Self-adaptive control method and device of electric drive system and vehicle-mounted terminal

Abstract

The invention relates to a self-adaptive control method and device of an electric drive system and a vehicle-mounted terminal, which are used for improving the driving experience of a user. The method comprises the steps that the actual rotating speed and the actual torque change rate of a motor are obtained, and the target rotating speed and the target torque change rate corresponding to the current vehicle running working condition are obtained, so that under different vehicle running working conditions, the zero-crossing rotating speed is calculated based on the rotating speed deviation between the actual rotating speed and the zero-crossing rotating speed; according to the actual torque change rate and the torque change rate deviation between the actual torque change rate and the target torque change rate, the sliding mode variable is determined, the motor torque can be controlled through the sliding mode variable to rapidly track the preset control target under each vehicle driving working condition before zero crossing, different vehicle driving working conditions are accurately adapted, and the control accuracy is improved. In addition, the target torque change rate is set under each vehicle working condition, the quick response capacity of the torque is reserved, the vehicle running smoothness is guaranteed, and then the driving experience of a user is improved.

Description

Technical Field

[0001] This application relates to the field of motor control technology, and in particular to an adaptive control method, device and vehicle terminal for an electric drive system. Background Technology

[0002] Electric vehicles are driven by an electric drive system, which includes a drive motor, a controller system, and a transmission system. Currently, more and more power strategies are no longer limited to the control of the vehicle control unit, but are integrated into the motor control unit, which directly responds to the vehicle control unit's requests to control the motor torque.

[0003] However, due to limitations in transmission system manufacturing processes, complete gear engagement is often impossible. In some cases, excessively fast motor torque response can lead to gear shifting (D / R), gear grinding or slight metallic impact during vehicle start-up, and zero-crossing shock in energy-saving and comfort modes. This can significantly impact the driving experience. Existing zero-crossing strategies often sacrifice torque response rate or use torque gradient control by calibrating the torque curve of the vehicle control unit in conjunction with the motor control unit. However, these methods delay the vehicle's acceleration performance by more than 200 milliseconds, resulting in a lack of responsiveness and a poor driving experience for the user. Summary of the Invention

[0004] Therefore, it is necessary to provide an adaptive control method, device, and vehicle terminal for an electric drive system to address the aforementioned technical problems and improve the user's driving experience.

[0005] In a first aspect, embodiments of this application provide an adaptive control method for an electric drive system, comprising: acquiring the actual speed and actual torque change rate of the motor, and acquiring the target speed and target torque change rate corresponding to the current vehicle driving condition, wherein the target speed and target torque change rate are preset values, and the target torque change rate includes the minimum change rate corresponding to the current vehicle driving condition without collision perception; determining a sliding diaphragm variable based on the speed deviation between the actual speed and the zero-crossing speed, and the torque change rate deviation between the actual torque change rate and the target torque change rate; and controlling the electric drive system according to the sliding diaphragm variable so that the actual torque change rate approaches the target torque change rate.

[0006] In one embodiment, controlling the electric drive system based on sliding mode variables includes: determining an adaptive control law based on the sliding mode variables, the actual speed of the motor, and the actual torque change rate; wherein the sliding mode variables represent the control reference for eliminating zero-crossing impact torque, and the adaptive control law is used to converge the actual torque change rate based on the sliding mode variables, so that the actual torque change rate approaches the target torque change rate.

[0007] In one embodiment, determining the sliding diaphragm variables includes: establishing the state equation of the electric drive system based on the speed deviation and torque change rate deviation, combined with kinematic relationships and dynamic equations; defining system error terms in the state equation of the electric drive system, including the target motor angular velocity error term, the target motor angular acceleration error term, and the target motor angular impact error term; differentiating the target motor angular velocity error term to obtain the correlation of the system error terms; constructing a second-order nonlinear system based on the correlation of the system error terms and the nonlinear terms; and designing the sliding diaphragm variables based on the target motor torque change rate and the second-order nonlinear system.

[0008] In one embodiment, the adaptive control law is determined based on the sliding mode variable, the actual speed of the motor, and the actual torque change rate. This includes: inversely solving for the control input required to maintain the sliding mode variable based on the sliding mode variable to obtain an equivalent control term; designing gain coefficients corresponding to each motor speed range based on the motor speed range corresponding to the vehicle driving conditions, and designing an adaptive boundary layer based on the motor speed range to obtain a switching control term that adapts to speed changes, wherein the gain coefficients include a reference gain coefficient and an integral gain coefficient; designing anti-torque disturbance feedforward compensation control based on the target torque change rate and the hyperbolic tangent function to obtain a torque feedforward compensation term; and integrating the equivalent control term, the switching control term that adapts to speed changes, and the torque feedforward compensation term to obtain the adaptive control law.

[0009] In one embodiment, a switching control term adapted to speed changes is obtained, including: generating a reference gain coefficient based on a sliding diaphragm variable, the reference gain coefficient representing the minimum control force required to overcome external disturbances; calculating the speed error at the current moment under each vehicle operating condition and predicting the speed error at the next moment, the speed error representing the difference between the actual speed and the target speed; constructing a dynamic modulation factor based on the speed error at the current moment and the speed error at the next moment, the dynamic modulation factor being used to adjust the motor speed according to the vehicle operating condition; and integrating the reference gain coefficient and the dynamic modulation factor to obtain an adaptive integral gain coefficient.

[0010] In one embodiment, an adaptive boundary layer is designed based on the motor speed range, including: establishing a basic boundary layer for each motor speed range, generating a dynamic fluctuation compensation term based on the speed error and the maximum motor speed within the motor range; and obtaining the adaptive boundary layer by superimposing the basic boundary layer with the dynamic fluctuation compensation term.

[0011] In one embodiment, anti-torque disturbance feedforward compensation control is designed based on the target torque change rate and the hyperbolic tangent function to obtain the torque feedforward compensation term, including: defining the hyperbolic tangent function as the compensation function with the target torque change rate as the activation condition of the feedforward compensation control; multiplying the compensation function by the feedforward compensation gain coefficient so that the feedforward compensation matches the motor speed, and the feedforward gain coefficient is inversely proportional to the motor speed.

[0012] In one embodiment, the method further includes stability verification, the verification steps of which include: constructing a positive definite function representing the system energy or error, defining half of the square of the sliding mode variable as the Lyapunov function; differentiating the Lyapunov function, substituting the differential equation of the sliding mode variable into the differentiated Lyapunov function to obtain the function to be analyzed; proving that all terms in the function to be analyzed are non-positive, thus completing the stability verification.

[0013] Secondly, embodiments of this application provide an adaptive control device for an electric drive system, comprising: an electric drive parameter acquisition module, used to acquire the actual speed and actual torque change rate of the motor, and to acquire the target speed and target torque change rate corresponding to the current vehicle driving condition, wherein the target speed and target torque change rate are preset values, and the target torque change rate is the minimum change rate corresponding to the current vehicle driving condition without collision perception; a control algorithm construction module, used to determine a sliding diaphragm variable based on the speed deviation between the actual speed and the zero-crossing speed, and the torque change rate deviation between the actual torque change rate and the target torque change rate; and an electric drive system control module, used to control the electric drive system according to the sliding diaphragm variable, so that the actual torque change rate approaches the target torque change rate.

