Vehicle actuator parameter determination method and apparatus

Abstract

The application discloses a vehicle actuator parameter determination method and device. The method comprises the following steps: establishing a first vehicle actuator model of a vehicle based on a motor driving mode of the vehicle and an air brake mode of an EBS electronic brake system, wherein the first vehicle actuator model comprises a regulating variable parameter; identifying the regulating variable parameter based on a current running state of the vehicle to obtain an online identification result of the regulating variable; updating the first vehicle actuator model into a second vehicle actuator model according to the online identification result of the regulating variable; and adapting the current running state of the vehicle according to the second vehicle actuator model. The application provides a self-adaptive vehicle actuator, and the vehicle actuator parameter determined through online identification is more suitable for a vehicle adopting the EBS electronic brake system for air brake and a vehicle with variable load. The application can be applied to commercial vehicles.

Description

Technical Field

[0001] This application relates to the field of autonomous driving technology, and in particular to a method and apparatus for determining vehicle actuator parameters. Background Technology

[0002] The goal of autonomous driving control is to use feasible control quantities to reduce deviations from the target trajectory and provide passenger comfort.

[0003] In related technologies, autonomous driving control typically only considers the delay control of vehicle actuators, such as taking into account the delay characteristics of actuators to achieve early control. There are no solutions specifically designed for commercial vehicle actuators that are modeled and applied to control technology.

[0004] However, since commercial vehicles, such as heavy trucks, mostly use EBS (Electronic Brake Systems) to achieve air braking, the actuator characteristics of commercial vehicles differ significantly from those of ordinary passenger cars using hydraulic braking. Furthermore, different trailers or different loads on commercial vehicles also have a significant impact on braking characteristics, resulting in substantial differences in actual braking performance. Summary of the Invention

[0005] This application provides a method and apparatus for determining vehicle actuator parameters, enabling online identification of vehicle actuator parameters and providing vehicle actuators that can be used in commercial vehicles.

[0006] The embodiments of this application adopt the following technical solutions:

[0007] In a first aspect, embodiments of this application provide a method for determining vehicle actuator parameters, wherein the method includes:

[0008] Based on the vehicle's motor drive mode and the EBS electronic braking system's air brake mode, a first vehicle actuator model is established, which includes adjustment variable parameters.

[0009] Based on the current operating status of the vehicle, the adjustment variable parameters are identified online to obtain the online identification result of the adjustment variable;

[0010] Based on the online identification results of the adjustment variables, the first vehicle actuator model is updated to the second vehicle actuator model;

[0011] The current operating state of the vehicle is adapted according to the second vehicle actuator model.

[0012] In some embodiments, the method further includes:

[0013] Obtain the overshoot parameters generated during vehicle braking;

[0014] When the braking deceleration applied to the vehicle is less than a preset threshold, the rising slope and falling slope of the acceleration are determined.

[0015] Based on the relationship between the acceleration drive and deceleration braking of the actual input and the acceleration and deceleration of the actual response, delay response parameters are established.

[0016] In some embodiments, the step of obtaining an online identification result of the adjustment variable based on the current operating state of the vehicle includes:

[0017] Based on the current operating state of the vehicle, the overshoot coefficient and cutoff frequency in the adjustment variable parameters are identified online after multiple iterations.

[0018] In some embodiments, after obtaining the online identification result of the regulation variable based on the current operating state of the vehicle by identifying the regulation variable parameters online, the method further includes:

[0019] The delay response parameter is determined based on the cutoff frequency among the adjustment variable parameters identified online.

[0020] In some embodiments, the vehicle has an autonomous driving function, and the method further includes: when the vehicle activates the autonomous driving function, recording the requested acceleration data from the start of sending the vehicle braking request to the end of sending the braking request within a preset time period, as well as the actual acceleration data collected by the IMU in the vehicle.

[0021] In some embodiments, the method further includes: obtaining an online identification result of the adjustment variable based on the current operating state of the vehicle, including:

[0022] The sampling overshoot coefficient is obtained by uniform sampling according to the preset sampling interval;

[0023] The sampling overshoot coefficient, the initial sampling cutoff frequency, and the acceleration input value are input into the first vehicle actuator model to obtain the calculated acceleration value.

[0024] The calculated acceleration value is compared with the measured acceleration value collected by the vehicle's IMU to determine the final overshoot coefficient.

