Integrated control method for handling stability of distributed drive electric vehicle and related device

Abstract

An integrated control method for handling stability of a distributed drive electric vehicle, and a related device, which belong to the technical field of vehicle handling stability control. According to the method, piecewise linear fitting is performed on a magic formula tire model to establish a piecewise affine tire model, a hybrid logic dynamic model is established on the basis of the piecewise affine tire model, an upper hybrid model prediction controller and a lower four-wheel torque optimization distribution controller are established by using a hierarchical integrated control policy, and additional yaw moment, an additional front wheel angle and wheel drive torque are calculated, so that stability control over vehicle handling is achieved. The method can accurately describe change conditions of tire lateral force during vehicle operation, and reduce the calculation amount of a control system to improve the response speed.

Description

Distributed drive electric vehicle handling stability integrated control method and related equipment

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to the Chinese patent application with application number 202311402934.3 filed with the Chinese Patent Office on October 26, 2023, entitled "Integrated Control Method and Related Equipment for Handling Stability of Distributed Drive Electric Vehicles", the entire contents of which are incorporated by reference into this application. Technical Field

[0003] The present invention belongs to the technical field of vehicle handling stability control, and in particular relates to an integrated control method for the handling stability of a distributed drive electric vehicle and related equipment. Background Art

[0004] When driving at high speeds or on low-adhesion roads, vehicles can deviate from their target trajectory due to understeering or oversteering. Under these circumstances, the vehicle is susceptible to instability due to factors such as the external environment or unstable vehicle parameters. Distributed drive electric vehicles, with multiple in-wheel motors or wheel-mounted motors independently driving each of the four wheels, offer a compact design, improved mechanical efficiency, and control precision. These structural features have promoted the further development of active safety technologies.

[0005] Tire forces currently play a crucial role in vehicle dynamics research for distributed drive electric vehicles. During vehicle stability studies, the establishment of a tire force model determines the controller's control accuracy to a certain extent. Under extreme operating conditions, when tires enter nonlinear or even saturated regions, considering only the linear characteristics of the tires significantly impacts vehicle stability control decisions and accuracy. Furthermore, the large computational effort involved in the control system's response leads to slow response speed, significantly hindering the improvement of control accuracy.

[0006] Summary of the Invention

[0007] In order to overcome the shortcomings of the above-mentioned technologies, the present invention provides a distributed drive electric vehicle handling stability integrated control method and related equipment, which can solve the technical problems of the existing control system due to the large amount of calculation, resulting in slow system response speed and difficulty in improving control accuracy.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions:

[0009] A distributed drive electric vehicle handling stability integrated control method, comprising:

[0010] Perform piecewise linear fitting on the magic formula tire model to obtain a piecewise affine tire model;

[0011] Based on the piecewise affine tire model and combined with the hybrid logic dynamic modeling method, a hybrid logic dynamic model is obtained;

[0012] An upper-level hybrid model predictive controller is established based on the hybrid logic dynamic model, and the additional yaw moment and the additional front wheel steering angle are calculated based on the upper-level hybrid model predictive controller;

[0013] A lower-layer four-wheel torque optimization distribution controller is established based on the additional yaw moment, and the wheel driving torque is calculated based on the lower-layer four-wheel torque optimization distribution controller.

[0014] Furthermore, the magic formula tire model is constructed using a model stiffness factor, a model shape factor, a model peak factor, a model curvature factor, and a tire slip angle.

[0015] Furthermore, the specific steps of constructing the hybrid logic dynamic model are as follows:

[0016] Based on the real-time vehicle status information, the ideal reference vehicle status parameters are calculated;

[0017] Based on the ideal reference vehicle state parameters, the error state equation is constructed;

[0018] Based on the error state equation and piecewise affine tire model, a hybrid logic dynamic model is obtained.

[0019] Furthermore, the real-time status information of the vehicle includes: vehicle speed, yaw rate, sideslip angle of center of mass and front wheel angle.

[0020] Furthermore, based on the upper hybrid model predictive controller, a mixed integer quadratic programming algorithm is used to calculate the additional yaw moment and the additional front wheel angle.

[0021] Furthermore, based on the lower-layer four-wheel torque optimization distribution controller, the tire workload rate is minimized as the optimization goal, the wheel longitudinal force is calculated, and the wheel driving torque is obtained based on the wheel longitudinal force.