[0014] In one embodiment, the electric drive system control module is used to determine an adaptive control law based on the sliding mode variable, the actual speed of the motor, and the actual torque change rate; wherein, the sliding mode variable represents the control reference for eliminating zero-crossing impact torque, and the adaptive control law is used to converge the actual torque change rate based on the sliding mode variable, so that the actual torque change rate approaches the target torque change rate.

[0015] In one embodiment, the control algorithm construction module is used to establish the state equation of the electric drive system based on the target motor speed and the target motor torque change rate, combined with kinematic relationships and dynamic equations; define system error terms in the state equation of the electric drive system, including the target motor angular velocity error term, the target motor angular acceleration error term, and the target motor rotational impact error term; differentiate the target motor angular velocity error term to obtain the correlation of the system error terms; construct a second-order nonlinear system based on the correlation of the system error terms and the nonlinear terms; and design a sliding surface based on the target motor torque change rate and the second-order nonlinear system, the sliding surface including a preset motor torque control target.

[0016] In one embodiment, the control algorithm construction module is used to solve for the control input required to maintain the sliding mode variables based on the sliding mode variables, and obtain the equivalent control term; design the gain coefficients corresponding to each motor speed range according to the motor speed range corresponding to the vehicle driving conditions, and design an adaptive boundary layer according to the motor speed range to obtain the switching control term that adapts to speed changes, wherein the gain coefficients include the reference gain coefficient and the integral gain coefficient; design the anti-torque disturbance feedforward compensation control based on the target torque change rate and the hyperbolic tangent function to obtain the torque feedforward compensation term; integrate the equivalent control term, the switching control term that adapts to speed changes, and the torque feedforward compensation term to obtain the adaptive control law.

[0017] In one embodiment, the control algorithm construction module is used to generate a reference gain coefficient based on the sliding diaphragm variable, the reference gain coefficient representing the minimum control force required to overcome external disturbances; calculate the speed error at the current moment under each vehicle operating condition, and predict the speed error at the next moment, the speed error representing the difference between the actual speed and the target speed; construct a dynamic modulation factor based on the speed error at the current moment and the speed error at the next moment, the dynamic modulation factor being used to adjust the motor speed according to the vehicle operating condition; and integrate the reference gain coefficient and the dynamic modulation factor to obtain an adaptive integral gain coefficient.

[0018] In one embodiment, the control algorithm construction module is used to establish a basic boundary layer for each motor speed range, and generate a dynamic fluctuation compensation term based on the speed error and the maximum motor speed within the motor range; by superimposing the basic boundary layer with the dynamic fluctuation compensation term, an adaptive boundary layer is obtained.

[0019] In one embodiment, the control algorithm construction module is used to define the hyperbolic tangent function as the activation condition for feedforward compensation control with the target torque change rate as the target torque change rate; the compensation function is multiplied by the feedforward compensation gain coefficient so that the feedforward compensation matches the motor speed, and the feedforward gain coefficient is inversely proportional to the motor speed.

[0020] In one embodiment, the adaptive control device of the electric drive system further includes a stability verification module for constructing a positive definite function representing the system energy or error, defining half of the square of the sliding mode variable as a Lyapunov function; differentiating the Lyapunov function, substituting the differential equation of the sliding mode variable into the differentiated Lyapunov function to obtain the function to be analyzed; proving that all terms in the function to be analyzed are non-positive, thus completing the stability verification of the high-order terminal sliding mode algorithm.

[0021] Thirdly, embodiments of this application provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the first aspect and any possible implementation method.

[0022] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method of the first aspect and any possible implementation.

[0023] The beneficial effects of this application are as follows: By acquiring the actual speed and actual torque change rate of the motor, as well as the target speed and target torque change rate corresponding to the current vehicle driving condition, this application determines the sliding diaphragm variable based on the speed deviation between the actual speed and the zero-crossing speed, and the torque change rate deviation between the actual torque change rate and the target torque change rate under different vehicle driving conditions. This allows the motor torque to be controlled by the sliding diaphragm variable to quickly track the preset control target for each vehicle driving condition before zero crossing, accurately adapting to different vehicle driving conditions and avoiding the power lag caused by fixed torque adjustment. In addition, setting the target torque change rate for each vehicle driving condition not only preserves the rapid response capability of torque but also takes into account driving power and smoothness, thereby improving the user's driving experience. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0025] Figure 1 This is an application environment diagram of the adaptive control method for an electric drive system in one embodiment. Figure 2 This is a flowchart illustrating an adaptive control method for an electric drive system in one embodiment; Figure 3 This is a technical roadmap for adaptive control of an electric drive system in one embodiment; Figure 4 This is a diagram of an adaptive torque control architecture based on a high-order terminal sliding mode algorithm in one embodiment; Figure 5 This is a structural block diagram of a device for an vehicle software architecture in one embodiment; Figure 6 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Unless otherwise specified, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than that shown here.

[0027] The terms "first" and "second" in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the term "comprising" and any variations thereof are intended to cover non-exclusive protection. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices. The term "multiple" in this application can mean at least two, for example, two, three, or more, and the embodiments of this application do not impose limitations.

[0028] To facilitate understanding of the technical solutions provided in the embodiments of this application, the design concept of the embodiments of this application will be introduced first below: The electric drive system is the core powertrain of electric vehicles and hybrid vehicles, and its core function is to efficiently convert the electrical energy from the battery into driving force for the wheels. The core components of a vehicle's electric drive system include: a motor controller (MCU), a drive motor, a reducer, a vehicle control unit (VCU), and auxiliary components. Therefore, the performance of the electric drive system directly determines the vehicle's power and ride comfort.

[0029] In electric vehicles, zero-crossing specifically refers to the process where the drive torque crosses zero between forward (driving) and reverse (regenerative braking or energy recovery). For example, when the driver releases the accelerator pedal, the torque command changes from positive to zero. Then, by lightly pressing the brake pedal, the torque command changes from zero to negative, entering energy recovery. This process of torque changing from positive to zero and then to negative is a typical example of torque zero-crossing. During zero-crossing, due to factors such as transmission system manufacturing processes, complete gear engagement is often not achieved. Excessive motor torque response can even lead to D / R shifting, gear grinding or slight metallic impact during start-up, and shocks during zero-crossing in energy-saving and comfort modes. This significantly impacts the driving experience. Over time, this accelerates gear wear and reduces product competitiveness.

[0030] The core objective of existing zero-crossing strategies is to suppress the impact and jitter at the moment of torque zero crossing, which is mainly achieved through two compromise solutions: The first approach sacrifices torque response rate for smoothness. Specifically, it involves actively limiting the rate of torque change in the zero-crossing range, i.e., reducing the torque response rate, in order to avoid gear shock caused by sudden torque changes. Essentially, it trades power response delay for driving smoothness.

[0031] The second approach is VCU-MCU calibration and optimization. The specific solution is to pre-calibrate the zero-crossing torque curve in the VCU and smooth the driver's instantaneous torque demand into a slowly changing command sent to the MCU. Through hardware and software cooperation, the zero-crossing impact is weakened. Essentially, it uses fixed calibration logic to adapt to dynamic operating conditions.

[0032] It can be seen that most existing zero-crossing strategies improve motor smoothness by sacrificing torque response rate or by calibrating the VCU torque curve in conjunction with MCU zero-crossing. Specifically: Both solutions require limiting the rate of torque change or delaying command execution to achieve smoothness, resulting in a torque response delay that is generally greater than or equal to 200 milliseconds. When the driver presses or releases the pedal, the vehicle cannot respond immediately, resulting in a noticeable lack of responsiveness in the driving experience.

