Lateral–vertical dynamics coordinated control method for distributed drive electric vehicle, and related device

Abstract

A lateral–vertical dynamics coordinated control method for a distributed drive electric vehicle, and a related device. The lateral–vertical dynamics coordinated control method for a distributed drive electric vehicle comprises the following steps: creating a fourteen-degree-of-freedom full vehicle model which considers unbalanced magnetic force in an in-wheel motor, so as to accurately represent a state response of a vehicle under different inputs; a three-dimensional piecewise affine tire model performing piecewise linearization of a lateral force of a tire under different side slip angles and vertical loads, so that nonlinear features of the tire can be described under extreme operating conditions; creating an active front-wheel steering controller on the basis of hybrid model predictive control, obtaining a reference center of mass side slip angle and a yaw rate by means of a bicycle model, and then calculating an additional steering angle of the vehicle, thereby ensuring vehicle stability; and designing a multi-constraint input and multi-constraint output active suspension controller based on dual-model predictive control by using roll stability and ride comfort, that is, a tire dynamic load, a vehicle body acceleration, and an in-wheel motor stator-rotor eccentricity, as suspension design control targets.

Description

Distributed wheel hub drive electric vehicle lateral and vertical cooperative control method and related equipment

[0001] Cross-reference to Related Applications

[0002] This application claims priority to the Chinese patent application No. 202411066020.9, filed on August 5, 2024, entitled “Distributed wheel hub drive electric vehicle lateral and vertical cooperative control method and related equipment”, the entire content of which is incorporated herein by reference. TECHNICAL FIELD

[0003] The present application belongs to the technical field of vehicle control, and specifically relates to a distributed wheel hub drive electric vehicle lateral and vertical cooperative control method and related equipment. BACKGROUND

[0004] Distributed drive electric vehicle (DDEV) has the characteristics of four-wheel independent drive control, which makes it have better power control and energy transmission path, and is convenient for realizing more complex and reliable active safety control technology.

[0005] Wheel hub motor drive as a new type of driving form of electric vehicle, in simplifying the vehicle structure, improving the transmission efficiency and the driving stability, also makes the vehicle unsprung mass increase, affects the lateral-vertical dynamics performance. Existing research shows that the vertical component of the unbalanced magnetic force generated by the relative eccentricity of the stator and rotor of the wheel hub motor is coupled with the suspension system, which affects the vehicle ride comfort and comfort. At the same time, the eccentricity of the stator and rotor of the wheel hub motor is positively related to the unbalanced magnetic force, which leads to the aggravation of bearing wear and the reduction of motor life. In addition, the unbalanced magnetic force of the wheel hub motor also directly acts on the tire, affecting the tire road holding ability, and then affecting the vehicle handling stability. When the vehicle is in the steering working condition, the body roll leads to the increase of the eccentricity of the stator and rotor of the wheel hub motor, which increases the unbalanced magnetic force, and then worsens the lateral dynamics performance. However, existing lateral dynamics research does not consider this problem. When the vehicle is in the steering working condition, the body roll will lead to the transfer of axle load, and there is an obvious coupling relationship between the lateral and vertical dynamics of the vehicle. However, most researchers in the design of lateral and vertical integrated controller often use linear tire model, and the designed controller is difficult to cope with the unstable state of the vehicle when the tire enters the nonlinear region or even the saturation region, and even may cause the vehicle to enter a more dangerous state because of the decision error of the controller. In addition, the tire lateral force is not only related to the side slip angle, but also related to the tire vertical load and the road adhesion coefficient.

[0006] Therefore, the existing hub motor driving control method only uses a single tire lateral force linear model under vertical load, which is difficult to accurately describe the nonlinear characteristics of the tire, resulting in poor control performance under extreme conditions. SUMMARY

[0007] The application provides a kind of distributed hub drive electric vehicle horizontal and vertical collaborative control method and related equipment, to solve the technical problems of poor control accuracy and poor control effect of the existing distributed drive electric vehicle stability control under extreme conditions.

[0008] In order to achieve the above purpose, the technical scheme adopted by the application is as follows:

[0009] A kind of distributed hub drive electric vehicle horizontal and vertical collaborative control method, comprising:

[0010] Based on the unbalanced magnetic force model of the hub motor, a fourteen-degree-of-freedom vehicle model is constructed;

[0011] Based on the fourteen-degree-of-freedom vehicle model and the optimized magic tire correction formula, a three-dimensional segmented affine tire model of lateral force-camber angle-vertical load is constructed;

[0012] Based on the three-dimensional segmented affine tire model, a hybrid model predictive control-based active front wheel steering controller is built; at the same time, with roll stability, ride comfort and hub motor stator eccentricity as the design control target, a multi-constraint input and multi-constraint output-based active suspension controller is built;

[0013] Based on the front wheel steering angle, phase plane and lateral load transfer rate, a coordinated control strategy of the active front wheel steering controller and the active suspension controller is constructed to realize the horizontal and vertical collaborative control of the distributed hub drive electric vehicle.