[0022] Furthermore, based on the lower-level four-wheel torque optimization distribution controller, the effective set method is adopted in combination with the minimum tire workload rate as the optimization goal to calculate the wheel longitudinal force, and based on the wheel longitudinal force, the wheel driving torque is calculated.

[0023] A distributed drive electric vehicle handling stability integrated control system, used to implement the above-mentioned distributed drive electric vehicle handling stability integrated control method, includes:

[0024] The piecewise affine tire model construction module is used to perform piecewise linear fitting processing on the magic formula tire model to obtain a piecewise affine tire model;

[0025] A hybrid logic dynamic model building module is used to obtain a hybrid logic dynamic model based on a piecewise affine tire model and a hybrid logic dynamic modeling method;

[0026] a first calculation module, configured to establish an upper-layer hybrid model predictive controller based on the hybrid logic dynamic model, and calculate an additional yaw moment and an additional front wheel steering angle based on the upper-layer hybrid model predictive controller;

[0027] The second calculation module is used to establish a lower-level four-wheel torque optimization distribution controller based on the additional yaw moment, and calculate the wheel driving torque based on the lower-level four-wheel torque optimization distribution controller.

[0028] A device comprising:

[0029] memory for storing computer programs;

[0030] The processor is used to implement the steps of the above-mentioned distributed drive electric vehicle handling stability integrated control method when executing the computer program.

[0031] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-mentioned method for integrated control of the handling stability of a distributed drive electric vehicle. Compared to the prior art, the present invention has the following beneficial effects:

[0032] The present invention also provides an integrated control method for the handling stability of a distributed drive electric vehicle. This method establishes a piecewise affine tire model through piecewise linear fitting of the magic formula tire model, establishes a hybrid logic dynamic model based on the piecewise affine tire model, and adopts a hierarchical integrated control strategy to establish an upper-layer hybrid model prediction controller and a lower-layer four-wheel torque optimization distribution controller, thereby calculating the additional yaw moment, the additional front wheel angle and the wheel drive torque, thereby achieving stability control of vehicle handling; the piecewise affine model can not only expand the traditional linear characteristics, but also simulate the nonlinear characteristics existing in the system with high precision; and can also efficiently handle linear threshold events and mode switching; the hybrid logic dynamic model can combine logic, dynamics and constraints to efficiently handle the problem of coupling continuous variables and discrete variables in the hybrid system; the use of this method can accurately describe the changes in tire lateral force during vehicle operation, while reducing the computational complexity of the control system and improving the response speed.

[0033] Preferably, in the present invention, based on the lower-level four-wheel torque optimization distribution controller, the wheel driving torque is calculated with the minimum tire workload rate as the optimization target. In this way, the lateral adhesion ability of the vehicle under extreme working conditions can be further improved, and the lateral anti-interference ability of the vehicle when driving is increased. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] FIG1 is a flow chart of a method for integrated control of handling stability of a distributed drive electric vehicle according to an embodiment of the present invention;

[0035] FIG2 is a nonlinear two-degree-of-freedom vehicle dynamics model provided by an embodiment of the present invention;

[0036] FIG3 is a piecewise affine result of the lateral force of the magic tire provided by an embodiment of the present invention;

[0037] FIG4 is a block diagram of a distributed drive electric vehicle handling stability integrated control system according to an embodiment of the present invention;

[0038] FIG5 is a flow chart of a method for integrated control of handling stability of a distributed drive electric vehicle provided by the present invention;

[0039] FIG6 is a schematic structural diagram of a distributed drive electric vehicle handling stability integrated control system provided by the present invention. DETAILED DESCRIPTION

[0040] The present invention provides a distributed drive electric vehicle handling stability integrated control method, as shown in FIG5 , comprising the following steps:

[0041] S1: Perform piecewise linear fitting on the magic formula tire model to obtain a piecewise affine tire model.

[0042] Among them, the magic formula tire model is constructed using model stiffness factor, model shape factor, model peak factor, model curvature factor and tire side slip angle.

[0043] S2: Based on the piecewise affine tire model and combined with the hybrid logic dynamic modeling method, a hybrid logic dynamic model is obtained.

[0044] The specific steps for constructing the hybrid logic dynamic model are as follows:

[0045] S201: Calculate ideal reference vehicle state parameters based on real-time vehicle state information; the real-time vehicle state information includes: vehicle speed, yaw rate, center of mass sideslip angle, and front wheel steering angle.

