Vehicle control method and device, vehicle, computer equipment and storage medium

Abstract

The invention discloses a vehicle control method and device, a vehicle, computer equipment and a storage medium. Estimating based on the required torque of the previous control period to obtain an estimated vehicle speed of the current control period, thereby determining the required torque of the current control period based on the deviation between the target vehicle speed of the current control period and the estimated vehicle speed, and controlling the vehicle according to the required torque of the current control period. Compared with the mode of feeding back the actual vehicle speed in the related technology, the estimated vehicle speed can be provided in advance, so that low-hysteresis following of the constant-speed cruise vehicle speed is achieved, and the actual vehicle speed of the vehicle is close to the target vehicle speed.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicles, and in particular, to a vehicle control method, device, vehicle, computer device, and storage medium. Background Art

[0002] With the rapid extension of the highway network in both vertical and horizontal directions, cruise control has also had a wide range of development and application prospects. After adopting cruise control, when the vehicle is driving on the highway for a long time, the driver no longer needs to control the accelerator pedal, which can reduce the driver's burden. However, the cruise control method in the related art needs to be improved. Summary of the Invention

[0003] The present invention aims to solve at least one of the technical problems in the related art to some extent. For this purpose, the present invention provides a vehicle control method, device, vehicle, computer device, and storage medium.

[0004] The present invention provides a vehicle control method, the method including: estimating based on the demand torque of the previous control cycle to obtain the estimated vehicle speed of the current control cycle; determining the demand torque of the current control cycle based on the deviation between the target vehicle speed and the estimated vehicle speed of the current control cycle; and controlling the vehicle according to the demand torque of the current control cycle.

[0005] In the present invention, by estimating based on the demand torque of the previous control cycle to obtain the estimated vehicle speed of the current control cycle, thereby determining the demand torque of the current control cycle based on the deviation between the target vehicle speed and the estimated vehicle speed of the current control cycle, and controlling the vehicle according to the demand torque of the current control cycle, compared with the method of feeding back the actual vehicle speed in the related art, it can provide the estimated vehicle speed in advance, so as to achieve low-latency following of the cruise control vehicle speed and make the actual vehicle speed of the vehicle close to the target vehicle speed.

[0006] In one of the embodiments, the estimating based on the demand torque of the previous control cycle to obtain the estimated vehicle speed of the current control cycle includes: determining the estimated vehicle speed of the current control cycle based on the demand torque of the previous control cycle and a preset transfer function; wherein, the preset transfer function is used to describe the relationship between the demand torque and the vehicle speed.

[0007] In this embodiment, by determining the estimated vehicle speed of the current control cycle, it can be fed back to the PID controller or the fuzzy PID controller earlier than the actual vehicle speed, which helps to reduce the delay of vehicle control.

[0008] In one of the embodiments, the preset transfer function is a second-order lag transfer function. Using the second-order lag transfer function as the preset transfer function in this embodiment can improve the accuracy of calculating the estimated vehicle speed of the current control cycle.

[0009] In one embodiment, determining the estimated vehicle speed of the current control cycle based on the required torque of the previous control cycle and a preset transfer function includes: performing a Laplace transform on the required torque of the previous control cycle to obtain a first transformation result; obtaining the product of the first transformation result and the preset transfer function to obtain a second transformation result corresponding to the estimated vehicle speed; and performing an inverse Laplace transform on the second transformation result to obtain the estimated vehicle speed.

[0010] In this embodiment, the estimated vehicle speed is obtained through Laplace transform, multiplication with a preset transfer function, and inverse Laplace transform based on the required torque of the previous control cycle, providing a data basis for accurately obtaining the required torque of the current control cycle.

[0011] In one embodiment, the preset transfer function is determined by the following method: obtaining a plurality of initial transfer functions, required torque time series data, and vehicle speed time series data corresponding to the required torque time series data; performing parameter identification on the initial transfer functions based on the required torque time series data and the vehicle speed time series data to obtain an identification result; and determining the preset transfer function from the plurality of initial transfer functions based on the identification result.

[0012] In this embodiment, parameter identification is respectively performed on a plurality of initial transfer functions through required torque time series data and vehicle speed time series data to obtain an identification result, and thus the preset transfer function is determined from the plurality of initial transfer functions based on the identification result, and a preset transfer function suitable for the actual system can be found.

[0013] In one embodiment, before determining the required torque of the current control cycle based on the deviation between the target vehicle speed and the estimated vehicle speed of the current control cycle, the method further includes: determining the deviation amount and deviation change rate between the target vehicle speed and the estimated vehicle speed of the current control cycle; correspondingly, determining the required torque of the current control cycle based on the deviation between the target vehicle speed and the estimated vehicle speed of the current control cycle includes: performing online correction based on the deviation change rate and the deviation amount to obtain the PID parameters of the current control cycle; and determining the required torque of the current control cycle based on the PID parameters of the current control cycle.

[0014] In this embodiment, the PID parameters of the current control cycle are determined through the deviation amount and deviation change rate between the target vehicle speed and the estimated vehicle speed of the current control cycle, and then the required torque of the current control cycle is determined, which can better adapt to the change of speed and improve the stability of control.

[0015] In one of the embodiments, online correction is performed based on the deviation change rate and the deviation amount to obtain the PID parameters for the current control cycle, including: processing the deviation change rate and the deviation amount based on fuzzy rules to obtain a change amount of the PID parameters; and performing online correction based on the change amount of the PID parameters and the initial PID parameters obtained by calibration to obtain the PID parameters for the current control cycle. Thereby, the control requirements under different working conditions can be adapted, and the adaptability and stability of the system can be improved.

[0016] The present invention provides a vehicle control device, which includes:

[0017] An estimated vehicle speed determination module, configured to estimate based on the required torque of the previous control cycle to obtain the estimated vehicle speed for the current control cycle;

[0018] A required torque determination module, configured to determine the required torque for the current control cycle based on the deviation between the target vehicle speed and the estimated vehicle speed for the current control cycle;

[0019] A vehicle control module, configured to control the vehicle according to the required torque for the current control cycle.

[0020] The present invention provides a vehicle, which includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the method described in any one of the above embodiments is implemented.

[0021] The present invention provides a computer device, which includes: a memory, and one or more processors communicatively connected to the memory; instructions executable by the one or more processors are stored in the memory, and when the instructions are executed by the one or more processors, the steps of the method described in any one of the above embodiments are implemented.

[0022] The present invention provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method described in any one of the above embodiments are implemented.

[0023] The present invention provides a computer program product, which includes instructions, and when the instructions are executed by a processor of a computer device, the computer device can execute the steps of the method described in any one of the above embodiments.

