Vehicle stability control method and device, vehicle and medium

Abstract

The application provides a vehicle stability control method and device, a vehicle and a medium. The method comprises the following steps: obtaining a target internal model controller for vehicle handling stability control and a current state parameter of the vehicle; determining a target control function for vehicle stability control based on the state parameter and the target internal model controller; the input parameter of the target control function comprises an error between an actual value and an expected value of a yaw angular velocity of the vehicle, and the output parameter of the target control function comprises a target yaw moment of the vehicle; obtaining an actual yaw angular velocity value and an expected yaw angular velocity value of the vehicle; determining a target yaw moment value based on the actual yaw angular velocity value, the expected yaw angular velocity value and the target control function; and distributing and controlling wheel end torques of the vehicle based on the target yaw moment value to meet the handling stability requirement of the vehicle. The scheme has the effects of fast response speed, good stability and strong robustness.

Description

Technical Field

[0001] This application relates to communication technology, and more particularly to methods, devices, vehicles, and media for controlling vehicle stability. Background Technology

[0002] Vehicle stability control is a key technology in intelligent vehicle control systems, aiming to improve handling stability and safety during driving by actively controlling parameters such as yaw moment. With the development of electric vehicles, their distributed drive / braking systems offer new possibilities for achieving more precise and flexible stability control. However, as a complex dynamic system, the control performance of a vehicle is affected by many factors.

[0003] In existing technologies, vehicle stability control methods typically rely on model predictive control or robust control strategies to generate control signals by solving optimization problems online. While these methods can improve control accuracy, their high computational complexity makes them difficult to meet real-time requirements. This results in lag in the actual control effect, impacting system stability. Summary of the Invention

[0004] To address the aforementioned issues, this application provides at least one method, device, vehicle, and medium for controlling vehicle stability, which offers advantages such as fast response, good stability, and strong robustness.

[0005] The technical solution of this application is implemented as follows: In a first aspect, this application provides a vehicle stability control method, which includes at least: acquiring a target internal model controller for vehicle handling stability control and the current state parameters of the vehicle; the state parameters include at least vehicle speed; determining a target control function for vehicle stability control based on the state parameters and the target internal model controller; the input parameters of the target control function include the error between the actual and expected values ​​of the vehicle's yaw rate, and the output parameters of the target control function include the target yaw moment of the vehicle; acquiring the current actual yaw rate value and the expected yaw rate value of the vehicle; determining the target yaw moment value based on the actual yaw rate value, the expected yaw rate value, and the target control function; and distributing and controlling the wheel-end torque of the vehicle based on the target yaw moment value to meet the vehicle's handling stability requirements.

[0006] Secondly, this application provides a vehicle stability control device, which includes at least a first acquisition unit, a first determination unit, a second acquisition unit, a second determination unit, and a control unit.

[0007] The first acquisition unit is used to acquire the target internal model controller for vehicle handling stability control and the current state parameters of the vehicle; the state parameters include at least the vehicle speed; the first determination unit is used to determine the target control function for vehicle stability control based on the state parameters and the target internal model controller; the input parameters of the target control function include the error between the actual and expected values ​​of the vehicle's yaw rate, and the output parameters of the target control function include the target yaw moment of the vehicle; the second acquisition unit is used to acquire the current actual yaw rate value and the expected yaw rate value of the vehicle; the second determination unit is used to determine the target yaw moment value based on the actual yaw rate value, the expected yaw rate value, and the target control function; the control unit is used to distribute and control the wheel-end torque of the vehicle based on the target yaw moment value to meet the vehicle's handling stability requirements.

[0008] Thirdly, this application provides a vehicle including a target internal mold controller, multiple wheels, a processor, and a memory. The memory stores a computer program or instructions. When the computer program or instructions are executed by the processor, they call the target internal mold controller to distribute and control the yaw torque at the wheel ends of the multiple wheels, thereby implementing the method provided in the first aspect.

[0009] Fourthly, this application also provides a storage medium storing a computer program or instructions that, when executed by a processor, implement any of the methods provided in the first aspect above.

[0010] Fifthly, this application also provides a computer program product comprising a computer program or instructions that, when executed by a processor, implement any of the methods provided in the first aspect above.

[0011] In this scheme, after obtaining the vehicle's current state parameters, these parameters are directly substituted into the target internal model controller to obtain the target control function for vehicle stability control. Since the input parameters of the target control function include the error between the actual and expected yaw rates, and the output parameters include the target yaw moment, the target yaw moment value can be obtained by directly substituting the actual and expected yaw rates into the target control function. The target internal model controller is pre-defined; during vehicle operation, the target yaw moment value can be obtained through simple assignment and calculation, avoiding the computational delay caused by online optimization problems. The target yaw moment value is then used for wheel-end torque distribution, improving the vehicle's handling stability, response speed, and robustness. Attached Figure Description

[0012] Figure 1 This is a schematic diagram of a first optional process for a vehicle stability control method provided in an embodiment of this application; Figure 2 A schematic diagram of a second optional process for a vehicle stability control method provided in an embodiment of this application; Figure 3 A schematic diagram of a third optional process for a vehicle stability control method provided in an embodiment of this application; Figure 4 A schematic diagram of a fourth optional process for a vehicle stability control method provided in an embodiment of this application; Figure 5 A schematic diagram of a fifth optional process for a vehicle stability control method provided in an embodiment of this application; Figure 6 A schematic diagram of an optional structure for a delay-free controlled system internal model control strategy provided in an embodiment of this application; Figure 7 A schematic diagram of another optional structure for the equivalent control strategy of the anti-delay control system provided in the embodiments of this application; Figure 8 This is a schematic diagram of an optional structure of the vehicle stability control device provided in an embodiment of this application.

[0013] It should be noted that the terms "first" and "second" mentioned above are only used to distinguish between different options and do not represent the degree of superiority or inferiority of the options or their priority in the implementation process. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the specific technical solutions of the application will be further described in detail below with reference to the accompanying drawings of the embodiments of this application. The following embodiments are used to illustrate this application, but are not intended to limit the scope of this application.

[0015] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0016] In the following description, the terms "first," "second," and "third" are used only to distinguish different objects and do not represent a specific order of objects, nor are they constituting a chronological order. It is understood that "first," "second," and "third" may be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.

[0017] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0018] This application provides a method, apparatus, vehicle, storage medium, and program product for controlling vehicle stability. The following describes various embodiments of the vehicle stability control method, apparatus, vehicle, storage medium, and program product provided in this application.

[0019] To facilitate understanding, some technical terms will be explained first.

[0020] Internal Model Controller (IMC): In a control system, an IMC is a model-based control strategy. Its core idea is to predict the system response using an internal model with the same dynamic characteristics as the controlled object, and adjust the control input based on the error between the actual output and the desired output. In this embodiment, the target IMC refers to a specific type of controller designed for vehicle stability control requirements. It can generate the desired yaw moment based on the yaw rate error to achieve effective control of vehicle handling stability.

[0021] Yaw Rate Error: Yaw rate is a physical quantity that describes how fast a vehicle rotates about its vertical axis, and is commonly used to measure the stability of a vehicle's steering behavior. Yaw rate error refers to the difference between the vehicle's current actual yaw rate and its expected yaw rate.

[0022] Wheel Torque Distribution: In distributed drive electric vehicles, each wheel can independently apply driving or braking forces, thereby achieving precise control of the vehicle's attitude. Wheel torque distribution refers to the rational distribution of a calculated target yaw moment to each wheel to generate the required yaw moment, thus improving the vehicle's handling stability. This process requires consideration of the vehicle's dynamic characteristics and the real-time status of each wheel to ensure the safety and effectiveness of torque distribution.

[0023] In a first aspect, embodiments of this application provide a vehicle stability control method, which can be executed by a vehicle stability control device. The vehicle stability control device can be deployed in an electronic device or a vehicle and implemented through a program stored in a memory by a processor.

[0024] The following section uses a vehicle as an example to explain the method for controlling the stability of the vehicle.

