A vehicle speed control method for a hybrid electric vehicle

Abstract

The application relates to a vehicle speed control method for a hybrid electric vehicle. The method comprises: establishing a vehicle dynamics model; designing an improved disturbance observer according to the vehicle dynamics model and obtaining a disturbance estimation value; designing a speed control law, wherein the speed control law comprises feedforward control and feedback control; determining a control input of the improved disturbance observer according to the disturbance estimation value and the speed control law; designing motor torque based on the control input of the improved disturbance observer; and dynamically adjusting the motor torque and engine torque based on a PID controller of a neural network, so that the vehicle speed dynamically approaches a target vehicle speed. The PID control based on the neural network is used to dynamically adjust the torque of the vehicle, so that the vehicle speed dynamically approaches the target vehicle speed.

Description

Technical Field

[0001] The present application relates to the technical field of electric vehicle speed control, and in particular to a method for controlling the speed of a hybrid electric vehicle. Background Art

[0002] Adaptive cruise control (ACC) emerged as a core technology for autonomous driving. While related research has a long history, longitudinal vehicle speed control is a key area of ​​research in the automotive industry because it is closely linked to the challenges faced by fully automated vehicle control systems, such as safety, fuel economy, traffic congestion, and ride comfort. Simultaneously, automakers are adding various additional features to vehicle designs, such as highway driving assistance systems and emergency braking systems. To meet these market demands, it is crucial to design and develop a longitudinal vehicle control algorithm that is easy to implement, efficient to develop, and meets the control performance requirements for mass production.

[0003] Typically, the longitudinal vehicle speed controller consists of two layers: an upper-level controller and a lower-level controller. The upper-level controller determines the speed profile, which can be derived from the driver's set speed or from a speed planning algorithm that considers fuel economy, traffic congestion, and safety. The lower-level controller's role is to track the speed profile requested by the upper-level controller while regulating the torque demand of the propulsion system.

[0004] For the lower-level controller. To meet performance specifications and develop an effective control system, it is necessary to obtain zero steady-state error and well suppress external disturbances. At the same time, the impact of model uncertainty should be minimized to provide the required acceleration / deceleration behavior to ensure occupant comfort. To meet these goals, many longitudinal vehicle control methods have been proposed. In the automotive industry, the classic method is a PID controller because of its simple structure, robustness and stability. Subsequently, many studies have focused on PID-based learning algorithms, sliding mode control and artificial intelligence methods. However, most of these methods need to be verified in vehicle experiments under different driving conditions. In addition, the algorithms need to be implemented on real-time controllers.

[0005] In related technologies, in order to improve the performance of the lower-level controller, a gain scheduling scheme is adopted, but this requires linearizing the nonlinear system to obtain the gain of each equilibrium point. Research based on optimal control theory, such as linear quadratic control and model predictive control, is used to design longitudinal vehicle control. However, these methods require an accurate vehicle model. The inaccuracy of the model and the complexity and variability of the vehicle power system may lead to certain uncertainties in the established model, thereby affecting the performance and control effect of the disturbance observer. In addition, real-time performance limitations are one of the key indicators of the control system. The disturbance observer and its related controller may require faster computing speed and response capabilities to meet the requirements of actual driving conditions. Although the disturbance observer can compensate for model uncertainty and external interference to a certain extent, its performance may be affected by changes in the external environment, resulting in challenges to the stability of the control system.

[0006] Therefore, it is necessary to provide a new technical solution to improve one or more problems existing in the above solutions.

[0007] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of this application, and therefore may include information that does not constitute prior art known to ordinary technicians in this field. Summary of the Invention

[0008] The purpose of the present application is to provide a method for controlling the speed of a hybrid electric vehicle, thereby overcoming one or more problems caused by limitations and defects of related technologies, at least to a certain extent.

[0009] According to an embodiment of the present application, a method for controlling the speed of a hybrid electric vehicle includes:

[0010] Establish vehicle dynamics model;

[0011] Designing an improved disturbance observer based on the vehicle dynamics model and obtaining a disturbance estimation value;

[0012] designing a speed control law, wherein the speed control law includes feedforward control and feedback control, and determining a control input of an improved disturbance observer based on the disturbance estimate and the speed control law;

[0013] The motor torque is designed based on the control input of the improved disturbance observer, and the motor torque and the engine torque are dynamically adjusted by a PID controller based on a neural network, so that the vehicle speed dynamically approaches the target vehicle speed.

