A vehicle multi-dimensional stability control method, device, equipment and storage medium

Abstract

This application relates to a method, apparatus, device, and storage medium for multi-dimensional vehicle stability control. The method includes: establishing a multi-degree-of-freedom dynamic model; determining a comprehensive stability evaluation index for a target vehicle in multiple dimensions based on the model; the dimensions include at least two of longitudinal slip, sideslip, and roll; acquiring real-time dynamic parameters of the target vehicle; and determining desired control quantities for the dynamic parameters of the target vehicle in different dimensions based on the comprehensive stability evaluation index; and performing vehicle control based on the desired control quantities in different dimensions. This application enables coupled adjustment of the vehicle's stability state in different dimensions, solving the problem in existing technologies where different-dimensional stability control is incompatible under extreme vehicle operating conditions.

Description

Technical Field

[0001] This application relates to the field of vehicle control, and in particular to a method, device, equipment and storage medium for multidimensional stability control of vehicles. Background Technology

[0002] Existing stability control methods for vehicles under extreme conditions have limitations due to their reliance on a single dimension and a single actuator. For example, differential braking controls the sideslip dimension, differentials or hub motors control the longitudinal slip dimension, and active suspension controls the roll dimension. However, the instability process of a vehicle under extreme conditions involves multi-dimensional coupling. The multi-dimensional instability processes in the longitudinal slip, sideslip, and roll directions are difficult to distinguish in time and space, and the phase relationships of instability in different dimensions lead to significant uncertainties in the instability process and mechanism. Secondly, due to the lack of nonlinear analysis and linearized quantitative analysis of the degree of multi-dimensional coupling, existing stability domain calculations and comprehensive evaluation indicators only achieve high accuracy under relatively idealized single-dimensional instability conditions. Finally, single-dimensional control schemes can significantly impact the dynamic behavior characteristics of the vehicle in other dimensions, specifically manifested as coupling contradictions arising from the independent operation of multiple actuators. Therefore, in order to improve the effectiveness of vehicle stability control under extreme conditions, it is necessary to study the instability process and mechanism of vehicles under extreme conditions, establish the stability domain and comprehensive evaluation index of the multi-dimensional coupled instability process, and apply the stability domain and comprehensive evaluation index to the formulation of multi-actuator collaborative control strategies.

[0003] There is currently no effective solution to the problem that related technologies cannot be compatible with stability control in different dimensions under extreme vehicle operating conditions. Summary of the Invention

[0004] This embodiment provides a vehicle multidimensional stability control method, device, equipment, and storage medium to solve the problem in related technologies that the vehicle cannot be compatible with stability control of different dimensions under extreme operating conditions.

[0005] In a first aspect, the present invention provides a vehicle multidimensional stability control method, comprising:

[0006] A multi-degree-of-freedom dynamic model is established, and a comprehensive stability evaluation index for the target vehicle in multiple dimensions is determined based on the multi-degree-of-freedom dynamic model; the dimensions include at least two of longitudinal slip, sideslip, and roll.

[0007] The real-time dynamic parameters of the target vehicle are obtained, and combined with the comprehensive stability evaluation index of the target vehicle in multiple dimensions, the expected control quantities of the dynamic parameters of the target vehicle in different dimensions are determined.

[0008] Vehicle control is performed on the target vehicle based on the desired control quantity under different dimensions.

[0009] In some of these embodiments, the multi-degree-of-freedom dynamic model includes: a lateral motion model, a yaw motion model, a longitudinal motion model, a roll motion model, and a wheel rotation motion model;

[0010] Wherein, the wheel rotation motion model and the longitudinal motion model correspond to the longitudinal slip dimension;

[0011] The lateral motion model and the yaw motion model correspond to the sideslip dimension;

[0012] The tilt motion model corresponds to the tilt dimension.

[0013] In some embodiments, the comprehensive stability evaluation index of the target vehicle in multiple dimensions is determined based on the multi-degree-of-freedom dynamics model, including:

[0014] The stability boundaries of the target vehicle under different stability conditions are determined based on the multi-degree-of-freedom dynamic model.

