Layered chassis coordination control method and device, electronic equipment and storage medium

Abstract

The invention provides a layered chassis coordination control method and device, electronic equipment and a storage medium, which are applied to an electric vehicle, and the method comprises the following steps: establishing a nonlinear vehicle model, and carrying out segmented ray imitation on the nonlinear vehicle model to obtain a vehicle system model; and designing a front wheel steering and yawing moment coordinated controller based on the vehicle system model, and performing chassis coordinated control on the electric vehicle based on the controller. By combining electronic stability control and an active front wheel steering system and utilizing a composite model predictive control strategy, the front wheel steering angle and the yaw moment are comprehensively processed, and stable control over the vehicle is achieved. The vehicle stability under different driving conditions is ensured by establishing a nonlinear vehicle model, carrying out linear processing and introducing a polyhedral constraint condition. According to the control strategy, optimal control input is generated by constructing an invariant control set and solving a mixed integer quadratic programming problem so as to cope with uncertain factors in a complex driving environment, and quick response and stability of the vehicle are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle stability control, and in particular to a layered chassis coordination control method, device, electronic equipment and storage medium. Background Art

[0002] With the increasing application of electric vehicles in the automotive industry, the stability and handling of vehicles have become the focus of research. Electronic stability control (ESC) system and active front steering (AFS) system are two important technologies developed in recent years. They improve the stability and safety of vehicles during driving by adjusting the steering angle and braking torque of the vehicle. However, it is difficult to achieve the optimal coordinated control effect when these two systems operate independently. Therefore, an effective control strategy is urgently needed to combine the advantages of these two systems. Summary of the invention

[0003] In view of this, the purpose of the present invention is to provide a layered chassis coordinated control method, device, electronic device and storage medium, so as to combine electronic stability control and active front-wheel steering technology to coordinately control the front wheel steering angle and yaw moment of the vehicle to improve the stability and maneuverability of the vehicle under various driving conditions.

[0004] In the first aspect, an embodiment of the present invention provides a layered chassis coordination control method, which is applied to electric vehicles. The method includes: establishing a nonlinear vehicle model based on the tire slip angle and yaw angular velocity of the front and rear wheels, and performing piecewise affine linearization on the nonlinear vehicle model to obtain a vehicle system model; designing a controller for coordinating front wheel steering and yaw moment based on the vehicle system model, and performing chassis coordination control of the electric vehicle based on the controller.

[0005] In an optional embodiment of the present application, the above-mentioned step of establishing a nonlinear vehicle model based on the tire slip angle and yaw velocity of the front and rear wheels includes: obtaining a two-degree-of-freedom model of the electric vehicle, and determining the tire slip angles of the front and rear wheels and the yaw velocity of the vehicle based on the two-degree-of-freedom model; determining a kinematic relationship based on the tire slip angle and the yaw velocity, and determining a lateral dynamics equation based on the kinematic relationship; and determining a differential equation for the sideslip angle based on the lateral dynamics equation.

[0006] In an optional embodiment of the present application, the above-mentioned step of performing piecewise affine linearization on the nonlinear vehicle model to obtain a vehicle system model includes: constructing a piecewise linear system based on the piecewise linear relationship between the nonlinear vehicle model and the nonlinear tire force; and modeling based on the piecewise linear system to obtain a vehicle system model.

[0007] In an optional embodiment of the present application, the above-mentioned step of constructing a piecewise linear system based on the piecewise linear relationship between the nonlinear vehicle model and the nonlinear tire force includes: determining the state, input and switching conditions of the piecewise linear system based on the nonlinear vehicle model; constructing a piecewise affine dynamic equation based on the state, input and switching conditions of the piecewise linear system; and discretizing the piecewise affine dynamic equation within the sampling period.

[0008] In an optional embodiment of the present application, the above-mentioned vehicle system model is used to convert the driving parameters of the hub motor of the electric vehicle into a discrete-time linear model.

[0009] In an optional embodiment of the present application, the above discrete-time linear model is suitable for predicting the control design of an electric vehicle.

[0010] In an optional embodiment of the present application, the above-mentioned controller is used to ensure the stability of the vehicle through time-varying driver steering instructions and minimize the use of the brake actuator.