[0025] refer to Figure 1 The process may include, but is not limited to, S101 to S105 described below.

[0026] S101, The vehicle acquires the target internal model controller for vehicle handling stability control and the current state parameters of the vehicle.

[0027] The vehicle here can be a distributed drive electric vehicle. A distributed drive electric vehicle refers to an electric vehicle with multiple independent drive units, typically with each wheel equipped with an independent motor, enabling independent control of the driving force of each wheel. This architecture allows the vehicle to flexibly adjust the driving and braking forces of each wheel, thereby achieving higher handling precision and driving stability. This embodiment is a stability control system designed based on a distributed drive electric vehicle platform, fully utilizing its advantages in control flexibility.

[0028] A target internal model controller (DMM) is a controller structure derived from a vehicle dynamics model. The core idea of ​​a DMM is to predict the system response using an internal model with the same dynamic characteristics as the controlled object, and to adjust the control input of the DMM based on the error between the actual output and the desired output.

[0029] The status parameters include at least vehicle speed.

[0030] The vehicle's current state parameters may include, but are not limited to, vehicle speed and steering angle, reflecting the vehicle's current operating state. These state parameters can be acquired through a sensor network or read from the controller's configuration information. For example, vehicle speed information can be obtained through wheel speed sensors, and steering angle information can be obtained through steering angle sensors. These state parameters provide the basic data support for subsequent calculations of the target control function.

[0031] S101 can be implemented as follows: The on-board controller first reads the current state parameters of the vehicle from various sensors or configuration information, and obtains the pre-set target internal model controller. There can be one or more internal model controllers. If there are multiple internal model controllers, the appropriate target internal model controller is selected according to the preset control algorithm.

[0032] For example, the target internal model controller can be switched according to the current driving conditions. A high-gain controller can be selected during emergency obstacle avoidance to improve response speed, while a low-gain controller can be selected during normal cruising to reduce unnecessary control actions. By switching the target internal model controller according to the current driving conditions, adaptive control can be achieved for different driving scenarios, thereby improving the overall vehicle handling stability and safety.

[0033] S102. Based on the state parameters and the target internal model controller, the vehicle determines the target control function for vehicle stability control.

[0034] The input parameters of the target control function include the error between the actual and expected values ​​of the vehicle's yaw rate, and the output parameters of the target control function include the vehicle's target yaw moment.

[0035] The target internal model controller includes three types of parameters: input parameters, output parameters, and variable parameters. The vehicle's state parameters correspond to the variable parameters. By inputting the state parameters into the target internal model controller, the relationship between the input parameters and the output parameters can be obtained.

[0036] In this embodiment, the target internal model controller is predetermined. For a given type of vehicle, or a vehicle in a fixed state or scenario, the target internal model controller is predefined. The target internal model controller is not a fixed function; it contains variable parameters. By changing the values ​​of these parameters, the target control function for the current vehicle state can be directly obtained. This implementation method satisfies the requirement of rapid response.

[0037] The target control function is a mathematical mapping relationship. Its input is the error between the actual and desired yaw rate, and its output is the required yaw torque. Specifically, the yaw rate error refers to the difference between the vehicle's current actual yaw rate and the desired yaw rate. The yaw rate error signal serves as one of the controller's inputs, used to assess whether the vehicle has deviated from the desired trajectory and to generate corresponding control commands. The target control function can take the form of a transfer function, a state-space model, or other forms of mathematical expression. The design of the target control function must meet real-time requirements while ensuring the stability of the control effect.

[0038] In practical implementation, the onboard controller automatically calculates and generates the corresponding target control function based on the current vehicle state parameters and the selected target internal model controller. The generation of the target control function does not require online optimization problem solving; it is obtained directly through analytical expressions, thus ensuring the real-time performance of the control process. Furthermore, the target control function can be dynamically adjusted according to different driving conditions (different state parameters) to adapt to constantly changing road environments and driving needs.

[0039] S103. The vehicle obtains the current actual yaw rate and the expected yaw rate.

[0040] Yaw rate is a physical quantity that describes how quickly a vehicle rotates about its vertical axis. It is commonly used to measure the stability of a vehicle's steering behavior. The actual yaw rate is the yaw rate measured under the vehicle's current actual motion conditions, while the desired yaw rate is the ideal yaw rate set by the autonomous driving system or calculated based on vehicle speed, steering angle, acceleration, and lateral acceleration. The difference between the actual and desired yaw rate is the yaw rate error. This error is a crucial input signal for the controller.

[0041] The vehicle obtains the current actual yaw rate and the expected yaw rate in real time.

[0042] The actual yaw rate can be obtained by measuring sensors such as an inertial measurement unit (IMU), while the desired yaw rate can be calculated from driver input (such as steering wheel angle), the path planned by the autonomous driving system, or the vehicle dynamics model. In practical applications, setting the desired yaw rate requires comprehensive consideration of factors such as vehicle dynamics, road conditions, and driving intentions to ensure the rationality and effectiveness of the control effect.

[0043] S104. The vehicle determines the target yaw moment value based on the actual yaw rate value, the desired yaw rate value, and the target control function.

[0044] The target yaw moment value is the control output calculated based on the target control function. This value is used to adjust the vehicle's yaw motion, making it approach or reach the desired yaw rate. The calculation of the target yaw moment value involves multiple variables, such as the actual yaw rate and the desired yaw rate. Through the comprehensive calculation of these variables, a precise target yaw moment value can be obtained, enabling effective control of the vehicle's yaw motion.

[0045] In actual implementation, the vehicle controller first determines the current yaw rate error value based on the actual yaw rate value and the desired yaw rate value, and then substitutes the yaw rate error value into the target control function to calculate the target yaw torque value in the current state in real time.

[0046] The calculation process performed by the onboard controller based on the current yaw rate error and the target control function is entirely based on analytical expressions, without involving any form of online training and optimization, thus ensuring the efficiency and real-time performance of the control process.

[0047] S105. The vehicle distributes and controls the wheel-end torque based on the target yaw moment value to meet the vehicle's handling stability requirements.

[0048] Wheel-end torque distribution refers to the process of rationally distributing a calculated target yaw moment value to each wheel to generate the required yaw moment, thereby improving the vehicle's handling stability. In distributed drive electric vehicles, each wheel can independently apply driving or braking force, thus achieving precise control of the vehicle's attitude. The wheel-end torque distribution process needs to consider the vehicle's dynamic characteristics and the real-time status of each wheel to ensure the safety and effectiveness of torque distribution.

[0049] Various strategies can be employed to distribute wheel-end torque, such as distribution methods based on the principle of minimum energy, distribution methods based on tire adhesion limits, or distribution methods based on vehicle dynamics models. Regardless of the method used, the core objective is to maximize the efficiency of yaw moment generation and control precision while ensuring vehicle stability.

[0050] In practice, the onboard controller distributes the calculated target yaw moment to each wheel via a distributed drive system. This torque distribution process can be adjusted in real time to adapt to constantly changing driving conditions and road environments. Through this torque distribution process, precise control of the vehicle's yaw motion can be achieved, thereby improving the vehicle's handling stability and driving safety.

[0051] The method provided in this embodiment of the application includes at least: acquiring a target internal model controller for vehicle handling stability control and the current state parameters of the vehicle; determining a target control function for vehicle stability control based on the state parameters and the target internal model controller; the input parameters of the target control function include the error between the actual and expected values ​​of the vehicle's yaw rate, and the output parameters of the target control function include the target yaw moment of the vehicle; acquiring the current actual yaw rate value and the expected yaw rate value of the vehicle; determining the target yaw moment value based on the actual yaw rate value, the expected yaw rate value, and the target control function; and distributing and controlling the wheel-end torque of the vehicle based on the target yaw moment value to meet the vehicle's handling stability requirements.