[0033] A fixed torque change rate or calibration curve cannot adapt to the system characteristics at different vehicle speeds. For example, at low speeds, the transmission system has low stiffness and gear backlash has a significant impact, while at high speeds, the load inertia is large and the response requirements are higher, resulting in residual impact at low speeds and amplified delay at high speeds, leading to unbalanced performance across all operating conditions.

[0034] Regardless of whether the driver accelerates or brakes suddenly, or accelerates or brakes gradually, the same control logic is used, which cannot accurately match diverse operating intentions, resulting in a lack of personalization and adaptability in the driving experience.

[0035] To address the above shortcomings, this application proposes an adaptive control method, device, and vehicle terminal for an electric drive system. This application obtains the actual speed and actual torque change rate of the motor, as well as the target speed and target torque change rate corresponding to the current vehicle driving condition. Thus, under different vehicle driving conditions, based on the speed deviation between the actual speed and the zero-crossing speed, and the torque change rate deviation between the actual torque change rate and the target torque change rate, a sliding diaphragm variable is determined. Before zero-crossing, the motor torque can be controlled by the sliding diaphragm variable to quickly track the preset control target for each vehicle driving condition, accurately adapting to different vehicle driving conditions and avoiding power lag caused by fixed torque adjustment. Furthermore, setting a target torque change rate for each vehicle driving condition not only preserves the rapid torque response capability but also considers driving power and smoothness, thereby improving the user's driving experience.

[0036] The adaptive control method for electric drive systems provided in this application can be applied to, for example... Figure 1 In the application environment shown, terminal 102 communicates with server 104 via a network. Terminal 102 can be used to obtain the actual speed and actual torque change rate of the motor under different vehicle driving conditions, as well as the target speed and target torque change rate corresponding to the current vehicle driving condition. It then sends these electric drive parameters to server 104 via the network, enabling server to construct a control algorithm based on the electric drive parameters. The control algorithm includes sliding mode variables and adaptive control laws.

[0037] The terminal 102 can be of different types of data acquisition devices. The terminal 102 can be, but is not limited to, data acquisition sensors, various personal computers, laptops, smartphones, and tablets. The server 104 can be implemented using a standalone server or a server cluster consisting of multiple servers.

[0038] The adaptive control method for an electric drive system proposed in this application will be described next: During vehicle operation, tip-in / out conditions (sudden pedal press / release) are a high-incidence scenario for torque fluctuations, transmission shocks, and vibrations in vehicle electric drive systems. Tip-in refers to rapidly pressing the accelerator pedal, and tip-out refers to rapidly releasing it. Electric vehicle drive assemblies lack buffer components such as clutches and torsional dampers, and the motor has high torque at low speeds. During the transition between these two conditions, the torque reverses rapidly. Since the transmission system has gear backlash, the torque reversal process releases elastic potential energy first, causing transmission components to accelerate through the gap area, leading to gear knocking, producing high-frequency metallic noise, and causing torsional vibration in the transmission system, ultimately transmitting to the vehicle interior as noticeable shaking. Furthermore, the parameters of tip-in / out conditions fluctuate greatly, and a fixed torque gradient cannot adapt to all scenarios; the operating characteristics differ significantly at different vehicle speeds and gears. For example, tip-in at low speeds with negative torque results in the highest frequency of shocks and shaking; at high speeds, the torque response speed requirement is even higher. Using a fixed torque gradient will result in residual low-speed shocks and delayed high-speed response.

[0039] It can be seen that traditional tip-in / out control often falls into a dilemma where smoothness and responsiveness are mutually exclusive. It either sacrifices smoothness by fixing a small torque gradient, resulting in a delayed power response, or it prioritizes power by allowing rapid torque changes, sacrificing smoothness. Therefore, an excessively large or small torque gradient leads to the dilemma of not being able to achieve both smooth driving and rapid response.

[0040] Therefore, this application implements hierarchical adaptive control for tip-in / out conditions under different vehicle driving conditions. The adaptive torque gradient control can be dynamically adjusted according to real-time conditions. For example, during rapid acceleration tip-in conditions, the torque gradient is appropriately increased to ensure power response, while during slow driving tip-in / out conditions, the gradient is decreased to ensure smoothness. In this way, different target torque change rates correspond to different vehicle driving conditions, thereby adapting to different vehicle driving conditions. Under each condition, the corresponding target torque change rate is used as the control target, adaptively converging the actual torque change rate to the target torque change rate. This can preserve the rapid response capability of torque, limit the torque change to be too fast through the target torque change rate, and also ensure the smoothness of vehicle driving.

[0041] Based on the above analysis, such as Figure 2 As shown, this application provides an adaptive control method for an electric drive system, which is applied to... Figure 1 Taking the server in the example, the following steps are included: Step S210: Obtain the actual speed and actual torque change rate of the motor, and obtain the target speed and target torque change rate corresponding to the current vehicle driving condition. The target speed and target torque change rate are preset values, and the target torque change rate includes the minimum change rate corresponding to the current vehicle driving condition without collision perception.

[0042] In one possible embodiment, vehicle driving conditions include vehicle start-up, low-speed driving, and high-speed driving. During vehicle start-up, the motor torque transitions from zero to forward drive, representing a critical zero-crossing scenario. During low-speed driving, the motor torque fluctuates within a small range, representing a high-frequency zero-crossing scenario. During high-speed driving, the torque demand is high, representing a rapid zero-crossing response scenario. For example, vehicle start-up could be a hill start scenario with a speed less than or equal to 10 km / h; low-speed driving could be following another vehicle in congested traffic with a speed between 10 km / h and 30 km / h; and high-speed driving could be highway cruising with a speed greater than or equal to 60 km / h.

[0043] After defining different vehicle driving scenarios, the actual speed and actual torque change rate of the motor under different vehicle driving scenarios are obtained, specifically: Obtain the actual rotational speed and compare the actual torque change rate with the motor torque change rate. Real-time calculation: From the formula: , ,have to: = ·ω; In the formula, ω is the angular velocity of the motor. The motor's rotational speed, Let ξ be the electric drive transmission ratio, and ξ be the conversion coefficient. It is a constant. This represents the actual speed of the motor.

[0044] From the electric drive system model: J· = ; In the formula, J is the moment of inertia of the motor. This is the motor torque. For other losses, This is the angular acceleration of the motor.

[0045] Based on engineering experience: J· = =η· ; In the formula, J is the moment of inertia of the motor. For the angular acceleration of the motor, This is the motor torque. For other losses, η is the efficiency of the electric drive system, which depends on the project. Assuming ideal conditions, we disregard overall vehicle system losses and only consider electric drive losses, typically taking 0.93. The actual torque change rate of the motor is obtained by differentiation. ).

[0046] Step S220: Determine the sliding diaphragm variable based on the speed deviation between the actual speed and the zero-crossing speed, and the torque change rate deviation between the actual torque change rate and the target torque change rate.

[0047] As mentioned earlier, the tip-in / out condition is a high-incidence scenario for torque fluctuations, transmission shocks, and vibrations in vehicle electric drive systems. This condition is not a single, fixed state but encompasses multiple combinations of variables, and its characteristics differ significantly at different vehicle speeds and gears. For example, tip-in under low-speed negative torque conditions results in the highest frequency of shock and vibration issues; at high speeds, the requirements for torque response speed are even higher. If a fixed torque gradient is used, problems such as residual low-speed shocks and high-speed response lag will occur. Adaptive control, on the other hand, can adjust the gradient according to real-time operating parameters to adapt to diverse scenarios.