[0014] Compared with the prior art, the application has the following beneficial effects:

[0015] The application provides a distributed hub drive electric vehicle lateral and vertical collaborative control method, a vehicle fourteen-degree-of-freedom model considering unbalanced magnetic force of a hub motor is established, and the state response of the vehicle under different inputs can be accurately represented; a three-dimensional segmented affine tire model linearizes lateral force of the tire under different side slip angles and vertical loads in segments, and the nonlinear characteristics of the tire under extreme working conditions can be described; an AFS controller establishes an active front wheel steering controller based on a hybrid model predictive control, a reference vehicle body side slip angle and yaw rate are obtained from a bicycle model, and then an additional steering angle of the vehicle is calculated to ensure vehicle stability; an ASS controller takes roll stability, ride comfort, i.e., tire dynamic load, vehicle body acceleration, and hub motor stator and rotor eccentricity as suspension design control targets, and designs a double model predictive control-based active suspension controller with multiple constraint inputs and multiple constraint outputs. The coordination control strategy is designed according to the front wheel steering angle, phase plane and lateral load transfer rate, and the two controllers complement each other, so as to improve the dynamic performance of the vehicle, realize lateral and vertical collaborative control of the vehicle, and improve the stability and comfort of the vehicle control.

[0016] Preferably, in the application, the fourteen-degree-of-freedom vehicle model covers longitudinal, lateral, yaw, pitch, roll and vertical motion of each mass of the vehicle, and can comprehensively analyze the dynamic response of the vehicle under complex working conditions, thereby providing strong support for the design of the controller.

[0017] Preferably, in the application, the lateral force under different tire side slip angles and vertical loads can be more accurately calculated by optimizing a magic tire correction formula and combining wheel dynamics analysis, so as to improve the accuracy and reliability of the tire model. BRIEF DESCRIPTION OF DRAWINGS

[0018] Fig. 1 is a flowchart of the distributed hub drive electric vehicle lateral and vertical collaborative control method provided by the embodiment of the application;

[0019] Fig. 2 is a lateral dynamics model diagram provided by the embodiment of the application;

[0020] Fig. 3 is a vertical dynamics model diagram provided by the embodiment of the application;

[0021] Fig. 4 is a three-dimensional segmented affine tire model identification flowchart provided by the embodiment of the application;

[0022] Fig. 5 is a three-dimensional segmented affine tire model diagram provided by the embodiment of the application;

[0023] Fig. 6 is an AFS / ASS integrated control block diagram provided by the embodiment of the application;

[0024] Fig. 7 is a flowchart of the distributed hub drive electric vehicle lateral and vertical collaborative control method provided by the application;

[0025] Fig. 8 is a structural schematic diagram of a lateral and vertical collaborative control system of a distributed wheel hub drive electric vehicle provided by the application. DETAILED DESCRIPTION

[0026] The application provides a lateral and vertical collaborative control method of a distributed wheel hub drive electric vehicle, as shown in Fig. 7, comprising the following steps:

[0027] S1: constructing a fourteen-degree-of-freedom vehicle model based on a wheel motor unbalanced magnetic force model; S2: constructing a three-dimensional segmented affine tire model of lateral force-side slip angle-vertical load based on the fourteen-degree-of-freedom vehicle model and an optimized magic tire correction formula; S3: building an active front wheel steering controller based on the three-dimensional segmented affine tire model and a hybrid model predictive control; at the same time, building an active suspension controller based on multiple constraint inputs and multiple constraint outputs, with roll stability, ride comfort and wheel motor stator-rotor eccentricity as design control targets; S4: building a coordination control strategy of the active front wheel steering controller and the active suspension controller based on the front wheel steering angle, the phase plane and the lateral load transfer rate, to realize lateral and vertical collaborative control of the distributed wheel hub drive electric vehicle.

[0028] As shown in Fig. 8, the application further provides a lateral and vertical collaborative control system of a distributed wheel hub drive electric vehicle, comprising: a vehicle model construction module for constructing a fourteen-degree-of-freedom vehicle model based on a wheel motor unbalanced magnetic force model; a three-dimensional segmented affine tire model construction module for constructing a three-dimensional segmented affine tire model of lateral force-side slip angle-vertical load based on the fourteen-degree-of-freedom vehicle model and an optimized magic tire correction formula; a controller building module for building an active front wheel steering controller based on the three-dimensional segmented affine tire model and a hybrid model predictive control; at the same time, building an active suspension controller based on multiple constraint inputs and multiple constraint outputs, with roll stability, ride comfort and wheel motor stator-rotor eccentricity as design control targets; a collaborative control module for building a coordination control strategy of the active front wheel steering controller and the active suspension controller based on the front wheel steering angle, the phase plane and the lateral load transfer rate, to realize lateral and vertical collaborative control of the distributed wheel hub drive electric vehicle.

[0029] The application further provides a device comprising: a memory for storing a computer program; and a processor for executing the computer program to realize the steps of the lateral and vertical collaborative control method of the distributed wheel hub drive electric vehicle.

[0030] The processor implements the above-mentioned steps of the distributed wheel hub drive electric vehicle lateral and vertical collaborative control when executing the computer program, for example: based on the wheel motor imbalance magnetic force model, a fourteen-degree-of-freedom vehicle model is constructed; based on the fourteen-degree-of-freedom vehicle model combined with the optimized magic tire correction formula, a three-dimensional segmented affine tire model of lateral force-lateral angle-vertical load is constructed; based on the three-dimensional segmented affine tire model, an active front wheel steering controller based on hybrid model predictive control is built; at the same time, with roll stability, ride comfort and wheel motor stator and rotor eccentricity as the design control target, an active suspension controller based on multiple constraint inputs and multiple constraint outputs is built; based on the front wheel steering angle, The phase plane and the lateral load transfer rate, the coordination control strategy of the active front wheel steering controller and the active suspension controller is constructed to realize the lateral and vertical collaborative control of the distributed wheel hub drive electric vehicle.