[0014] In the embodiment of the present application, the expression of the vehicle dynamics model is:

[0015] (1);

[0016] The expression of the transfer function is:

[0017] (2);

[0018] By rewriting Formula 1 through the transfer function, the expression of the vehicle dynamics model is obtained as follows:

[0019] (3);

[0020] Where, The Laplace transform form of the vehicle speed, that is, the complex frequency representation of the vehicle speed change over time, Indicates the vehicle weight, represents a complex frequency variable, represents the rolling resistance coefficient, Indicates tire load, represents the air density, represents the air resistance coefficient, represents the frontal area of ​​the vehicle, Indicates vehicle speed, represents the acceleration due to gravity, Indicates the slope angle.

[0021] In an embodiment of the present application, the step of designing an improved disturbance observer based on the vehicle dynamics model and obtaining a disturbance estimate includes:

[0022] The expression of the improved disturbance observer is:

[0023] (4);

[0024] Where, represents the nominal model, represents the control input of the improved disturbance observer, represents the disturbance response;

[0025] Rewrite Formula 4 as:

[0026] (5);

[0027] Where, represents the actual control system, represents the adjustment function, represents the input disturbance, represents the measurement noise;

[0028] Under low frequency conditions, assuming , then rewrite Formula 5 as follows:

[0029] (6);

[0030] Where, Represents the Laplace transform of the input perturbation.

[0031] In an embodiment of the present application, the step of designing an improved disturbance observer based on the vehicle dynamics model and obtaining a disturbance estimate further includes:

[0032] Design a low-pass filter, the expression of which is as follows:

[0033] (7);

[0034] Where, Represents the time constant.

[0035] In an embodiment of the present application, the step of designing a speed control law, wherein the speed control law includes feedforward control and feedback control, and determining a control input of an improved disturbance observer based on the disturbance estimate and the speed control law, includes:

[0036] According to the improved disturbance observer, the vehicle speed control system is obtained, and the expression of the vehicle speed control system is:

[0037] (9);

[0038] Where, v represents the vehicle speed;

[0039] By using formula 2 and formula 9, we can get the control input The concrete expression of (s) is:

[0040] (10);

[0041] Where, express (s) in the form of a specific expression, Indicates the acceleration of the vehicle;

[0042] Through the vehicle speed and target speed, the tracking error of the vehicle speed is obtained as:

[0043] (11);

[0044] (12);

[0045] Where, represents the tracking error of vehicle speed, Indicates the target speed. represents the first-order derivative of the tracking error, that is, the rate of change of the error, >0;

[0046] Furthermore, the acceleration tracking error is:

[0047] (13);

[0048] Where, represents the tracking error of acceleration, represents the target acceleration;

[0049] According to Formula 10, Formula 11, Formula 12 and Formula 13, the specific expression of the control input is further obtained as follows:

[0050] (14);

[0051] Where, represents the feedforward control term, represents the feedback control item;

[0052] The control input of the improved disturbance observer is:

[0053] (15).

[0054] In an embodiment of the present application, the step of designing the motor torque based on the control input of the improved disturbance observer, and dynamically adjusting the motor torque and the engine torque based on a neural network PID controller so as to dynamically approach the target vehicle speed includes:

[0055] The expression of the designed motor torque is:

[0056] (16);

[0057] Where, represents the engine torque error, , represents the gear ratio of the powertrain, Indicates the engine braking torque, The engine outputs effective torque, referred to as engine torque, and R represents the rotation radius of the tire.

[0058] In an embodiment of the present application, the step of designing the motor torque based on the control input of the improved disturbance observer, and dynamically adjusting the motor torque and the engine torque based on a neural network PID controller so that the vehicle speed dynamically approaches the target vehicle speed, further includes:

[0059] Design a neural network. The speed control output expression of the neural network is as follows:

[0060] (17);

[0061] Where, is the weight vector, , Represents the mth weight vector, radial basis vector , Represents the output value of the mth neuron;

[0062] The expression of the performance index function of the neural network is as follows:

[0063] (18);

[0064] Where, Represents the value of the k-th moment output in the neural network, Represents the output value of the mth layer;

[0065] The sensitivity of the output of the object to the change of the control input is expressed by the Jacobian matrix as follows:

[0066] (19);

[0067] represents the weight connecting neurons i and j. represents the change of the control variable at time k, represents the bias term of the j-th neuron.