[0046] S202: Constructing an error state equation based on ideal reference vehicle state parameters.

[0047] S203: Obtaining a hybrid logic dynamic model based on the error state equation and the piecewise affine tire model.

[0048] S3: An upper-level hybrid model predictive controller is established based on the hybrid logic dynamic model. Based on the upper-level hybrid model predictive controller, a mixed integer quadratic programming algorithm is used to calculate the additional yaw moment and the additional front wheel steering angle.

[0049] S4: Based on the additional yaw moment, a lower-level four-wheel torque optimization distribution controller is established. Based on the lower-level four-wheel torque optimization distribution controller, the effective set method is used in combination with the minimum tire workload rate as the optimization goal to calculate the wheel longitudinal force. Based on the wheel longitudinal force, the wheel driving torque is calculated.

[0050] As shown in FIG6 , the present invention further provides a distributed drive electric vehicle handling stability integrated control system, comprising: a piecewise affine tire model construction module, used to perform piecewise linear fitting processing on the magic formula tire model to obtain a piecewise affine tire model; a hybrid logic dynamic model construction module, used to obtain a hybrid logic dynamic model based on the piecewise affine tire model in combination with a hybrid logic dynamic modeling method; a first calculation module, used to establish an upper-layer hybrid model prediction controller based on the hybrid logic dynamic model, and calculate the additional yaw moment and the additional front wheel turning angle based on the upper-layer hybrid model prediction controller; a second calculation module, used to establish a lower-layer four-wheel torque optimization distribution controller based on the additional yaw moment, and calculate the wheel driving torque based on the lower-layer four-wheel torque optimization distribution controller.

[0051] The present invention also provides a device comprising: a memory for storing a computer program; and a processor for implementing the steps of the distributed drive electric vehicle handling stability integrated control method when executing the computer program.

[0052] When the processor executes the computer program, the steps of the above-mentioned integrated control of the handling stability of the distributed drive electric vehicle are implemented, for example: performing piecewise linear fitting processing on the magic formula tire model to obtain a piecewise affine tire model; based on the piecewise affine tire model, combining the hybrid logic dynamic modeling method, obtaining a hybrid logic dynamic model; establishing an upper-level hybrid model prediction controller based on the hybrid logic dynamic model, and calculating the additional yaw moment and the additional front wheel angle based on the upper-level hybrid model prediction controller; establishing a lower-level four-wheel torque optimization distribution controller based on the additional yaw moment, and calculating the wheel driving torque based on the lower-level four-wheel torque optimization distribution controller.

[0053] Alternatively, when the processor executes the computer program, it implements the functions of each module in the above-mentioned system, for example: a piecewise affine tire model construction module, which is used to perform piecewise linear fitting processing on the magic formula tire model to obtain a piecewise affine tire model; a hybrid logic dynamic model construction module, which is used to obtain a hybrid logic dynamic model based on the piecewise affine tire model in combination with a hybrid logic dynamic modeling method; a first calculation module, which is used to establish an upper-level hybrid model prediction controller based on the hybrid logic dynamic model, and calculate the additional yaw moment and the additional front wheel angle based on the upper-level hybrid model prediction controller; a second calculation module, which is used to establish a lower-level four-wheel torque optimization distribution controller based on the additional yaw moment, and calculate the wheel driving torque based on the lower-level four-wheel torque optimization distribution controller.

[0054] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of completing preset functions, and the instruction segments are used to describe the execution process of the computer program in the distributed drive electric vehicle handling stability integrated control device. For example, the computer program can be divided into a segmented affine tire model construction module, a hybrid logic dynamic model construction module, a first calculation module and a second calculation module; the specific functions of each module are as follows: a segmented affine tire model construction module, used to perform segmented linear fitting processing on the magic formula tire model to obtain a segmented affine tire model; a hybrid logic dynamic model construction module, used to obtain a hybrid logic dynamic model based on the segmented affine tire model in combination with a hybrid logic dynamic modeling method; a first calculation module, used to establish an upper-level hybrid model prediction controller based on the hybrid logic dynamic model, and calculate the additional yaw moment and the additional front wheel turning angle based on the upper-level hybrid model prediction controller; a second calculation module, used to establish a lower-level four-wheel torque optimization distribution controller based on the additional yaw moment, and calculate the wheel driving torque based on the lower-level four-wheel torque optimization distribution controller.