[0025] In some embodiments, the step of obtaining an online identification result of the adjustment variable based on the current operating state of the vehicle includes:

[0026] The cutoff frequency is obtained by uniformly sampling according to the preset sampling interval;

[0027] The cutoff frequency, the initial sampling cutoff frequency, and the acceleration input value are input into the first vehicle actuator model to obtain the calculated acceleration value.

[0028] The final cutoff frequency is determined by comparing the calculated acceleration value with the measured acceleration value collected by the vehicle's IMU.

[0029] In some embodiments, updating the first vehicle actuator model to a second vehicle actuator model based on the online identification result of the adjustment variable further includes:

[0030] Within a preset overshoot coefficient range, the final overshoot coefficient is obtained by optimization based on a preset solution method.

[0031] Within a preset cutoff frequency range, the final cutoff frequency is obtained by optimizing the solution based on a preset solution method.

[0032] Based on the final overshoot coefficient and the final cutoff frequency, the first vehicle actuator model is updated to the second vehicle actuator model.

[0033] In some embodiments, this is used in scenarios where the characteristics of the vehicle actuators in a commercial vehicle are variable and the commercial vehicle is equipped with different trailers or different loads.

[0034] Secondly, embodiments of this application also provide a vehicle actuator parameter determination device, wherein the device includes:

[0035] The vehicle actuator establishment module is used to establish a first vehicle actuator model of the vehicle based on the vehicle's motor drive mode and the air brake braking mode of the EBS electronic braking system. The first vehicle actuator model includes adjustment variable parameters.

[0036] The online identification module is used to identify the adjustment variable parameters online based on the current operating status of the vehicle to obtain the online identification result of the adjustment variable;

[0037] The update module is used to update the first vehicle actuator model to the second vehicle actuator model based on the online identification result of the adjustment variable;

[0038] An adaptation module is used to adapt the current operating state of the vehicle according to the second vehicle actuator model.

[0039] Thirdly, embodiments of this application also provide an electronic device, including: a processor; and a memory arranged to store computer-executable instructions, which, when executed, cause the processor to perform the above-described method.

[0040] Fourthly, embodiments of this application also provide a computer-readable storage medium that stores one or more programs, which, when executed by an electronic device including multiple applications, cause the electronic device to perform the above-described method.

[0041] The at least one technical solution adopted in this application embodiment can achieve the following beneficial effects: First, a first vehicle actuator model is established. Since the first vehicle actuator model includes adjustment variable parameters, it can be identified online for different vehicles, thereby adaptively obtaining the actual vehicle actuator. Further, based on the current operating state of the vehicle, the adjustment variable parameters are identified online to obtain the online identification result of the adjustment variables. According to the online identification result of the adjustment variables, the first vehicle actuator model is updated to a second vehicle actuator model. By extracting important adjustment variable parameters and obtaining the actual actuator model adapted to the current vehicle through online identification, an important reference is provided for subsequent vehicle speed planning and control. Finally, the current operating state of the vehicle is adapted according to the second vehicle actuator model. The vehicle's operating state includes acceleration through motor torque adjustment in motor drive mode or braking deceleration through the EBS electronic braking system. Attached Figure Description

[0042] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0043] Figure 1 This is a schematic diagram of the actuator response curve in an embodiment of this application;

[0044] Figure 2(a) is one of the schematic diagrams of the actuator acceleration model obtained based on Simulink in the embodiments of this application;

[0045] Figure 2(b) is a second schematic diagram of the actuator acceleration model obtained based on Simulink in the embodiments of this application;

[0046] Figure 3 This is a schematic diagram of the vehicle actuator parameter determination method in the embodiments of this application;

[0047] Figure 4 This is a schematic diagram of the vehicle actuator parameter determination device in the embodiments of this application;

[0048] Figure 5 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0050] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0051] During their research, the inventors discovered that commercial vehicles typically use EBS (Electronic Braking System) for air braking. While related technologies consider delayed advance control, it is difficult to achieve comfortable and accurate control over the depressurization characteristics of air brakes. Furthermore, when commercial vehicles are equipped with different trailers or with varying loads, the braking characteristics are significantly affected and difficult to predict in advance.

[0052] Through further research, the inventors discovered that (based on the vehicle's motor drive mode), when motor torque is invoked for acceleration, the input acceleration command and the actual acceleration conform to a second-order transfer function model. Figure 1 Not shown in the diagram. When the EBS (Electronic Braking System) is activated for braking deceleration (applying deceleration), the input acceleration command and the actual acceleration do not conform to a simple second-order transfer function model. This is because the EBS cannot immediately release pressure to reduce braking force during braking; it only begins to release pressure when the requested deceleration value is small. Furthermore, there is a certain overshoot during the braking process, specifically as follows: Figure 1 As shown, when the input acceleration is input_acceleration, the actual vehicle response acceleration is output_acceleration.