[0068] In an embodiment of the present application, the step of designing the motor torque based on the control input of the improved disturbance observer, and dynamically adjusting the motor torque and the engine torque based on a neural network PID controller so that the vehicle speed dynamically approaches the target vehicle speed, further includes:

[0069] The motor torque and the engine torque satisfy the total driving torque, and the expression of the total driving torque is as follows:

[0070] (20);

[0071] Where, Indicates the total driving torque.

[0072] In an embodiment of the present application, the step of designing the motor torque based on the control input of the improved disturbance observer, and dynamically adjusting the motor torque and the engine torque based on a neural network PID controller so that the vehicle speed dynamically approaches the target vehicle speed, further includes:

[0073] The expression for designing PID controller is as follows:

[0074] (twenty one);

[0075] Where, Indicates the deviation between the actual output and the expected output, K p is the proportional control coefficient, K i Indicates the integral control coefficient, K d represents the differential control coefficient, represents the control output of the PID controller, represents the motor reference torque, Indicates the current motor output torque.

[0076] In an embodiment of the present application, the step of designing the motor torque based on the control input of the improved disturbance observer, and dynamically adjusting the motor torque and the engine torque based on a neural network PID controller so that the vehicle speed dynamically approaches the target vehicle speed, further includes:

[0077] The PID controller is optimized by a neural network to obtain a neural network-based PID controller. The expression of the neural network-based PID controller is as follows:

[0078] (twenty two);

[0079] Where, represents the change of the control variable at time t, represents the value of the error at time k, represents the value of the reference input at time k, Represents the value of the output of the neural network at time k;

[0080] The input variables of the neural network-based PID controller are:

[0081] (twenty three);

[0082] Where, represents the adjustment coefficient of the neural network in the input layer, represents the adjustment coefficient of the neural network in the hidden layer, represents the adjustment coefficient of the neural network in the output layer, represents the value of the error at time k-1, represents the value of the error at time k-2;

[0083] The control law is:

[0084] (twenty four);

[0085] Where, represents the control input at the current moment k, represents the control input at the previous moment k-1, represents the set of control coefficient gains, Represents the state variables of the control system;

[0086] The expression of the performance index evaluation function of the engine system is as follows:

[0087] (25);

[0088] According to the evaluation function, the neural network is used to optimize the PID controller;

[0089] Using the control output of the neural network-based PID controller as the control input of the engine system to achieve control of the engine torque;

[0090] Based on the control of the motor torque and the generator torque, the vehicle speed is controlled.

[0091] The technical solutions provided by the embodiments of the present application may have the following beneficial effects:

[0092] In one embodiment of the present application, robust vehicle speed control is achieved through the above-described method, using an improved disturbance observer and obtaining a disturbance estimate. This method effectively estimates and compensates for system uncertainties and internal and external disturbances, better adapting to uncertainties and changes in various environments and operating conditions, and facilitating subsequent vehicle speed control. Simultaneously, a speed control law is designed through feedforward and feedback control, enabling more accurate tracking of the target speed and rapid, real-time response to speed changes, thereby improving the vehicle's dynamic performance and driving comfort. Furthermore, the vehicle's torque is dynamically adjusted through neural network-based PID control, enabling the vehicle speed to dynamically approach the target speed.

[0093] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0094] The accompanying drawings are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0095] Figure 1 A flowchart schematically illustrates a method for controlling the speed of a hybrid electric vehicle in an exemplary embodiment of the present application;

[0096] Figure 2 A block diagram schematically illustrates a vehicle speed control system of a hybrid electric vehicle in an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0097] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be embodied in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0098] In addition, the accompanying drawings are merely schematic illustrations of the present application and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the blocks shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0099] This example embodiment first provides a method for controlling the speed of a hybrid electric vehicle. Figure 1 As shown in , the method may include: steps S101 to S103.

[0100] Wherein, step S101: establishing a vehicle dynamics model.

[0101] Step S102: Design an improved disturbance observer according to the vehicle dynamics model and obtain a disturbance estimation value.

[0102] Step S103: designing a speed control law, which includes feedforward control and feedback control, and determining a control input of an improved disturbance observer according to the disturbance estimation value and the speed control law.

[0103] Step S103: Designing the motor torque based on the control input of the improved disturbance observer, and dynamically adjusting the motor torque and the engine torque based on the neural network PID controller to make the vehicle speed dynamically approach the target vehicle speed.