[0011] In an optional embodiment of the present application, the above-mentioned step of designing a controller for coordinating front wheel steering and yaw moment based on the vehicle system model includes: calculating the equilibrium point in the linear region of the discretized piecewise affine dynamic equation to obtain the equilibrium condition of the piecewise affine dynamic equation; determining the tracking target of the controller for coordinating front wheel steering and yaw moment.

[0012] In an optional embodiment of the present application, the above-mentioned tracking target is used to track the desired yaw rate by operating the steering wheel angle and the yaw moment of the vehicle, and the tracking target retains information about the driver's desired trajectory.

[0013] In an optional embodiment of the present application, the above-mentioned step of designing a controller for coordinating front wheel steering and yaw moment based on a vehicle system model further includes: adding an integral action in tracking the target and expanding the vehicle system model; and determining the constraints of the controller.

[0014] In an optional embodiment of the present application, after the above step of designing a controller for coordinating front wheel steering and yaw moment based on a vehicle system model, the method further includes: optimizing the controller based on a finite time domain.

[0015] In an optional embodiment of the present application, the above-mentioned steps based on the finite time domain optimization controller include: determining the optimal control problem of the controller; solving the optimal control problem through mixed integer quadratic programming to obtain the front wheel steering angle and yaw moment.

[0016] In an optional embodiment of the present application, the above-mentioned step of performing coordinated chassis control of the electric vehicle based on the controller includes: the controller sends the front wheel steering angle and yaw torque to the actuator of the electric vehicle through the controller local area network bus; the actuator performs coordinated chassis control of the electric vehicle based on the front wheel steering angle and yaw torque.

[0017] In the second aspect, an embodiment of the present invention also provides a layered chassis coordination control device, which is applied to electric vehicles. The device includes: a vehicle system model establishment module, which is used to establish a nonlinear vehicle model based on the tire slip angle and yaw angular velocity of the front and rear wheels, and the nonlinear vehicle model is piecewise affine linearized to obtain a vehicle system model; a controller design module, which is used to design a controller for coordinating front wheel steering and yaw moment based on the vehicle system model, and perform chassis coordination control of the electric vehicle based on the controller.

[0018] In a third aspect, an embodiment of the present invention further provides an electronic device, including a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the above-mentioned layered chassis coordination control method.

[0019] In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the above-mentioned layered chassis coordination control method.

[0020] The embodiments of the present invention bring the following beneficial effects:

[0021] The embodiment of the present invention provides a layered chassis coordinated control method, device, electronic device and storage medium, which realizes vehicle stability control by combining electronic stability control (ESC) and active front steering (AFS) systems and using a composite model predictive control strategy to comprehensively process the front wheel steering angle and yaw moment. The method establishes a nonlinear vehicle model and linearizes it, introduces polyhedral constraints, and ensures vehicle stability under different driving conditions. The control strategy generates optimal control inputs by constructing an invariant control set and solving a mixed integer quadratic programming problem to cope with uncertainties in complex driving environments and ensure rapid response and stability of the vehicle.

[0022] Other features and advantages of the present disclosure will be set forth in the following description, or some features and advantages may be inferred or unambiguously determined from the description, or may be learned by implementing the above-mentioned technology of the present disclosure.

[0023] In order to make the above-mentioned objectives, features and advantages of the present disclosure more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0025] Figure 1 A flowchart of a hierarchical chassis coordination control method provided by an embodiment of the present invention;

[0026] Figure 2 A schematic diagram of a two-degree-of-freedom model of an electric vehicle provided by an embodiment of the present invention;

[0027] Figure 3 A schematic diagram of a layered chassis coordination control method applicable to an electric vehicle provided by an embodiment of the present invention;

[0028] Figure 4 A schematic structural diagram of a hierarchical chassis coordination control device provided by an embodiment of the present invention;

[0029] Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] At present, with the increasing application of electric vehicles in the automotive industry, the stability and handling of vehicles have become the focus of research. Electronic stability control (ESC) system and active front steering (AFS) system are two important technologies developed in recent years. They improve the stability and safety of vehicles during driving by adjusting the steering angle and braking torque of the vehicle. However, when these two systems operate independently, it is difficult to achieve the optimal coordinated control effect. Therefore, an effective control strategy is urgently needed to combine the advantages of these two systems.