[0048] Therefore, in one possible embodiment, after obtaining the electric drive parameters under different vehicle driving conditions, the identification of the tip-in / out collision-free threshold includes confirming the collision-free critical torque gradient through vehicle calibration, and analyzing the speed range in which the speed drop occurs through the collected electric drive parameters.

[0049] During the vehicle calibration process, due to differences in gear backlash, electric drive system stiffness, and motor torque characteristics among different vehicle models, it is impossible to set a fixed value uniformly. It is necessary to find the minimum torque response gradient that is just right without collision sensation through real vehicle testing (bench test + road test), which serves as the critical value for the target torque change rate. .

[0050] Specifically, the zero-crossing speed range where the engine speed changes sharply due to the idle travel during vehicle start-up, low-speed driving, and high-speed driving is defined as the vehicle start-up speed range. Vehicle low-speed driving speed range Vehicle high-speed driving speed range Then, the critical value of the torque change rate within each zero-crossing speed range is calibrated, for example, during vehicle start-up (…). Within the specified range: gear backlash has the most significant impact, with a relatively small critical value. This ensures slow torque changes and avoids hard gear contact, for example, 0.5-0.8 N·m / ms; high-speed vehicle driving conditions ( Within the specified range: The load inertia is large, making it sensitive to response speed; the critical value is relatively large. For example, to balance responsiveness and smoothness, the critical value can be 1.2-1.5 N·m / ms; low-speed vehicle driving conditions ( Within the range): Adapts to small torque switching that frequently crosses zero, with the critical value between the two, for example, the critical value can be 0.8-1.0 N·m / ms.

[0051] After determining the speed range and target torque change rate corresponding to each vehicle operating condition, a high-order terminal sliding film algorithm can be constructed based on the above parameters, which includes sliding film variables and adaptive control laws. Before introducing the construction algorithm, this application first introduces the high-order terminal sliding film algorithm.

[0052] The advanced terminal sliding mode algorithm combines the anti-chattering characteristics of advanced sliding mode with the finite-time convergence characteristics of terminal sliding mode. It is used to solve multi-objective control problems of nonlinear systems (such as automotive electric drive systems) under dynamic conditions, and is a high-performance control scheme for handling tipping / out zero-crossing conditions. The advanced terminal sliding mode algorithm constructs an advanced sliding surface (containing advanced derivatives of state variables, such as the first / second derivatives of rotational speed and the rate of change of torque) and a terminal convergence term (nonlinear reaching law), enabling the system to converge to the ideal state within a finite time. Simultaneously, it suppresses the chattering defects of traditional sliding mode control, balancing real-time performance and stability.

[0053] In one possible embodiment, the design of the high-order terminal sliding mode algorithm can be tailored to the zero-crossing requirements of tip-in / out conditions, including sliding surface construction and control law design. The sliding surface construction uses the target motor speed and the target torque change rate as core parameters to build the sliding surface, directly tracking the control objective of the zero-crossing condition. The control law design incorporates speed range and range critical value constraints (e.g., low-gradient convergence in the low-speed range and high-gradient convergence in the high-speed range) to achieve hierarchical adaptive control. When the vehicle state is far from the sliding surface, a high-gain reaching law is used to quickly increase the torque gradient and ensure responsiveness; when the vehicle state is close to the sliding surface, a low-gain nonlinear reaching law is switched to smoothly transition the torque and avoid shocks.

[0054] The following describes the steps for constructing the high-order terminal sliding membrane algorithm in this application: Step S230: Determine the sliding diaphragm variable based on the speed deviation between the actual speed and the zero-crossing speed, and the torque change rate deviation between the actual torque change rate and the target torque change rate.

[0055] In one possible embodiment, the high-order terminal sliding mode algorithm includes sliding mode variables and adaptive control laws. First, the adaptive control law is determined based on the sliding mode variables, the actual speed of the motor, and the actual torque change rate. This includes: designing a sliding surface based on the target motor speed and the target motor torque change rate; and designing the adaptive control law based on the speed range and the range's critical value. The sliding surface represents the target torque control method for eliminating zero-crossing impacts. The adaptive control law is used to control the electric drive system and determine the sliding mode variables based on the deviation of the current torque control method and the sliding surface. This includes: establishing the state equation of the electric drive system based on the speed deviation and torque change rate deviation, combined with kinematic relationships and dynamic equations; defining system error terms in the state equation of the electric drive system, including target motor angular velocity error terms, target motor angular acceleration error terms, and target motor angular impact error terms; differentiating the target motor angular velocity error terms to obtain the correlation of the system error terms; constructing a second-order nonlinear system based on the correlation of the system error terms and the nonlinear terms; and designing the sliding mode variables based on the target motor torque change rate and the second-order nonlinear system.

[0056] In the advanced terminal sliding mode algorithm, the sliding surface is constructed using the target motor speed and the target motor torque change rate, allowing the sliding mode control to directly align with the control target and operating characteristics of the electric drive system. This ensures that the advanced terminal sliding mode algorithm can accurately track the driver's intentions and adapt to the dynamic requirements of tip in / out conditions.

[0057] As an example, the state equation is constructed with motor speed and motor torque gradient as control objectives, including the design of a sliding surface with motor speed and motor torque change rate as control objectives; Depend on Can be launched ; have to: ; In the formula, This refers to the actual rotational speed. Let J be the first derivative of the wheel tip rotational speed, and J be the moment of inertia of the motor. For the angular acceleration of the motor, The impact force of the motor rotation angle. The target threshold for motor angular velocity. The target threshold for motor rotation angle impact; For the target speed, take , , , The target torque change rate. The torque change rate is generally not explicitly differentiated in engineering, but can be calculated numerically. ; Next, the order is: The rate of change of motor torque. For the current moment, for Motor torque at any given moment for The motor torque at any given time. Let , , ,have Therefore, the dynamic model of the electric vehicle described above can be represented by a second-order nonlinear system as follows: ; in, For the system's nonlinear terms, This refers to the actual rotational speed. To control the input, d represents an unknown disturbance.

[0058] The control objective is to design a control law u to reasonably control the torque change gradient of the system during tip in / out conditions, so that the torque can respond quickly without impact.

[0059] ; Since the tip-in / out condition is strongly correlated with the torque gradient, this parameter must be used to construct the sliding surface. Simultaneously, due to the nonlinearity of speed changes, a nonlinear function needs to be constructed to avoid system discontinuities. Considering the high real-time requirements of the system, rapid convergence is also necessary. Taking all these factors into account, the sliding surface is constructed as follows: ; In the formula, λ is the dynamic adjustment parameter, β is the terminal attraction integral factor, and τ is the terminal attraction exponential factor. For synovial membrane variables.

[0060] Since the sliding surface is the control target benchmark of the high-order terminal sliding mode algorithm, after its construction, a control law needs to be designed. The control law generates specific control commands for the electric drive system, forcing the system state to quickly and smoothly approach and maintain on the sliding surface, ultimately achieving torque variation according to the target.

[0061] After obtaining the sliding mode variable, the adaptive control law is determined based on the sliding mode variable, the actual speed of the motor, and the actual torque change rate. The sliding mode variable represents the control benchmark for eliminating zero-crossing impact torque, and the adaptive control law is used to converge the actual torque change rate based on the sliding mode variable, so that the actual torque change rate approaches the target torque change rate.