[0052] In this scheme, after obtaining the vehicle's current state parameters, these parameters are directly substituted into the target internal model controller to obtain the target control function for vehicle stability control. Since the input parameters of the target control function include the error between the actual and expected yaw rates, and the output parameters include the target yaw moment, the target yaw moment value can be obtained by directly substituting the actual and expected yaw rates into the target control function. The target internal model controller is pre-defined; during vehicle operation, the target yaw moment value can be obtained through simple assignment and calculation, avoiding the computational delay caused by online optimization problems. The target yaw moment value is then used for wheel-end torque distribution, improving vehicle stability and response speed.

[0053] The process in S102 where the vehicle determines the target control function for vehicle stability control based on state parameters and the target internal model controller will be described below. This process may include, but is not limited to, S1021 or S1022 described below.

[0054] S1021. When the variable parameters of the target internal model controller include vehicle speed, the vehicle inputs the vehicle speed value from the state parameters to the target internal model controller to determine the target control function.

[0055] Vehicle speed refers to the speed at which a vehicle is currently traveling. It is typically collected in real time by onboard sensors (such as wheel speed sensors or GPS) and is a crucial state parameter affecting the vehicle's dynamic behavior. The magnitude of vehicle speed affects vehicle stability, and different vehicle speeds correspond to different target control functions. Therefore, vehicle speed is incorporated as an input variable into the calculation process of the target internal model controller to more accurately determine the target control function.

[0056] There is a nonlinear relationship between vehicle speed and yaw moment, especially at high speeds, where the vehicle's response to control output signals becomes more sensitive. Therefore, when designing an internal model controller, considering vehicle speed as a variable can improve the controller's adaptability to different operating conditions. By introducing this variable, control system designers can enhance the robustness and stability of the control system.

[0057] In practical applications, such as when a vehicle is traveling at a high speed, the internal model controller can adjust the output intensity of the desired yaw moment based on the current vehicle speed, thereby avoiding vehicle instability caused by excessively strong control output signals. This dynamic adjustment of the control strategy based on vehicle speed changes helps improve overall vehicle handling performance and driving safety.

[0058] By setting vehicle speed as a variable parameter in the internal model controller, more accurate modeling of vehicle dynamics can be achieved. This allows the internal model controller to maintain good tracking capability and stability under different vehicle speed conditions, thereby improving the efficiency and reliability of the vehicle stability control system.

[0059] S1022. When the variable parameters of the target internal model controller include vehicle speed and the wheel end angle of the active steering wheel, the vehicle inputs the vehicle speed value and the wheel end angle value of the active steering wheel from the state parameters to the target internal model controller to determine the target control function.

[0060] The wheel-end angle of an active steering wheel refers to the actual deflection angle of the active steering wheel (e.g., the front wheel) relative to the vehicle's central axis, and is typically provided by the steering system's feedback mechanism. The wheel-end angle of the active steering wheel reflects the driver's desired steering behavior and the vehicle's current steering state, and is crucial for predicting and controlling yaw rate.

[0061] In this embodiment, the wheel-end angle of the active steering wheel is introduced as another variable parameter into the internal model controller, enabling the internal model controller to simultaneously consider the influence of vehicle speed and steering state on vehicle dynamic behavior. Introducing the wheel-end angle of the active steering wheel as another variable parameter into the internal model controller not only improves the accuracy of the control model but also enhances the internal model controller's responsiveness to complex road conditions and driver operations.

[0062] For example, when driving on a curve, if the driver suddenly turns the steering wheel, the internal model controller can quickly adjust the yaw torque output by detecting changes in the wheel-end angle of the active steering wheels to maintain the vehicle's stable posture. The adoption of a multi-variable input mechanism helps improve the vehicle's handling performance under extreme conditions.

[0063] By using the wheel-end angle of the active steering wheel as a variable parameter of the internal model controller, this design enables the internal model controller to have greater flexibility and accuracy when dealing with complex driving scenarios, thereby effectively improving the response speed and control precision of the vehicle stability control system.

[0064] By using vehicle speed and the wheel-end angle of the active steering wheel as variable parameters in the internal model controller, vehicle dynamics response can be modeled more accurately. This improves tracking accuracy and stability. Enhanced tracking accuracy and stability allow for effective handling of dynamic changes in the vehicle under complex operating conditions. Furthermore, the effective handling of these dynamic changes improves overall handling performance, further enhancing driving safety and comfort.

[0065] The vehicle stability control process provided in this application embodiment also includes the process of determining the internal model controller.

[0066] In one possible implementation, refer to Figure 2 The process may include, but is not limited to, S201 to S203 described below.

[0067] S201. Construct a time-domain dynamic model of the vehicle for vehicle handling stability control.

[0068] A dynamic model refers to the mathematical modeling of the mechanical behavior of a vehicle during motion, describing the dynamic relationships between key variables such as the vehicle's center of gravity sideslip angle and yaw rate. A linear two-degree-of-freedom vehicle dynamics model can express the influence of factors such as vehicle mass, speed, center of gravity position, and front and rear axle sideslip stiffness on the yaw rate. Constructing a time-domain model means that it is suitable for real-time control calculations and can directly process time-series input and output signals.

[0069] The purpose of constructing a dynamic model is to provide a basic framework for the subsequent design of a stability control system, enabling the control strategy to accurately predict and adjust the vehicle's response. For example, in a distributed drive electric vehicle, the main control input is the yaw moment. By establishing a dynamic model, the system can clearly define how the yaw moment affects the yaw rate.

[0070] In this embodiment of the application, by constructing a time-domain dynamic model, the dynamic behavior in vehicle handling stability control can be accurately described; the constructed dynamic model can provide a reliable basis for the design of the target internal model controller, thereby improving the accuracy and real-time performance of the vehicle stability control system.

[0071] For example, the dynamic model can refer to the following formula (1).

[0072] Formula (1); In formula (1), The output parameter represents the derivative of the state. , As an intermediate quantity, , It can be obtained through formulas (2-1) to (2-4). Indicates the steering angle of the steering wheel (e.g., when the steering wheels of the vehicle are the front wheels). (This can be the front wheel steering angle). This indicates the yaw moment.

[0073] Formula (2-1); Formula (2-2); Formula (2-3); Formula (2-4); In formulas (2-1) to (2-4), The sideslip angle is the angle of the center of mass, which can be obtained by dividing the lateral velocity by the longitudinal velocity. The yaw rate is angular velocity. For the front wheel steering angle, For yaw moment, For the overall vehicle quality, Current vehicle speed The distance from the center of gravity to the front axle. The distance from the center of gravity to the rear axle. For front axle lateral stiffness, For rear axle lateral stiffness, This represents the yaw moment of inertia of the entire vehicle.

[0074] S202. The vehicle's stability control system is constructed based on a dynamic model, using a state-space model in the time domain.

[0075] The input to the state-space model includes the yaw moment, and the output of the state-space model includes the yaw angular velocity.

[0076] A state-space model is a mathematical model that describes the behavior of a system using state variables, typically consisting of state equations and output equations. In this embodiment, state variables may include the sideslip angle and yaw rate, with the yaw moment as the input and the yaw rate as the output. The advantage of a state-space model is that it can clearly represent the internal state changes of the system and is suitable for controller design in modern control theory.

[0077] Yaw moment is a control input used in vehicle stability control systems to generate the desired yaw rate. It can be achieved by applying different driving or braking forces to different wheel ends using distributed drive motors. Yaw rate is the speed at which a vehicle rotates about its vertical axis and is an important indicator for measuring a vehicle's steering response.

[0078] The purpose of state-space models is to transform complex vehicle dynamics into a computable mathematical form, facilitating controller design and optimization. Transfer functions, which describe the dynamic relationship between inputs and outputs, can be derived from state-space models.

[0079] For example, the state-space model can refer to the following formulas (3-1) and (3-2).

[0080] Formula (3-1); Formula (3-2); in, The differential representing the state. This represents the output of the state-space model. The intermediate quantity can be obtained through formulas (3-3) to (3-5).

[0081] Formula (3-3); Formula (3-4); Formula (3-5); For explanations of other parameters, please refer to the explanations in formulas (2-1) to (2-4).

[0082] The control input of the controlled system is The control output is .