[0032] As an advanced control strategy, the composite model predictive control method can handle multi-input and multi-output systems and achieve optimal control while considering system constraints and uncertainties. The composite system model can combine continuous dynamics, discrete dynamics and logical rules to provide a unified framework to describe the complex dynamic behavior of the vehicle. In recent years, this method has been successfully applied to the field of vehicle dynamic control, which can optimize the control input to keep the vehicle stable and respond quickly under various conditions.

[0033] Based on this, the embodiments of the present invention provide a layered chassis coordinated control method, device, electronic device and storage medium, which are applicable to electric vehicles. Specifically, a layered chassis coordinated control method applicable to electric vehicles is provided, which realizes the comprehensive control of ESC and AFS systems by constructing a reference model based on piecewise linear perturbation and a coordinated control algorithm. The method can effectively deal with uncertain factors in complex driving environments and improve the stability and safety of vehicles under variable working conditions.

[0034] To facilitate understanding of this embodiment, a layered chassis coordination control method disclosed in an embodiment of the present invention is first introduced in detail.

[0035] Embodiment 1:

[0036] The embodiment of the present invention provides a layered chassis coordination control method, which is applied to electric vehicles to solve the coordination problem between yaw moment control and front wheel steering control. Figure 1 The flowchart of a hierarchical chassis coordination control method is shown, and the hierarchical chassis coordination control method comprises the following steps:

[0037] Step S102: establishing a nonlinear vehicle model based on the tire slip angles and yaw angular velocity of the front and rear wheels, and performing piecewise affine linearization on the nonlinear vehicle model to obtain a vehicle system model.

[0038] In this embodiment, a vehicle system model can be constructed based on composite logic. First, a nonlinear vehicle model based on the tire slip angle and yaw rate of the front and rear wheels is established to simulate and predict the behavior of the vehicle under different driving conditions. A piecewise linear system is constructed through the piecewise linear relationship of the nonlinear tire force, and the vehicle system model is modeled using a composite logic dynamic system.

[0039] In some embodiments, the vehicle system model is used to convert the wheel hub motor drive parameters of the electric vehicle into a discrete time linear model. The discrete time linear model is suitable for predicting the control design of the electric vehicle.

[0040] Among them, the vehicle system model takes into account the wheel hub motor drive characteristics of electric vehicles and can be converted into a discrete time linear model, which is suitable for predictive control design.

[0041] Step S104, designing a controller for front wheel steering and yaw moment coordination based on the vehicle system model, and performing chassis coordination control of the electric vehicle based on the controller.

[0042] In this embodiment, a controller for coordinating the front wheel steering and yaw moment can also be designed. First, the tracking target is determined, and the control of the front wheel steering angle and yaw moment is coordinated through a composite predictive control strategy to improve the lateral stability and response speed of the vehicle. Using a piecewise linear model, a discrete time model of the state and input is constructed, and a mixed integer quadratic programming problem is solved to generate the optimal control input to maintain the stability of the vehicle under various driving conditions. This strategy effectively copes with uncertainties in complex driving environments and ensures the stability and rapid response of the vehicle under different working conditions.

[0043] The embodiment of the present invention provides a layered chassis coordination control method, which combines the electronic stability control (ESC) and active front steering (AFS) systems, uses a composite model predictive control strategy, and comprehensively processes the front wheel steering angle and yaw moment to achieve vehicle stability control. The method establishes a nonlinear vehicle model and performs linearization processing, introduces polyhedral constraints, and ensures vehicle stability under different driving conditions. The control strategy generates optimal control inputs by constructing an invariant control set and solving a mixed integer quadratic programming problem to cope with uncertainties in complex driving environments and ensure rapid response and stability of the vehicle.

[0044] Embodiment 2:

[0045] This embodiment provides another hierarchical chassis coordination control method, which is implemented on the basis of the above embodiment, and focuses on describing the way of constructing a vehicle system model based on complex logic.