[0104] In one embodiment of the present application, robust vehicle speed control is achieved through the above-described method, using an improved disturbance observer and obtaining a disturbance estimate. This method effectively estimates and compensates for system uncertainties and internal and external disturbances, better adapting to uncertainties and changes in various environments and operating conditions, and facilitating subsequent vehicle speed control. Simultaneously, a speed control law is designed through feedforward and feedback control, enabling more accurate tracking of the target speed and rapid, real-time response to speed changes, thereby improving the vehicle's dynamic performance and driving comfort. Furthermore, the vehicle's torque is dynamically adjusted through neural network-based PID control, enabling the vehicle speed to dynamically approach the target speed.

[0105] Below, we will refer to Figures 1 to 2 Each step of the above method in this exemplary embodiment is described in more detail.

[0106] In step S101, before building the vehicle dynamics model, relevant vehicle parameters must be obtained. The manufacturer's specifications for vehicle mass, wheelbase, engine output, and other related parameters may differ from the data obtained from field testing. In this application, the manufacturer's theoretical values ​​are used as the parameter values ​​for this design.

[0107] This theoretical value includes the vehicle weight m, m=2300kg.

[0108] In addition to the vehicle weight parameters, there are also wheel rotation speed, vehicle speed and engine torque. This application measures the wheel rotation speed by installing a vehicle speed sensor near the vehicle's wheel hub; the vehicle speed sensor is installed between the vehicle's transmission system to measure the speed of the entire vehicle (and vehicle speed); the engine torque sensor is installed in the engine control unit to measure the engine torque.

[0109] Based on the above references, the vehicle is subjected to force analysis, ignoring the dynamic effects of the powertrain and tire slip. The vehicle dynamics model is expressed as follows:

[0110] (26);

[0111] Where, represents the total tire force, represents air resistance, represents rolling resistance, Represents the vehicle's gravity.

[0112] In one embodiment, the vehicle dynamics model is expressed as:

[0113] (1);

[0114] Ignoring the dynamic models of the engine and motor, Equation 1 can be rewritten using the transfer function. The expression of the transfer function is:

[0115] (2);

[0116] By rewriting Formula 1 through the transfer function, the expression of the vehicle dynamics model is obtained as follows:

[0117] (3);

[0118] Where, The Laplace transform form of the vehicle speed, that is, the complex frequency representation of the vehicle speed change over time, Indicates the vehicle weight, represents a complex frequency variable, represents the rolling resistance coefficient, Indicates tire load, represents the air density, represents the air resistance coefficient, represents the frontal area of ​​the vehicle, Indicates vehicle speed, represents the acceleration due to gravity, Indicates the slope angle.

[0119] It's understandable that vehicle load cannot be accurately modeled due to factors such as uncertainty in the vehicle dynamics model and unmeasured information. In this application, changes in vehicle load and weight are combined into a single disturbance component. A disturbance observer is used as feedback to measure the uncertainties and unknown disturbances in the vehicle system and to compensate the control system for these disturbances.

[0120] Secondly, the main design of this application is a hybrid system (i.e., a vehicle dynamics model) consisting of an electric motor and an internal combustion engine, both of which simultaneously provide power to the drivetrain. The total tire force of the hybrid drivetrain can be equivalent to the sum of the engine torque and the electric motor torque.

[0121] (27);

[0122] Where, Indicates the rolling radius of the tire, represents the gear ratio of the powertrain, represents the engine torque, Indicates the motor torque.

[0123] According to the design of the vehicle dynamics model of the electric vehicle and the acquisition of vehicle parameters, the obtained parameter information is transmitted to the vehicle speed controller, disturbance observer and torque controller (ie torque) respectively.

[0124] Furthermore, after the vehicle dynamics model is established, a vehicle speed controller is designed. The step of designing the vehicle speed controller includes step S102 and step S103.

[0125] In step S102, electric vehicles are subject to varying degrees of internal and external disturbances during driving due to the external environment and driving conditions. Longitudinal vehicle speed control (i.e., vehicle speed control) requires that the vehicle maintain good tracking performance under various driving conditions. To ensure compliance with road speed limits, a disturbance observer control system is added to effectively reduce external disturbances and address vehicle dynamics model uncertainties caused by changes in vehicle weight.

[0126] The step of designing an improved disturbance observer according to the vehicle dynamics model and obtaining a disturbance estimation value includes steps S1021 and S1022.