[0083] S203. The vehicle constructs a time-domain target internal model controller based on the time-domain state-space model.

[0084] The input to the target internal mold controller is the yaw rate error (i.e., the desired yaw rate minus the actual yaw rate), and the output is the desired yaw torque.

[0085] The time domain dimension means that the target internal model controller (PMC) design is performed in the time domain, rather than the frequency domain, which is more suitable for the real-time computing requirements of embedded systems. The role of the PMC is to generate appropriate yaw torque commands based on the deviation between the current yaw rate and the desired value, enabling the vehicle to reach the desired motion state as quickly as possible. Because the PMC uses an analytical approach, it does not require online solving of complex optimization problems, thus exhibiting good real-time performance and computational efficiency.

[0086] In this embodiment, the operation of constructing the target internal model controller enables high-precision tracking of the vehicle's yaw rate. This high-precision tracking effectively improves the vehicle's handling stability. Improved handling stability further enhances driving safety and ride comfort.

[0087] The process of a defined target internal model controller can be implemented entirely in the time domain, but some processes can be transformed to the frequency domain and then back to the time domain.

[0088] In this embodiment, by constructing a time-domain dynamic model, a state-space model, and a target internal model controller for vehicle handling stability control, precise control of the vehicle's yaw rate can be achieved. By constructing a time-domain dynamic model, a state-space model, and a target internal model controller for vehicle handling stability control and achieving precise control of the vehicle's yaw rate, good vehicle stability can be ensured under complex operating conditions, thereby significantly improving driving safety and meeting the stringent real-time requirements of modern intelligent electric vehicles.

[0089] The following describes the process of constructing a time-domain target internal model controller for the vehicle based on the time-domain state-space model in S203.

[0090] In one possible implementation, refer to Figure 3 The process may include, but is not limited to, S301 to S304.

[0091] S301, The vehicle converts the state-space model in the time domain into the first transfer function model in the frequency domain.

[0092] The first transfer function model is used to characterize the first relationship between the vehicle's yaw rate and yaw moment.

[0093] State-space models are used for multi-input multi-output systems and are convenient for handling nonlinear or time-varying systems, while transfer function models are more suitable for frequency domain analysis and applications of classical control theory. In this embodiment, the time-domain state-space model is transformed into a first transfer function model in the frequency domain using mathematical tools such as Laplace transform or Z-transform, thereby providing a more intuitive description of the dynamic relationship between the vehicle's yaw rate and yaw moment. The process of transforming the time-domain state-space model into a first transfer function model in the frequency domain using mathematical tools such as Laplace transform or Z-transform is helpful for the subsequent design of the compensation model and the derivation of the controller.

[0094] The specific form of the first transfer function model can be a second-order or higher-order rational function. The numerator and denominator of the first transfer function model represent the proportional relationship between the output and the input, respectively. For example, in this embodiment, the yaw rate is taken as the output and the yaw moment is taken as the input. The first transfer function model can be obtained by the following formulas (4-1) to (4-3). The coefficients of the first transfer function model are determined by the vehicle dynamics parameters.

[0095] Formula (4-1); Formula (4-2); Formula (4-3); Where s represents the complex frequency operator of the Laplace transform, , For calculation The intermediate quantity, This represents the first transfer function model. Indicates the yaw moment. The yaw rate is angular velocity. For the front wheel steering angle, For yaw moment, For the overall vehicle quality, Current vehicle speed The distance from the center of gravity to the front axle. The distance from the center of gravity to the rear axle. For front axle lateral stiffness, For rear axle lateral stiffness, This represents the yaw moment of inertia of the entire vehicle. For explanations of other parameters, please refer to the explanations in formulas (2-1) to (2-4) above.

[0096] S302, Target compensation model for vehicles with defined frequency domain dimensions.

[0097] The target compensation model is used for time compensation or torque compensation.

[0098] The target compensation model can include time compensation and torque compensation.

[0099] A target compensation model is an auxiliary model designed in the frequency domain to improve the dynamic performance of a system or compensate for delays. Depending on the specific needs, the compensation model can compensate for time delays (i.e., phase lag) or enhance insufficient torque response. For example, the design of a target compensation model is often based on the Smith Predictor concept. By introducing a delay term to simulate the delay behavior in the actual system, and performing advance compensation in the controller, the system's anti-delay capability is improved.

[0100] The target compensation model can also be a low-pass filter, a high-pass filter, or a PID controller with a specific structure. For example, if the system has significant phase lag, a lead element can be added to the controller to counteract the lag effect. In practice, the target compensation model can adjust the gain, zeros, and poles to make the overall closed-loop system response faster and more stable.

[0101] In practical implementation, the target compensation model and the first transfer function model are closely related. Together, they form the foundation of the internal model controller. The target compensation model is used to correct system defects, while the first transfer function model is used to model the dynamic characteristics of the system itself. Combining the target compensation model and the first transfer function model can effectively improve the accuracy and robustness of the controller.

[0102] S303. The vehicle is based on the target compensation model in the frequency domain dimension and the first transfer function model in the frequency domain dimension to determine the first internal model controller in the frequency domain dimension.

[0103] In this embodiment, the designers first use the first transfer function model obtained in the first step and the target compensation model designed in the second step to jointly construct the first internal model controller in the frequency domain.

[0104] In one possible implementation, the first internal mold controller can be determined by the following formula (5).

[0105] Formula (5); In formula (5), Indicates the first internal mold controller. Represents the target compensation model. This represents the first transfer function model.

[0106] In another possible implementation, the first internal mold controller can be determined by the following formula (6).

[0107] Formula (6); In formula (6), Indicates the first internal mold controller. Represents the target compensation model. This represents the second transfer function model derived from the first transfer function model. Compared to the first transfer function model, the second transfer function model takes into account the effect of time delay.

[0108] S304, The vehicle's first internal model controller based on the frequency domain dimension determines the target internal model controller.

[0109] The target internal model controller is designed in the frequency domain and then converted back to the time domain for implementation in embedded systems. Since modern vehicle control systems typically run in real-time operating systems, the target internal model controller must possess high computational performance and a well-defined algorithmic structure. Therefore, this embodiment employs a state-space modeling method to convert the transfer function of the first internal model controller in the frequency domain back into a state-space model in the time domain.

[0110] The advantage of state-space models lies in their suitability for implementing digital control systems, especially multivariable systems, as their matrix form facilitates programming. The state-space model of a target internal model controller can be obtained through the inverse transformation of the transfer function, ultimately forming a series of state equations that guide the generation of actual control signals.

[0111] In this embodiment, by converting the time-domain state-space model into a frequency-domain first transfer function model, and designing a target compensation model and a first internal model controller, anti-delay control can be achieved without relying on online optimization, while maintaining good real-time performance and stability. Using this method, the yaw stability of distributed drive electric vehicles can be significantly improved, thereby further enhancing driving safety and handling experience.

[0112] In this embodiment, firstly, the time-domain state-space model is converted into a frequency-domain first transfer function model. Next, a target compensation model is designed based on system characteristics and control objectives to compensate for system deficiencies. Subsequently, the first transfer function model is combined with the target compensation model to construct a first internal model controller, thereby optimizing the system's dynamic performance. Finally, the first internal model controller model is converted back to the time domain to facilitate the implementation of the embedded system. This entire process forms a complete closed-loop control scheme, which ensures the controller's high efficiency and reliability in practical applications.

[0113] The following explains the process of determining the target compensation model for the frequency domain dimension of the vehicle in S302.

[0114] The process may include, but is not limited to, method 1 and / or method 2 below.

[0115] Method 1: Determine the target compensation model based on the time compensation principle; Method 2: Determine the target compensation model based on the torque compensation principle.

[0116] The following explains the process of determining the target compensation model based on the time compensation principle in Method 1.

[0117] The process may include: determining the target compensation model based on a second-order filter; using the second-order filter to simulate the actual time delay of stability control in order to compensate for the time delay of the vehicle's stability control system.