[0046] In some embodiments, a two-degree-of-freedom model of an electric vehicle can be obtained, and the tire slip angles of the front and rear wheels and the yaw rate of the vehicle can be determined based on the two-degree-of-freedom model; a kinematic relationship can be determined based on the tire slip angles and the yaw rate, and a lateral dynamics equation can be determined based on the kinematic relationship; and a differential equation for the sideslip angle can be determined based on the lateral dynamics equation.

[0047] 1. Vehicle model construction:

[0048] See also Figure 2 The figure shows a schematic diagram of a two-degree-of-freedom model of an electric vehicle. In order to model the vehicle dynamics in high-speed cornering, a two-degree-of-freedom model is used for modeling.

[0049] The tire slip angle is the angle between the tire direction and the tire speed direction, and is an important tire state parameter in modeling. First, the tire slip angles of the front and rear wheels are defined. The tire slip angle of the front wheel is denoted as αf , the rear tire slip angle is denoted as α r Since the longitudinal velocity component on the wheel is the same as the velocity at the center of mass, it is recorded as v x , and the lateral velocity is calculated by dividing the lateral velocity v at the center of mass by y The tire slip angle is calculated by adding the contribution due to rotation to the tire slip angle:

[0050]

[0051] Where a and b are the distances between the front and rear axles and the center of mass of the vehicle, δ is the steering angle, and r is the vehicle yaw rate.

[0052] The force F on the front and rear wheels f and F r They are tire slip angle α f and α r and a nonlinear function of the longitudinal slip s∈[0,1]. For a constant longitudinal slip s, the tire force is piecewise linear and can be expressed as:

[0053]

[0054] Among them, c f,r , d f,r and e f,r is a tire parameter that can be determined experimentally. It is through experiments

[0055] ^

[0056] It should be noted here that the actual value of the vehicle is represented and will not be described in detail hereafter.

[0057] Since the tire slip angle is very small in high-speed cornering, we can approximate tanα≈α, thus obtaining a simplified kinematic relationship:

[0058]

[0059] Assume that during the turn the longitudinal velocity v x Constant, by differentiating the above formula, we get:

[0060]

[0061] It should be noted here that · What is represented is the first-order derivative, which will not be repeated hereafter.

[0062] From formulas (5) and (6), we can get Therefore, the tire slip angle α f , α rThe relationship between and the vehicle yaw rate r is:

[0063]

[0064] When the longitudinal velocity v x When constant, the acceleration of the vehicle can be decomposed into the acceleration of rotation around the constant yaw rate r and the lateral acceleration at the center of mass. The only force acting on the vehicle is the lateral force F on the front and rear wheels. f and F r , so the lateral dynamics can be expressed by formula (10):

[0065]

[0066] The yaw acceleration of the vehicle is calculated from the forces acting on the vehicle:

[0067]

[0068] Among them, I z is the vehicle inertia relative to the center of mass, and Y is the yaw moment generated by differential braking. For small steering angles δ, cosδ≈1, so combining the above formulas, we get the differential equation for the sideslip angle:

[0069]

[0070] Wherein, m is a constant.

[0071] In some embodiments, a piecewise linear system may be constructed based on the nonlinear vehicle model and the piecewise linear relationship of the nonlinear tire force; and a vehicle system model may be obtained by modeling based on the piecewise linear system.

[0072] 2. Piecewise affine linearization of the model:

[0073] In some embodiments, the state, input and switching conditions of the piecewise linear system can be determined based on the nonlinear vehicle model; the piecewise affine dynamic equations are constructed based on the state, input and switching conditions of the piecewise linear system; and the piecewise affine dynamic equations are discretized within the sampling period.