[0031] For example, the computer program can be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present application. The one or more modules / units can be a series of computer program instruction segments capable of completing the preset functions, which are used to describe the execution process of the computer program in the distributed wheel hub drive electric vehicle lateral and vertical collaborative control device. For example, the computer program can be divided into a vehicle model construction module, a three-dimensional segmented affine tire model construction module, a controller building module and a collaborative control module.

[0032] The distributed wheel hub drive electric vehicle lateral and vertical collaborative control device can be a desktop computer, a notebook, a palm computer and a cloud server, etc. The distributed wheel hub drive electric vehicle lateral and vertical collaborative control device can include, but is not limited to, a processor, a memory. Those skilled in the art can understand that the above is an example of the distributed wheel hub drive electric vehicle lateral and vertical collaborative control device, and does not constitute a limitation on the distributed wheel hub drive electric vehicle lateral and vertical collaborative control device, which can include more components than the above, or combine certain components, or different components, for example, the distributed wheel hub drive electric vehicle lateral and vertical collaborative control device can also include an input / output device, a network access device, a bus, etc.

[0033] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor or the like, which is a control center of the distributed hub-driven electric vehicle horizontal and vertical collaborative control, and connects various parts of the distributed hub-driven electric vehicle horizontal and vertical collaborative control device through various interfaces and lines.

[0034] The memory can be used to store the computer program and / or modules, and the processor realizes various functions of the distributed hub-driven electric vehicle horizontal and vertical collaborative control device by running or executing the computer program and / or modules stored in the memory, and calling the data stored in the memory.

[0035] The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application program required by a function (such as a sound playing function, an image playing function, etc.), etc.; and the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, for example, a hard disk, a 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 devices.

[0036] The application further provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the steps of the distributed hub-driven electric vehicle horizontal and vertical collaborative control method.

[0037] If the modules / units of the distributed hub-driven electric vehicle horizontal and vertical collaborative control system are realized in the form of software function units and sold or used as independent products, the modules / units can be stored in a computer readable storage medium.

[0038] Based on such understanding, the present application implements all or part of the processes in the above-mentioned lateral and vertical collaborative control method for distributed wheel hub drive electric vehicles, and can also be completed by a computer program instructing relevant hardware. The computer program can be stored in a computer readable storage medium, and the computer program can implement the steps of the above-mentioned lateral and vertical collaborative control method for distributed wheel hub drive electric vehicles when executed by a processor. The computer program includes computer program codes, which can be in the form of source codes, object codes, executable files or preset intermediate forms, etc.

[0039] The computer readable storage medium can include any entity or device, recording medium, U disk, 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. that can carry the computer program codes.

[0040] It should be noted that the contents contained in the computer readable storage medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction, for example, in some jurisdictions, according to legislation and patent practice, the computer readable storage medium does not include electrical carrier signals and telecommunication signals.

[0041] The present application will be further described below in conjunction with the embodiments and the accompanying drawings:

[0042] Embodiment

[0043] The present application provides a lateral and vertical collaborative control method for distributed wheel hub drive electric vehicles, which can solve the technical problems of the influence of unbalanced magnetic force on the suspension and tire and the low modeling and control accuracy of lateral controller caused by linear tires in the prior art.

[0044] Among them, the following terms are explained as follows: AFS represents an active front steering system; ASS represents an active suspension system; PWA represents a piecewise affine; hMPC represents a hybrid model predictive control; and DMPC represents a dual model predictive control.

[0045] As shown in FIG. 1, the present embodiment provides a lateral and vertical collaborative control method for distributed wheel hub drive electric vehicles, which includes four aspects and specifically includes the following steps:

[0046] In the first aspect, a fourteen-degree-of-freedom model of the whole vehicle considering the unbalanced magnetic force of the wheel hub motor is established.

[0047] Step 1, the hub motor stator and rotor are separated, the tire and the rotor are considered as a whole, the fourteen degrees of freedom vehicle model includes longitudinal, lateral, yaw, pitch, roll, vertical motion of sprung mass, stator mass and rotor (tire) mass respectively;

[0048] The longitudinal and lateral motion differential equation expressions are as follows: x = F x1 cos(δ) + F x2 cos(δ) + F x3 + F x4 y = F y1 cos(δ) + F y2 cos(δ) + F y3 + F y4

[0049] Wherein, m is the total mass of the vehicle, δ is the front wheel angle, a x is the longitudinal acceleration, the expression is a y is the lateral acceleration, the expression is v x is the longitudinal vehicle speed, v y is the lateral vehicle speed, F xi is the wheel longitudinal force, F yi is the wheel lateral force, i = 1, 2, 3, 4, representing the left front, right front, left rear, right rear tire respectively. The following formula is the same.

[0050] The yaw, pitch and roll motion differential equation expressions are as follows:

[0051] Wherein, ω is the yaw angular velocity, a, b are the distances between the mass center and the front and rear axles respectively, B f , B r are the front and rear axle track respectively, θ is the vehicle body pitch angle, h g is the height of the mass center to the ground, h s is the height of the roll center to the ground, g is the acceleration of gravity, F si is the suspension force, m b is the sprung mass, φ is the vehicle body roll angle, I x , I y , I z are the moments of inertia around the vehicle coordinate axes X, Y, Z respectively.