[0024] In the present invention, based on the demand torque of the previous control cycle, an estimated vehicle speed for the current control cycle is obtained. Then, based on the deviation between the target vehicle speed and the estimated vehicle speed of the current control cycle, the demand torque of the current control cycle is determined, and the vehicle is controlled according to the demand torque of the current control cycle. Compared with the method of feeding back the actual vehicle speed in the related art, the estimated vehicle speed can be provided in advance, so as to achieve low-latency following of the constant-speed cruise vehicle speed and make the actual vehicle speed of the vehicle close to the target vehicle speed. Description of the Drawings

[0025] Figure 1a It is a schematic diagram of the vehicle control method provided by the embodiment of the present specification;

[0026] Figure 1b It is a flowchart of the vehicle control method provided by the embodiment of the present specification;

[0027] Figure 1c It is a schematic diagram of controlling the vehicle according to the demand torque of the current control cycle provided by the embodiment of the present specification;

[0028] Figure 2 It is a schematic diagram of determining the estimated vehicle speed of the current control cycle provided by the embodiment of the present specification;

[0029] Figure 3 It is a flowchart of obtaining the estimated vehicle speed provided by the embodiment of the present specification;

[0030] Figure 4 It is a flowchart of determining the preset transfer function provided by the embodiment of the present specification;

[0031] Figure 5 It is a flowchart of determining the demand torque of the current control cycle provided by the embodiment of the present specification;

[0032] Figure 6a It is a flowchart of obtaining the PID parameters of the current control cycle provided by the embodiment of the present specification;

[0033] Figure 6b It is a schematic diagram of determining the PID parameters of the current control cycle provided by the embodiment of the present specification;

[0034] Figure 7 It is a flowchart of the vehicle control method provided by the embodiment of the present specification;

[0035] Figure 8 It is a schematic diagram of the vehicle control device provided by the embodiment of the present specification;

[0036] Figure 9 It is the internal structure diagram of the computer device provided by the embodiment of the present specification. Detailed Embodiments

[0037] Embodiments of the present invention will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where like or similar reference numerals denote like or similar elements or elements having like or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.

[0038] In the related art, taking the deviation between the actual distance and the safety distance between the cruising vehicle and the target vehicle and the speed difference between the cruising vehicle and the target vehicle as inputs, and the increment of the accelerator pedal or the brake pedal as the output, fuzzy rules between the inputs and the output are formulated. Then, through defuzzification processing, the increment of the accelerator pedal or the brake pedal is calculated, so as to realize the control of the throttle opening or the brake pedal, and further realize the following of the target vehicle. However, when using the increment of the accelerator pedal or the brake pedal to control the throttle opening or the brake pedal, the actual vehicle speed has often changed. Therefore, there is a certain hysteresis in calculating the increment of the accelerator pedal or the brake pedal through defuzzification processing.

[0039] In the related art, first, the difference between the actual vehicle speed and the target vehicle speed can be used as the input, and the classical PID algorithm is used to calculate the vehicle speed adjustment amount. Then, the incremental required torque is calculated through the torque module according to the vehicle speed adjustment amount. Finally, the incremental required torque is output by the power system and transmitted to the wheels to realize the following of the actual vehicle speed to the target vehicle speed. However, when the incremental required torque is transmitted to the wheels, the actual vehicle speed has often changed. The vehicle speed adjustment amount calculated by the PID controller is no longer the current required vehicle speed adjustment amount, that is, there is a certain hysteresis in the calculated vehicle speed adjustment amount, resulting in fluctuations in the following vehicle speed controlled by the PID controller, and further affecting the riding comfort.

[0040] Since the hysteresis in the transmission process from the required torque to the vehicle speed in a real vehicle objectively exists, the embodiments of this specification provide a vehicle control method. Based on the required torque in the previous control cycle, an estimated vehicle speed for the current control cycle is obtained, so as to determine the required torque for the current control cycle based on the deviation between the target vehicle speed and the estimated vehicle speed for the current control cycle, and control the vehicle according to the required torque for the current control cycle. Compared with the method of feeding back the actual vehicle speed in the related art, it can provide the estimated vehicle speed in advance, so as to achieve low-hysteresis following of the constant-speed cruise vehicle speed. Exemplarily, on the basis of a PID controller, a vehicle speed estimation model is introduced, and the required torque in the previous control cycle is input into the vehicle speed estimation model. The vehicle speed estimation model estimates the required torque in the previous control cycle to obtain the estimated vehicle speed for the current control cycle. By obtaining the estimated vehicle speed for the current control cycle in advance through the vehicle speed estimation model, the estimated vehicle speed for the current control cycle is fed back to the PID controller in advance. The PID controller determines the required torque for the current control cycle based on the deviation between the target vehicle speed and the estimated vehicle speed for the current control cycle, and thus controls the vehicle according to the required torque for the current control cycle.

[0041] Exemplarily, a vehicle speed estimation model can be introduced on the basis of a fuzzy PID controller, and the estimated vehicle speed is fed back to the fuzzy PID controller to calculate the required torque, and then the required torque is provided to the real vehicle execution component to output the actual vehicle speed. It should be noted that the fuzzy PID controller includes a fuzzy controller and a PID controller. Specifically, based on the required torque in the previous control cycle, an estimated vehicle speed is obtained, which can be fed back to the fuzzy PID controller earlier than the actual vehicle speed. Then, it is determined that the fuzzy PID controller determines the required torque for the current control cycle based on the deviation between the target vehicle speed and the estimated vehicle speed for the current control cycle. Next, the vehicle is controlled according to the required torque for the current control cycle, so that the actual vehicle speed of the vehicle gradually approaches the target vehicle speed, thereby achieving low-hysteresis following of the constant-speed cruise vehicle speed and improving the effect of the constant-speed cruise.

[0042] The vehicle control method provided by the embodiments of this specification can be applied to a vehicle. Among them, the vehicle can be a traditional driving vehicle or an autonomous driving vehicle. The autonomous driving vehicle can be a vehicle with partial autonomous driving functions or a vehicle with all autonomous driving functions. That is to say, the level of autonomous driving of this vehicle can be classified into no automation (L0), driving assistance (L1), partial automation (L2), conditional automation (L3), high automation (L4) or full automation (L5) with reference to the classification standard of the Society of Automotive Engineers (SAE) in the United States. The vehicle or other devices can implement this vehicle control method through its included components (including hardware and software).