[0074] The only nonlinearity in the model is due to the relationship between the tire forces and the tire slip angle. For a constant longitudinal slip s, the nonlinear function can be written as a piecewise affine function. Therefore, the overall vehicle dynamics equations can also be linearized in piecewise affine form. Let x = [α f α r ] T , u=[Y δ] T is the state and input of the system. Among them, [] TIn order to express the nonlinearity in the formula, two Boolean variables γ can be defined: f ,γ r ∈0,1, define the following switching conditions:

[0075]

[0076]

[0077] Therefore, the piecewise affine dynamics equation is:

[0078]

[0079] Where i,j∈0,1, the matrix A ij , B ij and vector f ij From formulas (12) and (13), we can get: s =100ms, we can get:

[0080] x(k+1)=A i x(k)+B i u(k)+f i ,i∈{1,…,4}:H i x(k)≤K i , (17)

[0081] Among them, A i ,B i ,f i Determined by formula (16), and H i ,K i is an inequality describing the polyhedral region of different linear expressions. Discrete-time piecewise affine linear models can be used as logical dynamic systems in the design of low-level control.

[0082] Embodiment three:

[0083] This embodiment provides another layered chassis coordination control method, which is implemented on the basis of the above embodiment, and focuses on describing the design of a controller for coordinating front wheel steering and yaw moment.

[0084] In some embodiments, the equilibrium point of the linear region of the discretized piecewise affine dynamics equation can be calculated to obtain the equilibrium condition of the piecewise affine dynamics equation; and the tracking target of the controller for coordinating the front wheel steering and yaw moment is determined.

[0085] 1. Reference trajectory generation:

[0086] In some embodiments, the controller is configured to ensure vehicle stability by time-varying driver steering commands and minimize use of brake actuators.

[0087] In the control framework of this embodiment, the purpose of the controller is to ensure vehicle stability through the time-varying driver steering command δ(k) while minimizing the use of the brake actuator. Here, δ(k) refers to the steering command at the kth moment. By calculating the linear region (i.e., γ f =γ r =0), the equilibrium condition can be obtained from the equilibrium point of formula (17):

[0088]

[0089] Among them, a and b are preset constants. At the same time, the tracking target of the control problem is defined as follows:

[0090]

[0091] For ease of calculation, the tracking target is a time-invariant constant. is the actual longitudinal velocity measurement of the vehicle currently available. Typically, The value of will be different from the longitudinal velocity in the controller.

[0092] In some embodiments, an integral action may be added to the tracking of the tracking target, the vehicle system model may be expanded, and the constraints of the controller may be determined.

[0093] 2. Controller design reference model:

[0094] In some embodiments, the tracking target is used to track the desired yaw rate by operating the steering wheel angle and the yaw moment of the vehicle, and the tracking target retains information of the driver's desired trajectory.

[0095] The main control goal in this embodiment is to track the desired yaw rate by manipulating the steering wheel angle δ and the vehicle's yaw moment Y It retains information about the driver's desired trajectory. In order to eliminate constant disturbances, reduce the impact of model inaccuracies, and ensure zero tracking error in steady state, an integral action is added to the center of mass sideslip angle tracking. Specifically, the system can be expanded as follows. Where r s is the ideal yaw rate, r is the current yaw rate, I z is the moment of inertia.

[0096]

[0097] r s (k+1)=r s (k) (23)

[0098] Therefore, after adding equations (22) and (23) to the system model of equation (17), the expanded system equation can be expressed in the form of equation (24). Z is the expanded state variable, y is the output of the system, A i B i C i D i They are the expanded system matrices respectively.

[0099]

[0100] In addition, the control input must satisfy the following constraints related to the actuator:

[0101] |τ b (k)|≤1000Nm, |δ(k)|≤0.35rad (25)

[0102] Also to restrict the subset of tire slip angle space, the following constraints are imposed:

[0103]

[0104] In some embodiments, the controller may also be optimized based on a finite time domain.

[0105] 3. Optimization controller based on finite time domain:

[0106] In some embodiments, the optimal control problem of the controller can be determined; the optimal control problem is solved by mixed integer quadratic programming to obtain the front wheel steering angle and yaw moment.

[0107] At any time step k, the optimal control problem can be formulated as follows:

[0108]

[0109] stz j+k|k ∈K i

[0110] z - {k|k}=z(k) (27)

[0111] Where N is the control time domain, u k =(u k|k ,u k+1|k , ..., u k+N-1|k ) is the control variable sequence of the optimization problem, z k =(z k|k , z k+1|k , …, z k+N-1|k) is the state variable sequence of the optimization problem. z(k) and y(k) are the measured state and output of the system at time k. and are reference values ​​for the settings, which are based on the longitudinal speed of the vehicle and The measured value of Q z and Q u is a positive definite semidefinite weight matrix.