[0052] The vertical motion of the sprung mass, stator mass and tire + rotor mass can be expressed as:

[0053] Wherein, m si is the stator mass, m​ri Ktire + rotor mass si C suspension stiffness coefficient si K suspension damping coefficient mi K stiffness coefficient between motor stator and rotor ti Z tire stiffness coefficient i1 Z vertical displacement of tire + motor rotor i2 Z vertical displacement of motor stator i3 Z vertical displacement of sprung mass 0i F four-wheel road roughness i F suspension active force Ei Vertical unbalance excitation of in-wheel motor

[0054] Step 2, establish the magic tire correction formula with the introduction of road adhesion coefficient, as follows:

[0055] Where, F y wheel lateral force, μ is the road adhesion coefficient, α is the tire side slip angle, C y stiffness factor, D y shape factor, E y peak factor, and y curvature factor.

[0056] The fourteen-degree-of-freedom vehicle model of the embodiment is shown in FIGS. 2 and 3.

[0057] In a second aspect, the application designs an identification method for a three-dimensional segmented affine tire model of lateral force-side slip angle-vertical load.

[0058] The lateral force-side slip angle-vertical load data obtained by the above magic tire correction formula is taken as the original data set Ω, and the improved K-plane clustering is used to cluster the original data Ω, and the steps are as follows:

[0059] Step 1, divide the original data set along the x, y axes into n x m uniform sub-data sets Ω j (j = 1... n x m), and use the least square estimation method to fit the plane parameters ε j of each sub-data set.

[0060] Assume that there are p initial clustering planes, randomly select one plane as the first initial plane, denoted as: M1= {a1, b1, c1, d1};

[0061] Step 2, take the average value of the parameters of the determined initial planes {M1, M2,..., M q} as: Use the Euclidean distance to calculate the difference D between the remaining plane parameters and the average value.i (Ω j M i ), and select the one with the largest difference, i.e., maxD. i (Ω j M i () as the next initial plane;

[0062] Step 3: Repeat step 2 until p initial planes {M1, M2, ..., M} are selected. p};

[0063] The above steps are to complete the initialization of K-plane clustering.

[0064] Step 4: Cluster the tire data points using p planes, resulting in a data subset {J1, J2, ..., J...} p}, will the data points (x) i ,y i ,z i Classified to the nearest plane J j The data classification rules are as follows:

[0065] Among them, Dis i-j Representing point (x) i ,y i ,z i The distance from the j-th plane.

[0066] Step 5: Identify and classify boundary outliers. When data point (x i ,y i ,z i A point is considered a boundary outlier if the number of points whose distance to a certain plane is less than a certain threshold is greater than or equal to 2, i.e., it meets the following conditions:

[0067] A = [Dis] i-1 Dis i-2 ,...,Dis i-p ]

[0068] num(A(i)≤ε boundary )≥2

[0069] Where A is point (x i ,y i ,z i The distance from ε to each plane boundary This is the threshold for boundary outliers.

[0070] After identifying boundary outliers, the minimum distance from the outlier to the centers of p data subsets is used to reclassify the outlier.

[0071] Then, according to the data subset corresponding to each plane, the plane parameters are re-identified using LSM, and the data classification, boundary outlier identification and classification steps are repeated, and the three-dimensional segmented affine tire sub-model dataset is obtained through multiple iterative cycles

[0072] Step 6, according to the data subset corresponding to each plane, the plane parameters are re-identified using LSM, and steps 4-5 are repeated, and the three-dimensional segmented affine model is obtained through multiple iterative cycles of K-plane clustering; that is, the plane parameters are re-identified using LSM, and the data classification, boundary outlier identification and classification steps are repeated, and the three-dimensional segmented affine tire sub-model dataset is obtained through multiple iterative cycles

[0073] Step 7, identify and classify the cutting outliers, and the data set is projected onto the x, y or z axis to convert it into one-dimensional data, and k-means clustering method is used for clustering analysis, and the number of cutting outliers is identified by the proportion of the number of sub-datasets after clustering:

[0074] wherein, is the sub-dataset obtained by k-means clustering, e=1,2,...,p, and j is the number of k-means clustering classifications, which should be determined according to the distribution of the data set , size(*) represents the number of data sets, and ξ cut is the cutting outlier threshold.

[0075] After identifying the boundary outliers, the outliers are classified again using the minimum distance from the outliers to the centers of the p data subsets, and the final tire sub-model dataset is obtained, and the plane parameters are re-identified using LSM.

[0076] Step 8, support vector machine is used to estimate the coefficient matrix of the segmented affine tire sub-model interface. The steps are as follows:

[0077] First, find two adjacent data sets, i.e. satisfy: m i ,m j are the subset centers of the data subsets and . The data points of the two adjacent subsets are taken as input data, and are denoted as X={X1,X2,...X N}, each sample of the input data contains multiple features, and forms a feature space X i ={x1,x2,...x n}, given the learning goal y = {y1, y2,... y N}, let The sample data in the subset is labeled as y(i) = 1, indicating a positive class; The sample data in the subset is labeled as y(j) = -1, indicating a negative class. In order to prevent the existence of data that cannot be linearly separated, a slack variable υ is introduced to construct the following convex quadratic programming problem: 0 ≤ α i ≤ υ, i = 1, 2,... N

[0078] Wherein, α i is the Lagrange multiplier, and α i ≥ 0, κ (·) is the kernel function, and N is the number of samples of the training data set.