[0043] Specifically, please refer to Figure 1a , the fuzzy PID controller 102 can transmit the required torque T q (k - 1) of the previous control cycle to the vehicle speed prediction module 104. Based on the required torque T q (k - 1) of the previous control cycle, the vehicle speed prediction module 104 can calculate and obtain the predicted vehicle speed V e (k) of the current control cycle. Determine the target vehicle speed V o (k) of the current control cycle. Based on the target vehicle speed V o (k) and the predicted vehicle speed V e (k) of the current control cycle, the deviation amount δ(k) and the deviation change rate θ(k) between the target vehicle speed and the predicted vehicle speed of the current control cycle can be calculated. Transmit the deviation amount δ(k) and the deviation change rate θ(k) between the target vehicle speed and the predicted vehicle speed of the current control cycle to the fuzzy PID controller 102. Based on the deviation amount δ(k) and the deviation change rate θ(k) between the target vehicle speed and the predicted vehicle speed of the current control cycle, the fuzzy controller 106 in the fuzzy PID controller 102 obtains the PID parameter change amounts ΔK p , ΔK i , ΔK d . The fuzzy controller 106 transmits the PID parameter change amounts ΔK p , ΔK i , ΔK d to the online correction module 108. Based on the PID parameter change amounts ΔK p , ΔK i , ΔK d of the current control cycle and the initial PID parameters K p0、 K i0 , K d0 obtained by calibration, the online correction module 108 in the fuzzy PID controller 102 performs online correction to obtain the PID parameters Kp, Ki, Kd of the current control cycle. The online correction module 108 transmits the PID parameters Kp, Ki, Kd of the current control cycle to the PID controller 110. Based on the PID parameters Kp, Ki, Kd of the current control cycle, the deviation amount δ(k) and the deviation change rate θ(k) between the target vehicle speed and the predicted vehicle speed of the current control cycle, the PID controller 110 can obtain the required torque T q (k) of the current control cycle. The PID controller 110 transmits the required torque T q (k) of the current control cycle to the in-vehicle actuator 112 to output the actual vehicle speed. The PID controller 110 transmits the required torque T q(k) is transmitted to the vehicle speed prediction module 104, and the vehicle speed prediction module 104 calculates based on the required torque T of the current control cycle q (k), and the predicted vehicle speed V of the next control cycle can be obtained e (k + 1). The predicted vehicle speed V of the next control cycle e (k + 1) can continue to be used as an input together with the target vehicle speed of the next control cycle. The deviation δ(k + 1) and the deviation change rate θ(k + 1) between the target vehicle speed and the predicted vehicle speed of the next control cycle are fed back to the fuzzy PID controller 102, and the required torque of the next control cycle is recalculated and applied to the actual vehicle execution component 112 to achieve vehicle speed following.

[0044] An embodiment of this specification provides a vehicle control method. Please refer to Figure 1b This vehicle control method may include the following steps:

[0045] S110. Predict based on the required torque of the previous control cycle to obtain the predicted vehicle speed of the current control cycle.

[0046] Among them, the required torque can be the torque (i.e., torque) that needs to be generated during vehicle operation to achieve specific power requirements, which can be understood as the power output and control ability required by the vehicle in a specific situation to meet acceleration, deceleration, and power requirements. The predicted vehicle speed can be obtained by predicting the vehicle speed of the current control cycle, for example, predicting the vehicle speed of the current control cycle based on the required torque of the previous control cycle.

[0047] Specifically, a vehicle speed prediction model between the required torque and the predicted vehicle speed can be established in advance. The required torque of the previous control cycle obtained is used as the input of the pre-established vehicle speed prediction model to realize the prediction of the vehicle speed from the required torque and obtain the predicted vehicle speed of the current control cycle. Compared with the feedback moment of the actual vehicle speed, by obtaining the predicted vehicle speed, the speed state of the vehicle can be fed back in advance, thereby reducing the influence of hysteresis on vehicle speed control and improving the accuracy of vehicle speed control.

[0048] In some embodiments, a vehicle speed prediction model describing the relationship between the required torque and the predicted vehicle speed can be established based on the dynamic response characteristics (such as the response time of acceleration and deceleration) of the actual vehicle execution component (such as the engine). In other embodiments, a vehicle speed prediction model between the required torque and the predicted vehicle speed can be established by using historical vehicle driving data and the required torque corresponding to each driving data through a machine learning algorithm. In still other embodiments, a vehicle speed prediction model between the required torque and the predicted vehicle speed can be established by using vehicle dynamics principles, according to detailed vehicle parameters (such as engine characteristics, tire characteristics) and dynamic characteristics, and considering the influence of environmental factors such as road surface friction coefficient and slope.

[0049] S120. Determine the required torque for the current control cycle based on the deviation between the target vehicle speed and the predicted vehicle speed in the current control cycle.

[0050] S130. Control the vehicle according to the required torque for the current control cycle.

[0051] Among them, the target vehicle speed can be the desired speed that the control system hopes the vehicle will reach at the end of the current control cycle. The target vehicle speed can be dynamically adjusted according to changes in road conditions, traffic conditions, and driver intentions to achieve good safety performance. The deviation between the target vehicle speed and the predicted vehicle speed can be used to represent the gap between the predicted vehicle speed and the target vehicle speed, and is the basis for subsequent vehicle control. For example, the deviation can be the deviation amount between the target vehicle speed and the predicted vehicle speed. For example, the deviation can be the deviation amount and the deviation change rate between the target vehicle speed and the predicted vehicle speed. The target vehicle speed can be used to determine the required torque for the control system to adjust the vehicle in each control cycle, so that the vehicle gradually approaches and finally reaches the desired vehicle speed, that is, the target vehicle speed.

[0052] Specifically, after determining the target vehicle speed and the predicted vehicle speed of the current control cycle, compare the target vehicle speed and the predicted vehicle speed of the current control cycle, and calculate the deviation between the two. Take the deviation between the target vehicle speed and the predicted vehicle speed of the current control cycle as the input of the PID controller, and determine the required torque to be applied to the vehicle in the current control cycle according to the magnitude of the deviation. The required torque for the current control cycle can be applied to the vehicle, and the vehicle can be controlled through the execution components of the vehicle. In some embodiments, the set desired cruise speed in the current control cycle can be used as the target vehicle speed of the current control cycle. In other embodiments, the vehicle's navigation system can provide data such as route information, road speed limits, and traffic information, so the target vehicle speed applicable to the current control cycle can be determined according to the current section and navigation path.

[0053] Exemplarily, please refer to Figure 1c , input the deviation amount δ(k) between the target vehicle speed and the predicted vehicle speed of the current control cycle into the fuzzy PID controller 102, and the fuzzy PID controller 102 can output the required torque T q (k) to the real vehicle execution component 112. The real vehicle execution component 112 controls the vehicle according to the required torque T q (k) of the current control cycle, and controls the vehicle to travel at a speed V a (k). It should be noted that the deviation amount δ(k) and the deviation change rate θ(k) between the target vehicle speed and the predicted vehicle speed of the current control cycle can also be input into the fuzzy PID controller 102 to determine the required torque for the current control cycle.

[0054] In some embodiments, after determining the required torque for the current control cycle, the required torque can be converted into a corresponding pulse-width modulation signal PWM to control the throttle actuator or the brake pedal actuator, thereby controlling the throttle opening or the brake pedal stroke, so that the actual driving speed of the vehicle gradually approaches the target speed, realizing the control of the vehicle.

[0055] In the above embodiments, based on the required torque of the previous control cycle, an estimated vehicle speed for the current control cycle is obtained. Then, based on the deviation between the target vehicle speed and the estimated vehicle speed of the current control cycle, the required torque for the current control cycle is determined, and the vehicle is controlled according to the required torque of the current control cycle. Compared with the method of feeding back the actual vehicle speed in the related art, the estimated vehicle speed can be provided in advance, so as to realize low-latency following of the cruise control speed and make the actual vehicle speed of the vehicle approach the target vehicle speed.