[0055] The distributed drive electric vehicle handling stability integrated control device can be a computing device such as a desktop computer, laptop, PDA, or cloud server. The distributed drive electric vehicle handling stability integrated control device may include, but is not limited to, a processor and memory. Those skilled in the art will appreciate that the above are examples of distributed drive electric vehicle handling stability integrated control devices and do not constitute a limitation on the distributed drive electric vehicle handling stability integrated control device. The distributed drive electric vehicle handling stability integrated control device may include more components than those described above, or a combination of certain components, or different components. For example, the distributed drive electric vehicle handling stability integrated control device may also include input and output devices, network access devices, buses, etc.

[0056] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor, or the processor may be any conventional processor, etc. The processor is the control center of the distributed drive electric vehicle handling stability integrated control system, and utilizes various interfaces and lines to connect various parts of the entire distributed drive electric vehicle handling stability integrated control system.

[0057] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the distributed drive electric vehicle handling stability integrated control device by running or executing the computer program and / or module stored in the memory, and calling the data stored in the memory.

[0058] The memory may mainly include a program storage area and a data storage area. The program storage area may store an operating system and at least one application required for a function (such as a sound playback function, an image playback function, etc.); the data storage area may store data created based on the use of the mobile phone (such as audio data, a phone book, etc.). In addition, the memory may include a high-speed random access memory and may also include a non-volatile memory, such as a hard disk, internal memory, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0059] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the integrated control method for the handling stability of a distributed drive electric vehicle.

[0060] If the modules / units integrated in the distributed drive electric vehicle handling stability integrated control system are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0061] Based on this understanding, the present invention implements all or part of the process steps in the above-mentioned distributed drive electric vehicle handling stability integrated control method, and can also be completed by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, the computer program can implement the steps of the above-mentioned distributed drive electric vehicle handling stability integrated control method. The computer program includes computer program code, which can be in source code form, object code form, executable file, or preset intermediate form.

[0062] The computer-readable storage medium may include: any entity or device that can carry the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium, etc.

[0063] It should be noted that the content contained in the computer-readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practices in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practices, computer-readable storage media do not include electrical carrier signals and telecommunication signals.

[0064] The present invention will be further described below in conjunction with the embodiments and drawings:

[0065] Example

[0066] As mentioned in the background, under extreme operating conditions, when tires enter nonlinear or even saturated regions, solely considering the tire's linear characteristics can significantly impact vehicle stability control decisions and accuracy. Furthermore, the high computational complexity of the control system's response leads to slow response times, significantly hindering precision.

[0067] To address the aforementioned issues, the present invention provides an integrated control method for the handling stability of a distributed drive electric vehicle. The design concept of the present invention is as follows: tires are modeled using a piecewise affine method, the tire system state input domain is divided into a finite number of polyhedral regions, and an affine sub-model is obtained in each sub-region. However, the stability control process of a distributed drive electric vehicle necessarily involves adaptive discrete switching of multiple tire characteristics through sub-model switching changes of the piecewise affine tire model. In the subsystem composed of each sub-model, the state update process is a typical continuous dynamic process. Therefore, the stability control of a distributed drive electric vehicle can be viewed as a hybrid system with distinct hybrid dynamic characteristics, including continuous dynamic changes and discrete mode switching. The present invention uses a hybrid logic dynamic model for vehicle handling stability control. A hierarchical integrated control strategy is adopted. The upper layer calculates the output additional yaw moment and front wheel angle according to the hybrid model predictive control strategy (upper hybrid model predictive controller). The lower layer calculates the output wheel driving torque based on the establishment of the lower-layer four-wheel torque optimization distribution controller, and adopts a driving torque optimization distribution method with the minimum tire workload rate as the goal. The adoption of this method can solve the problem of large calculation amount and influence on system response speed of the current control system, and at the same time further improve the lateral adhesion ability of the vehicle under extreme working conditions and increase the lateral anti-interference ability of the vehicle when driving.

[0068] The technical solution of the present invention is explained in more detail below with reference to the embodiments and drawings:

[0069] As shown in FIG1 , this embodiment provides a method for integrated control of the handling stability of a distributed drive electric vehicle, which specifically includes the following steps:

[0070] S1 includes:

[0071] Step 1: Establish the magic formula tire model. The tire lateral force expression is:

[0072] F y =D sin{C arctan[Bα-E(Bα-arctan(Bα))]}

[0073] Where, F y is the tire cornering force, B, C, D, and E are the model stiffness factor, model shape factor, model peak factor, and model curvature factor, respectively, which can be obtained from the fitting parameters, and α is the tire slip angle.