[0079] Finally, the PWA tire model can be expressed as: F y = θ i-1 α + θ i-2 F z + θ i-3 , i = (1, 2,..., p)

[0080] Wherein, θ i-1 , θ i-2 and θ i-3 are the identified PWA tire sub-model parameters.

[0081] It should be noted that the traditional K-plane clustering clusters each data point to the plane with the minimum distance, but when the data points are close to multiple planes, clustering only according to the distance will produce boundary abnormal points, that is, the data points that cannot be separated; In addition, the plane after K-plane clustering has infinite extension, which may cause multiple planes to cut each other in a three-dimensional space, thereby producing cutting abnormal points. Therefore, the two kinds of abnormal points need to be identified and classified separately.

[0082] The improved magic tire three-dimensional segmented affine process of the embodiment is shown in FIG. 4, and the affine result is shown in FIG. 5. It can be seen that the three-dimensional surface is divided into nine linear planes.

[0083] In a third aspect, the application designs an active front wheel steering controller of hMPC based on the established segmented affine tire model.

[0084] Step 1: Based on the fourteen-degree-of-freedom vehicle model, assuming that the front wheel steering angle is very small and the longitudinal forces of the four wheels are equal, the lateral dynamics model is simplified: ∑F Y = F y1 + F y2 + F y3 + F y4 ∑M z = a (Fy1 +F y2 )-b(F y3 +F y4 )

[0085] Let x = [Δβ, Δω] T Let y = [Δβ, Δω] be the state vector. T For the output quantity, u = Δδ f Let Δω be the input quantity, where Δω = ω - ω d Δβ=β-β d ,Δδ f To add active front wheel steering angle, ω d β d Let the parameters be ideal. The error state-space equation is obtained, and its expression is:

[0086] Where l and r are the PWA tire sub-models of the left and right front wheels, respectively, l = (1, 2, ..., p), r = (1, 2, ..., p), A lr B lr ,f lr ,C1,D1,g lr The coefficient matrix expression for the error state space equation is as follows:

[0087] Where, θ l-i-m and θ r-i-m The parameters for the left and right PWA tire models are i = (1,2,…,p), m = (1,2,3), and K, respectively. r This refers to the lateral stiffness of the left and right rear tires.

[0088] Step 2, introduce auxiliary discrete variable σ n ∈{0,1},n=(1,2,…,p 2 Combining the IF-THEN-ELSE rule, the error state-space equation can be rewritten as:

[0089] The above formula indicates that when the system enters the nth control region, it is equivalent to σ. n =1, otherwise, σ n =0. Therefore, a continuous auxiliary variable z is introduced. n (k)=[A n x(k)+B n u(k)+f n ]·σ n (k)

[0090] The MLD prediction model can be expressed in the following form: x(k+1)=A * 1x(k)+B * 1u(k)+B* 2σ(k)+B * 3z(k) y(k)=C1 * x(k)+D * 1u(k)+D * 2σ(k)+D * 3z(k) E * 1σ(k)+E * 2z(k)≤E * 3x(k)+E * 4u(k)+E * 5

[0091] Step 3, the optimization objective function can be expressed as:

[0092] Where, N p is the prediction horizon, N c is the control horizon.

[0093] Solving the following quadratic programming problem, the additional corner of control can be obtained;

[0094] In the formula, ξ(t)=[U(t) Δ(t) Z(t)] T , U(t), Δ(t), Z(t) are the sequences of system output, control variable, auxiliary discrete variable and auxiliary continuous variable in the prediction horizon respectively; matrix Λ, H, F, is the coefficient matrix obtained by recursion based on the solution of the MLD prediction model.

[0095] It should be noted that when the AFS controller is designed, in order to prevent the calculation amount from increasing and the controller from responding slowly due to the complexity of the hybrid logic, the present application only uses the PWA tire model for the left and right front wheels, and uses the traditional linear model for the rear wheel.

[0096] In the fourth aspect, the present application uses roll stability, ride comfort and hub motor stator and rotor eccentricity as design control targets, and uses DMPC to establish a multi-constraint input and multi-constraint output active suspension controller. And a coordinated control strategy of active front wheel steering / active suspension is designed:

[0097] Step 1, simplify the fourteen-degree-of-freedom vehicle vertical model to obtain a six-degree-of-freedom left and right half-car suspension model, and the state space expression is: y roll =C2x+D2u+Ew

[0098] Wherein, the state variable is The system input quantity is u = [F1, F2], that is, the active force output by the active suspension system, the unbalanced magnetic force and the road unevenness are regarded as external disturbances, so w = [FE1, FE2, Z 01 ,Z 02 ], the system output quantity is y roll = [φ], that is, the roll angle of the vehicle body, that is, the eccentricity of the left and right motor stators and the tire dynamic load and the vehicle body acceleration.

[0099] Step 2, taking the MPC controller derivation of roll stability as an example, the ride comfort is set as follows:

[0100] The forward Euler method is used to discretize the above state space equation to obtain:

[0101] In the formula, the matrix expressions are:

[0102] Wherein, T = 0.01 is the simulation step, Nx is the number of state variables, Nu is the number of control variables, and Nz is the number of disturbance variables. The output matrix of the system at future time can be expressed as: Y(t) = Ψξ(t|t) + ΘΔU(t) + ΩW(t|t)

[0103] In the formula, the coefficient matrixes can be derived by iteration.