[0053] Based on the above problems, an actuator acceleration model is established in this application, as shown in Figure 2(a) and Figure 2(b). The following describes in detail how to calculate the actual vehicle response acceleration output_acceleration from the input acceleration input_acceleration.

[0054] a. When the input acceleration input_acceleration < 0 m / s 2 At that time, input_acceleration2 = input_accleration*overshoot_coeff, which corresponds to overshoot_coeff=1.4 in Figure 2(a).

[0055] b. Calculate the rising and falling slopes of input_acceleration. The rising slope limit up_limit varies according to the magnitude of input_acceleration. When input_acceleration > -0.5 m / s 2 At that time, the slope is limited to 10 m / s. 3 Otherwise, it is 0.07 m / s 3 Because the actuator is for speeds <-0.5m / s 2 The pressure relief response is very slow. Similarly, the descent slope lo_limt is obtained as -3 m / s. 3 The value of input_acceleration after slope limiting is input_limit. This corresponds to Figure 2(b). In specific implementation, when the braking deceleration is less than a certain value (e.g., the calibration value -0.5 m / s²),... 2 When the depressurization is slow, the rate of increase of the deceleration acc_limit (up_rate_limit) is relatively slow, while the rate of decrease is relatively fast. The specific rates of increase and decrease are obtained through observation and calculation based on measured data.

[0056] c. Transfer input_limit via a second-order transfer function After calculation, output_acceleration is obtained, that is, the actual acceleration response of the actuator model input to input_acceleration is output_acceleration, as shown in Figure 2(a).

[0057] It is important to note that the above second-order transfer function is for a second-order system. A second-order system is a control system with second-order differential equations as its equations of motion. Zeta is the damping coefficient, the specific value of which is determined by the actual application scenario of the model. w is the cutoff frequency. S represents the variables of the second-order transfer function.

[0058] In addition to the factors mentioned above, the actuator acceleration model should also consider that there will be a certain overshoot during braking. Let the overshoot coefficient be overshoot_coeff. The overshoot coefficient needs to be identified online and is related to the actuator state and the load.

[0059] Therefore, the main parameters affecting the vehicle actuator model are the overshoot coefficient (overshoot_coeff) and the cutoff frequency. Specifically, the optimal model parameters (overshoot_coeff and w_n) that best fit the entire process can be found through multiple iterations using equal-interval sampling and the golden section method. By determining the overshoot coefficient (overshoot_coeff) and the cutoff frequency (w_n), a more accurate vehicle actuator model can be obtained (the actual acceleration response, output_acceleration, can be obtained from the input acceleration command input_acceleration).

[0060] This application provides a method for determining vehicle actuator parameters, such as... Figure 3 The diagram shows a flowchart of a method for determining vehicle actuator parameters in an embodiment of this application. The method includes at least the following steps S310 to S340:

[0061] Step S310: Based on the vehicle's motor drive mode and the EBS electronic braking system's air brake mode, establish a first vehicle actuator model for the vehicle, which includes adjustment variable parameters.

[0062] As mentioned earlier, when accelerating by invoking motor torque, the acceleration command and the actual acceleration conform to a second-order transfer function model. However, when braking and decelerating by invoking the EBS electronic braking system, the input acceleration command and the actual acceleration do not conform to a simple second-order transfer function model. To meet the requirements of acceleration and braking / deceleration, a first vehicle actuator model is established.

[0063] The adjustment variable parameters in the first vehicle actuator model mainly include the vehicle's overshoot coefficient and cutoff frequency, which can be used to obtain the actual actuator model of the vehicle through online identification.

[0064] For example, taking a commercial vehicle as an example, a vehicle actuator model that conforms to the EBS air brake and electric motor drive of a commercial heavy truck is established, important model parameters are extracted, and the actual actuator model of the commercial vehicle can be obtained through online identification.

[0065] Step S320: Based on the current operating status of the vehicle, the adjustment variable parameters are identified online to obtain the online identification result of the adjustment variable.

[0066] During online identification, the vehicle's current operating status can be obtained when the autonomous driving function is activated. This mainly includes the vehicle's input acceleration and actual output acceleration. Depending on the vehicle, the adjustment variable parameters need to be identified online to obtain the online identification results. The adjustment variable parameters obtained through the online identification method can be applied to heavy trucks with volatile actuator characteristics, mounted on different trailers, or in scenarios with different load capacities.