[0015] The stability boundaries of the target vehicle under different stability conditions are fitted to determine the stability domain of the target vehicle in different dimensions.

[0016] The stability domains of the target vehicle under different dimensions are coordinated and coupled to determine the comprehensive stability evaluation index of the target vehicle under multiple dimensions.

[0017] In some embodiments, the stability boundaries of the target vehicle under different stability conditions are determined based on the multi-degree-of-freedom dynamic model, including:

[0018] A Jacobian matrix is ​​constructed, and the eigenvalues ​​of the Jacobian matrix are solved. Based on the solution results, the bifurcation values ​​of each dynamic parameter of the target vehicle under different stability conditions and instability in different dimensions are determined. The Jacobian matrix contains the dynamic parameters in the multi-degree-of-freedom dynamic model.

[0019] Based on the bifurcation values ​​of various dynamic parameters of the target vehicle when it becomes unstable in different dimensions, the stability boundaries of the target vehicle under different stability conditions are determined.

[0020] In some embodiments, the stability boundaries of the target vehicle under different stability conditions are fitted to determine the stability domain of the target vehicle in different dimensions, including:

[0021] The stability boundary of the target vehicle under different stability conditions is fitted using a nonlinear numerical method and a linearized analytical method to determine the stability domain boundary of the target vehicle. The stability domain boundary is used to divide the target vehicle into a stability domain and a danger domain in different dimensions. The stability domain includes an absolute stability domain and a relative stability domain.

[0022] In some embodiments, by combining the comprehensive stability evaluation index of the target vehicle across multiple dimensions, the desired control quantities of the dynamic parameters of the target vehicle under different dimensions are determined, including:

[0023] The phase sequence of the target vehicle's instability in different dimensions is determined based on the dynamic parameters of the target vehicle at the time of instability.

[0024] Simulate the instability conditions of the target vehicle in each single dimension according to the phase sequence, collect the dynamic parameters corresponding to other dimensions, and determine the weight ratio of the influence of different dynamic parameters on the stability state of the target vehicle.

[0025] The desired control quantity of the dynamic parameters of the target vehicle is determined based on the comprehensive evaluation index of the target vehicle in multiple dimensions and the weight ratio of the influence of different dynamic parameters on the stability state of the target vehicle.

[0026] In some embodiments, performing vehicle control on the target vehicle according to the desired control quantity includes:

[0027] Determine the degree of influence of the actuators of the target vehicle in different dimensions on the stability of the target vehicle in other dimensions, and obtain the degree of influence results;

[0028] The desired control quantity is optimized and compensated based on the results of the degree of influence.

[0029] The actuators of the target vehicle in the corresponding dimension are controlled according to the expected control quantity after optimization and compensation.

[0030] Secondly, this invention provides a vehicle multi-dimensional stability control device, comprising:

[0031] The index determination module is used to establish a multi-degree-of-freedom dynamic model and determine the comprehensive stability evaluation index of the target vehicle in multiple dimensions based on the multi-degree-of-freedom dynamic model; the dimensions include at least two of longitudinal slip, sideslip, and roll.

[0032] The data processing module acquires the real-time dynamic parameters of the target vehicle, and combines them with the comprehensive stability evaluation index of the target vehicle in multiple dimensions to determine the expected control quantities of the dynamic parameters of the target vehicle in different dimensions.

[0033] The control execution module performs vehicle control on the target vehicle according to the desired control quantity.

[0034] Thirdly, the present invention provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the vehicle multidimensional stability control method described in the first aspect above.

[0035] Fourthly, the present invention provides a storage medium storing a computer program that, when executed by a processor, implements the vehicle multidimensional stability control method described in the first aspect above.