[0074] Step 2: Use a linear piecewise function to perform piecewise affine fitting on the pure cornering magic formula tire model, i.e., piecewise linear fitting processing, to obtain a piecewise model that is symmetrical about the origin as follows:

[0075] Where i∈{f,r} represents the front and rear equivalent wheels of the vehicle freedom dynamics model, α1 is the segmentation point where the tire enters the nonlinear region, α2 is the segmentation point where the tire enters the saturation region, and α max is the maximum side slip angle, k0, k1, k2, k3, k4 and m0, m1, m2, m3, m4 are the slope and intercept of each piecewise function respectively; among them, the comparison between the piecewise radial model curve and the original magic formula tire curve is shown in Figure 3. It can be seen that the front wheel tire cornering force contains 5 linear control regions, and the rear wheel tire cornering force also contains 5 linear control regions. Therefore, the piecewise linear system has a total of 25 linear control regions. That is, the tire lateral force changes as follows: max ,-α2], (-α2,-α1), [-α1,α1], (α1,α2) and [α2,α max ] dynamically switch between α1, α2, α max The segmentation points for the piecewise linearization of the tire.

[0076] When α∈[-α max ,-α2] or α∈[α2,α max ], the lateral tire force is in a saturated state, and it is difficult to generate a yaw moment by changing the tire slip angle due to the influence of the tire force boundary effect;

[0077] When α∈(-α2,-α1) or α∈(α1,α2), the lateral tire force is in a critical state. Increasing the tire slip angle will lead to saturation of the tire force area. Similarly, decreasing the tire slip angle can restore the tire force to a linear state.

[0078] When α∈[-α1,α1], the lateral tire force is in a linear state, and changing the tire slip angle can generate a certain yaw moment.

[0079] The piecewise affine model established above is transformed into a piecewise affine model with automatic switching subsystem by introducing auxiliary variables to express it as a set of mixed integer linear inequality constraints. i ∈{0,1},i∈I,I={1,2,...,25},the logical proposition expression for establishing the piecewise affine model is as follows:

[0080] Where, α i min =[α ml , α nl ], α i max =[α mu , α nu ], i is any linear control area, m and n correspond to the corresponding piecewise linear range, that is, m, n = {1, 2, .., 5}, l and u represent the minimum and maximum values ​​of the segmented area where the tire is currently located, α = [α f,α r ] is the tire slip angle of the front and rear wheels at this time, indicating that when the rear wheel slip angle is in a combination of a certain linear region, the corresponding auxiliary discrete variable is triggered. In the logical proposition expression generated by the piecewise affine model, Logical operators for equivalence relations.

[0081] According to the conversion relationship between compound logic propositions and linear inequalities, the piecewise affine tire model can be converted into the following linear inequality:

[0082] Where M i1 、m i1 They are respectively the expressions -α+α imin The maximum and minimum values ​​of M i2 、m i2 They are respectively the expressions α-α imax The maximum and minimum values ​​of , ε is the tolerance error, and when it exceeds this value, the constraint is violated.

[0083] In addition, all auxiliary discrete variables must satisfy the XOR condition as follows:

[0084] The above equation can be converted into an integer linear inequality as follows:

[0085] S2 includes:

[0086] Step 3: As shown in Figure 2, Figure 2 is a nonlinear two-degree-of-freedom vehicle dynamics model, which is used to obtain the ideal reference vehicle state and lay the foundation for establishing the error state space equation.

[0087] The established two-degree-of-freedom dynamic model is:

[0088] Where m s is the vehicle mass, a and b are the distances from the center of mass to the front and rear axles of the vehicle, v is the longitudinal velocity of the center of mass of the vehicle, I z is the moment of inertia of the vehicle around the z-axis, F yf 、F yr are the sum of the cornering forces of the front and rear wheels, δ f is the front wheel turning angle, β and γ are the vehicle's center of mass side slip angle and yaw rate around the z axis, respectively, ΔM z It is the additional yaw moment obtained by the vehicle through differential drive or braking.