[0127] Step S1021: The expression of the improved disturbance observer is:

[0128] (4);

[0129] Where, represents the nominal model, represents the control input of the improved disturbance observer, represents the disturbance response;

[0130] Rewrite Formula 4 as:

[0131] (5);

[0132] Where, represents the actual control system, represents the adjustment function, represents the input disturbance, represents the measurement noise;

[0133] Under low frequency conditions, assuming , then rewrite Formula 5 as follows:

[0134] (6);

[0135] Where, Represents the Laplace transform of the input perturbation.

[0136] It can be understood that the actual closed-loop system of the disturbance observer (DOB) constitutes a closed-loop system on the nominal model and can be combined with any external loop controller. The design of the filter can achieve the stability of the disturbance observer loop system, compensate for the uncertainty factors in the system, and improve the control performance of the system.

[0137] Step S1022: Design a low-pass filter. The expression of the low-pass filter is as follows:

[0138] (7);

[0139] Where, Represents the time constant.

[0140] It can be understood that the low-pass filter is a third-order low-pass filter, and its model is shown in Formula 7. From the disturbance estimate value of DOB, the expression of the disturbance estimate value of DOB is:

[0141] (27);

[0142] In the formula, when the nominal model is close to the actual control system When , Formula 27 is further expressed as:

[0143] (28);

[0144] Where d is the actual disturbance.

[0145] If the time constant of the low-pass filter is designed to be small enough, that is, , then the disturbance estimate is similar to the actual disturbance. Further, the expression of the nonlinear function is set as follows:

[0146] (29);

[0147] Where, is the acceleration obtained from the acceleration sensor, and U is the coefficient matrix.

[0148] Step S1022: Input the disturbance response into the low-pass filter to obtain the disturbance estimate of the disturbance observer. The expression of the disturbance estimate is as follows:

[0149] (8);

[0150] Where, represents the nonlinear function, and d represents the actual disturbance.

[0151] In step S103, the vehicle speed control law is a set of rules for controlling vehicle speed, enabling the electric vehicle to achieve a desired speed under different driving conditions. Therefore, the vehicle speed control law is set to coordinate vehicle speed control. Furthermore, the step of designing a speed control law, which includes feedforward control and feedback control, and determining the control input of an improved disturbance observer based on the disturbance estimate and the speed control law, includes step S1031.

[0152] Step S1031: Based on the improved disturbance observer, the vehicle speed control system is obtained. The expression of the vehicle speed control system is:

[0153] (9);

[0154] Where, v represents the vehicle speed;

[0155] By using formula 2 and formula 9, we can get the control input The concrete expression of (s) is:

[0156] (10);

[0157] Where, express (s) in the form of a specific expression, Indicates the acceleration of the vehicle;

[0158] Through the vehicle speed and target speed, the tracking error of the vehicle speed is obtained as:

[0159] (11);

[0160] (12);

[0161] Where, represents the tracking error of vehicle speed, Indicates the target speed. represents the first-order derivative of the tracking error, that is, the rate of change of the error, >0;

[0162] Furthermore, the acceleration tracking error is:

[0163] (13);

[0164] Where, represents the tracking error of acceleration, represents the target acceleration;

[0165] According to Formula 10, Formula 11, Formula 12 and Formula 13, the specific expression of the control input is further obtained as follows:

[0166] (14);

[0167] Where, represents the feedforward control term, represents the feedback control item;

[0168] The control input of the improved disturbance observer is:

[0169] (15).

[0170] It is understood that the feedforward control term is set, which is determined by the traction As the speed control of the vehicle, the reverse control item uses the difference between the target speed and the vehicle speed as the reverse control to make feedback adjustments to the actual control.

[0171] The speed controller is designed to achieve accurate tracking and dynamic adjustment of vehicle speed. By adding an improved disturbance observer, disturbances are estimated and compensated, the robustness of the control system is improved, and the calculated control input u (as the speed control signal) is applied to the vehicle dynamics model to adjust the vehicle speed to its target speed. This ensures that the vehicle's actual speed dynamically tracks the target speed while compensating for disturbances caused by the external environment and modeling errors. This section generates the vehicle speed control signal u, providing fundamental information for the subsequent torque coordination control of the powertrain.

[0172] It should be noted that the present application utilizes an improved disturbance observer to achieve robust vehicle speed control. The disturbance observer can effectively estimate and compensate for system uncertainties and internal and external disturbances in the vehicle dynamics system, thereby improving the robustness and tracking performance of the control system composed of disturbance compensation and feedback control.