[0056] In some embodiments, estimating the estimated vehicle speed of the current control cycle based on the required torque of the previous control cycle may include: determining the estimated vehicle speed of the current control cycle based on the required torque of the previous control cycle and a preset transfer function.

[0057] Among them, the preset transfer function is used to describe the relationship between the required torque and the vehicle speed. The transfer function is the ratio of the input required torque and the output vehicle speed after Laplace transform, and is used to describe the dynamic response characteristics of the system. Both the vehicle speed and the required torque are variables related to the time domain and are related to frequency and phase. The transfer function is a mathematical model introduced in the process of solving linear ordinary differential equations using the Laplace transform method.

[0058] Specifically, the functional relationship between the required torque and the vehicle speed can be constructed based on specific situations and actual requirements as the preset transfer function. For example, the preset transfer function can be constructed based on the required torque time series data and the vehicle speed time series data corresponding to the required torque time series data. On the premise of knowing the preset transfer function and the required torque of the previous control cycle, the preset transfer function is used to calculate the required torque of the previous control cycle to obtain the estimated vehicle speed of the current control cycle.

[0059] Exemplarily, please refer to Figure 2 , the deviation amount δ(k - 1) and the deviation change rate θ(k - 1) between the target vehicle speed and the estimated vehicle speed of the previous control cycle can be input into the fuzzy PID controller 102 to determine the required torque T q (k - 1) of the previous control cycle. The fuzzy PID controller 102 can output the required torque T q (k - 1) of the previous control cycle to the vehicle speed estimation module 104. The vehicle speed estimation module 104 uses the preset transfer function to calculate the required torque T q(k - 1) is calculated to obtain the estimated vehicle speed V of the current control cycle e (k).

[0060] In the above embodiment, the estimated vehicle speed of the current control cycle is determined based on the required torque of the previous control cycle and the preset transfer function, and can be fed back to the PID controller or the fuzzy PID controller earlier than the actual vehicle speed, which helps to reduce the delay of vehicle control.

[0061] In some embodiments, the preset transfer function is a second-order lag transfer function. Among them, the second-order lag transfer function is used to describe the relationship between the required torque and the vehicle speed.

[0062] In some cases, the control system of the actual vehicle execution component is a complex high-order control system. For such a high-order control system, the second-order lag model can be used to describe its dynamic characteristics, that is, the vehicle speed estimation module is simplified to a second-order lag object, and its corresponding transfer function is the second-order lag transfer function.

[0063] Specifically, the second-order lag transfer function is as follows:

[0064]

[0065] Among them, τ, τ 1 , τ 2 are time constants, Q is a constant related to the transmission characteristics of the required torque, s is a complex variable, and the complex variable s = δ + jw, where δ contains the phase information (real part) and w contains the frequency information (imaginary part). H(s) is a fluctuating curve with a certain frequency and phase in the time domain.

[0066] It can be understood that is the series connection of the second-order lag link and the pure lag link e -τs .

[0067] The control system in this embodiment can be equivalent to a second-order system with time lag. The determination process of the second-order lag transfer function is exemplarily described as follows:

[0068]

[0069] y(t) = y1(t - τ) (2)

[0070] Among them, x(t) is the required torque. y(t) represents the estimated vehicle speed. The estimated vehicle speed y(t) has a time lag τ added to y1(t), which is equivalent to y1(t) being the output after the required torque x(t) is calculated by the second-order lag transfer function.

[0071] Laplace transform the input and output of formula (1), and divide the transformed input and output to obtain a second-order lag link:

[0072]

[0073] Laplace transform the input and output of formula (2), and divide the transformed input and output to obtain a pure delay link:

[0074]

[0075] Connect the above second-order lag link and pure delay link in series to obtain the transfer function of the second-order plus pure delay model:

[0076]

[0077] It should be noted that by transforming the differential equation of the control system in the time domain into a transfer function in the complex domain for description, the differential and integral operations in the time domain are simplified to algebraic operations, which facilitates the analysis and design of the control system. τ, τ 1 , τ 2 , Q can be obtained by fitting experimental data. For a specific vehicle model, the dynamic characteristics of the required torque transmission are basically stable. Under the condition that the constant speed cruise is turned on, record the actual vehicle speed change after the required torque passes through the actual vehicle execution components, import the recorded data into the Matlab toolbox and enter the model parameter identification module, and the parameters τ, τ 1 , τ 2 , Q corresponding values can be obtained.

[0078] In the above implementation, presetting the transfer function as a second-order lag transfer function can improve the accuracy of calculating the predicted vehicle speed in the current control cycle.

[0079] In some implementations, please refer to Figure 3 , determining the predicted vehicle speed in the current control cycle based on the required torque in the previous control cycle and the preset transfer function may include the following steps:

[0080] S310. Perform Laplace transform on the required torque in the previous control cycle to obtain a first transformation result.

[0081] S320. Obtain the product of the first transformation result and the preset transfer function to obtain a second transformation result corresponding to the predicted vehicle speed.

[0082] S330. Perform inverse Laplace transform on the second transformation result to obtain the predicted vehicle speed.

[0083] Among them, the Laplace transform is a mathematical tool commonly used in fields such as signal processing, control theory, and circuit analysis. The Laplace transform can convert a function from the time domain to the complex domain, which helps to simplify the solution of differential equations and analyze the dynamic characteristics of complex systems. The inverse Laplace transform can refer to the process of performing an inverse transform on a given Laplace transform function to convert it back to the original domain.

[0084] Specifically, the required torque in the previous control cycle is expressed as a function of time. According to the specific form of the required torque in the previous control cycle, the corresponding Laplace transform formula is found. The required torque in the previous control cycle is substituted into the corresponding Laplace transform formula, and calculations are performed according to the specific function form to obtain the first transformation result with respect to the complex variable. Then, the first transformation result is multiplied by the preset transfer function to obtain the second transformation result corresponding to the estimated vehicle speed, which helps to analyze and understand the dynamic characteristics of the system in the frequency domain, including the response to different required torques. Next, according to the second transformation result, an appropriate inverse Laplace transform formula needs to be found, and an appropriate integration path is selected, usually by choosing appropriate real values to ensure that the integration path is far enough away from all poles to avoid crossing the poles. Finally, the second transformation result is substituted into the inverse Laplace transform formula, and then numerical integration calculations are performed to obtain the time-domain representation corresponding to the estimated vehicle speed.

[0085] In the above implementation, the Laplace transform is performed on the required torque in the previous control cycle to obtain the first transformation result, the product of the first transformation result and the preset transfer function is obtained to get the second transformation result corresponding to the estimated vehicle speed, and the inverse Laplace transform is performed on the second transformation result to obtain the estimated vehicle speed, providing a data basis for obtaining the required torque in the current control cycle.