[0104] Step 3, the optimization objective function can be expressed as:

[0105] Wherein, the matrices Q and R are the weight matrices of the system output and the control increment respectively, and ε is the relaxation factor.

[0106] Since the active suspension actuator can only generate a limited active force, in order to prevent the suspension from frequently hitting the limit block, the suspension stroke should be specified within a safe range, in addition, the eccentricity of the motor stator cannot exceed the structural limit, finally, considering the stability performance of the vehicle, the tire dynamic load should also be constrained, therefore, the following constraint conditions are set for the active suspension system: u min ≤u≤u max ,Δu min ≤Δu≤Δu max |Z ri -Z si |≤Zh max ,|Z si -Z bi |≤Zs max

[0107] Therefore, the optimization problem can finally be expressed as the following quadratic programming problem: s.t. lb≤[ΔU,ε]≤ub A_cons*[ΔU,ε]≤b_cons

[0108] where H, G, lb, ub, A_cons, b_cons are derived from the state-space equations and the constraints.

[0109] Considering the coordination strategy of roll stability index and ride comfort index, the final ASS optimization objective function can be expressed as:

[0110] where J1 and ε J1 are the roll stability objective function and its coordination parameter, respectively, and J2 and ε J2 are the ride comfort objective function and its coordination parameter, respectively.

[0111] Step 4, in order to coordinate the AFS and ASS systems, the present application uses the phase plane to make regional judgments on vehicle stability. The stable region Ψ is obtained using the double straight line method, and the stable region boundary can be expressed as:

[0112] where, is the stable boundary coefficient;

[0113] At the same time, the lateral load transfer rate is introduced to evaluate the roll stability of the vehicle, and its expression is as follows:

[0114] The present application designs the following coordination strategy according to the vehicle driving conditions:

[0115] (1) When the vehicle is driving in a straight line, the AFS does not work, and the ASS works with the goal of optimizing ride comfort, i.e. p1=0, p2=1;

[0116] (2) When the vehicle is in a steering condition, both the AFS and the ASS work, i.e. p1=1, p2 is designed in combination with the LTR index and the phase plane stability boundary, and its expression is as follows:

[0117] where LTR * =0.5 and are the stability threshold and the rollover threshold, respectively. This formula shows that when the vehicle state is within the stable region and the LTR is lower than the rollover threshold, the active suspension partially intervenes; when the vehicle state is outside the stable region or the LTR is greater than the rollover threshold, the active suspension fully intervenes.

[0118] The controller designed in the application comprises an AFS controller, an ASS controller and a coordination strategy of the two. The phase plane and the transverse load transfer rate are designed, the two controllers complement each other, and thus the dynamic performance of the vehicle is improved.

[0119] Therefore, the distributed hub-driven electric vehicle horizontal and vertical collaborative control method provided in the embodiment has the following advantages: (I) a decentralized integrated horizontal and vertical control method is established, the coupling between different chassis electronic control systems is effectively solved, the vehicle handling stability is improved, the influence of the unbalanced magnetic force on the suspension and the tire is reduced, and specifically, the vehicle roll stability, ride comfort and hub motor performance are improved. (II) An identification method of a three-dimensional piecewise affine tire model of lateral force, side slip angle and vertical load is provided. The problem that the linear tire model is difficult to describe the nonlinear characteristics of the tire under extreme conditions is effectively solved, and the control ability of the lateral controller is improved. (III) The coordination strategy of the decentralized integrated horizontal and vertical control method designed in the application comprehensively considers the needs of the vehicle under different working conditions, and combines The phase plane and the transverse load transfer rate are designed, the two controllers complement each other, and thus the dynamic performance of the vehicle is improved.

[0120] In summary, the distributed hub-driven electric vehicle horizontal and vertical collaborative control method provided in the application comprises a vehicle model module, a three-dimensional piecewise affine tire model identification, an AFS controller, an ASS controller and a coordination control strategy. A fourteen-degree-of-freedom vehicle model considering the unbalanced electromagnetic force of the hub motor is established. Secondly, in order to improve the modeling accuracy of the lateral controller, a three-dimensional piecewise affine tire model based on lateral force, side slip angle and vertical load is established by using a piecewise affine (PWA) method, and a hybrid logical dynamic model is established. An active front wheel steering controller based on hybrid model predictive control (hMPC) is designed. Considering the influence of the unbalanced magnetic force of the hub motor on the suspension system and the tire, an active suspension controller based on double model predictive control (DMPC) is designed, and a front wheel steering angle, The AFS / ASS coordination strategy of phase plane and transverse load transfer rate solves the technical problems of the influence of unbalanced magnetic force on the suspension and tire and the low modeling and control accuracy of the lateral controller caused by the linear tire, and improves the steering stability, roll stability and ride comfort of the vehicle.

[0121] The above embodiment is only one of the implementation manners of the technical scheme of the present application, and the scope of the present application is not limited to the above embodiment, but also includes any changes, substitutions and other implementation manners that are easily thought of by those skilled in the art within the technical scope disclosed by the present application.