[0173] In step S104, the motor torque is designed based on the control input of the improved disturbance observer, and the motor torque and the engine torque are dynamically adjusted based on the PID controller of the neural network so that the vehicle speed dynamically approaches the target vehicle speed, including steps S1041 to S1045.

[0174] Step S1041: The designed expression of the motor torque is:

[0175] (16);

[0176] Where, represents the engine torque error, , represents the gear ratio of the powertrain, Indicates the engine braking torque, It represents the effective torque output by the engine, that is, the engine torque, and R represents the rotation radius of the tire.

[0177] It can be understood that the control input of the improved disturbance observer obtained in step S1021 is , from the motor and engine, converting the control input into a driving force as a control signal (i.e. total tire force), in the nominal model, the engine torque , motor torque and drivetrain gear ratios Take the optimal value.

[0178] In actual systems, engine braking fuel consumption has significant differences in engine efficiency. Engine efficiency affects the efficiency of the hybrid system, causing changes in the motor torque. The torque of the motor is further distributed to the engine and the motor of the hybrid system. Taking into account the dynamic changes of the engine response and the control input, the expression of the motor torque can be expressed as the above formula 16.

[0179] Step S1042:

[0180] Design a neural network. The speed control output expression of the neural network is as follows:

[0181] (17);

[0182] Where, is the weight vector, , Represents the mth weight vector, radial basis vector , Represents the output value of the mth neuron;

[0183] The expression of the performance index function of the neural network is as follows:

[0184] (18);

[0185] Where, Represents the value of the k-th moment output in the neural network, Represents the output value of the mth layer;

[0186] The sensitivity of the output of the object to the change of the control input is expressed by the Jacobian matrix as follows:

[0187] (19);

[0188] represents the weight connecting neurons i and j. represents the change of the control variable at time k, It is understood that the neural network is a radial basis function (RBF) neural network, which is a single hidden layer feedforward neural network that uses radial basis functions as the activation function of the hidden layer neurons.

[0189] Step S1043: The motor torque and the engine torque satisfy the total driving torque. The total driving torque is expressed as follows:

[0190] (20);

[0191] Where, Indicates the total driving torque.

[0192] It is understandable that in a hybrid system, the engine torque The response speed is usually slower than the motor torque , so a PID controller is needed to dynamically adjust the torque so that the combination of the engine torque and the motor torque can accurately satisfy the above formula 20.

[0193] Step S1044: Design the expression of the PID controller as follows:

[0194] (twenty one);

[0195] Where, Indicates the deviation between the actual output and the expected output, K p is the proportional control coefficient, K i Indicates the integral control coefficient, K d represents the differential control coefficient, represents the control output of the PID controller, represents the motor reference torque, Indicates the current motor output torque.

[0196] It is understandable that for hybrid electric vehicles, the PID controller controls the torque adjustment based on the output characteristics of the motor torque. Since factors such as load, operating conditions, and disturbances can cause changes in torque demand and power system characteristics during vehicle operation, the parameters of the traditional fixed PID controller may not be able to provide optimal performance in all cases. Therefore, this application uses a PID controller based on an RBF neural network to dynamically adjust the torque to address the problem of the engine torque responding slower than the motor torque. The expression of the PID controller is shown in the above formula 21.

[0197] Step S1045: Optimize the PID controller through the neural network to obtain a neural network-based PID controller. The expression of the neural network-based PID controller is as follows:

[0198] (twenty two);

[0199] Where, represents the change of the control variable at time t, represents the value of the error at time k, represents the value of the reference input at time k, Represents the value of the output of the neural network at time k;

[0200] The input variables of the neural network-based PID controller are:

[0201] (twenty three);

[0202] Where, represents the adjustment coefficient of the neural network in the input layer, represents the adjustment coefficient of the neural network in the hidden layer, represents the adjustment coefficient of the neural network in the output layer, represents the value of the error at time k-1, represents the value of the error at time k-2;

[0203] The control law is:

[0204] (twenty four);

[0205] Where, represents the control input at the current moment k, represents the control input at the previous moment k-1, represents the set of control coefficient gains, Represents the state variables of the control system;

[0206] The expression of the performance index evaluation function of the engine system is as follows:

[0207] (25);

[0208] According to the evaluation function, the neural network is used to optimize the PID controller;

[0209] The control output of the neural network-based PID controller is used as the control input of the engine system to achieve control of the engine torque;

[0210] Vehicle speed is controlled based on the control of motor torque and generator torque.