[0086] In some implementations, to determine a more accurate transfer function, multiple initial transfer functions can be pre-constructed, and then parameter identification techniques are used to select the function with the highest accuracy from the multiple initial transfer functions as the preset transfer function. Specifically, please refer to Figure 4 , the preset transfer function can be determined in the following way:

[0087] S410. Obtain multiple initial transfer functions, required torque time-series data, and vehicle speed time-series data corresponding to the required torque time-series data.

[0088] S420. Perform parameter identification on the initial transfer function based on the required torque time-series data and vehicle speed time-series data to obtain the identification result.

[0089] S430. Determine the preset transfer function from the multiple initial transfer functions based on the identification result.

[0090] Among them, the time-series data can be a set of data arranged in chronological order, which can include the values observed at consecutive time points or the sampled data at discrete time points. Parameter identification can be to determine the parameters in the mathematical model from the actual observed data for system modeling, analysis, and control.

[0091] Specifically, conduct tests on a test bench or an actual vehicle, and obtain the demand torque time-series data and the vehicle speed time-series data corresponding to the demand torque time-series data through devices such as in-vehicle sensors and fuzzy PID controllers. According to the data situation and actual requirements, select multiple initial transfer functions suitable for the current task. Select a suitable system identification method (such as the least squares method, state space method, frequency domain analysis method) according to the data characteristics and system properties. Use the selected system identification method to perform parameter identification on any initial transfer function, which can be understood as determining the identification result of the initial transfer function by fitting the demand torque time-series data and the vehicle speed time-series data corresponding to the demand torque time-series data. Compare the identification results for each initial transfer function and evaluate the fitting effect and prediction performance of each transfer function model. According to different control requirements, select different initial transfer functions as the preset transfer functions. For example, the initial transfer function corresponding to the identification result with a small deviation can be selected as the preset transfer function.

[0092] In some embodiments, parameter identification is performed on each initial transfer function based on the demand torque time-series data and the vehicle speed time-series data respectively to obtain the identification result of each initial transfer function, and finally, the preset transfer function is selected from multiple initial transfer functions based on the identification result of each initial transfer function.

[0093] In other embodiments, multiple initial transfer functions, the demand torque time-series data, and the vehicle speed time-series data corresponding to the demand torque time-series data can be uploaded to a computer device for data storage. Then, the computer device performs parameter identification on each initial transfer function based on the demand torque time-series data and the vehicle speed time-series data respectively to obtain the identification result of each initial transfer function, and then selects the preset transfer function from multiple initial transfer functions based on the identification result of each initial transfer function. Finally, the computer device sends the preset transfer function to the vehicle.

[0094] Exemplarily, collect the demand torque time-series data Tq corresponding to the vehicle when the cruise control is turned on and the vehicle speed time-series data V corresponding to the demand torque time-series data, and the time interval between each data point is the sampling interval t.

[0095] V = [88.0625, 85.3750, 83.3125, 86.6250, 86.4688, 85.1563, 86.0938, 86.2500, 86.2500, 85.8750, 87.1250, 85.6563, 87.0313, 88.0625, 87.1563, 84.1563, 86.7188, 87.0625, 86.6563, 88.4063, 86.3750];

[0096] Tq = [24.7, 24.6, 24.5, 24.5, 24.45, 24.4, 24.45, 24.45, 24.45, 24.45, 24.45, 24.35, 24.4, 24.4, 24.4, 24.35, 24.35, 24.35, 24.35, 24.35];

[0097] Import the acquired demand torque time - series data Tq and the corresponding vehicle speed time - series data V into the systemIdentification (system identification) toolbox in the matlab toolbox, and select the transfer function structure as Process Models (process model). How to determine the preset transfer function can be solved through the MATLAB system identification toolbox. It should be noted that the demand torque time - series data Tq and the vehicle speed time - series data V are used to exemplarily illustrate the parameter identification process, and can have a more accurate representation in combination with the actual situation of the sampling interval. In some cases, the sampling interval t can be set to 0.01 s.

[0098] Select the first - order lag function and click the Estimate button to perform parameter identification to obtain the identification result of the first - order lag function.

[0099] Select the second - order lag function and click the Estimate button to perform parameter identification to obtain the identification result of the second - order lag function:

[0100] Select the second - order lag function with zeros, click the Estimate button to perform parameter identification to obtain the identification result of the second - order lag function with zeros. Select the first - order lag function with zeros, click the Estimate button to perform parameter identification to obtain the identification result of the first - order lag function with zeros.

[0101] Compare the identification results of the first - order lag function, the second - order lag function, the second - order lag function with zeros, and the first - order lag function with zeros, and select the transfer function corresponding to the identification result with higher accuracy (i.e., smaller deviation) as the preset transfer function, that is, select the second - order lag function as the preset transfer function.

[0102] In the above embodiments, a plurality of initial transfer functions, demand torque time series data, and vehicle speed time series data corresponding to the demand torque time series data are obtained. Parameter identification is performed on the plurality of initial transfer functions respectively based on the demand torque time series data and the vehicle speed time series data to obtain an identification result. Based on the identification result, a preset transfer function is determined among the plurality of initial transfer functions, and a preset transfer function suitable for the actual system can be found.

[0103] In some embodiments, referring to Figure 5 , determining the demand torque of the current control cycle based on the deviation between the target vehicle speed and the estimated vehicle speed of the current control cycle may include the following steps:

[0104] S510. Determine the deviation amount and deviation change rate between the target vehicle speed and the estimated vehicle speed of the current control cycle.

[0105] Among them, the deviation amount can be understood as the difference between the target vehicle speed and the estimated vehicle speed. The deviation change rate can be understood as the degree of change of the difference between the target vehicle speed and the estimated vehicle speed over time per unit time.

[0106] Specifically, the demand torque can be input into the vehicle speed estimation model to estimate the vehicle speed, and the estimated vehicle speed of the current control cycle can be obtained. By subtracting the obtained target vehicle speed and estimated vehicle speed of the current control cycle, the deviation amount between the target vehicle speed and the estimated vehicle speed of the current control cycle can be obtained. The sampling period can be directly obtained according to the control system. It is also possible to record the current time at the start of the control cycle as the starting point of time, record the time again at the end of the control cycle as the ending point of time, and perform a subtraction operation on the ending point of time and the starting point of time to obtain the sampling period. Based on the deviation amount between the target vehicle speed and the estimated vehicle speed of the current control cycle and the sampling period, the deviation change rate between the target vehicle speed and the estimated vehicle speed of the current control cycle can be obtained.

[0107] Exemplarily, the vehicle speed estimation module outputs the estimated vehicle speed V e (k) of the current control cycle. Determine the target vehicle speed V o (k) of the current control cycle. By using the obtained target vehicle speed V o (k) and estimated vehicle speed V e (k) of the current control cycle for subtraction calculation, the deviation amount δ(k) between the target vehicle speed and the estimated vehicle speed of the current control cycle can be obtained. Similarly, the deviation amount δ(k - 1) of the previous control cycle can be obtained, and the sampling period can be denoted as T. Then, subtracting the deviation amount δ(k) between the target vehicle speed and the estimated vehicle speed of the current control cycle from the deviation amount δ(k - 1) of the previous control cycle, and dividing the obtained difference by the sampling period T can obtain the deviation change rate θ(k).