[0118] Second-order filters are used to approximate the actual time delay characteristics of stability control in vehicle stability control systems. By adjusting the filter parameters, the system's response behavior at different frequencies can be simulated, thereby compensating for the system's time delay.

[0119] Stability control real-time delay refers to the physical delay in a vehicle stability control system between the controller issuing a control command (such as a desired yaw moment) and the system generating an actual response (such as a change in yaw rate). Stability control real-time delay can originate from multiple factors, including the time difference in sensor data acquisition, the processing time of the computing unit, and the response time of the actuators. If stability control real-time delay is not compensated for, its presence will lead to a lag in system response, thereby affecting vehicle stability and handling.

[0120] Time compensation refers to modeling the actual stability control delay in a system and introducing a corresponding time compensation mechanism into the controller design to offset or mitigate the negative impact of this delay. In this embodiment, a second-order filter is used to simulate the actual stability control delay, and the simulation results are incorporated into the target compensation model design, achieving dynamic compensation for the actual stability control delay. This method can improve the system's response speed and stability without affecting control accuracy.

[0121] Using the above methods, a more accurate compensation model can be constructed in the frequency domain. The control system can more effectively track the reference yaw rate while taking into account the actual time delay of stability control, thereby improving the stability and safety of the vehicle under complex operating conditions.

[0122] In this embodiment, the operation method of constructing a target compensation model based on a second-order filter can effectively simulate the actual time delay characteristics of the vehicle stability control system. Through this operation method, the system can compensate for the time delay of the vehicle control system, avoiding fluctuations caused by excessively fast response. Due to the introduction of the above compensation mechanism, the vehicle's driving stability and real-time response capability under complex operating conditions are significantly improved.

[0123] The setting of the second-order filter can be referenced in formula (7).

[0124] Formula (7); In formula (7), Let s represent the output of the second-order filter, and let s represent the complex frequency operator of the Laplace transform. The time parameter can be obtained through calibration. The value of τ is related to speed and rotation angle; essentially, τ is related to error.

[0125] The process of determining the target compensation model based on the torque compensation principle in Method 2 is explained below.

[0126] In one possible implementation, refer to Figure 4 The process may include, but is not limited to, S401 to S403 described below.

[0127] S401, Vehicle determines the first time delay function.

[0128] The first time delay function is used to simulate the time delay between the yaw torque and the yaw angular velocity given by the stability control system.

[0129] A time delay function is a mathematical modeling tool used to describe the time difference between a system's input and output. Specifically, the first time delay function indicates that the effect of the yaw moment on the yaw rate of a vehicle is not instantaneous, but rather occurs with a certain lag time. This lag may be caused by factors such as the actuator's response time, the processing time in the signal transmission path, and the delay in sensor feedback.

[0130] For example, in an electric vehicle stability control system, when the controller issues a yaw moment command, the drive motor or braking system needs a certain amount of time to respond, and the change in the yaw moment response also needs time to be reflected in the vehicle's actual yaw rate. Control system designers can establish a first time delay function to accurately characterize the process of the drive motor or braking system responding to the yaw moment command and the process of the yaw moment response change being reflected in the vehicle's actual yaw rate, providing a foundation for subsequent compensation strategies.

[0131] For example, the first time delay function can be represented as follows: .For example, The delay between the control input and response in the analog system, where s represents the complex frequency operator of the Laplace transform, and the coefficients... This represents the time delay between the application of control input and the generation of a response by the system. It needs to be generated by calibration.

[0132] By introducing a first time delay function, control system designers can more accurately describe the actual delay phenomena in the system, thus providing a theoretical basis and data support for designing anti-delay controllers.

[0133] S402. The vehicle determines the second transfer function model based on the first time delay function and the first transfer function model.

[0134] The second transfer compensation function model is used to pre-compensate for the yaw moment.

[0135] The second transfer function model is a new dynamic model built upon consideration of system time delay. It combines the first time delay function with the first transfer function model to form a modified expression for the yaw moment. The purpose of the second transfer function model is to adjust the yaw moment in advance, so as to better achieve the desired yaw rate tracking when considering system delay.

[0136] For example, during vehicle operation, if a specific yaw rate is desired at a certain moment, the controller can calculate the appropriate yaw moment value based on the second transfer function model and add an appropriate lead time to this value to offset the effects of system delay. By calculating the appropriate yaw moment value based on the second transfer function model and adding an appropriate lead time, the corresponding control action can be completed before the actual yaw rate changes, thereby improving the system's response speed and accuracy.

[0137] For example, the second transfer function model can be represented by formula (8).

[0138] Formula (8); In formula (8), This represents the first transfer function model. This represents the second transfer function model. This represents the first time delay function. For example, The delay between the control input and response in the analog system, where s represents the complex frequency operator of the Laplace transform, and the coefficients... This represents the time delay between the application of control input and the generation of a response by the system. It needs to be generated by calibration.

[0139] By constructing a second transfer function model, the yaw moment can be compensated before the control command is issued. Technicians can use this model to effectively reduce control errors caused by system delays and improve overall control performance.

[0140] S403. The vehicle processes the second transfer function model based on the Smith predictor to determine the target compensation model.

[0141] The Smith predictor is used to process the second transfer function model, which already includes time delay information, to generate the final compensation model—the target compensation model. The target compensation model considers the dynamic characteristics of the system and incorporates the time delay factor, achieving high-precision prediction and compensation for system behavior.

[0142] The Smith predictor extracts the impact of delay by constructing an ideal model that is similar to the real system but without delay, and then comparing the output of the actual system with the output of the ideal model. Subsequently, the Smith predictor feeds back the delay information extracted from the comparison between the actual system output and the ideal model output to the controller. The controller can then adjust its control strategy based on the delay characteristics, thereby achieving better control performance.

[0143] By introducing a Smith predictor into the control system, the system's adaptability to delays can be significantly improved. This improvement enables the controller to maintain high control accuracy and system stability even in complex environments, ultimately achieving more precise and efficient yaw rate control.

[0144] In this embodiment, a second transfer function model is constructed by determining a first time delay function and combining it with a first transfer function model, and then a target compensation model is generated using a Smith predictor. This method of determining a first time delay function, constructing a second transfer function model by combining it with a first transfer function model, and then generating a target compensation model using a Smith predictor can effectively compensate for control deviations caused by delays in the system, thereby improving the real-time performance and robustness of the control system, and ultimately achieving more stable and safer vehicle handling.

[0145] The following describes the process by which the first internal model controller of the vehicle in S304 determines the target internal model controller based on the frequency domain dimension.

[0146] In one possible implementation, refer to Figure 5 The process may include, but is not limited to, S501 and S502 described below.

[0147] S501, The vehicle's first internal model controller based on the frequency domain dimension and the target transfer function model determine the second internal model controller in the frequency domain dimension.

[0148] The transfer function model includes either a first transfer function model or a second transfer function model; the second internal model controller in the frequency domain is used to characterize the relationship between the yaw moment and the yaw rate error.

[0149] Frequency domain dimension refers to the mathematical representation used in control system design for modeling and calculation using frequency variables (such as s). Frequency domain methods are typically used to describe the response characteristics of a system to input signals of different frequencies, clearly demonstrating key performance indicators such as system stability and bandwidth. The transfer function model is used to describe the mapping relationship between yaw moment and yaw rate. Depending on different control requirements, either the first transfer function model or the second transfer function model can be selected, corresponding to different control strategies.

[0150] The second internal model controller in the frequency domain is used to construct a new controller model by combining the first internal model controller with the selected transfer function model. This controller model, constructed by combining the second internal model controller in the frequency domain with the first internal model controller and the selected transfer function model, can better adapt to changes in vehicle dynamics and external disturbances, thereby improving control accuracy and robustness.

[0151] By introducing a second internal model controller in the frequency domain and combining it with different transfer function models, the system can enhance the tracking ability of the second internal model controller for vehicle yaw motion without increasing computational complexity, thereby improving the system's response speed and stability.

[0152] For example, the second internal mold controller can be represented by formula (9).