[0036] Compared with related technologies, this invention provides a multi-dimensional stability control method for vehicles. It establishes a multi-degree-of-freedom dynamic model to determine the comprehensive stability evaluation index of the target vehicle in multiple dimensions. Real-time dynamic parameters of the target vehicle are acquired, and combined with the comprehensive stability evaluation index, the desired control quantities for the target vehicle's dynamic parameters in different dimensions are determined. Finally, vehicle control is executed based on the desired control quantities. This method helps the vehicle comprehensively consider the impact of changes in dynamic parameters on the stability state in different dimensions, and couples and adjusts the vehicle's stability state in different dimensions, solving the problem in existing technologies where multi-dimensional stability control is incompatible under extreme vehicle operating conditions.

[0037] Details of one or more embodiments of this application are set forth in the following drawings and description to make other features, objects and advantages of this application more readily apparent. Attached Figure Description

[0038] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0039] Figure 1 This is a block diagram of the terminal hardware structure for implementing the vehicle multidimensional stability control method provided in this invention.

[0040] Figure 2 This is a flowchart of the vehicle multidimensional stability control method of the present invention;

[0041] Figure 3 This is a lateral schematic diagram of a multi-degree-of-freedom dynamic model;

[0042] Figure 4 This is a schematic diagram of the longitudinal and transverse directions of a multi-degree-of-freedom dynamic model;

[0043] Figure 5This is a schematic diagram of the wheel rotation motion in a multi-degree-of-freedom dynamic model;

[0044] Figure 6 This is a structural block diagram of the vehicle multidimensional stability control device of the present invention. Detailed Implementation

[0045] To better understand the purpose, technical solution, and advantages of this application, the application is described and illustrated below in conjunction with the accompanying drawings and embodiments.

[0046] Unless otherwise defined, the technical or scientific terms used in this application shall have the general meaning understood by one of ordinary skill in the art to which this application pertains. Words such as “a,” “an,” “an,” “the,” “the,” and “these” used in this application do not indicate quantitative limitation and may be singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that comprises a series of steps or modules (units) is not limited to the listed steps or modules (units) but may include steps or modules (units) not listed, or may include other steps or modules (units) inherent to these processes, methods, products, or devices. Words such as “connected,” “linked,” and “coupled” used in this application are not limited to physical or mechanical connections but may include electrical connections, whether direct or indirect. “Multiple” used in this application refers to two or more. “And / or” describes the relationship between related objects, indicating that three relationships may exist; for example, “A and / or B” can represent: A alone, A and B simultaneously, and B alone. Normally, the character " / " indicates that the objects before and after it are in an "or" relationship. The terms "first," "second," "third," etc., used in this application are merely to distinguish similar objects and do not represent a specific order of objects.

[0047] The method embodiments provided in this invention can be executed on a terminal, computer, or similar computing device. For example, running on a terminal. Figure 1 This is a block diagram of the terminal hardware structure for implementing the vehicle multi-dimensional stability control method provided in this invention. For example... Figure 1 As shown, a terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 120 and a memory 140 for storing data are also included. The processor 120 may be, but is not limited to, a microprocessor (MCU) or a programmable logic device (FPGA). The terminal may also include a transmission device 160 for communication functions and an input / output device 180. Those skilled in the art will understand that… Figure 1The structure shown is for illustrative purposes only and does not limit the structure of the terminal described above. For example, the terminal may also include components that are larger than... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown are illustrated.

[0048] The memory 140 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the vehicle multi-dimensional stability control method in this invention. The processor 120 executes various functional applications and data processing by running the computer program stored in the memory 140, thereby implementing the aforementioned method. The memory 140 may include high-speed random access memory and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 140 may further include memory remotely located relative to the processor 120, and these remote memories can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0049] Transmission device 160 is used to receive or send data via a network. This network includes a wireless network provided by the terminal's communication provider. In one example, transmission device 160 includes a Network Interface Controller (NIC), which can connect to other network devices via a base station to communicate with the Internet. In another example, transmission device 160 can be a Radio Frequency (RF) module used for wireless communication with the Internet.

[0050] This invention provides a method for multidimensional stability control of vehicles. Figure 2 This is a flowchart of the vehicle multidimensional stability control method of the present invention, such as... Figure 2 As shown, the process includes the following steps:

[0051] Step S201: Establish a multi-degree-of-freedom dynamic model and determine the comprehensive stability evaluation index of the target vehicle in multiple dimensions based on the multi-degree-of-freedom dynamic model; the dimensions include at least two of longitudinal slip, sideslip and roll.