[0108] Accordingly, determining the required torque for the current control cycle based on the deviation between the target vehicle speed and the estimated vehicle speed in the current control cycle includes:

[0109] S520. Perform online correction based on the deviation change rate and the deviation amount to obtain the PID parameters for the current control cycle.

[0110] S530. Determine the required torque for the current control cycle based on the PID parameters for the current control cycle.

[0111] Among them, the PID parameters may include a proportional parameter P, an integral parameter I, and a derivative parameter D, and the PID parameters can be corrected according to specific control objects and system requirements.

[0112] Specifically, it is first necessary to design appropriate rules to correct the PID parameters of the previous control cycle according to the deviation change rate and the deviation amount. This rule can be based on experience or designed based on system identification theory and adaptive control algorithms. In some embodiments, according to the parameter update rule, using the deviation change rate and the deviation amount of the current control cycle, the PID parameters of the current control cycle can be directly calculated. In other embodiments, according to the parameter update rule, using the deviation change rate and the deviation amount of the current control cycle, the correction amount of the PID parameters of the current control cycle can be obtained. Apply the calculated correction amount of the PID parameters to the current control cycle for online correction to obtain the PID parameters of the current control cycle. Based on the PID parameters, deviation change rate, and deviation amount of the current control cycle, the required torque for the current control cycle can be calculated. Exemplarily, the deviation change rate θ(k) and the deviation amount δ(k) of the current control cycle are used as the inputs of the fuzzy PID controller for online correction to obtain the PID parameters K p (k), K i (k), K d (k).

[0113] Exemplarily, the required torque increment for the current control cycle can be determined by the following formula:

[0114]

[0115] Among them, K p (k), K i (k), K d (k) are the PID parameters of the current control cycle, δ(k) is the deviation amount of the current control cycle, and δ(k - 1) is the deviation amount of the previous control cycle. T is the sampling period, is the deviation change rate θ(k).

[0116] On the basis of obtaining the required torque increment, the required torque increment is added to the required torque calculated based on the vehicle longitudinal dynamics to obtain the final required torque, which is used as the required torque for the current control cycle.

[0117] In the above embodiment, the deviation amount and the deviation change rate between the target vehicle speed and the estimated vehicle speed in the current control cycle are determined, and online correction is performed based on the deviation change rate and the deviation amount to obtain the PID parameters for the current control cycle. Determining the required torque for the current control cycle based on the PID parameters of the current control cycle can better adapt to speed changes and improve the stability of vehicle control.

[0118] In some embodiments, refer to Figure 6a , performing online correction based on the deviation change rate and the deviation amount to obtain the PID parameters for the current control cycle may include the following steps:

[0119] S610. Process the deviation change rate and the deviation amount based on fuzzy rules to obtain the PID parameter change amount.

[0120] Among them, fuzzy rules are important concepts in fuzzy logic, which are used to describe the relationship between input variables and output variables for decision-making and reasoning in fuzzy control systems. In fuzzy rules, a causal relationship is established between the fuzzified input variables and the fuzzified output variables, enabling the system to make corresponding fuzzy outputs based on fuzzy conditions. The PID parameter change amount refers to the amount of dynamically adjusting the PID parameters according to the actual response of the system in the PID controller, which can be the amplitude of adjusting the PID parameters.

[0121] Specifically, first, map the deviation change rate and the deviation amount to the membership functions of the fuzzy rules, and the deviation change rate and the deviation amount are transformed into fuzzy inputs. The Gaussian type (gaussmf type) membership function can be used to represent the fuzzified inputs. Next, define the fuzzy output of the PID parameter change amount. Similarly, it is necessary to map the change amount of the PID parameters to the membership function in the fuzzy set. Define a set of fuzzy rules that describe the way of adjusting the fuzzified PID parameter change amount according to the fuzzified deviation change rate and deviation amount. According to the fuzzy rules and the fuzzified inputs, that is, the fuzzified deviation change rate and deviation amount, use the fuzzy inference engine to perform inference and calculate the fuzzified PID parameter change amount. Finally, transform the fuzzy output obtained by fuzzy inference, that is, the fuzzified PID parameter change amount, into a specific PID parameter change amount. Usually, defuzzification methods (such as the centroid method, the maximum value method, etc.) are used to map the fuzzy output to the actual parameter change amount.

[0122] In some embodiments, the fuzzy controller can be established using the MATLAB Fuzzy Logic Toolbox and Simulink. First, it is necessary to define the input variables and output variables of the fuzzy controller, including their physical meanings, value ranges, etc. Define membership functions (such as Gaussian type) for each input variable to fuzzify it. Next, the output variables need to be defined. Based on experience or system data, define the fuzzy rule matrix to map the fuzzified input variables to the fuzzy output. After obtaining the fuzzified output variables, defuzzification processing is required to convert the fuzzy output into specific output variables. This can use defuzzification methods such as the maximum membership degree method or the weighted average method.

[0123] Exemplarily, select the deviation δ between the predicted vehicle speed and the target vehicle speed in the current control cycle and the deviation change rate θ as the input variables of the fuzzy controller, and select ΔK p 、ΔK i 、ΔK d as the output of the fuzzy controller. The universe of discourse ranges of the input and output variables can be defined as [-6, 6]. The linguistic variables of the input variables and output variables are selected as follows:

[0124] δ: [NB, NM, NS, Z, PS, PM, PB]

[0125] θ: [NB, NM, NS, Z, PS, PM, PB]

[0126] ΔK p : [NB, NM, NS, Z, PS, PM, PB]

[0127] ΔK i : [NB, NM, NS, Z, PS, PM, PB]

[0128] ΔK d : [NB, NM, NS, Z, PS, PM, PB]

[0129] Among them, NB corresponds to negative large, NM corresponds to negative medium, NS corresponds to negative small, Z corresponds to positive zero, PS corresponds to positive small, PM corresponds to positive medium, and PB corresponds to positive large.

[0130] Then define the membership functions of the input variables: The input variables δ and θ of the fuzzy control can use the gaussmf type membership function, and the output variables ΔK p 、ΔK i 、ΔK d can use the trimf type membership function.

[0131] Then define the fuzzy rules based on the following rules:

[0132] 1) When the deviation is large, larger \(K_p\) and smaller \(K_d\) should be taken to improve the system response speed and avoid the differential oversaturation caused by the sudden increase in deviation at startup. To prevent integral saturation and large overshoot during the initial tuning, the value of \(K_i\) is set to 0.

[0133] 2) When the deviation and the deviation change rate are medium, smaller \(K_p\) and \(K_d\) are taken, which can reduce the response overshoot to a certain extent and meet the speed requirements. The value of \(K_i\) should be moderate.