[0067] Step S330: Based on the online identification result of the adjustment variable, update the first vehicle actuator model to the second vehicle actuator model.

[0068] The first vehicle actuator model is updated to the second vehicle actuator model based on the online identification results of the adjustment variables. It should be noted that the "first vehicle actuator model" is usually used as the initial vehicle actuator model, while the "second vehicle actuator model" is usually used as the vehicle actuator model after updating the adjustment variable parameters.

[0069] The terms "first vehicle actuator model" or "second vehicle actuator model" are not intended to specifically define a vehicle actuator, but rather to indicate that they refer to actuator models of the same type.

[0070] Step S340: Adapt the current operating state of the vehicle according to the second vehicle actuator model.

[0071] The second vehicle actuator model can be adapted to the vehicle's current operating state, i.e., the current input acceleration and actual output acceleration of the vehicle. This input and output acceleration can then provide a reference for subsequent speed planning and control. In this case, the second vehicle actuator model more closely reflects the vehicle's actual characteristics. Specifically, this refers to situations where air brakes using EBS electronic braking systems are employed in heavy-duty commercial vehicles or cargo trucks, and in situations where different loads significantly affect the braking characteristics of heavy-duty commercial vehicles or cargo trucks.

[0072] The above method provides a parameter determination method for vehicle actuators used in commercial vehicles. By using online identification, the adjustment variable parameters in the vehicle actuator model are determined, and the online identification results of these adjustment variables are used to obtain a vehicle actuator model suitable for commercial vehicles. This vehicle actuator model not only considers the pressure relief characteristics of EBS air brakes used in commercial vehicles, but also takes into account the impact of different trailers or different loads on the actual braking effect. It can provide adaptive adjustment variable parameters to adapt to the vehicle's current operating state.

[0073] The above method provides a modeling approach for actuators that conforms to the real characteristics of commercial vehicles. After online identification, the results of online identification of adjustment variables are obtained, and then the vehicle actuator model is adaptively updated. For example, by online identification of overshoot coefficients and cutoff frequencies, an adaptively updated vehicle actuator model can be obtained.

[0074] The actuator model established using the above method conforms to the real characteristics of heavy trucks, and can be adapted to the actuator characteristics of commercial vehicles when they are equipped with different trailers or different loads through online identification methods. For example, by identifying the overshoot coefficient and cutoff frequency online, a vehicle actuator that is more in line with the current vehicle load characteristics can be obtained.

[0075] Unlike related technologies that lack vehicle actuators for commercial vehicles, the above method was used to establish a first vehicle actuator model. Then, based on the current operating state of the vehicle, the regulating variable parameters were identified online to obtain the online identification result of the regulating variable. Finally, based on the online identification result of the regulating variable, the first vehicle actuator model was updated to a second vehicle actuator model, so that the actuator model conforms to the actual characteristics of heavy trucks.

[0076] Unlike related technologies that address the issue of fluctuating loads in commercial vehicles, this approach establishes a first vehicle actuator model based on the vehicle's motor drive mode and the air brake mode of the EBS electronic braking system. Then, according to the online identification results of the adjustment variables, the first vehicle actuator model is updated to a second vehicle actuator model. This makes it suitable for heavy-duty trucks with varying actuator characteristics and different trailers and load conditions.

[0077] Unlike related technologies, when using air brakes in EBS (Electronic Braking System) for commercial vehicles, there are issues such as the EBS not immediately depressurizing to reduce braking force, only depressurizing when the requested deceleration value is small, and a certain overshoot during the braking process. The above method achieves the modeling of vehicle actuators that conforms to the real characteristics of commercial vehicles, thereby reducing the impact of EBS electronic braking system on vehicle actuators when braking and decelerating, thus providing an important reference for subsequent speed planning and control.

[0078] In one embodiment of this application, the method further includes: acquiring overshoot parameters generated when the vehicle brakes; determining the rising and falling slopes of acceleration when the braking deceleration applied to the vehicle is less than a preset threshold; and establishing delay response parameters based on the relationship between the actual input acceleration drive and deceleration braking and the actual response acceleration and deceleration.

[0079] The execution process is not specifically limited and can be performed in different orders. The main consideration is the impact of the adjustment variable parameters in the vehicle actuator acceleration model on the vehicle actuator.

[0080] a. For the overshoot parameter, obtain the overshoot parameter generated when the vehicle brakes.