[0052] Step S202: Obtain the real-time dynamic parameters of the target vehicle, and combine them with the comprehensive stability evaluation index of the target vehicle in multiple dimensions to determine the expected control quantities of the dynamic parameters of the target vehicle in different dimensions.

[0053] Step S203: Perform vehicle control on the target vehicle according to the desired control quantity under different dimensions.

[0054] In this method, firstly, a multi-degree-of-freedom (DOF) dynamic model is established, containing dynamic parameters of the vehicle in different dimensions. This model characterizes the stability state of the target vehicle under different dynamic parameters in the corresponding dimensions. By comprehensively considering the impact of changes in the dynamic parameters of the target vehicle in different dimensions on its stability state, a comprehensive stability evaluation index is determined. This index comprehensively considers the influence of dynamic parameter changes on the stability state in different dimensions, facilitating the determination of optimal values ​​for the dynamic parameters that maintain vehicle stability. Then, by substituting the real-time dynamic parameters of the target vehicle into the comprehensive stability evaluation index, and combining this with the road parameters of the target vehicle's current operating environment and the vehicle's own structural parameters, the real-time stability state of the target vehicle can be determined. Furthermore, based on the real-time stability state, optimal values ​​of the dynamic parameters that allow the target vehicle to adjust to or maintain a stable state are determined. Combining these optimal values ​​with the real-time dynamic parameters of the target vehicle, the desired control quantities for the target vehicle's dynamic parameters in different dimensions can be determined. By executing vehicle control on the target vehicle according to the determined desired control parameters, and issuing the optimal dynamic parameters to the actuators controlling the stability of the target vehicle in various dimensions, the vehicle can be promptly adjusted to or maintained in a stable state with the optimal dynamic parameters. This method helps the vehicle comprehensively consider the impact of changes in dynamic parameters on the stability state in different dimensions, and couples and adjusts the vehicle's stability state in different dimensions, solving the problem in existing technologies where different dimensions of stability control cannot be compatible under extreme vehicle operating conditions.

[0055] In some embodiments, the multi-degree-of-freedom dynamic model includes: a lateral motion model, a yaw motion model, a longitudinal motion model, a roll motion model, and a wheel rotation motion model. The wheel rotation motion model and the longitudinal motion model correspond to the longitudinal slip dimension; the lateral motion model and the yaw motion model correspond to the sideslip dimension; and the roll motion model corresponds to the roll dimension.

[0056] In this embodiment, a schematic diagram of the multi-degree-of-freedom dynamic model is shown below. Figure 3-5 As shown, Figure 3 This is a lateral schematic diagram of a multi-degree-of-freedom dynamic model. Figure 4 These are schematic diagrams of the longitudinal and transverse directions of a multi-degree-of-freedom dynamic model. Figure 5 This is a schematic diagram of the wheel rotation motion in a multi-degree-of-freedom dynamic model. Specifically, the expression for the lateral motion model is as follows:

[0057]

[0058] The expression for the yaw motion model is as follows:

[0059]

[0060] The expression for the longitudinal motion model is as follows:

[0061]

[0062] The expression for the roll motion model is as follows:

[0063]

[0064] The expression for the wheel rotation motion model is as follows:

[0065]

[0066] In the above expressions, For the overall vehicle quality, , These are the longitudinal and lateral vehicle speeds, respectively. The yaw rate is angular velocity. , These are the longitudinal force and lateral force of the four wheels, respectively. For the front wheel steering angle, Let z be the moment of inertia about the z-axis. , These are the distances from the center of mass to the front and rear axles, respectively. The wheelbase is the distance between the wheels. Let x be the moment of inertia about the x-axis. For the sprung mass, The tilt angle of the sprung mass. For the height of the center of mass, , These are equivalent stiffness and equivalent damping, respectively. The moment of inertia of the four wheels. For four-wheel drive torque, For four-wheel braking torque, Let be the wheel radius. The slip ratio can be calculated from the angular velocities of each wheel. The ratio of the theoretical speed to the actual speed of the target vehicle is used to obtain the result.