[0112] In some embodiments, the controller sends the front wheel steering angle and yaw torque to the actuator of the electric vehicle through the controller area network bus; the actuator performs chassis coordination control of the electric vehicle based on the front wheel steering angle and yaw torque.

[0113] The above problem (27) can be solved by mixed integer quadratic programming. The solved front wheel steering angle and yaw moment are sent to the actuator through the controller area network (CAN) bus to complete the entire control process.

[0114] See also Figure 3 A schematic diagram of a hierarchical chassis coordination control method suitable for electric vehicles is shown. In this embodiment, a vehicle system model based on composite logic can be first constructed, and then the piecewise affine linearization of the nonlinear model can be performed. After that, a reference trajectory is generated, and the control optimization problem based on mixed integer quadratic programming is solved, thereby realizing the hierarchical chassis coordination control of the electric vehicle.

[0115] The above method provided by the embodiment of the present invention combines the electronic stability control (ESC) and active front steering (AFS) systems, utilizes the composite model predictive control strategy, and comprehensively processes the front wheel steering angle and yaw moment to achieve vehicle stability control. The method establishes a nonlinear vehicle model and performs linearization processing, introduces polyhedral constraints, and ensures vehicle stability under different driving conditions. The control strategy generates the optimal control input by constructing an invariant control set and solving a mixed integer quadratic programming problem to cope with uncertainties in complex driving environments and ensure rapid response and stability of the vehicle.

[0116] Embodiment 4:

[0117] Corresponding to the above method embodiment, this embodiment provides a hierarchical chassis coordination control device, which is applied to electric vehicles, see Figure 4 The structure diagram of a hierarchical chassis coordination control device is shown, and the hierarchical chassis coordination control device includes:

[0118] The vehicle system model building module 41 is used to build a nonlinear vehicle model based on the tire slip angle and yaw rate of the front and rear wheels, and to obtain a vehicle system model by performing piecewise affine linearization on the nonlinear vehicle model;

[0119] The controller design module 42 is used to design a controller for front wheel steering and yaw moment coordination based on a vehicle system model, and to perform chassis coordination control of the electric vehicle based on the controller.

[0120] The embodiment of the present invention provides a hierarchical chassis coordination control device, which combines the electronic stability control (ESC) and active front steering (AFS) systems, uses a composite model predictive control strategy, and comprehensively processes the front wheel steering angle and yaw moment to achieve vehicle stability control. The method establishes a nonlinear vehicle model and performs linearization processing, introduces polyhedral constraints, and ensures vehicle stability under different driving conditions. The control strategy generates optimal control inputs by constructing an invariant control set and solving a mixed integer quadratic programming problem to cope with uncertainties in complex driving environments and ensure rapid response and stability of the vehicle.

[0121] The above-mentioned vehicle system model building module is used to obtain a two-degree-of-freedom model of the electric vehicle, determine the tire slip angles of the front and rear wheels and the yaw rate of the vehicle based on the two-degree-of-freedom model; determine the kinematic relationship based on the tire slip angle and the yaw rate, determine the lateral dynamics equation based on the kinematic relationship; and determine the differential equation of the sideslip angle based on the lateral dynamics equation.

[0122] The vehicle system model building module is used to build a piecewise linear system based on the nonlinear vehicle model and the piecewise linear relationship between the nonlinear tire forces; and to obtain a vehicle system model by modeling based on the piecewise linear system.

[0123] The above-mentioned vehicle system model building module is used to determine the state, input and switching conditions of the piecewise linear system based on the nonlinear vehicle model; construct the piecewise affine dynamic equation based on the state, input and switching conditions of the piecewise linear system; and discretize the piecewise affine dynamic equation within the sampling period.

[0124] The above vehicle system model is used to convert the wheel hub motor drive parameters of the electric vehicle into a discrete time linear model.