[0062] The control law design in this application can resist changes in internal parameters and external excitation disturbances. The specific design steps include: First, design the adaptive update law: In one possible embodiment, determining the adaptive control law based on the sliding mode variable, the actual speed of the motor, and the actual torque change rate includes: determining the adaptive control law based on the sliding mode variable, the actual speed of the motor, and the actual torque change rate includes: solving the control input required to maintain the sliding mode variable based on the sliding mode variable to obtain the equivalent control term; designing gain coefficients corresponding to each motor speed range based on the motor speed range corresponding to the vehicle driving condition, and designing an adaptive boundary layer based on the motor speed range to obtain a switching control term that adapts to speed changes, wherein the gain coefficients include a reference gain coefficient and an integral gain coefficient; designing anti-torque disturbance feedforward compensation control based on the target torque change rate and the hyperbolic tangent function to obtain a torque feedforward compensation term; and integrating the equivalent control term, the switching control term that adapts to speed changes, and the torque feedforward compensation term to obtain the adaptive control law.

[0063] As an example, since sudden torque changes often occur during driving, the impact of these changes on the system needs to be considered to enhance system stability. This invention, based on the Super-Twisting control law, introduces a feedforward torque compensation control term to address these torque changes. It consists of equivalent system control, switching control adapted to speed changes, and torque gradient feedforward compensation control, as shown in the following control law. It can be represented as: ; Where τ is the terminal attraction index factor, For synovial membrane variables, For the system's nonlinear terms, This is the robust gain coefficient, used to suppress high-frequency vibrations. The integral gain coefficient eliminates steady-state error; φ represents the speed boundary thickness adaptation layer for smooth system switching. This is a torque gradient feedforward compensation term to reduce torque abrupt disturbances. , , ,have .

[0064] In one possible embodiment, obtaining a switching control term that adapts to changes in rotational speed includes: generating a reference gain coefficient based on a sliding diaphragm variable, the reference gain coefficient representing the minimum control force required to overcome external disturbances; calculating the rotational speed error at the current moment under each vehicle operating condition and predicting the rotational speed error at the next moment, the rotational speed error representing the difference between the actual rotational speed and the target rotational speed; constructing a dynamic modulation factor based on the rotational speed error at the current moment and the rotational speed error at the next moment, the dynamic modulation factor being used to adjust the motor speed according to the vehicle operating condition; and integrating the reference gain coefficient and the dynamic modulation factor to obtain an adaptive integral gain coefficient.

[0065] Based on the Super-Twisting adaptive update law, this invention incorporates a disturbance design into the integral term to improve the integral control accuracy, addressing the sharp drop in speed caused by sudden torque changes or road excitation. The new update law can be expressed as: ; In the formula, It is the gain constant. The robust gain coefficient is... For synovial membrane variables, For a moment The motor speed, For the target speed, for The motor speed at any given time.

[0066] Next, design the speed boundary transition function: In one possible embodiment, an adaptive boundary layer is designed based on the motor speed range, including: establishing a basic boundary layer for each motor speed range, generating a dynamic fluctuation compensation term based on the speed error and the maximum motor speed within the motor range, and obtaining the adaptive boundary layer by superimposing the basic boundary layer with the dynamic fluctuation compensation term.

[0067] As an example, to enable smooth switching between low, medium, and high rotational speeds, a rotational speed boundary thickness adaptation layer function is designed based on rotational speed variations: ; Where φ is the rotational speed boundary thickness adaptation layer, and δ is the rotational speed transition bandwidth. This represents the change in rotational speed. This is the highest speed during the current operating cycle.

[0068] Finally, the torque mutation feedforward compensation function is designed: In one possible embodiment, anti-torque disturbance feedforward compensation control is designed based on the target torque change rate and the hyperbolic tangent function to obtain the torque feedforward compensation term, including: defining the hyperbolic tangent function as the compensation function with the target torque change rate as the activation condition for the feedforward compensation control; multiplying the compensation function by the feedforward compensation gain coefficient so that the feedforward compensation matches the motor speed, and the feedforward gain coefficient is inversely proportional to the motor speed.

[0069] As an example, to address sudden torque changes, the gentle gradient of the hyperbolic tangent function is used to suppress its effect. Simultaneously, a torque gradient gain compensation coefficient is increased to weaken the impact of increased speed on gradient changes. When the sliding mode variable exceeds the torque gradient threshold, feedforward compensation is activated. ; In the formula, It is the gain constant. For correction factor, For a moment The motor speed, For synovial membrane variables, The target torque change rate.

[0070] Because the high-order terminal sliding mode algorithm combines the continuous control characteristics of high-order sliding mode with the nonlinear convergence characteristics of terminal sliding mode, its control law contains nonlinear terms and higher-order derivative terms. The nonlinear terms may cause the system to escape within a finite time under sliding mode switching or parameter boundary conditions (such as divergence caused by sudden changes in torque gradient at zero crossing). The higher-order derivative terms can easily amplify system noise (such as current sampling noise and speed fluctuations). If the stability is not verified, it may lead to increased oscillation of the control law, causing problems such as electric drive system jitter and gear impact, which can easily cause hardware damage. Therefore, stability verification is a key barrier for the high-order terminal sliding mode algorithm to be transformed from theoretical design into an engineering-usable solution.

[0071] The following section describes the stability verification of the high-order terminal sliding mode algorithm: In one possible embodiment, the stability verification of the high-order terminal sliding mode algorithm is further included. The verification steps include: constructing a positive definite function representing the system energy or error, defining half of the square of the sliding mode variable as the Lyapunov function; differentiating the Lyapunov function, substituting the differential equation of the sliding mode variable into the differentiated Lyapunov function to obtain the function to be analyzed; proving that all terms in the function to be analyzed are non-positive, thus completing the stability verification of the high-order terminal sliding mode algorithm.

[0072] As an example, the stability of the designed control algorithm is verified; System convergence only requires verification Construct the Lyapunov function: ; in, It is a Lyapunov function. For synovial membrane variables.

[0073] right Differentiating, we get: ; in, The first derivative of the Lyapunov function, For synovial membrane variables, It is the first derivative of the synovial variable.

[0074] in, ; in, Let λ be the first derivative of the synovial fluid variable, λ be the dynamic adjustment parameter, β be the terminal attraction integral factor, and τ be the terminal attraction exponential factor. , , ,have .

[0075] Will Substituting u into the above equation, we get: ; in, For the first derivative of the synovial variable, The robust gain coefficient is... Here, φ represents the integral gain coefficient, and φ represents the rotational speed boundary thickness adaptation layer. This is the torque gradient feedforward compensation term. Let d be an unknown disturbance term that can be adjusted by the system and unified to the feedforward. In the middle, Substitution ,get: ; in, The first derivative of the Lyapunov function. The robust gain coefficient is... This is the integral gain coefficient. Let φ be the slip film variable, and φ be the thickness of the speed boundary adaptation layer. This is the torque gradient feedforward compensation term. Let be the time interval, and d be the unknown disturbance term.

[0076] Expand It is easy to prove that for - If and only if or hour, At all other times, the value is less than 0.

[0077] Symbolic characteristic analysis: when φ, the system is located outside the boundary layer. ; when φ, where The system is located within the boundary layer. ; in, Let φ be the slip film variable, and φ be the thickness of the speed boundary adaptation layer. For a moment.