Claims

1. A lateral and vertical cooperative control method for a distributed wheel drive electric vehicle, characterized in that, include: A 14-DOF vehicle model was constructed based on the unbalanced magnetic force model of the hub motor. Based on the 14-DOF vehicle model and the optimized magic tire correction formula, a three-dimensional segmented affine tire model is constructed, which combines lateral force, slip angle, and vertical load. Based on a three-dimensional segmented affine tire model, an active front wheel steering controller based on hybrid model predictive control is built; at the same time, with roll stability, ride comfort and hub motor stator-rotor eccentricity as design control objectives, an active suspension controller based on multi-constraint input and multi-constraint output is built. based on the front wheel steering angle, Based on phase plane and lateral load transfer rate, a coordinated control strategy for active front wheel steering controller and active suspension controller is constructed to achieve coordinated lateral and vertical control of distributed hub-driven electric vehicles.

2. The lateral and vertical cooperative control method for a distributed wheel drive electric vehicle according to claim 1, characterized in that, The in-wheel motor unbalanced magnetic model is obtained by separating the stator and rotor of the in-wheel motor, while treating the tire and rotor as a single unit; the fourteen-degree-of-freedom vehicle model includes longitudinal, lateral, yaw, pitch, roll, and vertical movements of the sprung mass, stator mass, and rotor and tire mass; among which, The differential equations of motion for the longitudinal and lateral directions are expressed as follows: ma x = F x1 cos(δ) + F x2 cos(δ) + F x3 + F x4 ma y = F y1 cos(δ) + F y2 cos(δ) + F y3 + F y4 where m is the total mass of the vehicle, δ is the front wheel steering angle, a x is the longitudinal acceleration, expressed as a y For lateral acceleration, the expression is v x is the longitudinal vehicle speed, v y is the lateral vehicle speed, F xi is the longitudinal wheel force, F yi is the lateral wheel force, i = 1,2,3,4, representing the front left, front right, rear left, and rear right tires, respectively; The differential equation expressions for yaw, pitch, and roll are as follows: where ω is the yaw rate, a, b are the distances between the mass center and the front and rear axles, B f , B r are the wheel base of the front and rear axles, θ is the pitch angle of the vehicle body, h g is the height of the mass center to the ground, h s is the height of the roll center to the ground, g is the acceleration of gravity, F si is the suspension force, m b is the sprung mass, φ is the roll angle of the vehicle body, I x , I y , I z are the moments of inertia about the X, Y, Z axes of the vehicle coordinate system, respectively. The vertical motions of the sprung mass, the stator mass, and the tire and rotor mass can be expressed as: wherein m si m is the mass of the stator ri K is the tire and rotor mass si C is the suspension stiffness coefficient si K is the suspension damping coefficient mi K is the stiffness coefficient between the motor stator and rotor ti Z is the tire stiffness coefficient i1 Z is the tire and motor rotor vertical displacement i2 Z is the motor stator vertical displacement i3 Z is the sprung mass vertical displacement 0i F is the four-wheel road roughness i F is the suspension active force Ei is the wheel motor vertical unbalance excitation.

3. The lateral and vertical cooperative control method for a distributed wheel drive electric vehicle according to claim 1, characterized in that, The optimization steps of the magic tire correction formula are as follows: The road adhesion coefficient is introduced into the magic tire correction formula, and the specific formula is as follows: where F y is the wheel side force, μ is the road adhesion coefficient, a is the tire side slip angle, C y is the stiffness factor, D y is the shape factor, E y is the peak factor, and F y is the curvature factor. Based on the wheel dynamics analysis, the tire side slip angle is expressed as: The tire vertical load is expressed as: where F z is the vertical load on the tire.

4. The lateral and vertical cooperative control method for a distributed wheel drive electric vehicle according to claim 3, characterized in that, The construction process of the three-dimensional segmented affine tire model based on lateral force, slip angle, and vertical load is as follows: The lateral force-slip angle-vertical load data obtained from the optimized magic tire correction formula are used as the original dataset Ω. Improved K-plane clustering is used to cluster the original dataset Ω, as follows: Step 1, divide the original data set into uniform n x m sub-data sets Ω along x, y axes j (j = 1...n x m), the plane parameters of each sub-data set Ω are fitted by least square estimation method, expressed as follows: j (j = 1...n x m), the plane parameters of each sub-data set Ω are fitted by least square estimation method, expressed as follows: wherein Plane parameters for each subset of data; Suppose there are p initial clustering planes. We randomly select one plane as the first initial plane, denoted as: M1 = {a1, b1, c1, d1}; Step 2, the parameter mean values of the initial plane {M1, M2,..., M q} are noted as: The difference between the remaining plane parameters and M is calculated using the Euclidean distance, which can be expressed as: Where q is the number of initial planes that have been determined, and q≤p; selecting the difference maximum, i.e. maxD i (Ω j , M i ) as the next initial plane; Step 3, repeat Step 2 until p initial planes {M1, M2,..., Mp} are selected to complete the initialization of K-plane clustering. p}, to complete the initialization of K-plane clustering. Step 4, clustering the tire data points using p planes, the corresponding data subsets are {J1, J2, …, Jp}, the data classification rules are as follows: p}, the data classification rules are as follows: wherein Dis i-j represents the distance from the point (x i ,y i ,z i ) to the jth plane, [a j ,b j ,c j ,d j ] is the parameter of the jth plane, and the data classification rule is used to classify the data point (x i ,y i ,z i ) to the nearest plane J j ; Step 5, identify and secondary classify boundary outliers, when the number of data points (x i ,y i ,z i ) to a certain plane distance is greater than or equal to 2, it is judged as a boundary outlier, then the following conditions are met: A = [Dis i-1 , Dis i-2 ,..., Dis i-p ] num(A(i)≤ε boundary )≥2 wherein A is the distance from the point (x i ,y i ,z i ) to each plane, and ε boundary is the threshold value of the boundary outlier point; After the boundary abnormal points are identified, the abnormal points are reclassified using the minimum distance of the abnormal points to the centers of the p data subsets, to obtain a three-dimensional segmented affine tire sub-model data set Step 6: Based on the data subset corresponding to each plane, use LSM to re-identify its plane parameters and repeat steps 4-5. Through multiple iterative K-plane clustering, a three-dimensional piecewise affine model is obtained. Step 7, identifying and secondary classifying the cutting abnormal points, the data set with cutting abnormal points The projection into the x, y or z axis converts into one-dimensional data, and the k-means clustering method is used for clustering analysis, and the number ratio of the sub-data set after clustering is used for cutting abnormal point identification: wherein For the sub-datasets obtained by k-means clustering, e = 1, 2,..., p, j is the number of classifications of k-means clustering, and should be seen as a function of the dataset depending on the distribution of the data, size(*) represents the number of data in the dataset, ξ cut is the threshold for cutting outliers; After the cutting abnormal points are identified, the abnormal points are classified again using the minimum distance of the abnormal points to the centers of the p data subsets to obtain the final tire sub-model data And its planar parameters were re-identified using LSM; Step 8: Use a support vector machine to estimate the coefficient matrix of the interface of the three-dimensional piecewise affine tire sub-model; Finally, the three-dimensional segmented affine tire model is represented as: F y = θ i-1 α + θ i-2 F z + θ i-3 i = (1, 2,..., p) where θ i-1 , θ i-2 , and θ i-3 are the identified three-dimensional piecewise affine tire model parameters, respectively.