[0134] 3) When the deviation is small, to ensure the stability of the system, the values of \(K_p\) and \(K_i\) need to be increased. At the same time, to avoid the output variable oscillating near the set value, the value of \(K_d\) needs to be adjusted continuously. Among them, the principle for the value of \(K_d\) is: when the deviation change rate is small, a smaller \(K_d\) is taken; when the deviation change rate is large, a larger \(K_d\) is taken.

[0135] Finally, based on the above principles, \(\Delta K\) p 、\(\Delta K\) i 、\(\Delta K\) d The fuzzy rule control table is as follows:

[0136] Table 1 \(\Delta K\) p Fuzzy rule control table

[0137]

[0138] Table 2 \(\Delta K\) i Fuzzy rule control table

[0139]

[0140] Table 3 \(\Delta K\) d Fuzzy rule control table

[0141]

[0142] The center of gravity method can be used to numerically concretize the output based on fuzzy rules. The specific method is: according to the deviation \(\delta\) between the predicted vehicle speed and the target vehicle speed and the deviation change rate \(\theta\), look up the fuzzy rule control table of \(\Delta K\) p (Table 1) to obtain the membership value of \(\Delta K\) p . Assume that the membership values of the deviation \(\delta\) between the predicted vehicle speed and the target vehicle speed in the current control cycle are PM and PB, and the membership degrees are \(\varepsilon_1\) and \(\varepsilon_2\) respectively, and the membership value of \(\theta\) is NB and NM, and the membership degrees are \(\eta_1\) and \(\eta_2\) respectively. According to the fuzzy rules, the membership value of the output \(\Delta K\) p is determined to be PS and Z. After defuzzification, the output of \(\Delta K\) p is: \((\varepsilon_1 * \eta_2+\varepsilon_2 * \eta_1+\varepsilon_2 * \eta_2)*Z+\varepsilon_1 * \eta_1*PS\).

[0143] Based on the deviation δ between the estimated vehicle speed and the target vehicle speed and the deviation change rate θ, look up ΔK i in the fuzzy rule control table (Table 2) to obtain ΔK i in the universe of discourse value. Assume that the membership values of the deviation δ between the estimated vehicle speed and the target vehicle speed in the current control cycle are PM and PB, and the membership degrees are ε3 and ε4 respectively, and the membership values of θ are NB and NM, and the membership degrees are η3 and η4 respectively. Determine the output ΔK according to the fuzzy rules i in the membership value of Z. After defuzzification, ΔK p the output is: (ε3*η4 + ε4*η3 + ε4*η4 + ε3*η3)*Z.

[0144] Based on the deviation δ between the estimated vehicle speed and the target vehicle speed and the deviation change rate θ, look up ΔK d in the fuzzy rule control table (Table 3) to obtain ΔK d in the universe of discourse value. Assume that the membership values of the deviation δ between the estimated vehicle speed and the target vehicle speed in the current control cycle are PM and PB, and the membership degrees are ε5 and ε6 respectively, and the membership values of θ are NB and NM, and the membership degrees are η5 and η6 respectively. Determine the output ΔK according to the fuzzy rules d in the membership values of PB, PM and NS. After defuzzification, ΔK d the output is: (ε5*η5 + ε6*η5)*PB + ε6*η6*PM + ε5*η6*NS.

[0145] S620. Based on the PID parameter variation and the initial PID parameters obtained by calibration, perform online correction to obtain the PID parameters of the current control cycle.

[0146] Specifically, combine the obtained PID parameter variation with the initial PID parameters obtained by calibration to obtain the corrected PID parameters of the current control cycle. A common implementation method can be to correct the three parameters of proportional, integral, and differential according to a certain correction strategy. For example, the incremental PID algorithm can be used to perform incremental adjustment on the initial PID parameters according to the variation, or other adaptive control algorithms can be used for online correction.

[0147] Exemplarily, please refer to Figure 6b , the deviation δ and the deviation change rate θ between the target vehicle speed and the estimated vehicle speed can be input into the fuzzy controller 106 to determine the PID parameter variation ΔK p 、ΔK i 、ΔK d . The PID parameter variation ΔK output by the fuzzy controller 106 p 、ΔK i 、ΔK dPassed to the online correction module 108. The online correction module 108 adds ΔK p , ΔK i , ΔK d to the original parameters K p0 , K i0 , K d0 obtained by calibration, to obtain the PID parameters Kp, Ki, and Kd for the current control cycle.

[0148] It should be noted that an appropriate calibration method (such as the trial-and-error method, frequency-domain analysis method, optimization algorithm) can be selected according to the actual situation. Give the fuzzy PID controller an initial PID parameter value, which can be an initial value based on experience or a value calculated according to the system model or initial data. Conduct experimental operations on the system and record the data of the input signal and output response. Data such as step response and frequency response can be collected. According to the collected experimental data, use the selected calibration method to adjust the initial PID parameter value to make the actual response of the system approach the desired performance index and obtain the original parameters.

[0149] In the above embodiment, the rate of change of deviation and the deviation amount are processed based on fuzzy rules to obtain the change amount of PID parameters. The PID parameters for the current control cycle are obtained through online correction based on the change amount of PID parameters and the initial PID parameters obtained by calibration, which can adapt to the control requirements under different working conditions and improve the adaptability and stability of the system.

[0150] The embodiments of this specification also provide a vehicle control method. Exemplarily, please refer to Figure 7 , and the vehicle control method may include the following steps:

[0151] S702. Obtain a plurality of initial transfer functions, demand torque time-series data, and vehicle speed time-series data corresponding to the demand torque time-series data.

[0152] S704. Perform parameter identification on the initial transfer function based on the demand torque time-series data and the vehicle speed time-series data to obtain an identification result.

[0153] S706. Determine a preset transfer function from the plurality of initial transfer functions based on the identification result.

[0154] Among them, the preset transfer function is used to describe the relationship between the demand torque and the vehicle speed. The preset transfer function is a second-order lag transfer function.

[0155] S708. Perform Laplace transform on the demand torque of the previous control cycle to obtain a first transformation result.

[0156] S710. Obtain the product of the first transformation result and the preset transfer function to obtain a second transformation result corresponding to the estimated vehicle speed.

[0157] S712. Perform the inverse Laplace transform on the second transformation result to obtain the predicted vehicle speed.

[0158] S714. Determine the deviation amount and deviation change rate between the target vehicle speed and the predicted vehicle speed in the current control cycle.

[0159] S716. Process the deviation change rate and deviation amount based on fuzzy rules to obtain the change amount of PID parameters.

[0160] S718. Perform online correction based on the change amount of PID parameters and the initial PID parameters obtained by calibration to obtain the PID parameters in the current control cycle.

[0161] S720. Determine the required torque in the current control cycle based on the PID parameters in the current control cycle.

[0162] S722. Control the vehicle according to the required torque in the current control cycle.

[0163] An embodiment of this specification provides a vehicle control device 800. Please refer to Figure 8 , the vehicle control device 800 includes: a predicted vehicle speed determination module 810, a required torque determination module 820, and a vehicle control module 830.