[0078] In conclusion, <0, the system is stable and convergent.

[0079] Step S240: Control the electric drive system according to the sliding diaphragm variable so that the actual torque change rate approaches the target torque change rate.

[0080] In summary, the adaptive control method for the electric drive system proposed in this application obtains the actual speed and actual torque change rate of the motor, as well as the target speed and target torque change rate corresponding to the current vehicle driving condition. Thus, under different vehicle driving conditions, based on the speed deviation between the actual speed and the zero-crossing speed, and the torque change rate deviation between the actual torque change rate and the target torque change rate, a sliding diaphragm variable is determined. Before zero crossing, the motor torque can be controlled by the sliding diaphragm variable to quickly track the preset control target for each vehicle driving condition, accurately adapting to different vehicle driving conditions and avoiding the power lag caused by fixed torque adjustment. Furthermore, setting a target torque change rate for each vehicle driving condition not only preserves the rapid torque response capability but also ensures smooth vehicle operation, thereby improving the user's driving experience.

[0081] Based on the above introduction, Figure 3 An example of the technology roadmap of this application is shown, such as Figure 3As shown, electric drive parameters are obtained, and system state equations are constructed based on these parameters. The system sliding surface is designed, and tip-in / out collision-free critical thresholds are identified. A hierarchical speed update rate and anti-torque disturbance feedforward control are designed to construct the system's adaptive control law. Finally, the stability of the constructed algorithm is verified. In different speed ranges, a high-order terminal sliding mode algorithm can be used to control the motor torque to quickly track the preset control target for each speed range before zero crossing. This enables different torque control based on different speed ranges, accurately adapting to different speed ranges and torque change scenarios. It avoids the power lag caused by fixed torque adjustment, not only retaining the rapid torque response capability but also taking into account driving power and smoothness, thereby improving the user's driving experience.

[0082] Based on the above introduction, Figure 4 The diagram shows an adaptive torque control architecture based on a high-order terminal sliding film algorithm, such as... Figure 4 As shown, torque smooth control under tip-in / out conditions is achieved through a closed-loop logic of parameter acquisition, sliding surface construction, and control law output. First, the external input includes the target motor speed and the target motor torque change rate (corresponding to the control target derived from the driver's operating intention). Second, real-time operating parameters of the electric drive system are acquired, including the actual motor speed n and the actual motor torque change rate. These real-time parameters are transmitted to subsequent modules and simultaneously fed back to the control law module for dynamic adjustment of control commands. Then, based on pre-calibrated speed ranges and range thresholds, control parameters adapted to the current operating condition (such as s1, s2, s3, corresponding to the upper limit of torque change rate in different speed ranges) are output. The calibration threshold is transmitted to the sliding surface module, providing approximate values ​​for sliding surface construction. In the control process, by calculating the deviation between the target and actual parameters (i.e., speed deviation and torque change rate deviation) and incorporating calibration threshold constraints, a sliding surface s is generated. Based on the state of the sliding surface s, a feedforward torque gradient command Δτ is output in advance to actively compensate for disturbances such as transmission system backlash and parameter drift, improving control smoothness. Based on the sliding surface s, the feedforward command Δτ, and real-time parameter feedback, the final control command u of the electric drive system is generated. Upon receiving the control command u, torque output is executed (by hardware such as the drive motor and transmission system). Simultaneously, actual operating parameters are fed back to the parameter acquisition module, forming a closed-loop control. The sliding surface ensures rapid system convergence, while calibration thresholds and feedforward compensation suppress shocks, ultimately achieving a balance between dynamic performance and smoothness.

[0083] It should be understood that, although Figure 2-4 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 2-4At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0084] Based on the same inventive concept, embodiments of this application provide an adaptive control device for an electric drive system, comprising: an electric drive parameter acquisition module 501, a control algorithm construction module 502, and an electric drive system control module 503, wherein: The electric drive parameter acquisition module 501 is used to acquire the actual speed and actual torque change rate of the motor, as well as the target speed and target torque change rate corresponding to the current vehicle driving condition. The target speed and target torque change rate are preset values, and the target torque change rate is the minimum change rate corresponding to the current vehicle driving condition without collision perception.

[0085] The parameter calibration module 502 is used to determine the sliding film variables based on the speed deviation between the actual speed and the zero-crossing speed, and the torque change rate deviation between the actual torque change rate and the target torque change rate.

[0086] The electric drive system control module 503 is used to control the electric drive system according to the sliding diaphragm variable, so that the actual torque change rate approaches the target torque change rate.

[0087] In one embodiment, the electric drive system control module is used to determine an adaptive control law based on the sliding mode variable, the actual speed of the motor, and the actual torque change rate; wherein, the sliding mode variable represents the control reference for eliminating zero-crossing impact torque, and the adaptive control law is used to converge the actual torque change rate based on the sliding mode variable, so that the actual torque change rate approaches the target torque change rate.

[0088] In one embodiment, the control algorithm construction module is used to establish the state equation of the electric drive system based on the speed deviation and torque change rate deviation, combined with kinematic relationships and dynamic equations; define system error terms in the state equation of the electric drive system, including the target motor angular velocity error term, the target motor angular acceleration error term, and the target motor angular impact error term; differentiate the target motor angular velocity error term to obtain the correlation of the system error terms; construct a second-order nonlinear system based on the correlation of the system error terms and the nonlinear terms; and design the sliding film variables based on the target motor torque change rate and the second-order nonlinear system.

[0089] In one embodiment, the control algorithm construction module is used to solve for the control input required to maintain the sliding mode variables based on the sliding mode variables, and obtain the equivalent control term; design the gain coefficients corresponding to each motor speed range according to the motor speed range corresponding to the vehicle driving conditions, and design an adaptive boundary layer according to the motor speed range to obtain the switching control term that adapts to speed changes, wherein the gain coefficients include the reference gain coefficient and the integral gain coefficient; design the anti-torque disturbance feedforward compensation control based on the target torque change rate and the hyperbolic tangent function to obtain the torque feedforward compensation term; integrate the equivalent control term, the switching control term that adapts to speed changes, and the torque feedforward compensation term to obtain the adaptive control law.

[0090] In one embodiment, the control algorithm construction module is used to generate a reference gain coefficient based on the sliding diaphragm variable, the reference gain coefficient representing the minimum control force required to overcome external disturbances; calculate the speed error at the current moment under each vehicle operating condition, and predict the speed error at the next moment, the speed error representing the difference between the actual speed and the target speed; construct a dynamic modulation factor based on the speed error at the current moment and the speed error at the next moment, the dynamic modulation factor being used to adjust the motor speed according to the vehicle operating condition; and integrate the reference gain coefficient and the dynamic modulation factor to obtain an adaptive integral gain coefficient.

[0091] In one embodiment, the control algorithm construction module is used to establish a basic boundary layer for each motor speed range, and generate a dynamic fluctuation compensation term based on the speed error and the maximum motor speed within the motor range; by superimposing the basic boundary layer with the dynamic fluctuation compensation term, an adaptive boundary layer is obtained.

[0092] In one embodiment, the control algorithm building module is used to define the hyperbolic tangent function as the activation condition for feedforward compensation control with the target torque change rate as the target torque change rate; the compensation function is multiplied by the feedforward compensation gain coefficient so that the feedforward compensation matches the motor speed, and the feedforward gain coefficient is inversely proportional to the motor speed.