[0081] When a vehicle (especially a commercial vehicle) brakes, there is a certain amount of overshoot. Let the overshoot coefficient be overshoot_coeff, and this parameter needs to be identified online. The overshoot coefficient is related to the state of the vehicle's actuators and the vehicle's load.

[0082] b. Determine the upward and downward slopes of the acceleration.

[0083] When the braking deceleration is less than a certain value (e.g., -0.5 m / s²), 2 When the EBS electronic braking system used in commercial vehicles depressurizes very slowly, the rate of increase of deceleration acc_limit (up_rate_limit) is relatively slow, while the rate of decrease is relatively fast. Furthermore, the specific rates of increase and decrease are obtained through observation and calculation based on actual measurement data.

[0084] c. Establish delay response parameters.

[0085] Apart from the additional braking rules considered in a and b, the actual acceleration and deceleration responses conform to a second-order delay system. Among them, w_n of the second-order system is a crucial variable affecting model accuracy, and this parameter needs to be identified online. S represents the variables of the second-order transfer function, which are common variables in signal processing.

[0086] d. The main parameters affecting the vehicle actuator model include the overshoot coefficient (overshoot_coeff) and the cutoff frequency (w_n). The optimal model parameters (overshoot_coeff) and w_n that best fit the entire online recognition process can be found through multiple iterations using equal-interval sampling and the golden section method.

[0087] In one embodiment of this application, the step of obtaining the online identification result of the adjustment variable parameters based on the current operating state of the vehicle includes: based on the current operating state of the vehicle, identifying the overshoot coefficient and cutoff frequency in the adjustment variable parameters online after multiple iterations.

[0088] In practice, based on the current operating state of the vehicle, the acceleration input command is input into the acceleration calculation value obtained by the vehicle actuator model, and compared with the actual acceleration value measured by the IMU in the vehicle. After multiple iterations, the overshoot coefficient and cutoff frequency in the adjustment variable parameters are identified online.

[0089] In one embodiment of this application, after obtaining the online identification result of the adjustment variable based on the current operating state of the vehicle, the method further includes: determining the delay response parameter based on the cutoff frequency in the online identified adjustment variable parameter.

[0090] In practice, the delayed response parameters are determined based on the cutoff frequency. w_n of the second-order response system is an important variable affecting the accuracy of the model, and this parameter needs to be identified online.

[0091] The calculation principle is as follows: through the second-order transfer function (delay response parameter) After calculation, the output acceleration is obtained as output_acceleration, and the input acceleration is input to the actual acceleration response of the vehicle actuator model as output_acceleration.

[0092] In one embodiment of this application, the vehicle has an autonomous driving function, and the method further includes: when the vehicle activates the autonomous driving function, recording the requested acceleration data from the start of sending the vehicle braking request to the end of sending the braking request within a preset time period, as well as the actual acceleration data collected by the IMU in the vehicle.

[0093] When identifying the regulation variable parameters in the vehicle actuator model online, when autonomous driving is activated, all requested accelerations within a certain period of time, from the start of sending a braking request to a certain time (e.g., 2 seconds) after the braking request ends, can be recorded first. and the actual acceleration measured by the IMU The data.

[0094] The above request acceleration This refers to the actual acceleration command, or actual acceleration. It can be obtained through data collection and used for subsequent calculations of acceleration obtained by sampling and calculating the vehicle actuator model. .

[0095] In one embodiment of this application, the method further includes: obtaining an online identification result of the adjustment variable based on the current operating state of the vehicle by uniformly sampling at a preset sampling interval to obtain a sampling overshoot coefficient; inputting the sampling overshoot coefficient, an initial sampling cutoff frequency, and an acceleration input value into the first vehicle actuator model to obtain a calculated acceleration value; and comparing the calculated acceleration value with the measured acceleration value collected by the vehicle's IMU to determine the final overshoot coefficient.

[0096] Based on the actual acceleration obtained above Request acceleration Use the uniform sampling overshoot coefficient overshoot_coeff; sampling interval gap_overshoot, for example [0.6, 0.8, 1.0, 1.2, 1.4, 1.6, 1.8, 2.0].

[0097] The specific uniform sampling range and sampling interval are determined through multiple tests based on the characteristics of the specific vehicle. Substitute the sampling overshoot coefficient `overshoot_coeff` and the initial sampling cutoff frequency `w_n_init` into the input... Get from the model Compare the acceleration measured by the IMU and acceleration calculated by the model Find the sampling overshoot_coeff_temp that minimizes the cost.

[0098] in, , where n is the number of input_acceleration data records.