[0125] The above discrete-time linear model is suitable for predictive control design of electric vehicles.

[0126] The controller is used to ensure vehicle stability through time-varying driver steering commands and minimize the use of brake actuators.

[0127] The controller design module is used to calculate the equilibrium point of the linear region of the discretized piecewise affine dynamic equation, obtain the equilibrium condition of the piecewise affine dynamic equation, and determine the tracking target of the controller for coordinating the front wheel steering and yaw moment.

[0128] The tracking target is used to track the desired yaw rate by operating the steering wheel angle and the yaw moment of the vehicle, and the tracking target retains information about the driver's desired trajectory.

[0129] The controller design module is also used to add an integral action in the tracking of the tracking target, expand the vehicle system model, and determine the constraints of the controller.

[0130] The controller design module is also used to optimize the controller based on a finite time domain.

[0131] The controller design module is used to determine the optimal control problem of the controller; the optimal control problem is solved by mixed integer quadratic programming to obtain the front wheel steering angle and yaw moment.

[0132] The controller design module is used for the controller to send the front wheel steering angle and yaw moment to the actuator of the electric vehicle through the controller local area network bus; the actuator performs chassis coordination control of the electric vehicle based on the front wheel steering angle and yaw moment.

[0133] Technicians in the relevant field can clearly understand that for the convenience and simplicity of description, the specific working process of the hierarchical chassis coordination control system described above can refer to the corresponding process in the aforementioned embodiment of the hierarchical chassis coordination control method, and will not be repeated here.

[0134] Embodiment five:

[0135] The embodiment of the present invention also provides an electronic device for executing the above-mentioned layered chassis coordinated control method; see Figure 5 A structural schematic diagram of an electronic device is shown, which includes a memory 100 and a processor 101, wherein the memory 100 is used to store one or more computer instructions, and the one or more computer instructions are executed by the processor 101 to implement the above-mentioned layered chassis coordination control method.

[0136] Further, Figure 5 The electronic device shown further includes a bus 102 and a communication interface 103 , and the processor 101 , the communication interface 103 and the memory 100 are connected via the bus 102 .

[0137] The memory 100 may include a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk storage. The communication connection between the system network element and at least one other network element is realized through at least one communication interface 103 (which may be wired or wireless), and the Internet, wide area network, local area network, metropolitan area network, etc. may be used. The bus 102 may be an ISA bus, a PCI bus, or an EISA bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0138] The processor 101 may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by the hardware integrated logic circuit or software instructions in the processor 101. The above processor 101 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present invention can be implemented or executed. The general processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in conjunction with the embodiments of the present invention can be directly embodied as a hardware decoding processor for execution, or a combination of hardware and software modules in the decoding processor for execution. The software module may be located in a storage medium mature in the art, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 100, and the processor 101 reads the information in the memory 100 and completes the steps of the method of the above embodiment in combination with its hardware.

[0139] An embodiment of the present invention further provides a computer-readable storage medium, which stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the above-mentioned layered chassis coordination control method. The specific implementation can be found in the method embodiment, which will not be repeated here.

[0140] The computer program product of the layered chassis coordination control method, device, electronic device and storage medium provided in the embodiments of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods in the previous method embodiments. The specific implementation can be found in the method embodiments, which will not be repeated here.

[0141] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system and / or device described above can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0142] In addition, in the description of the embodiments of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0143] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0144] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0145] Finally, it should be noted that the above-described embodiments are only specific implementations of the present invention, which are used to illustrate the technical solutions of the present invention, rather than to limit them. The protection scope of the present invention is not limited thereto. Although the present invention is described in detail with reference to the above-mentioned embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-mentioned embodiments within the technical scope disclosed by the present invention, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A layered chassis coordination control method, characterized in that: Applied to electric vehicles, the method comprises: Establishing a nonlinear vehicle model based on the tire slip angle and yaw angular velocity of the front and rear wheels, and performing piecewise affine linearization on the nonlinear vehicle model to obtain a vehicle system model; A controller for coordinating front wheel steering and yaw moment is designed based on the vehicle system model, and chassis coordination control of the electric vehicle is performed based on the controller.