[0067] In some embodiments, step S201, determining the comprehensive stability evaluation index of the target vehicle in multiple dimensions based on the multi-degree-of-freedom dynamics model, includes: determining the stability boundary of the target vehicle under different stability conditions based on the multi-degree-of-freedom dynamics model; fitting the stability boundary of the target vehicle under different stability conditions to determine the stability domain of the target vehicle in different dimensions; and coordinating and coupling the stability domain of the target vehicle in different dimensions to determine the comprehensive stability evaluation index of the target vehicle in multiple dimensions.

[0068] In this embodiment, firstly, a numerical method is used to analyze the characterization of various dynamic parameters (wheel slip rate, yaw rate, lateral acceleration, longitudinal acceleration, and roll angle, etc.) in the multi-degree-of-freedom dynamic model during the multi-dimensional instability state of the vehicle and the generation mechanism of the target vehicle's stability state abrupt change. Different stability conditions are preset, and based on the values ​​of each dynamic parameter when the target vehicle experiences a stability state abrupt change under the corresponding stability conditions, the stability boundaries of the target vehicle under different stability conditions are determined. Specifically, a Jacobian matrix is ​​constructed, and the eigenvalues ​​of the Jacobian matrix are solved. Based on the solution results, the bifurcation values ​​of each dynamic parameter when the target vehicle experiences instability in different dimensions under different stability conditions are determined. Specifically, the bifurcation values ​​are determined by calculating the solution when the characteristic polynomial of the Jacobian matrix of the linear part of the target vehicle system is 0. The Jacobian matrix contains the dynamic parameters in the multi-degree-of-freedom dynamic model; based on the bifurcation values ​​of each dynamic parameter when the target vehicle experiences instability in different dimensions, the stability boundaries of the target vehicle under different stability conditions are determined.

[0069] Then, the stability boundaries of the target vehicle under different stability conditions are fitted to determine the stability domain of the target vehicle in different dimensions. Specifically, nonlinear numerical methods and linearized analytical methods are used to fit the stability boundaries of the target vehicle under different stability conditions to determine the stability domain boundaries of the target vehicle; the stability domain boundaries are used to divide the target vehicle into the stability domain and the danger domain in different dimensions.

[0070] For example, taking the study of the relationship between vehicle speed and front wheel steering angle as an example, a scenario is predefined according to the principle of controlling variables, such as the target vehicle traveling at a speed of... V Driving on a road with a fixed coefficient of friction, a functional relationship between vehicle speed and maximum steering angle is derived. In this functional relationship, the front wheel steering angle exceeds... When this happens, roll instability will inevitably occur. Therefore, the stability boundary is the maximum front wheel steering angle of the target vehicle under the current stability conditions. The stability conditions include not only vehicle speed but also parameters such as the coefficient of adhesion defined in the scenario. Furthermore, the front wheel steering angle is defined to be less than... For the boundary-related area, the front wheel steering angle is greater than This is a boundary-independent region. Furthermore, since the strength of the driving force of the target vehicle will affect the stability boundary (which may cause the expansion of the stability domain and the change in the shape of the stability boundary to occur simultaneously), after obtaining the stability boundary of the target vehicle under different stability conditions according to the above method, curve fitting is performed on the stability boundary to determine the stability domain boundary of the target vehicle.

[0071] The stability region includes the absolute stability region and the relative stability region. The absolute stability region indicates that under all stability conditions, the dynamic parameters of the target vehicle are within this range and instability will definitely not occur. The relative stability region indicates that the dynamic parameters of the target vehicle are within this range and instability may occur. The danger region indicates that under all stability conditions, the dynamic parameters of the target vehicle are within this range and instability will definitely occur. For the boundary-related region between the absolute stability region and the danger region, where it is difficult to determine whether it is stable, a fuzzy recognition system based on radial basis function neural networks is used to perform fuzzy judgment on the stable and unstable points and their additional conditions (e.g., the properties of the same state variable may differ under different derivative values). A fuzzy recognizer based on radial basis function neural networks is designed to extend the stability region boundary into an intermediate zone, establishing a relative stability region between the absolute stability region and the danger region of the target vehicle.