[0099] In one embodiment of this application, the step of identifying the regulation variable parameters online based on the current operating state of the vehicle to obtain the online identification result of the regulation variable includes: uniformly sampling to obtain a cutoff frequency according to a preset sampling interval; inputting the cutoff frequency, the initial sampling cutoff frequency, and the acceleration input value into the first vehicle actuator model to obtain a calculated acceleration value; and comparing the calculated acceleration value with the measured acceleration value collected by the vehicle's IMU to determine the final cutoff frequency.

[0100] Based on the actual acceleration obtained above Request acceleration The uniform sampling cutoff frequency w_n and the sampling interval gap_w_n are used as inputs, for example, [2.0, 3.0, 4.0, 5.0, 6.0, 7.0]. The specific uniform sampling range and sampling interval are determined through multiple tests based on the characteristics of the specific vehicle. Substitute the overshoot coefficient overshoot_coeff_temp and the sampling cutoff frequency w_n into the input. Get from the model Compare the acceleration measured by the IMU and acceleration calculated by the model Find the sample w_n_temp with the minimum cost. , where n is the number of input_acceleration data records.

[0101] In one embodiment of this application, updating the first vehicle actuator model to the second vehicle actuator model based on the online identification result of the adjustment variable further includes: obtaining the final overshoot coefficient by performing an optimization solution based on a preset solution method within a preset overshoot coefficient range; obtaining the final cutoff frequency by performing an optimization solution based on a preset solution method within a preset cutoff frequency range; and updating the first vehicle actuator model to the second vehicle actuator model based on the final overshoot coefficient and the final cutoff frequency.

[0102] In practical implementation, within the overshoot coefficient range:

[0103] [overshoot_coeff_temp-gap_overshoot, vershoot_coeff_temp+gap_overshoot] is calculated using the golden section method to obtain the overshoot_coeff_temp with the minimum cost. gap_overshoot is the step size.

[0104] Within the cutoff frequency interval [w_n_temp - gap_w_n, w_n_temp + gap_w_n], the minimum cost w_n_temp is obtained by optimizing using the golden section method. gap_w_n is the step size.

[0105] To achieve greater accuracy, the above process can be repeated multiple times to obtain the actuator model parameters overshoot_coeff_optimal and w_n_optimal that are adapted to the real vehicle.

[0106] In one embodiment of this application, it is used for scenarios where the characteristics of the vehicle actuators in a commercial vehicle are volatile and the commercial vehicle is equipped with different trailers or different loads.

[0107] The vehicle actuator parameter determination method in this application can be applied to scenarios where the characteristics of vehicle actuators in commercial vehicles are volatile and the commercial vehicles are equipped with different trailers or have different loads. The vehicle actuator parameter determination method obtains the parameters through online identification, thus making the actuator model more consistent with the actual characteristics of heavy-duty trucks.

[0108] This application embodiment also provides a vehicle actuator parameter determination device 400, such as... Figure 4 The diagram shows a structural schematic of a vehicle actuator parameter determination device 400 according to an embodiment of this application. The vehicle actuator parameter determination device 400 includes at least: a vehicle actuator establishment module 410, an online identification module 420, an update module 430, and an adaptation module 440, wherein:

[0109] In one embodiment of this application, the vehicle actuator establishment module 410 is specifically used to: establish a first vehicle actuator model of the vehicle based on the vehicle's motor drive mode and the air brake braking mode of the EBS electronic braking system, wherein the first vehicle actuator model includes adjustment variable parameters.

[0110] As mentioned earlier, when accelerating by invoking motor torque, the acceleration command and the actual acceleration conform to a second-order transfer function model. However, when braking and decelerating by invoking the EBS electronic braking system, the input acceleration command and the actual acceleration do not conform to a simple second-order transfer function model. To meet the requirements of acceleration and braking / deceleration, a first vehicle actuator model is established.

[0111] The adjustment variable parameters in the first vehicle actuator model mainly include the vehicle's overshoot coefficient and cutoff frequency, which can be used to obtain the actual actuator model of the vehicle through online identification.

[0112] For example, taking a commercial vehicle as an example, a vehicle actuator model that conforms to the EBS air brake and electric motor drive of a commercial heavy truck is established, important model parameters are extracted, and the actual actuator model of the commercial vehicle can be obtained through online identification.

[0113] In one embodiment of this application, the online identification module 420 is specifically used to: identify the adjustment variable parameters online based on the current operating state of the vehicle to obtain the online identification result of the adjustment variable.