[0211] It is understandable that this application introduces RBF neural network to optimize PID controller, and the PID controller output and the actual output error , together they form the input to the neural network. After neural network training, the output is the Jacobian matrix shown in Equation 19, which is used to tune the parameters of the PID controller. To avoid the cumulative error generated by the neural network, the PID controller uses an incremental PID controller, as shown in Equation 22.

[0212] A neural network is added to the traditional PID controller to optimize the parameters of the PID controller and realize an incremental PID controller, so that it can gradually achieve zero steady-state error, meet the fast tracking response of the engine torque, and improve the driving comfort of hybrid vehicles.

[0213] Furthermore, the performance index is obtained according to the evaluation function, and the weight of the neural network is adjusted according to the gradient descent method to realize the dynamic adjustment of the parameters of the PID controller, so that the tuning index finally meets the required requirements.

[0214] Dynamic torque adjustment is achieved by balancing the motor torque and the engine torque. In a hybrid system, it can be concluded from Formula 4 that the total driving force of the powertrain is provided by both the motor torque and the engine torque. Combining the vehicle dynamics model of Formula 1 and Formula 26, after torque adjustment, the control law is dynamically optimized based on the improved disturbance observer and the neural network PID controller to achieve closed-loop control of the vehicle speed. At the same time, according to Formulas 11 and 12, through real-time monitoring of the vehicle speed deviation, the control system adjusts the torque output so that the vehicle speed dynamically approaches the target speed. .

[0215] The present application dynamically adjusts vehicle speed and driving torque requirements, determines engine torque and motor torque, and controls the hybrid system.

[0216] The performance of the vehicle speed control stability and the robustness of the control system to internal and external disturbances are evaluated on actual roads and roads with different slopes to evaluate the performance of the control system.

[0217] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of this application and include common knowledge or customary techniques in the art that are not disclosed herein.

Claims

1. A method for controlling the speed of a hybrid electric vehicle, characterized in that: include: A vehicle dynamics model is established; wherein the expression of the vehicle dynamics model is: （1）； The expression of the transfer function is: （2）； By rewriting Formula 1 through the transfer function, the expression of the vehicle dynamics model is obtained as follows: （3）； Where, represents the total tire force, The Laplace transform form of the vehicle speed, that is, the complex frequency representation of the vehicle speed change over time, Indicates the vehicle weight, represents a complex frequency variable, represents the rolling resistance coefficient, Indicates tire load, represents the air density, represents the air resistance coefficient, represents the frontal area of ​​the vehicle, Indicates vehicle speed, represents the acceleration due to gravity, Indicates the slope angle; An improved disturbance observer is designed based on the vehicle dynamics model, and a disturbance estimation value is obtained; wherein the step of designing an improved disturbance observer based on the vehicle dynamics model and obtaining a disturbance estimation value includes: The expression of the improved disturbance observer is: （4）； Where, represents the nominal model, represents the control input of the improved disturbance observer, represents the disturbance response; Rewrite Formula 4 as: （5）； Where, represents the actual control system, represents the adjustment function, represents the input disturbance, represents the measurement noise; Under low frequency conditions, assuming , then rewrite Formula 5 as follows: （6）； Where, represents the Laplace transform form of the input disturbance; Design a low-pass filter, the expression of which is as follows: （7）； Where, represents the time constant; The disturbance response is input into the low-pass filter to obtain the disturbance estimate of the disturbance observer. The expression of the disturbance estimate is as follows: （8）； Where, represents the nonlinear function, d represents the actual disturbance; designing a speed control law, wherein the speed control law includes feedforward control and feedback control, and determining a control input of an improved disturbance observer based on the disturbance estimate and the speed control law; The motor torque is designed based on the control input of the improved disturbance observer, and the motor torque and the engine torque are dynamically adjusted by a PID controller based on a neural network, so that the vehicle speed dynamically approaches the target vehicle speed.