[0089] The tire slip angles of the front and rear wheels are approximately:

[0090] The real-time vehicle status information is obtained, including vehicle speed, yaw rate, sideslip angle, front wheel angle, etc. The ideal reference vehicle state parameters are calculated based on the front wheel angle and longitudinal speed of the vehicle. Based on the vehicle's linear two-degree-of-freedom dynamic model, the expected yaw rate γ can be derived under steady-state rotation conditions when the lateral acceleration is small. d And the center of mass side slip angle β d If it is set to 0, the expression of the ideal value is:

[0091] β d =0

[0092] Where, L=a+b is the vehicle wheelbase, μ is the road adhesion coefficient, is the vehicle stability factor, k f and k r are the cornering stiffness of the front and rear tires respectively, a is the front wheelbase, b is the rear wheelbase, and sgn is the sign function.

[0093] Step 4: Combine the ideal reference state obtained in step 3 to construct the error state equation; T is the discrete sampling period, and the model is discretized using the forward Euler method. The discretized system state space expression can be expressed as:

[0094] Where m and n represent the front and rear slip angles at this moment on the mth and nth linear functions of the tire piecewise affine model, respectively. mn , B mn , C, D, f mn , g mn is the coefficient matrix of the state space equation, where each matrix is ​​specifically:

[0095] Based on the piecewise affine model of the automatically switchable subsystem, a hybrid logic dynamic model is established.

[0096] The error state space equation is combined with the piecewise affine tire model to rewrite it as a combination of linear terms and IF-THEN-ELSE rules, and the auxiliary discrete variable δ is introduced. i ∈{0,1}, i∈I,I={1,2,...,25}, its expression is

[0097] According to the hybrid logic dynamic modeling method of hybrid control system, a logical proposition expression is established for the constraints involved in the above equation and converted into a linear inequality containing logical variables. Among them, the above equation means that when the system state enters the i-th linear control region, the corresponding state equation is equivalent to δ i(k)=1.

[0098] After the conversion, when the system enters the i-th region at a certain moment, it can be expressed as:

[0099] In order to write the propositional logic expression that satisfies the above constraint conditions, an auxiliary continuous variable is introduced

[0100] From the above formula, we can get the propositional logic expression as follows:

[0101] In the formula, “→” is the logical operator of implication relation.

[0102] According to the conversion relationship between the logical proposition expression of the multiplication of logical variables and continuous problems and linear inequalities, the following mixed integer inequality constraints can be obtained:

[0103] Where M i =max(A i x(k)+B i u(k)+f i ), m i =min(A i x(k)+B i u(k)+f i ) represent the maximum and minimum values ​​of each state equation in the constraints.

[0104] In summary, considering the mixed integer linear inequalities and the piecewise linear state equations of the system shown above, the standard form of the hybrid logic dynamic model of the vehicle two-degree-of-freedom dynamic model is obtained:

[0105] x(k+1)=A1x(k)+B1u(k)+B2δ(k)+B3z(k)

[0106] y(k)=Cx(k)+D1u(k)+D2δ(k)+D3z(k)

[0107] E1δ(k)+E2z(k)≤E3x(k)+E4u(k)+E5

[0108] Wherein, A, B1, B2, B3, C, D1, D2, D3, E1, E2, E3, E4, and E5 are calculated according to the error state space equation and the mixed integer linear inequality.

[0109] S3 includes:

[0110] Step 5: Based on the hybrid logic dynamic model, an upper-level hybrid model predictive controller is established. It combines the piecewise affine model and the hybrid logic dynamic model with constraints suitable for optimal control as the prediction model of the hybrid model predictive control and adopts the mixed integer quadratic programming algorithm for rolling optimization solution.

[0111] Let t be the current moment and x(t) be the current state variable (obtained by direct measurement or through a state observer). When t = 0, x(t) is the initial state of the system. The optimal control problem solved by the hybrid model predictive control algorithm can be expressed as:

[0112] Where P is the prediction step length, U is the control step length, Q and R are the weights of the output variable and the control variable, respectively. R = diag(r1, r2), r1 and r2 are the weights of the control variables active front wheel angle and direct yaw moment, respectively; Q = diag(q1, q2), q1 and q2 are the weights of the output variables vehicle real-time yaw rate and center of mass sideslip angle. y is the reference value of the output variable, u is the reference value of the control variable, y ref =0,u ref =0. u mim 、u max are the lower and upper limits of the control variables, i.e., the physical constraints of the vehicle’s front wheel angle and direct yaw moment, y min 、y max are the lower and upper limits of the output variable. In addition, in this embodiment, the norm is set to 2.