[0072] For example, the process for fuzzy identification of boundary-related areas is as follows:

[0073] 1. Select the wheel slip rate, yaw rate, lateral acceleration, and roll angle of the target vehicle as the four-dimensional input layer of the fuzzy recognition system. As a hidden layer, Y For output layer;

[0074] 2. Establish the Gaussian function as follows: radial basis function structure Then training begins, in the formula, Indicates the first The central vector of each perception vector, Indicates the first One hidden layer width, This indicates the dimension of the hidden layer. Representing vectors norm, For output weights;

[0075] 3. Root mean square error If the accuracy of the radial basis function structure is satisfactory, the training ends. The preset accuracy standard, For the expected output value, The output value of the radial basis function structure represents whether the target vehicle is in a stable or unstable state given the four dynamic parameters of the target vehicle currently input.

[0076] Finally, the stability domains of the target vehicle across multiple dimensions are coordinated and coupled to determine a comprehensive stability evaluation index. Specifically, based on the dynamic parameters and dynamic disturbance excitations of the target vehicle in a stable state, the coupling boundaries and attachment constraints of the stability domain limits in each dimension are determined. The stability conditions for each polynomial coefficient in the steady-state system model of the target vehicle are derived based on the Hurwitz criterion. The steady-state system model is a multi-degree-of-freedom dynamic model of the target vehicle with its stable-state dynamic parameters as input. Combining the influence of the target vehicle's dynamic parameters and its own structural parameters on its instability behavior, a steady-state evaluation index based on the deviation characteristics of stability bifurcation points in different dimensions is determined. A comprehensive evaluation index for the stability of the target vehicle is established based on the Nash equilibrium method.

[0077] In this method, the structural parameters of the target vehicle itself include wheelbase, vehicle mass, sprung mass, distance from the center of gravity to the front and rear axles, distance from the center of gravity to the roll axis, equivalent roll stiffness and damping of the whole vehicle, roll and yaw moment of inertia at the center of gravity, tire size and longitudinal and lateral force characteristics; road parameters include road surface adhesion coefficient and lateral and longitudinal slope angles; real-time dynamic parameters of the target vehicle include sprung mass roll angle and angular velocity, yaw rate, lateral acceleration, and four-wheel slip ratio.

[0078] In some embodiments, step S202, combining the comprehensive stability evaluation index of the target vehicle in multiple dimensions, determines the expected control amount of the target vehicle's dynamic parameters in different dimensions, including: determining the phase sequence of the target vehicle's instability in different dimensions based on the dynamic parameters of the target vehicle when it becomes unstable; simulating the instability condition of the target vehicle in each single dimension according to the phase sequence, collecting the corresponding dynamic parameters in other dimensions, and determining the weight ratio of the influence of different dynamic parameters on the stability state of the target vehicle; and determining the expected control amount of the target vehicle's dynamic parameters based on the comprehensive evaluation index of the target vehicle in multiple dimensions and the weight ratio of the influence of different dynamic parameters on the stability state of the target vehicle.

[0079] Taking a comprehensive consideration of three dimensions—piston slip, sideslip, and roll—as an example: First, the dynamic parameters characterizing instability in these three dimensions are observed to determine the phase sequence of multidimensional instability under different operating conditions. These dynamic parameters include slip ratio, yaw rate, lateral acceleration, and roll angle. Slip ratio characterizes piston slip instability, yaw rate characterizes sideslip instability, and lateral acceleration and roll angle characterize roll instability. Then, the instability condition of the target vehicle in a single dimension is set, and dynamic parameters characterizing other dimensions are collected. An invertible matrix between the target vehicle's multidimensional motions is established, and decoupling solutions for instability in different dimensions are performed based on prediction theory to determine the weight ratios of different dynamic parameters' influence on the target vehicle's stability. Finally, based on the comprehensive stability evaluation index of the target vehicle and the aforementioned weight ratios, a multidimensional coupled cooperative controller is designed. Based on the real-time dynamic parameters of the target vehicle, it outputs the expected control quantities of the dynamic parameters in each dimension under different stability domains.