2. The method for controlling the speed of a hybrid electric vehicle according to claim 1, wherein: The step of designing a speed control law, wherein the speed control law includes feedforward control and feedback control, and determining a control input of an improved disturbance observer based on the disturbance estimate and the speed control law, includes: According to the improved disturbance observer, the vehicle speed control system is obtained, and the expression of the vehicle speed control system is: （9）； Where, v represents the vehicle speed; By using formula 2 and formula 9, we can get the control input The concrete expression of (s) is: （10）； Where, express (s) in the form of a specific expression, Indicates the acceleration of the vehicle; Through the vehicle speed and target speed, the tracking error of the vehicle speed is obtained as: （11）； （12）； Where, represents the tracking error of vehicle speed, Indicates the target speed. Represents the first-order derivative of the tracking error, that is, the rate of change of the error, >0; Furthermore, the acceleration tracking error is: （13）； Where, represents the tracking error of acceleration, represents the target acceleration; According to Formula 10, Formula 11, Formula 12 and Formula 13, the specific expression of the control input is further obtained as follows: （14）； Where, represents the feedforward control term, represents the feedback control item; The control input of the improved disturbance observer is: （15）。 3. The method for controlling the speed of a hybrid electric vehicle according to claim 2, wherein: The step of designing the motor torque based on the control input of the improved disturbance observer and dynamically adjusting the motor torque and the engine torque based on a neural network PID controller so as to make the vehicle speed dynamically approach the target vehicle speed includes: The expression of the designed motor torque is: （16）； Where, represents the engine torque error, , represents the gear ratio of the powertrain, Indicates the engine braking torque, It represents the effective torque output by the engine, referred to as engine torque, and R represents the rotation radius of the tire.

4. The method for controlling the speed of a hybrid electric vehicle according to claim 3, wherein: The step of designing the motor torque based on the control input of the improved disturbance observer and dynamically adjusting the motor torque and the engine torque based on a neural network PID controller so as to dynamically make the vehicle speed approach the target vehicle speed further includes: Design a neural network. The speed control output expression of the neural network is as follows: （17）； Where, is the weight vector, , Represents the mth weight vector, radial basis vector , Represents the output value of the mth neuron; The expression of the performance index function of the neural network is as follows: （18）； Where, Represents the value of the k-th moment output in the neural network, Represents the output value of the mth layer; The sensitivity of the output of the object to the change of the control input is expressed by the Jacobian matrix as follows: （19）； represents the weight connecting neurons i and j, represents the change of the control variable at time k, represents the bias term of the j-th neuron.

5. The method for controlling the speed of a hybrid electric vehicle according to claim 4, wherein: The step of designing the motor torque based on the control input of the improved disturbance observer and dynamically adjusting the motor torque and the engine torque based on a neural network PID controller so as to dynamically make the vehicle speed approach the target vehicle speed further includes: The motor torque and the engine torque satisfy the total driving torque, and the expression of the total driving torque is as follows: （20）； Where, Indicates the total driving torque.

6. The method for controlling the speed of a hybrid electric vehicle according to claim 5, wherein: The step of designing the motor torque based on the control input of the improved disturbance observer and dynamically adjusting the motor torque and the engine torque based on a neural network PID controller so as to dynamically make the vehicle speed approach the target vehicle speed further includes: The expression for designing PID controller is as follows: （21）； Where, Indicates the deviation between the actual output and the expected output, K p is the proportional control coefficient, K i Indicates the integral control coefficient, K d represents the differential control coefficient, represents the control output of the PID controller, represents the motor reference torque, Indicates the current motor output torque.

7. The method for controlling the speed of a hybrid electric vehicle according to claim 6, wherein: The step of designing the motor torque based on the control input of the improved disturbance observer and dynamically adjusting the motor torque and the engine torque based on a neural network PID controller so as to dynamically make the vehicle speed approach the target vehicle speed further includes: The PID controller is optimized by a neural network to obtain a neural network-based PID controller. The expression of the neural network-based PID controller is as follows: （22）； Where, represents the change of the control variable at time t, represents the value of the error at time k, represents the value of the reference input at time k, Represents the value of the output of the neural network at time k; The input variables of the neural network-based PID controller are: （23）； Where, represents the adjustment coefficient of the neural network in the input layer, represents the adjustment coefficient of the neural network in the hidden layer, represents the adjustment coefficient of the neural network in the output layer, represents the value of the error at time k-1, represents the value of the error at time k-2; The control law is: （24）； Where, represents the control input at the current moment k, represents the control input at the previous moment k-1, represents the set of control coefficient gains, Represents the state variables of the control system; The expression of the performance index evaluation function of the engine system is as follows: （25）； According to the evaluation function, the neural network is used to optimize the PID controller; Using the control output of the neural network-based PID controller as the control input of the engine system to achieve control of the engine torque; Based on the control of the motor torque and the generator torque, the vehicle speed is controlled.

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