[0113] The above process can be transformed into a mixed integer quadratic programming problem at each sampling time t and solved to obtain a set of optimal control sequences;

[0114] That is, ξ(t)=[u0,…,u U-1 ,δ0,…,δ U-1 ,z0,…,z U-1 ] T , where the first element u0 is the optimal control input at the current moment. In order to facilitate the optimization solution, the output Y(t), control input U(t), auxiliary discrete variable Δ(t), auxiliary continuous variable Z(t) in the prediction time domain, and their weighted matrices are defined Specifically:

[0115] Where P is the prediction step size and U is the control step size.

[0116] The optimization objective function can be rewritten as:

[0117] At the same time, the prediction model can be reformulated as:

[0118] Where, are matrices of suitable dimensions obtained by recursively deducing the original linear inequality.

[0119] Substituting the prediction model into the objective function, we can get

[0120] Where ξ(t), G, H, f T is the intermediate quantity, u c is the control output matrix, lb and ub are the maximum and minimum values ​​of the longitudinal force output respectively. The specific expression of each matrix is:

[0121] The linear inequality terms in the prediction model can be further written as:

[0122] Where,

[0123] Since x(t) in the above formula T The term Gx(t) is a known term at that moment, and the mixed integer quadratic programming problem is further simplified to:

[0124] The above problem is solved by the Branch & Bound algorithm to obtain the output of the upper hybrid model predictive controller, namely the additional yaw moment and the additional front wheel angle.

[0125] S4 includes:

[0126] Step 6: Use the upper hybrid model predictive controller to calculate u0 = {Δδ f ,ΔM z} contains three control quantities, among which the additional front wheel angle can be directly given to the wire control system after processing, and the additional yaw moment ΔM z It is a generalized quantity and needs to be converted into the driving force or braking force of the four wheels according to a certain distribution method.

[0127] In actual vehicle driving, higher tire load ratios result in lower reserve tire adhesion, which increases the likelihood of vehicle instability. Therefore, to coordinate with the upper-level controller to achieve good stability under extreme operating conditions, this embodiment uses a drive torque optimization method that minimizes the tire load ratio.

[0128] Based on the four tire-road adhesion load rate formulas, the objective function is constructed:

[0129] Where C i is the weight coefficient of each wheel utilization, μ i is the longitudinal rolling friction coefficient, F zi is the current vertical load of each wheel, F xi 、F yi are the longitudinal force and lateral force of each wheel respectively, and i=1, 2, 3, 4 represent the left front wheel, right front wheel, left rear wheel and right rear wheel of the vehicle respectively.

[0130] Since the distribution of vehicle torque has little effect on lateral force, and the front wheel angle during steering is small, the lateral force is not large. Therefore, the present invention simplifies the tire working load rate into the following formula:

[0131] Constraints:

[0132] Where C i is the weight coefficient of each wheel utilization, is the longitudinal force of each wheel, is the vertical load of each wheel, F x is the total vertical demand force, μ i is the longitudinal rolling friction coefficient, d is the vehicle wheelbase, δ f is the front wheel angle, F xmax is the magnitude of the longitudinal force corresponding to the peak torque of the motor, ΔM z is the additional yaw moment, and i=1, 2, 3, 4 represent the left front wheel, right front wheel, left rear wheel, and right rear wheel of the vehicle, respectively.

[0133] By organizing the objective function and its constraints and transforming them into the standard form of quadratic programming, we can obtain:

[0134] stA eq u c =b eq lb≤u c ≤ub

[0135] Where W u 、A eq 、b eq is the intermediate quantity, u c is the control output matrix, lb and ub are the maximum and minimum values ​​of the longitudinal force output respectively, which can be expressed as:

[0136] u c =[F x1 F x2F x3 F x4 ] T

[0137] b eq =[F x M z ] T ;

[0138] lb=max(-μF zi ,-F max )

[0139] ub=min(μF zi ,F max )

[0140] The effective set method is used to solve the above quadratic programming problem to obtain the longitudinal forces of the four wheels, and then the wheel driving torque of the corresponding drive motor is solved.