5. The lateral and vertical cooperative control method for distributed wheel drive electric vehicle according to claim 1, characterized in that, The specific steps for building an active front wheel steering controller based on hybrid model predictive control, based on a three-dimensional segmented affine tire model, include: Based on the fourteen-degree-of-freedom vehicle model, the lateral dynamics model is simplified to obtain the error state space equation; By introducing auxiliary discrete variables and combining them with the IF-THEN-ELSE rule, the error state space equation is transformed. Based on a three-dimensional piecewise affine tire model and the transformed error state space equation, a quadratic programming problem is solved to obtain the additional steering angle for control, thereby completing the construction of an active front wheel steering controller based on hybrid model predictive control.

6. The lateral and vertical cooperative control method for distributed wheel drive electric vehicle according to claim 1, characterized in that, The specific steps for building an active suspension controller based on multiple constraint inputs and multiple constraint outputs include: The fourteen-degree-of-freedom vehicle model is simplified to obtain a semi-vehicle suspension model; Based on the half-car suspension model, the roll stability, ride comfort and the eccentricity of the rotor and stator of the wheel hub motor are taken as the design control targets, a quadratic programming problem is solved, the target functions corresponding to the roll stability and ride comfort and their coordination parameters are obtained, and the active suspension controller based on multiple constraint inputs and multiple constraint outputs is built.

7. The lateral and vertical cooperative control method for distributed wheel drive electric vehicle according to claim 1, characterized in that, Based on the front wheel steering angle, the phase plane and the lateral load transfer rate, the coordination control strategy of the active front wheel steering controller and the active suspension controller is constructed, including: (1) When the vehicle is driving straight, the active front wheel steering controller does not work, and the active suspension controller works with the target of optimizing ride comfort, i.e. p1=0, p2=1; (2) When the vehicle is in the cornering condition, both the active front wheel steering controller and the active suspension controller work, here, pi = 1, p2 is combined with the LTR index and The phase plane stability boundary is designed, and its expression is: wherein LTR * and The stability threshold and the rollover threshold are respectively.

8. A distributed wheel-hub drive electric vehicle lateral and vertical cooperative control system for implementing the steps of the distributed wheel-hub drive electric vehicle lateral and vertical cooperative control method according to any one of claims 1-7, characterized in that, It comprises: A vehicle model construction module is configured to construct a fourteen-degree-of-freedom vehicle model based on a wheel hub motor unbalanced magnetic force model; A three-dimensional segmented affine tire model construction module is configured to construct a three-dimensional segmented affine tire model of lateral force-side slip angle-vertical load based on the fourteen-degree-of-freedom vehicle model combined with the optimized magic tire correction formula; A controller construction module is configured to construct an active front wheel steering controller based on a hybrid model predictive control based on the three-dimensional segmented affine tire model, and to construct an active suspension controller based on multiple constraint inputs and multiple constraint outputs with the roll stability, ride comfort and the eccentricity of the rotor and stator of the wheel hub motor as the design control targets. a cooperative control module for controlling the front wheels based on the front wheel steering angle, The phase plane and the lateral load transfer rate are used to construct the coordination control strategy of the active front wheel steering controller and the active suspension controller to realize the horizontal and vertical collaborative control of the distributed wheel hub drive electric vehicle.

9. An apparatus, comprising: It comprises: A memory is configured to store a computer program; A processor is configured to execute the computer program to realize the steps of the horizontal and vertical collaborative control method of the distributed wheel hub drive electric vehicle according to any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The computer program is executed by the processor to realize the steps of the horizontal and vertical collaborative control method of the distributed wheel hub drive electric vehicle according to any one of claims 1-7.

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