[0153] Formula (9); Indicates the second internal mold controller. Indicates the yaw moment. This represents the desired yaw rate. This represents the actual yaw rate. Indicates the first internal mold controller. First transfer function model.

[0154] S502, The vehicle converts the second internal model controller in the frequency domain dimension into the time domain state space to obtain the target internal model controller.

[0155] Compared to time-domain models, state-space models are more suitable for embedded development and real-time control because they can be directly used for numerical solutions and implementation of controllers.

[0156] The process of converting a frequency-domain second internal model controller into a time-domain state-space model typically involves using the inverse Laplace transform or discretization methods to transform the controller, in transfer function form, into a matrix-form state-space model. This conversion enables the controller to be directly applied to real-world control systems, thus supporting real-time computation and execution.

[0157] The target internal model controller is the final controller model obtained after completing the above transformation. The structure of the target internal model controller is suitable for operation on embedded platforms. In actual control, the target internal model controller can accurately generate the desired yaw moment, thereby achieving stable control of the vehicle.

[0158] The operation of converting the frequency domain controller into a time-domain state-space model makes the controller suitable for embedded system deployment, thereby improving the feasibility and real-time performance of the control system, and ensuring that the controller has good robustness and stability under complex operating conditions.

[0159] There is a close logical relationship between the frequency domain dimension and the yaw moment and yaw rate error. The design purpose of the frequency domain controller is to better reflect the dynamic relationship between the yaw moment and the yaw rate error, and to ensure that the controller can effectively track the reference signal. The state-space model, as the final implementation of the controller, inherits the advantages of the frequency domain model, while enhancing its practicality.

[0160] The following example illustrates the vehicle stability control process.

[0161] Vehicle stability control systems, such as vehicle stability program and direct yaw moment control, play a significant role in improving vehicle handling stability. With the development of electronic and electrical technologies and advanced control theories, electric vehicle (EV) stability control systems have achieved substantial improvements in control performance and response speed compared to traditional EV stability control systems. This is due to the more flexible arrangement of drive / brake motors in EVs, enabling distributed drive / braking and higher control precision. Simultaneously, the rapid development of EV technology has placed higher demands on the control methods and performance of EV stability control systems.

[0162] Vehicle stability control systems (VSCs) control the vehicle to follow a desired reference state at every moment, typically the desired yaw rate, to improve the vehicle's handling stability. Considering that a vehicle is a complex dynamic system, the design of a VSC presents two core challenges: (1) there is often a certain degree of delay between the control input (e.g., wheel-end torque) and the control response (e.g., yaw rate) of an electric vehicle chassis, which affects the stability of the control system; (2) modern intelligent electric vehicles react rapidly, requiring the control system to have real-time computational efficiency. If these two challenges cannot be overcome, the performance of the control system will be severely affected, and in severe cases, it may lead to safety accidents.

[0163] In related technologies, the delay between control input and control response is not considered when modeling and designing control methods, which leads to hysteresis in control systems. In order to obtain a more accurate control signal, online optimization problems are often involved, such as the optimal control problem in predictive control and the H-infinity optimization problem in robust control, which greatly affects the real-time performance of the calculation.

[0164] The technical solution of this embodiment is as follows: This embodiment is based on a distributed drive electric vehicle platform and aims to develop a vehicle stability control system with anti-latency capabilities and real-time computing performance. This technical solution uses a reference yaw rate as input to establish a state-space model of the vehicle dynamics system from the reference state (reference yaw rate) to the control signal (desired yaw moment), and then derives the transfer function from the reference state to the control signal. The derived transfer function further considers the delay process of the actual controlled system. For this controlled system considering delay, this technical solution adopts an internal model control strategy based on the Smith predictor, derives the analytical expression of the controller's transfer function, and obtains the actual control signal for tracking the required reference signal.

[0165] Significance of this embodiment: This embodiment aims to develop an electric vehicle stability control system with anti-latency capabilities and real-time computing performance, thereby improving the control performance of the electric vehicle stability control system.

[0166] This embodiment provides a vehicle stability control system based on a distributed drive electric vehicle platform, aiming to develop a system with latency resistance and real-time computing performance. This control system takes a reference yaw rate and the actual yaw rate error as inputs to obtain an analytical expression for the back-transfer function of the controller, from the yaw rate error to the desired yaw torque. The derivation of this analytical expression does not involve solving any online optimization problems, thus possessing real-time performance. Furthermore, in the derivation of the controller, the actual system delay from the wheel-end torque (actual control input) to the yaw rate (controlled system response) of the distributed electric vehicle is considered, therefore the resulting controller also possesses latency resistance.

[0167] The processing procedure may include, but is not limited to, steps 1 through 5 below.

[0168] Step 1: Modeling the Vehicle Dynamics System Without Delay: This control system relies on a vehicle dynamics model. To balance computational performance and modeling accuracy, this technical solution adopts a linear two-degree-of-freedom vehicle dynamics model. The two-degree-of-freedom vehicle dynamics model can be described by referring to the above formulas (1), (2-1) to (2-4). Among them, vehicle weight, wheelbase, stiffness, and inertia can be defined as structural parameters, which can be determined after the vehicle is fixed; vehicle speed can be defined as a variable parameter, which can change with the state of the vehicle.

[0169] As can be seen from this dynamic model, the vehicle control signal is the yaw moment. and front wheel cornering Considering that electric vehicle chassis control systems, without steer-by-wire or active steering systems, generally lack the ability to control the front wheel angle, a separate controller for front wheel angle and yaw moment is constructed. These two controllers can be combined; therefore, the control input for this control strategy is the yaw moment. The control output is the yaw rate. Based on this analysis, a complete state-space model of the controlled system is obtained. Refer to the descriptions in equations (3-1) to (3-5) above.

[0170] The control input of this controlled system is The control output is This two-degree-of-freedom vehicle dynamics model does not consider the delay from control input to control output, and is therefore defined as a nominal vehicle dynamics system.

[0171] Step 2: Design of the nominal vehicle dynamics system state tracking controller based on internal model control: For the nominal vehicle dynamics control system that does not consider delay, it is converted into a transfer function model. The transfer function model can be referred to the following formulas (10-1) to (10-3).

[0172] Formula (10-1); Formula (10-2); Formula (10-3); in, This represents the actual yaw rate. This represents the actual yaw moment. For transfer function Given the desired yaw rate in the frequency domain In order to obtain the desired yaw moment to track the reference yaw rate This technical solution uses internal mold control to obtain the desired yaw moment. , For calculation The intermediate values ​​can be obtained using formulas (10-2) and (10-3). Explanations of other parameters can be found in the explanations and descriptions in formulas (2-1) to (2-4) above.

[0173] Based on the design concept of internal model control, the second-order filter is designed as follows: The formula of the filter is derived to prevent the control signal from being too fast; It is a standardized quantity, which can be obtained by looking up a table; The value is related to speed and turning angle; essentially... It is related to error. An excessively fast control signal can lead to overcorrection, larger oscillations, and reduced stability.

[0174] You can refer to the description of formula (7) above.

[0175] Furthermore, the controller is obtained. The controller can be referred to the following formula (11).

[0176] Formula (11); in, Indicates controller, This represents a second-order filter. This is a transfer function model. For explanations of other parameters, please refer to the explanations in formulas (2-1) to (2-4) above.

[0177] Finally, the transfer function of the internal model controller is obtained, which takes the yaw rate error (desired yaw rate minus actual yaw rate) as input and the desired yaw torque as output. The internal model controller transfer function can be referred to the following formula (12).

[0178] Formula (12).

[0179] in, Transfer function for internal model controller For the desired yaw moment, For yaw rate error, Indicates controller, This is a transfer function model.

[0180] Therefore, the yaw rate error signal in the given frequency domain Tracking the desired yaw rate The expected yaw moment can be obtained by referring to the following formula (13).

[0181] Formula (13).

[0182] The controller here Defined as a nominal controller, referring to the no-delay nominal vehicle dynamics system Yaw stabilization controller.