[0141] As shown in FIG4 , this embodiment further provides a distributed drive electric vehicle handling stability integrated control system, which is based on hybrid model predictive control. Specifically, as shown in FIG4 , this system is designed with upper and lower controllers. The upper controller calculates and outputs the additional yaw moment and front wheel angle based on the ideal vehicle reference state and the actual vehicle state through the hybrid model predictive control strategy. The lower controller achieves vehicle handling stability control by allocating the driving torque with the goal of minimizing the tire workload rate.

[0142] In summary, this invention provides an integrated control method and related equipment for the handling stability of distributed drive electric vehicles. This method transforms a nonlinear vehicle dynamics model into a hybrid logic dynamic model and studies the finite-time domain optimal control of the hybrid logic dynamic model based on model predictive control theory. This method significantly reduces the computational burden on the controller while further improving the vehicle's lateral adhesion under extreme operating conditions and enhancing the vehicle's lateral resistance to interference during driving.

[0143] The above embodiment is only one of the implementation methods that can realize the technical solution of the present invention. The scope of protection claimed by the present invention is not limited only to this embodiment, but also includes changes, replacements and other implementation methods that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed by the present invention.

Claims

1. A distributed drive electric vehicle handling stability integrated control method, characterized in that: include: Perform piecewise linear fitting processing on the magic formula tire model to obtain a piecewise affine tire model; Based on the piecewise affine tire model and the hybrid logic dynamic modeling method, a hybrid logic dynamic model is obtained; An upper hybrid model predictive controller is established based on the hybrid logic dynamic model, and additional yaw moment and additional front wheel steering angle are calculated based on the upper hybrid model predictive controller; A lower-layer four-wheel torque optimization distribution controller is established based on the additional yaw moment, and a wheel driving torque is calculated based on the lower-layer four-wheel torque optimization distribution controller; The specific steps of constructing the hybrid logic dynamic model are as follows: Based on the real-time vehicle status information, ideal reference vehicle status parameters are calculated; Based on the ideal reference vehicle state parameters, an error state equation is constructed; Based on the error state equation and piecewise affine tire model, a hybrid logic dynamic model is obtained; The real-time status information of the vehicle includes: vehicle speed, yaw rate, center of mass sideslip angle and front wheel turning angle.

2. The method for integrated control of handling stability of a distributed drive electric vehicle according to claim 1, characterized in that: The magic formula tire model is constructed using a model stiffness factor, a model shape factor, a model peak factor, a model curvature factor, and a tire slip angle.

3. The method for integrated control of handling stability of a distributed drive electric vehicle according to claim 1, characterized in that: Based on the upper hybrid model predictive controller, the additional yaw moment and the additional front wheel steering angle are calculated using a mixed integer quadratic programming algorithm.

4. The method for integrated control of handling stability of a distributed drive electric vehicle according to claim 1, characterized in that: Based on the lower-layer four-wheel torque optimization distribution controller, the tire working load rate is minimized as the optimization target, the wheel longitudinal force is calculated, and the wheel driving torque is obtained based on the wheel longitudinal force.

5. The method for integrated control of handling stability of a distributed drive electric vehicle according to claim 4, characterized in that: Based on the lower-layer four-wheel torque optimization distribution controller, the effective set method is adopted in combination with the minimum tire workload rate as the optimization goal to calculate the wheel longitudinal force, and based on the wheel longitudinal force, the wheel driving torque is calculated.

6. A distributed drive electric vehicle handling stability integrated control system, used to implement the steps of the distributed drive electric vehicle handling stability integrated control method according to any one of claims 1 to 5, characterized in that: include: A piecewise affine tire model building module is used to perform piecewise linear fitting processing on the magic formula tire model to obtain a piecewise affine tire model; A hybrid logic dynamic model building module is used to obtain a hybrid logic dynamic model based on a piecewise affine tire model and a hybrid logic dynamic modeling method; A first calculation module is used to establish an upper hybrid model predictive controller based on the hybrid logic dynamic model, and calculate an additional yaw moment and an additional front wheel steering angle based on the upper hybrid model predictive controller; The second calculation module is used to establish a lower-level four-wheel torque optimization distribution controller based on the additional yaw moment, and calculate the wheel drive torque based on the lower-level four-wheel torque optimization distribution controller.

7. A device, characterized in that: include: Memory for storing computer programs; A processor is used to implement the steps of the distributed drive electric vehicle handling stability integrated control method as described in any one of claims 1 to 5 when executing the computer program.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, it is used to implement the steps of the distributed drive electric vehicle handling stability integrated control method according to any one of claims 1 to 5.

Images