[0164] The predicted vehicle speed determination module 810 is used to perform prediction based on the required torque in the previous control cycle to obtain the predicted vehicle speed in the current control cycle;

[0165] The required torque determination module 820 is used to determine the required torque in the current control cycle based on the deviation between the target vehicle speed and the predicted vehicle speed in the current control cycle;

[0166] The vehicle control module 830 is used to control the vehicle according to the required torque in the current control cycle.

[0167] In some embodiments, the predicted vehicle speed determination module is further used to determine the predicted vehicle speed in the current control cycle based on the required torque in the previous control cycle and a preset transfer function; wherein, the preset transfer function is used to describe the relationship between the required torque and the vehicle speed.

[0168] In some embodiments, the preset transfer function is a second-order lag transfer function.

[0169] In some embodiments, the predicted vehicle speed determination module is further used to perform the Laplace transform on the required torque in the previous control cycle to obtain a first transformation result; obtain the product of the first transformation result and the preset transfer function to obtain a second transformation result corresponding to the predicted vehicle speed; perform the inverse Laplace transform on the second transformation result to obtain the predicted vehicle speed.

[0170] In some embodiments, the preset transfer function is determined by the following method: obtaining a plurality of initial transfer functions, demand torque time series data, and vehicle speed time series data corresponding to the demand torque time series data; performing parameter identification on the initial transfer functions based on the demand torque time series data and the vehicle speed time series data to obtain an identification result; and determining the preset transfer function from the plurality of initial transfer functions based on the identification result.

[0171] In some embodiments, the vehicle control device further includes: a deviation change determination module, configured to determine a deviation amount and a deviation change rate between a target vehicle speed and an estimated vehicle speed in the current control cycle.

[0172] The demand torque determination module is further configured to perform online correction based on the deviation change rate and the deviation amount to obtain PID parameters for the current control cycle; and determine the demand torque for the current control cycle based on the PID parameters for the current control cycle.

[0173] In some embodiments, the demand torque determination module is further configured to process the deviation change rate and the deviation amount based on fuzzy rules to obtain a change amount of PID parameters; and perform online correction based on the change amount of PID parameters and the calibrated initial PID parameters to obtain the PID parameters for the current control cycle.

[0174] For the specific description of the vehicle control device, reference may be made to the description of the vehicle control method in the foregoing text, which will not be elaborated herein.

[0175] An embodiment of this specification provides a vehicle, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the method in any one of the foregoing embodiments are implemented.

[0176] In some embodiments, a computer device is provided. The computer device includes a processor, a memory, and a communication interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program stored in the non-volatile storage medium to run. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a carrier network, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, a vehicle control method is implemented. The structure diagram of the computer device can be as Figure 9As shown, the computer device may further include a display screen and an input device. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device may be a touch layer covering the display screen, or may be a button, a trackball, or a touchpad provided on the housing of the computer device, or may also be an external keyboard, touchpad, or mouse, etc.

[0177] Those skilled in the art can understand that Figure 9 the structure shown in is only a block diagram of some structures related to the solution disclosed in this specification, and does not constitute a limitation on the computer device to which the solution disclosed in this specification is applied. Specifically, the computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different component layout.

[0178] In some embodiments, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the method steps in the above embodiments are implemented.

[0179] An embodiment of this specification provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method in any one of the above embodiments are implemented.

[0180] An embodiment of this specification provides a computer program product, which includes instructions. When the instructions are executed by the processor of the computer device, the computer device can execute the steps of the method in any one of the above embodiments.

[0181] Note that the logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definite ordered listing of executable instructions for implementing logical functions, which can be specifically implemented in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device). For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection part having one or more wirings (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium can even be paper or other suitable medium on which a program can be printed, as the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpretation, or other appropriate processing as necessary, and then stored in a computer memory.

Claims

1. A vehicle control method, characterized in that, the method includes: estimating based on the required torque in the previous control cycle to obtain the estimated vehicle speed in the current control cycle; determining the required torque in the current control cycle based on the deviation between the target vehicle speed and the estimated vehicle speed in the current control cycle; controlling the vehicle according to the required torque in the current control cycle.

2. The method according to claim 1, characterized in that, the estimating based on the required torque in the previous control cycle to obtain the estimated vehicle speed in the current control cycle includes: determining the estimated vehicle speed in the current control cycle based on the required torque in the previous control cycle and a preset transfer function; wherein, the preset transfer function is used to describe the relationship between the required torque and the vehicle speed.

3. The method according to claim 2, characterized in that, the preset transfer function is a second-order lag transfer function.

4. The method according to claim 2 or 3, characterized in that, the determining the estimated vehicle speed in the current control cycle based on the required torque in the previous control cycle and the preset transfer function includes: performing a Laplace transform on the required torque in the previous control cycle to obtain a first transform result; obtaining the product of the first transform result and the preset transfer function to obtain a second transform result corresponding to the estimated vehicle speed; performing an inverse Laplace transform on the second transform result to obtain the estimated vehicle speed.

5. The method according to claim 2 or 3, characterized in that, the preset transfer function is determined by the following method: obtaining a plurality of initial transfer functions, required torque time series data, and vehicle speed time series data corresponding to the required torque time series data; performing parameter identification on the initial transfer functions based on the required torque time series data and the vehicle speed time series data to obtain an identification result; determining the preset transfer function from the plurality of initial transfer functions based on the identification result.

6. The method according to claim 1, characterized in that, before determining the required torque in the current control cycle based on the deviation between the target vehicle speed and the estimated vehicle speed in the current control cycle, the method further includes: determining the deviation amount and the deviation change rate between the target vehicle speed and the estimated vehicle speed in the current control cycle; the determining the required torque in the current control cycle based on the deviation between the target vehicle speed and the estimated vehicle speed in the current control cycle includes: performing online correction based on the deviation change rate and the deviation amount to obtain the PID parameters in the current control cycle; determining the required torque in the current control cycle based on the PID parameters in the current control cycle.

7. The method according to claim 6, characterized in that, the performing online correction based on the deviation change rate and the deviation amount to obtain the PID parameters in the current control cycle includes: processing the deviation change rate and the deviation amount based on fuzzy rules to obtain a change amount of the PID parameters; performing online correction based on the change amount of the PID parameters and the initial PID parameters obtained by calibration to obtain the PID parameters in the current control cycle.

8. A vehicle control device, characterized in that, The device includes: A predicted vehicle speed determination module, configured to perform prediction based on the required torque in the previous control cycle to obtain the predicted vehicle speed in the current control cycle; A required torque determination module, configured to determine the required torque in the current control cycle based on the deviation between the target vehicle speed and the predicted vehicle speed in the current control cycle; A vehicle control module, configured to control the vehicle according to the required torque in the current control cycle.

9. A vehicle, characterized in that it includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer device, including a memory and a processor, the memory stores a computer program, characterized in that when the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

11. A computer-readable storage medium, on which a computer program is stored, characterized in that when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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