[0114] During online identification, the vehicle's current operating status can be obtained when the autonomous driving function is activated. This mainly includes the vehicle's input acceleration and actual output acceleration. Depending on the vehicle, the adjustment variable parameters need to be identified online to obtain the online identification results. The adjustment variable parameters obtained through the online identification method can be applied to heavy trucks with volatile actuator characteristics, mounted on different trailers, or in scenarios with different load capacities.

[0115] In one embodiment of this application, the update module 430 is specifically used to: update the first vehicle actuator model to a second vehicle actuator model based on the online identification result of the adjustment variable.

[0116] The first vehicle actuator model is updated to the second vehicle actuator model based on the online identification results of the adjustment variables. It should be noted that the "first vehicle actuator model" is usually used as the initial vehicle actuator model, while the "second vehicle actuator model" is usually used as the vehicle actuator model after updating the adjustment variable parameters.

[0117] The terms "first vehicle actuator model" or "second vehicle actuator model" are not intended to specifically define a vehicle actuator, but rather to indicate that they refer to actuator models of the same type.

[0118] In one embodiment of this application, the adaptation module 440 is specifically used to: adapt the current operating state of the vehicle according to the second vehicle actuator model.

[0119] The second vehicle actuator model can be adapted to the vehicle's current operating state, i.e., the current input acceleration and actual output acceleration of the vehicle. This input and output acceleration can then provide a reference for subsequent speed planning and control. In this case, the second vehicle actuator model more closely reflects the vehicle's actual characteristics. Specifically, this refers to situations where air brakes using EBS electronic braking systems are employed in heavy-duty commercial vehicles or cargo trucks, and in situations where different loads significantly affect the braking characteristics of heavy-duty commercial vehicles or cargo trucks.

[0120] It is understood that the above-mentioned vehicle actuator parameter determination device can implement each step of the vehicle actuator parameter determination method provided in the foregoing embodiments. The relevant explanations of the vehicle actuator parameter determination method are applicable to the vehicle actuator parameter determination device, and will not be repeated here.

[0121] Figure 5 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Please refer to it. Figure 5 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and memory. The memory may include main memory, such as high-speed random-access memory (RAM), or non-volatile memory, such as at least one disk drive. Of course, the electronic device may also include other hardware required for other business operations.

[0122] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 5 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0123] Memory is used to store programs. Specifically, programs may include program code, which includes computer operation instructions. Memory may include main memory and non-volatile memory, and provides instructions and data to the processor.

[0124] The processor reads the corresponding computer program from non-volatile memory into main memory and then executes it, forming a vehicle actuator parameter determination device at the logical level. The processor executes the program stored in memory and specifically performs the following operations:

[0125] Based on the vehicle's motor drive mode and the EBS electronic braking system's air brake mode, a first vehicle actuator model is established, which includes adjustment variable parameters.

[0126] Based on the current operating status of the vehicle, the adjustment variable parameters are identified online to obtain the online identification result of the adjustment variable;

[0127] Based on the online identification results of the adjustment variables, the first vehicle actuator model is updated to the second vehicle actuator model;

[0128] The current operating state of the vehicle is adapted according to the second vehicle actuator model.

[0129] The above is as stated in this application. Figure 3The method for determining vehicle actuator parameters disclosed in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; 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. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of this application can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.

[0130] The electronic device can also perform Figure 3 The method for determining vehicle actuator parameters is described, and the vehicle actuator parameter determining device is implemented in... Figure 3 The functions of the embodiments shown are not described in detail here.

[0131] This application also proposes a computer-readable storage medium that stores one or more programs, the programs including instructions that, when executed by an electronic device including multiple applications, enable the electronic device to perform... Figure 3 The method executed by the vehicle actuator parameter determination device in the illustrated embodiment is specifically used to execute:

[0132] Based on the vehicle's motor drive mode and the EBS electronic braking system's air brake mode, a first vehicle actuator model is established, which includes adjustment variable parameters.

[0133] Based on the current operating status of the vehicle, the adjustment variable parameters are identified online to obtain the online identification result of the adjustment variable;

[0134] Based on the online identification results of the adjustment variables, the first vehicle actuator model is updated to the second vehicle actuator model;

[0135] The current operating state of the vehicle is adapted according to the second vehicle actuator model.