[0183] Step 3: Design of a delay-resistant internal model controller based on the Smith predictor: based on the transfer function of the delayed nominal vehicle dynamics system. and its nominal controller The expression is given. Now, considering the actual delay between the generation of yaw torque (control input) and the generation of yaw angular velocity (system response) in the actual controlled system, the controlled system is modeled based on formula (14).

[0184] Formula (14); in, For controlled systems that take latency into account, For the transfer function model, The delay between the control input and the response in an analog system, the coefficient This represents the time delay between the application of control input and the generation of a response by the system. It needs to be generated by calibration.

[0185] The controlled system considering the delay can be approximated by the following formula (15).

[0186] Formula (15).

[0187] Based on this consideration, the controlled system with delay This technical solution combines the Smith predictor and internal model control to design a stable controller with anti-delay function to track the desired yaw rate.

[0188] First, based on the delay compensation principle of the Smith predictor, the desired closed-loop control system is designed with reference to the following formula (16).

[0189] Formula (16).

[0190] in, This is a closed-loop control system based on a Smith predictor. The Smith predictor functions similarly to a second-order filter, but while the second-order filter only affects time, the Smith predictor provides a lead compensation, meaning it is related to both time and output, i.e., it performs a pre-emptive torque compensation. Specifically, The nominal controller derived in step 2.

[0191] Based on this closed-loop system, the controller in the internal model controller is designed as follows (17).

[0192] Formula (17).

[0193] A controller based on the Smith predictor.

[0194] Finally, the transfer function of the internal model controller with the yaw rate error (desired yaw rate minus actual yaw rate) as input and the desired yaw torque as output is obtained as the following formula (18).

[0195] Formula (18).

[0196] An internal model controller based on the Smith predictor. This represents the desired yaw rate. This represents the final yaw moment acting on the vehicle platform. This represents the actual yaw rate.

[0197] Therefore, given the desired yaw rate error under a given frequency domain signal, the desired yaw torque for tracking this signal is obtained by the following formula (19).

[0198] . Formula (19).

[0199] The controller here Known as a delay-resistant controller, it refers to a vehicle dynamics controlled system that takes into account delay effects. The yaw stability controller. The desired yaw moment here. This represents the yaw moment that ultimately acts on the vehicle platform.

[0200] Step 4, State-space control strategy: Considering that in vehicle stability control systems, the reference yaw rate is often a time-domain signal, and to support embedded development, the controller needs to be... Transform it into a state-space expression, and refer to the following formulas (20-1) and (20-2) for details.

[0201] Formula (20-1); Formula (20-2).

[0202] in, , , For the transfer function The generated state-space model matrix.

[0203] Step 5, Wheel-end torque distribution based on distributed drive: The desired yaw moment calculated in Step 4 is distributed... The torque is further distributed to the wheel ends, and the wheel-end torque is distributed through a distributed drive system to generate the desired yaw torque in the vehicle. .

[0204] The control architecture will be explained below.

[0205] The internal model control strategy for a delay-free controlled system can be referenced. Figure 6 The content shown, , For a real but unknown controlled system. Q(s) and The explanation can be found in the description of the parameters in the above embodiments. This represents the desired yaw rate. Indicates the desired yaw moment. This represents the actual yaw rate.

[0206] The equivalent control strategy for the anti-delay control system can be referenced. Figure 7 The content shown, , A real but unknown controlled system.

[0207] This embodiment proposes a yaw stabilization control method based on analytical expressions, which is concise in form and computationally efficient. The analytical expression of the controller's transfer function from the yaw rate error to the desired yaw moment can be directly obtained. The proposed control method is based on a delay-free vehicle dynamics model, combined with internal model control to obtain a nominal controller. Furthermore, considering the delay of the controlled system in real-world applications, the designed nominal controller is further combined with internal model control and a Smith predictor to obtain a transfer function expression for the controller from the yaw rate error to the desired yaw moment, which possesses anti-delay capabilities. The derivation of this controller is entirely based on analytical expressions, making calculation simple and fully meeting the requirements of real-time computation. Its anti-delay characteristics also enhance the controller's practicality.

[0208] This control system is based on a distributed drive electric vehicle chassis platform and is applicable to any electric vehicle chassis platform equipped with distributed drive. It does not require the chassis to be equipped with more complex and advanced chassis systems, such as steer-by-wire systems and active steering systems. Its simplicity and universality in application scenarios should be protected.

[0209] The control system developed in this technical solution is designed for distributed drive electric vehicle chassis platforms. This is because, in practical applications, it is easier for electric vehicle chassis platforms to generate yaw moment through distributed drive, thus providing stronger real-time performance. For a wider range of chassis platforms, such as distributed drive + active steering, distributed drive + steer-by-wire, or distributed drive + four-wheel steering platforms, this control system can be easily extended to the corresponding platforms. This only requires introducing the front wheel angle / rear wheel angle as additional control inputs. Therefore, the design concept of this control system should protect the scalability of distributed drive + active steering / steer-by-wire / four-wheel independent steering chassis platforms.

[0210] This embodiment has the following technical effects: 1. Real-time performance: The derivation of this control system is entirely based on analytical expressions and does not involve any form of online training and optimization. Therefore, it has sufficient real-time performance and can also meet the computational complexity requirements of embedded development.

[0211] 2. Anti-delay capability: When deriving the yaw stabilization controller, this control system considers the actual delay from the control input to the output of the controlled system. Therefore, the designed control method has anti-delay capability and practicality.

[0212] 3. High Applicability: This technical solution is applicable to any electric vehicle chassis platform equipped with distributed drive, without requiring the chassis to be equipped with more complex and advanced chassis systems (such as steer-by-wire systems and active steering systems). Therefore, this technical solution has strong applicability, and the applicable platforms are simple and reliable.

[0213] Secondly, embodiments of this application provide a vehicle stability control device, with reference to... Figure 8 As shown, the vehicle stability control device 80 may include: a first acquisition unit 801, a first determination unit 802, a second acquisition unit 803, a second determination unit 804, and a control unit 805.

[0214] The vehicle stability control device includes: a first acquisition unit 801, used to acquire a target internal model controller for vehicle handling stability control and the current state parameters of the vehicle; the state parameters include at least the vehicle speed; a first determination unit 802, used to determine a target control function for vehicle stability control based on the state parameters and the target internal model controller; the input parameters of the target control function include the error between the actual and expected values ​​of the vehicle's yaw rate, and the output parameters of the target control function include the vehicle's target yaw moment; a second acquisition unit 803, used to acquire the vehicle's current actual yaw rate value and expected yaw rate value; a second determination unit 804, used to determine the target yaw moment value based on the actual yaw rate value, the expected yaw rate value, and the target control function; and a control unit 805, used to distribute and control the wheel-end torque of the vehicle based on the target yaw moment value to meet the vehicle's handling stability requirements.

[0215] In some embodiments, the first determining unit 801 is further configured to: input the vehicle speed value in the state parameters to the target internal model controller when the variable parameters of the internal model controller include vehicle speed, so as to determine the target control function; and input the vehicle speed value in the state parameters and the wheel end angle value of the active steering wheel to the target internal model controller when the variable parameters of the internal model controller include vehicle speed and the wheel end angle of the active steering wheel, so as to determine the target control function.

[0216] In some embodiments, the vehicle stability control device 80 further includes a model building module, which is used to build a time-domain dynamic model for vehicle handling stability control; build a time-domain state-space model of the stability control system based on the dynamic model; the input of the state-space model includes yaw moment, and the output of the state-space model includes yaw angular velocity; and build a time-domain target internal model controller based on the time-domain state-space model.

[0217] In some embodiments, the model building module is further configured to: convert the state space model in the time domain dimension into a first transfer function model in the frequency domain dimension; the first transfer function model is used to characterize a first relationship model between the yaw rate and yaw moment of the vehicle; determine a target compensation model in the frequency domain dimension; the target compensation model is used for time compensation or torque compensation; determine a first internal model controller in the frequency domain dimension based on the target compensation model in the frequency domain dimension and the first transfer function model in the frequency domain dimension; and determine a target internal model controller based on the first internal model controller in the frequency domain dimension.