[0093] In one embodiment, the adaptive control device of the electric drive system further includes a stability verification module 504, which is used to construct a positive definite function representing the system energy or error, define half of the square of the sliding mode variable as the Lyapunov function; differentiate the Lyapunov function, substitute the differential equation of the sliding mode variable into the differentiated Lyapunov function to obtain the function to be analyzed; prove that all terms in the function to be analyzed are non-positive, and complete the stability verification of the high-order terminal sliding mode algorithm.

[0094] Specific limitations regarding the adaptive control device for the electric drive system can be found in the limitations of the adaptive control method for the electric drive system described above, and will not be repeated here. Each module in the aforementioned adaptive control device for the electric drive system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in hardware or independently of the processor in the computer device, or stored in software in the memory of the computer device, so that the processor can call and execute the corresponding operations of each module.

[0095] Based on the same inventive concept, embodiments of this application provide a computer device, which may be a server, and its internal structure diagram may be as follows: Figure 6 As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements an adaptive control method for an electric drive system.

[0096] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0097] Based on the same inventive concept, this application provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it performs the following steps: The system obtains the actual speed and actual torque change rate of the motor, as well as the target speed and target torque change rate corresponding to the current vehicle driving condition. The target speed and target torque change rates are preset values, and the target torque change rate is the minimum change rate corresponding to the current vehicle driving condition without collision perception. Based on the speed deviation between the actual speed and the zero-crossing speed, and the torque change rate deviation between the actual torque change rate and the target torque change rate, the sliding diaphragm variable is determined. The system controls the electric drive system according to the sliding diaphragm variable so that the actual torque change rate approaches the target torque change rate.

[0098] In one embodiment, the processor, when executing a computer program, also performs the following steps: The adaptive control law is determined based on the sliding mode variable, the actual speed of the motor, and the actual torque change rate. The sliding mode variable represents the control benchmark for eliminating zero-crossing impact torque, and the adaptive control law is used to converge the actual torque change rate based on the sliding mode variable, so that the actual torque change rate approaches the target torque change rate.

[0099] In one embodiment, the processor, when executing a computer program, also performs the following steps: Based on the speed deviation and torque change rate deviation, the state equation of the electric drive system is established by combining kinematic relationships and dynamic equations. System error terms are defined in the state equation of the electric drive system, including the target motor angular velocity error term, the target motor angular acceleration error term, and the target motor angular impact error term. The correlation of the system error terms is obtained by differentiating the target motor angular velocity error term. A second-order nonlinear system is constructed based on the correlation of the system error terms and the nonlinear terms. The sliding film variables are designed based on the target motor torque change rate and the second-order nonlinear system.

[0100] In one embodiment, the processor, when executing a computer program, also performs the following steps: Based on the sliding mode variables, the control input required to maintain the sliding mode variables is solved to obtain the equivalent control term. According to the motor speed range corresponding to the vehicle driving conditions, the gain coefficients corresponding to each motor speed range are designed, and an adaptive boundary layer is designed according to the motor speed range to obtain the switching control term that adapts to speed changes. Among them, the gain coefficients include the reference gain coefficient and the integral gain coefficient. Based on the target torque change rate and the hyperbolic tangent function, the anti-torque disturbance feedforward compensation control is designed to obtain the torque feedforward compensation term. The equivalent control term, the switching control term that adapts to speed changes, and the torque feedforward compensation term are integrated to obtain the adaptive control law.

[0101] In one embodiment, the processor, when executing a computer program, also performs the following steps: A reference gain coefficient is generated based on the sliding diaphragm variable. The reference gain coefficient represents the minimum control force required to overcome external disturbances. The speed error at the current moment is calculated for each vehicle operating condition, and the speed error at the next moment is predicted. The speed error represents the difference between the actual speed and the target speed. A dynamic modulation factor is constructed based on the speed error at the current moment and the speed error at the next moment. The dynamic modulation factor is used to adjust the motor speed according to the vehicle operating condition. An adaptive integral gain coefficient is obtained by integrating the reference gain coefficient and the dynamic modulation factor.

[0102] In one embodiment, the processor, when executing a computer program, also performs the following steps: A basic boundary layer is established for each motor speed range. A dynamic fluctuation compensation term is generated based on the speed error and the maximum motor speed within the motor range. An adaptive boundary layer is obtained by superimposing the basic boundary layer with the dynamic fluctuation compensation term.

[0103] In one embodiment, when the processor executes the computer program, it further performs the following steps: defining the hyperbolic tangent function as the activation condition for feedforward compensation control with the target torque change rate; multiplying the compensation function by the feedforward compensation gain coefficient so that the feedforward compensation matches the motor speed, and the feedforward gain coefficient is inversely proportional to the motor speed.

[0104] In one embodiment, the processor, when executing a computer program, also performs the following steps: Construct a positive definite function representing the system energy or error, and define half of the square of the sliding mode variable as the Lyapunov function; differentiate the Lyapunov function, substitute the differential equation of the sliding mode variable into the differentiated Lyapunov function to obtain the function to be analyzed; prove that all terms in the function to be analyzed are non-positive, and complete the stability verification of the high-order terminal sliding mode algorithm.

[0105] Based on the same inventive concept, embodiments of this application provide a computer-readable storage medium storing a computer program thereon, wherein the computer program, when executed by a processor, performs the following steps: The system obtains the actual speed and actual torque change rate of the motor, as well as the target speed and target torque change rate corresponding to the current vehicle driving condition. The target speed and target torque change rates are preset values, and the target torque change rate is the minimum change rate corresponding to the current vehicle driving condition without collision perception. Based on the speed deviation between the actual speed and the zero-crossing speed, and the torque change rate deviation between the actual torque change rate and the target torque change rate, the sliding diaphragm variable is determined. The system controls the electric drive system according to the sliding diaphragm variable so that the actual torque change rate approaches the target torque change rate.

[0106] In one embodiment, when the computer program is executed by a processor, it also performs the following steps: The adaptive control law is determined based on the sliding mode variable, the actual speed of the motor, and the actual torque change rate. The sliding mode variable represents the control benchmark for eliminating zero-crossing impact torque, and the adaptive control law is used to converge the actual torque change rate based on the sliding mode variable, so that the actual torque change rate approaches the target torque change rate.

[0107] In one embodiment, when the computer program is executed by a processor, it also performs the following steps: Based on the speed deviation and torque change rate deviation, the state equation of the electric drive system is established by combining kinematic relationships and dynamic equations. System error terms are defined in the state equation of the electric drive system, including the target motor angular velocity error term, the target motor angular acceleration error term, and the target motor angular impact error term. The correlation of the system error terms is obtained by differentiating the target motor angular velocity error term. A second-order nonlinear system is constructed based on the correlation of the system error terms and the nonlinear terms. The sliding film variables are designed based on the target motor torque change rate and the second-order nonlinear system.

[0108] In one embodiment, when the computer program is executed by a processor, it also performs the following steps: Based on the sliding mode variables, the control input required to maintain the sliding mode variables is solved to obtain the equivalent control term. According to the motor speed range corresponding to the vehicle driving conditions, the gain coefficients corresponding to each motor speed range are designed, and an adaptive boundary layer is designed according to the motor speed range to obtain the switching control term that adapts to speed changes. Among them, the gain coefficients include the reference gain coefficient and the integral gain coefficient. Based on the target torque change rate and the hyperbolic tangent function, the anti-torque disturbance feedforward compensation control is designed to obtain the torque feedforward compensation term. The equivalent control term, the switching control term that adapts to speed changes, and the torque feedforward compensation term are integrated to obtain the adaptive control law.