2. The method according to claim 1, characterized in that The steps of establishing a nonlinear vehicle model based on the tire slip angle and yaw rate of the front and rear wheels include: Acquire a two-degree-of-freedom model of the electric vehicle, and determine tire slip angles of front and rear wheels and a yaw rate of the vehicle based on the two-degree-of-freedom model; determining a kinematic relationship based on the tire slip angle and the yaw rate, and determining a lateral dynamic equation based on the kinematic relationship; A differential equation for the sideslip angle is determined based on the lateral dynamics equation.

3. The method according to claim 1, characterized in that The step of performing piecewise affine linearization on the nonlinear vehicle model to obtain a vehicle system model comprises: constructing a piecewise linear system based on the nonlinear vehicle model and the piecewise linear relationship of the nonlinear tire forces; A vehicle system model is obtained by modeling based on the piecewise linear system.

4. The method according to claim 3, characterized in that The step of constructing a piecewise linear system based on the piecewise linear relationship between the nonlinear vehicle model and the nonlinear tire force comprises: determining states, inputs, and switching conditions of a piecewise linear system based on the nonlinear vehicle model; constructing piecewise affine dynamic equations based on the states, inputs and switching conditions of the piecewise linear system; The piecewise affine dynamic equations are discretized within a sampling period.

5. The method according to claim 1, characterized in that The vehicle system model is used to convert the hub motor driving parameters of the electric vehicle into a discrete time linear model.

6. The method according to claim 5, characterized in that The discrete-time linear model is suitable for predicting the control design of the electric vehicle.

7. The method according to claim 1, characterized in that The controller is used to ensure vehicle stability through time-varying driver steering commands and minimize the use of brake actuators.

8. The method according to claim 4, characterized in that The step of designing a controller for front wheel steering and yaw moment coordination based on the vehicle system model comprises: Calculating the equilibrium point of the linear region of the discretized piecewise affine dynamics equation to obtain the equilibrium condition of the piecewise affine dynamics equation; Determine the tracking target of the controller for coordinating the front wheel steering and yaw moment.

9. The method according to claim 8, characterized in that The tracking target is used to track the desired yaw rate by operating the steering wheel angle and the yaw moment of the vehicle, and the tracking target retains information of the driver's desired trajectory.

10. The method according to claim 8, characterized in that The step of designing a controller for front wheel steering and yaw moment coordination based on the vehicle system model also includes: adding an integral action in the tracking of the tracking target and expanding the vehicle system model; Constraints of the controller are determined.

11. The method according to claim 1, characterized in that: After the step of designing a controller for front wheel steering and yaw moment coordination based on the vehicle system model, the method further includes: The controller is optimized based on a finite time horizon.

12. The method according to claim 11, characterized in that The step of optimizing the controller based on a finite time domain comprises: determining an optimal control problem for the controller; The optimal control problem is solved by mixed integer quadratic programming to obtain the front wheel steering angle and yaw moment.

13. The method according to claim 12, characterized in that The step of performing chassis coordinated control of the electric vehicle based on the controller includes: The controller sends the front wheel steering angle and the yaw moment to the actuator of the electric vehicle via a controller area network bus; The actuator performs chassis coordinated control of the electric vehicle based on the front wheel steering angle and the yaw moment.

14. A layered chassis coordination control device, characterized in that: Applied to electric vehicles, the device comprises: A vehicle system model building module is used to build a nonlinear vehicle model based on the tire slip angle and yaw rate of the front and rear wheels, and to obtain a vehicle system model by performing piecewise affine linearization on the nonlinear vehicle model; A controller design module is used to design a controller for coordinating front wheel steering and yaw moment based on the vehicle system model, and to perform chassis coordination control of the electric vehicle based on the controller.

15. An electronic device, characterized in that: The invention comprises a processor and a memory, wherein the memory stores computer executable instructions that can be executed by the processor, and the processor executes the computer executable instructions to implement the layered chassis coordinated control method according to any one of claims 1 to 13.

16. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are called and executed by a processor, the computer-executable instructions prompt the processor to implement the layered chassis coordination control method according to any one of claims 1 to 13.

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