[0136] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0137] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0138] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0139] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0140] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0141] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0142] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0143] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0144] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0145] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for determining vehicle actuator parameters, wherein, The method includes: Based on the vehicle's motor drive mode and the EBS electronic braking system's air brake mode, a first vehicle actuator model is established. The first vehicle actuator model includes adjustment variable parameters, including overshoot coefficient and cutoff frequency. Based on the current operating status of the vehicle, the adjustment variable parameters are identified online to obtain the online identification result of the adjustment variable; The step of obtaining the online identification result of the adjustment variable parameters based on the current operating state of the vehicle includes: based on the current operating state of the vehicle, identifying the overshoot coefficient and cutoff frequency in the adjustment variable parameters online after multiple iterations; Based on the online identification results of the adjustment variables, the first vehicle actuator model is updated to the second vehicle actuator model; Based on the second vehicle actuator model, adapt to the current operating state of the vehicle; The first vehicle actuator model is used as the initial vehicle actuator model, and the second vehicle actuator model is used as the vehicle actuator model after the adjustment variable parameters have been updated.

2. The method as described in claim 1, wherein, The method further includes: Obtain the overshoot parameters generated during vehicle braking; When the braking deceleration applied to the vehicle is less than a preset threshold, the rising slope and falling slope of the acceleration are determined. Based on the relationship between the acceleration drive and deceleration braking of the actual input and the acceleration and deceleration of the actual response, delay response parameters are established.

3. The method as described in claim 2, wherein, After obtaining the online identification result of the adjustment variable based on the current operating state of the vehicle, the method further includes: The delay response parameter is determined based on the cutoff frequency among the adjustment variable parameters identified online.

4. The method as described in claim 1, wherein, The vehicle has an autonomous driving function, and the method further includes: when the vehicle activates the autonomous driving function, recording the requested acceleration data from the start of sending the vehicle braking request to the end of sending the braking request within a preset time period, as well as the actual acceleration data collected by the IMU in the vehicle.

5. The method as described in claim 4, wherein, The method further includes: obtaining an online identification result of the adjustment variable based on the current operating state of the vehicle, including: The sampling overshoot coefficient is obtained by uniform sampling according to the preset sampling interval; The sampling overshoot coefficient, the initial sampling cutoff frequency, and the acceleration input value are input into the first vehicle actuator model to obtain the calculated acceleration value. The calculated acceleration value is compared with the measured acceleration value collected by the vehicle's IMU to determine the final overshoot coefficient.

6. The method of claim 4, wherein, The step of obtaining the online identification result of the adjustment variable parameters based on the current operating state of the vehicle includes: The cutoff frequency is obtained by uniformly sampling according to the preset sampling interval; The cutoff frequency, the initial sampling cutoff frequency, and the acceleration input value are input into the first vehicle actuator model to obtain the calculated acceleration value. The final cutoff frequency is determined by comparing the calculated acceleration value with the measured acceleration value collected by the vehicle's IMU.

7. The method as described in claim 5 or 6, wherein, The step of updating the first vehicle actuator model to the second vehicle actuator model based on the online identification result of the adjustment variable further includes: Within a preset overshoot coefficient range, the final overshoot coefficient is obtained by optimization based on a preset solution method. Within a preset cutoff frequency range, the final cutoff frequency is obtained by optimizing the solution based on a preset solution method. Based on the final overshoot coefficient and the final cutoff frequency, the first vehicle actuator model is updated to the second vehicle actuator model.

8. The method according to any one of claims 1 to 6, wherein, This is applicable to scenarios where the characteristics of the vehicle actuators in commercial vehicles are variable and the commercial vehicles are equipped with different trailers or different loads.

9. A vehicle actuator parameter determining device, wherein, The device includes: The vehicle actuator establishment module is used to establish a first vehicle actuator model of the vehicle based on the vehicle's motor drive mode and the air brake braking mode of the EBS electronic braking system. The first vehicle actuator model includes adjustment variable parameters, including overshoot coefficient and cutoff frequency. The online identification module is used to identify the adjustment variable parameters online based on the current operating status of the vehicle to obtain the online identification result of the adjustment variable; The step of obtaining the online identification result of the adjustment variable parameters based on the current operating state of the vehicle includes: based on the current operating state of the vehicle, identifying the overshoot coefficient and cutoff frequency in the adjustment variable parameters online after multiple iterations; The update module is used to update the first vehicle actuator model to the second vehicle actuator model based on the online identification result of the adjustment variable; An adaptation module is used to adapt the current operating state of the vehicle according to the second vehicle actuator model; The first vehicle actuator model is used as the initial vehicle actuator model, and the second vehicle actuator model is used as the vehicle actuator model after the adjustment variable parameters have been updated.

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