[0109] In one embodiment, when the computer program is executed by a processor, it also performs the following steps: A reference gain coefficient is generated based on the sliding diaphragm variable. The reference gain coefficient represents the minimum control force required to overcome external disturbances. The speed error at the current moment is calculated for each vehicle operating condition, and the speed error at the next moment is predicted. The speed error represents the difference between the actual speed and the target speed. A dynamic modulation factor is constructed based on the speed error at the current moment and the speed error at the next moment. The dynamic modulation factor is used to adjust the motor speed according to the vehicle operating condition. An adaptive integral gain coefficient is obtained by integrating the reference gain coefficient and the dynamic modulation factor.

[0110] In one embodiment, when the computer program is executed by a processor, it also performs the following steps: A basic boundary layer is established for each motor speed range. A dynamic fluctuation compensation term is generated based on the speed error and the maximum motor speed within the motor range. An adaptive boundary layer is obtained by superimposing the basic boundary layer with the dynamic fluctuation compensation term.

[0111] In one embodiment, when the computer program is executed by a processor, it also performs the following steps: Using the interval critical value as the activation condition for feedforward compensation control, the hyperbolic tangent function is defined as the compensation function; the compensation function is multiplied by the feedforward compensation gain coefficient so that the feedforward compensation matches the motor speed, and the feedforward gain coefficient is inversely proportional to the motor speed.

[0112] In one embodiment, when the computer program is executed by a processor, it also performs the following steps: Construct a positive definite function representing the system energy or error, and define half of the square of the sliding mode variable as the Lyapunov function; differentiate the Lyapunov function, substitute the differential equation of the sliding mode variable into the differentiated Lyapunov function to obtain the function to be analyzed; prove that all terms in the function to be analyzed are non-positive, and complete the stability verification of the high-order terminal sliding mode algorithm.

[0113] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0114] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0115] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. An adaptive control method of an electric drive system, characterized by, The method comprises: acquiring an actual rotating speed and an actual torque change rate of the motor, and acquiring a target rotating speed and a target torque change rate corresponding to a current vehicle driving condition, the target rotating speed and the target torque change rate being preset values, and the target torque change rate comprising a minimum change rate corresponding to no collision feeling in the current vehicle driving condition; determining a sliding mode variable based on a rotating speed deviation between the actual rotating speed and a zero-crossing rotating speed, and a torque change rate deviation between the actual torque change rate and the target torque change rate; controlling the electric drive system according to the sliding mode variable, so that the actual torque change rate approaches the target torque change rate.

2. The method of claim 1, wherein, The method of controlling the electric drive system according to the sliding mode variable comprises: determining an adaptive control law according to the sliding mode variable, the actual rotating speed and the actual torque change rate of the motor; wherein the sliding mode variable represents a control reference for eliminating zero-crossing impact torque, and the adaptive control law is used to converge the actual torque change rate according to the sliding mode variable, so that the actual torque change rate approaches the target torque change rate.

3. The method of claim 2, wherein, The method of determining the sliding mode variable comprises: establishing a state equation of the electric drive system according to the rotating speed deviation and the torque change rate deviation, in combination with a kinematic relationship and a dynamic equation; defining a system error term in the state equation of the electric drive system, the system error term comprising a target motor angular velocity error term, a target motor angular acceleration error term and a target motor rotating angle impact degree error term; deriving the target motor angular velocity error term to obtain a correlation of the system error term; constructing a second-order nonlinear system according to the correlation of the system error term and a nonlinear term; designing the sliding mode variable according to the target motor torque change rate and the second-order nonlinear system.

4. The method of claim 3, wherein, The method of determining the adaptive control law according to the sliding mode variable, the actual rotating speed and the actual torque change rate of the motor comprises: solving a control input required for maintaining the sliding mode variable based on the sliding mode variable to obtain an equivalent control term; designing a gain coefficient corresponding to each motor rotating speed interval according to a motor rotating speed interval corresponding to a vehicle driving condition, and designing an adaptive boundary layer according to the motor rotating speed interval, to obtain a switching control term adaptive to rotating speed change, wherein the gain coefficient comprises a reference gain coefficient and an integral gain coefficient; designing an anti-torque disturbance feedforward compensation control based on the target torque change rate and a hyperbolic tangent function, to obtain a torque feedforward compensation term; integrating the equivalent control term, the switching control term adaptive to rotating speed change and the torque feedforward compensation term, to obtain the adaptive control law.

5. The method of claim 4, wherein, The method of obtaining the switching control term adaptive to rotating speed change comprises: generating a reference gain coefficient based on the sliding mode variable, the reference gain coefficient representing a minimum control effort required to overcome external disturbance; calculating a rotating speed error at a current time in each vehicle condition, and predicting a rotating speed error at a next time, the rotating speed error representing a difference between an actual rotating speed and a target rotating speed; constructing a dynamic modulation factor according to the rotating speed error at the current time and the rotating speed error at the next time, the dynamic modulation factor being used to adjust the motor rotating speed according to the vehicle condition; The adaptive integral gain coefficient is integrated according to the reference gain coefficient and the dynamic modulation factor.

6. The method of claim 4, wherein, An adaptive boundary layer is designed according to the motor speed interval, including: A basic boundary layer is established for each motor speed interval, and a dynamic fluctuation compensation term is generated according to the speed error and the maximum motor speed in the motor interval; The adaptive boundary layer is obtained by superimposing the dynamic fluctuation compensation term on the basic boundary layer.

7. The method of claim 4, wherein, A torque disturbance rejection feedforward compensation control is designed based on the target torque change rate and a hyperbolic tangent function, and a torque feedforward compensation term is obtained, including: The target torque change rate is used as the activation condition of the feedforward compensation control, and the hyperbolic tangent function is defined as a compensation function; The compensation function is multiplied by a feedforward compensation gain coefficient, so that the feedforward compensation matches the motor speed, and the feedforward gain coefficient is inversely proportional to the motor speed.

8. The method of claim 1, wherein, The method further includes stability verification, and the verification steps include: Construct a positive definite function representing the energy or error of the system, and define half the square of the sliding variable as a Lyapunov function; Derive the Lyapunov function, substitute the differential equation of the sliding variable into the derived Lyapunov function, and obtain an analyzed function; Prove that all terms in the analyzed function are non-positive, and complete the stability verification.

9. An adaptive control device for an electric drive system, characterized in that Including: An electric drive parameter acquisition module is configured to acquire an actual motor speed and an actual torque change rate, and to acquire a target speed and a target torque change rate corresponding to a current vehicle driving condition, the target speed and the target torque change rate being preset values, and the target torque change rate being a minimum change rate corresponding to no collision feeling under the current vehicle driving condition; A control algorithm construction module determines a sliding variable based on a speed deviation between the actual speed and the zero-crossing speed, and a torque change rate deviation between the actual torque change rate and the target torque change rate; An electric drive system control module controls the electric drive system according to the sliding variable, so that the actual torque change rate approaches the target torque change rate.

10. A vehicle terminal comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the adaptive control method of the electric drive system according to any one of claims 1 to 8. The processor executes the computer program to implement the adaptive control method of the electric drive system according to any one of claims 1 to 8.

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