[0218] In some embodiments, the model building module is further configured to: determine the target compensation model based on a second-order filter; the second-order filter is used to simulate the actual time delay of stability control in order to perform time compensation on the vehicle's stability control system.

[0219] In some embodiments, the model building module is further configured to: determine a first time delay function; the first time delay function is used to simulate the time delay between the yaw moment and the yaw angular velocity given by the stability control system; determine a second transfer function model based on the first time delay function and the first transfer function model; the second transfer compensation function model is used to pre-compensate the yaw moment; and process the second transfer function model based on the Smith predictor to determine the target compensation model.

[0220] In some embodiments, the model building module is further configured to: determine a second internal model controller in the frequency domain dimension based on a first internal model controller in the frequency domain dimension and a target transfer function model; the transfer function model includes a first transfer function model or a second transfer function model; the second internal model controller in the frequency domain dimension is used to characterize the relationship between yaw moment and yaw angular velocity error; and convert the second internal model controller in the frequency domain dimension into a time domain state space to obtain the target internal model controller.

[0221] It should be noted that the communication device of the application provided in this application embodiment includes all the units included, which can be implemented by a processor in an electronic device; of course, it can also be implemented by specific logic circuits; in the implementation process, the processor can be a central processing unit (CPU), a microprocessor (MPU), a digital signal processor (DSP), or a field-programmable gate array (FPGA), etc.

[0222] The descriptions of the above device embodiments are similar to those of the above method embodiments, and have similar beneficial effects. For technical details not disclosed in the device embodiments of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0223] It should be noted that, in the embodiments of this application, if the above-described vehicle driving control method is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of this application, or the part that contributes to the related technology, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), magnetic disks, or optical disks. Thus, the embodiments of this application are not limited to any specific hardware and software combination.

[0224] Thirdly, embodiments of this application provide a vehicle, which includes a target inner mold controller, multiple wheels, a processor, and a memory. The memory stores computer programs or instructions. When the computer programs or instructions are executed by the processor, they call the target inner mold controller to distribute and control the yaw torque at the wheel ends of the multiple wheels, thereby implementing the method provided in the first aspect.

[0225] Fourthly, embodiments of this application provide a storage medium, namely a computer-readable storage medium, on which a computer program or instructions are stored, which, when executed by a processor, implement the steps of any of the methods provided in the first aspect of the above embodiments.

[0226] Fifthly, embodiments of this application provide a computer program product, which includes a computer program or instructions that, when executed by a processor, implement the steps of any of the methods provided in the first aspect of the above embodiments.

[0227] It should be noted that the descriptions of the above embodiments of storage media, devices, apparatuses, and program products are similar to the descriptions of the above method embodiments and have similar beneficial effects. For technical details not disclosed in the embodiments of storage media, devices, apparatuses, and program products of this application, please refer to the descriptions of the method embodiments of this application for understanding.

[0228] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment" or "in some embodiments" appearing throughout the specification do not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of this application, the sequence numbers of the above-described processes do not imply a sequential order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. The sequence numbers of the above-described embodiments are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0229] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0230] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another electronic device, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0231] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0232] In addition, each functional unit in the various embodiments of this application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0233] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as mobile storage devices, read-only memory (ROM), magnetic disks, or optical disks.

[0234] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to related technologies, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0235] The above are merely embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for controlling vehicle stability, characterized in that, The method includes: Acquire the target internal model controller for vehicle handling stability control and the current state parameters of the vehicle; the state parameters include at least the vehicle speed. Based on the state parameters and the target internal model controller, a target control function for vehicle stability control is determined; the input parameters of the target control function include the error between the actual and expected values ​​of the vehicle's yaw rate, and the output parameters of the target control function include the vehicle's target yaw moment. Obtain the current actual yaw rate and the expected yaw rate of the vehicle; Based on the actual yaw rate value, the desired yaw rate value, and the target control function, the target yaw moment value is determined. The wheel-end torque of the vehicle is distributed and controlled based on the target yaw moment value to meet the vehicle's handling stability requirements.

2. The method according to claim 1, characterized in that, The step of determining the target control function for vehicle stability control based on the state parameters and the target internal model controller includes: When the variable parameters of the target internal model controller include vehicle speed, the vehicle speed value in the state parameters is input to the target internal model controller to determine the target control function; When the variable parameters of the target internal model controller include vehicle speed and the wheel end angle of the active steering wheel, the vehicle speed value and the wheel end angle value of the active steering wheel in the state parameters are input to the target internal model controller to determine the target control function.

3. The method according to claim 1 or 2, characterized in that, The method further includes: Construct a time-domain dynamic model for vehicle handling stability control; Based on the aforementioned dynamic model, a state-space model of the stability control system in the time domain is constructed; the input of the state-space model includes yaw moment, and the output of the state-space model includes yaw angular velocity. The target internal model controller in the time domain dimension is constructed based on the state space model in the time domain dimension.

4. The method according to claim 3, characterized in that, The construction of the time-domain target internal model controller based on the time-domain dimension of the state-space model includes: The state-space model in the time domain is converted into a first transfer function model in the frequency domain; the first transfer function model is used to characterize the first relationship model between the vehicle's yaw rate and yaw moment. A target compensation model is determined in the frequency domain dimension; the target compensation model is used for time compensation or torque compensation. Based on the target compensation model and the first transfer function model in the frequency domain, the first internal model controller in the frequency domain is determined. The target internal model controller is determined based on the first internal model controller in the frequency domain dimension.

5. The method according to claim 4, characterized in that, The target compensation model that determines the frequency domain dimension includes: The target compensation model is determined based on a second-order filter; the second-order filter is used to simulate the actual time delay of stability control in order to perform time compensation on the vehicle's stability control system.

6. The method according to claim 4, characterized in that, The target compensation model that determines the frequency domain dimension includes: Determine the first time delay function; the first time delay function is used to simulate the time delay between the yaw torque and the yaw angular velocity given by the stability control system; The second transfer function model is determined based on the first time delay function and the first transfer function model; the second transfer function model is used to compensate for the yaw moment in advance. The target compensation model is determined by processing the second transfer function model using the Smith predictor.

7. The method according to claim 4, characterized in that, The first internal model controller based on the frequency domain dimension determines the target internal model controller, including: A second internal model controller in the frequency domain dimension is determined based on the first internal model controller and the target transfer function model in the frequency domain dimension; the transfer function model includes either the first transfer function model or the second transfer function model; the second internal model controller in the frequency domain dimension is used to characterize the relationship between the yaw moment and the yaw rate error. The second internal model controller in the frequency domain is converted into the time domain state space to obtain the target internal model controller.

8. A vehicle stability control device, characterized in that, The device includes: The first acquisition unit is used to acquire the target internal model controller for vehicle handling stability control and the current state parameters of the vehicle; the state parameters include at least the vehicle speed. The first determining unit is used to determine a target control function for vehicle stability control based on the state parameters and the target internal model controller; the input parameters of the target control function include the error between the actual value and the expected value of the vehicle's yaw rate, and the output parameters of the target control function include the vehicle's target yaw moment; The second acquisition unit is used to acquire the current actual yaw rate value and the expected yaw rate value of the vehicle. The second determining unit is used to determine the target yaw moment value based on the actual yaw rate value, the desired yaw rate value, and the target control function. The control unit is used to distribute and control the wheel-end torque of the vehicle based on the target yaw moment value in order to meet the vehicle's handling stability requirements.

9. A vehicle, characterized in that, The vehicle includes a target inner mold controller, multiple wheels, a processor, and a memory. The memory stores computer programs or instructions. When the processor executes the computer programs or instructions, it calls the target inner mold controller to distribute and control the yaw torque at the wheel ends of the multiple wheels, thereby implementing the method described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The readable storage medium stores a computer program or instructions, which, when executed by a processor, implement the method described in any one of claims 1-7.

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