitute a limitation to the protection scope of the present disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principle of the present disclosure shall be included within the protection scope of the present disclosure.

Claims

1. A method for vehicle test control, characterized in that, Including: Obtain the current actual speed and vehicle state parameters of the target vehicle, and determine the current desired speed according to the mapping relationship between the desired speed and time, where the mapping relationship is generated according to different test conditions executed by the target vehicle; Based on the vehicle configuration information, the desired speed, and the actual speed, perform fusion processing through feedforward control and proportional-integral-derivative control to obtain a first control torque for controlling the target vehicle; According to the vehicle configuration information, the vehicle state parameters, and the actual speed, construct a vehicle dynamics model for model predictive control, and solve the vehicle dynamics model to obtain a second control torque for controlling the target vehicle; Fuse the first control torque and the second control torque to obtain a target control torque, and control the target vehicle to perform tests based on the target control torque.

2. The method according to claim 1, characterized in that, The step of, based on the vehicle configuration information, the desired speed, and the actual speed, performing fusion processing through feedforward control and proportional-integral-derivative control to obtain a first control torque for controlling the target vehicle includes: Construct a feedforward model based on the desired speed and the actual speed to perform feedforward control on the target vehicle, and solve the feedforward model to obtain a feedforward torque; Calculate the resistance torque of the target vehicle according to the actual speed and the air resistance coefficient and / or friction coefficient in the vehicle configuration information; Calculate the speed difference between the desired speed and the actual speed, and calculate the torque of each regulation item in the proportional-integral-derivative control according to the speed difference to obtain a regulation item torque; Fuse the feedforward torque, the resistance torque, and the regulation item torque to obtain the first control torque.

3. The method according to claim 1, wherein The step of, according to the vehicle configuration information, the vehicle state parameters, and the actual speed, constructing a vehicle dynamics model for model predictive control, and solving the vehicle dynamics model to obtain a second control torque for controlling the target vehicle includes: Based on the desired speed, establish a cost function of the vehicle dynamics model; Convert the vehicle dynamics model into a state-space equation, and discretize the state-space equation to obtain a discretized dynamics model; Use the cost function to perform predictive optimization calculation on the discretized dynamics model to obtain the second control torque.

4. The method according to claim 1, characterized in that, The step of fusing the first control torque and the second control torque to obtain a target control torque includes: Determine the test condition executed by the target vehicle, and determine the fusion weights corresponding to the first control torque and the second control torque under the test condition; Based on the fusion weights, fuse the first control torque and the second control torque to obtain a target control torque.

5. The method according to any one of claims 1-4, characterized in that, The method further includes: Calculate the throttle opening and braking opening corresponding to the target control torque to ensure the normal progress of the test items for adjusting the throttle opening and / or the braking opening.

6. The method according to claim 1, characterized in that, Before obtaining the current actual speed and vehicle state parameters of the target vehicle and determining the current desired speed according to the mapping relationship between the desired speed and time, the method further includes: Obtain the driving condition information of the vehicle test, and establish the mapping relationship between the expected speed and time during the vehicle test according to the driving condition information.

7. A device for vehicle test control, characterized in that, It includes: An acquisition unit, configured to acquire the current actual speed and vehicle state parameters of the target vehicle, and determine the current expected speed according to the mapping relationship between the expected speed and time, wherein the mapping relationship is generated according to different test driving conditions executed by the target vehicle; A processing unit, configured to perform fusion processing through feedforward control and proportional integral derivative control based on the vehicle configuration information, the expected speed, and the actual speed, to obtain a first control torque for controlling the target vehicle; A solving unit, configured to construct a vehicle dynamics model for model predictive control according to the vehicle configuration information, the vehicle state parameters, and the actual speed, and solve the vehicle dynamics model to obtain a second control torque for controlling the target vehicle; A fusion unit, configured to perform fusion processing on the first control torque and the second control torque to obtain a target control torque, and control the target vehicle to perform a test based on the target control torque.

8. An electronic device, characterized in that, It includes: At least one processor; And A memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor, so that the at least one processor can execute the method according to any one of claims 1-6.

9. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to execute the method according to any one of claims 1-6.

10. A vehicle, characterized in that, The vehicle includes: the device for vehicle test control according to claim 7, or the electronic device according to claim 8, or the non-transitory computer-readable storage medium according to claim 9.

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