[0080] For the practical application of a multidimensional coupled cooperative controller, the specific steps are as follows:

[0081] First, based on the control objective of the vehicle's driving stability, the sliding mode switching function is defined as follows:

[0082]

[0083] In the formula, , , Indicates the reference state value. , The weighting coefficients representing the deviations of state variables and The setting can be dynamically adjusted based on the road surface adhesion coefficient of the road where the vehicle is traveling. For example, under high adhesion conditions, the longitudinal lateral force limit of the wheels is higher, making them more prone to roll instability; therefore, the setting should be appropriately increased. The value; under low-adhesion conditions, the wheels often experience sideslip or longitudinal slippage instability even when the vehicle body roll angle is very small, therefore, appropriately increasing the value; The value of .

[0084] Then, the Lyapunov function is defined as follows:

[0085]

[0086] Finally, the stability condition of the multidimensional coupled cooperative controller is determined based on the defined Lyapunov function, and the specific expression is as follows:

[0087]

[0088] In some embodiments, step S203, performing vehicle control on the target vehicle according to the desired control quantity in different dimensions, includes: determining the degree of influence of the actuators (differential (or hub motor), differential braking, active suspension and active steering, etc.) of the target vehicle on the stability of the target vehicle in other dimensions, and obtaining the degree of influence result; optimizing and compensating the desired control quantity according to the degree of influence result; and controlling the actuators of the target vehicle in the corresponding dimension according to the optimized and compensated desired control quantity.

[0089] Specifically, firstly, a numerical method is used to determine the dynamic evolution mechanism of the target vehicle under the action of a single actuator in different instability dimensions. The degree of mutual coupling of the target vehicle in different dimensions under the control of multiple actuators is determined and compared with the instability improvement effect of the corresponding dimension under the action of a single actuator. A neural network self-learning algorithm is used to classify the strong and weak coupling regions in multiple dimensions, obtaining a parameterized representation of the multi-actuator coupling contradiction mechanism. Then, based on the parameterized representation of the multi-actuator coupling contradiction mechanism, the influence of actuators in different dimensions on the stability of the target vehicle in other dimensions is determined. Finally, a multi-actuator cooperative controller is designed, inputting the expected control quantities of the dynamic parameters in each dimension under different stability domains. Based on the above influence levels, the main instability dimensions and actuators are determined. In other dimensions, a priori feedback algorithm is used for control quantity compensation to achieve local optimal control. Then, an optimization control algorithm based on Hildreth quadratic programming is used to optimize the expected control quantity. The actuators corresponding to the target vehicle are controlled according to the optimized expected control quantity to achieve the optimal control effect.

[0090] In practical implementation, considering that longitudinal slip, sideslip, and roll instability essentially depend on the longitudinal, lateral, and vertical forces of the tire, an objective function based on tire force distribution can be established, as shown in the following expression:

[0091]

[0092] in:

[0093]

[0094]

[0095]

[0096]

[0097]

[0098] In the formula: Let the objective function be the tire force. and These are two sub-terms of the objective function. The former ensures the vehicle's driving capability and prevents tire longitudinal slippage, while the latter satisfies the yaw moment requirements of the upper-level controller for anti-skid purposes. For longitudinal tire force, T Represents the matrix transpose symbol. The matrix for calculating the yaw moment is derived from the known parameters; To control the allocation matrix and to adjust the magnitude of longitudinal tire forces, The weights are assigned coefficients and used to adjust the proportion of the two allocation terms in the objective function. M z This represents the yaw moment of the target vehicle. Considering that the longitudinal and vertical forces of the tires approximately satisfy a linear relationship, the control allocation matrix... The parameters can be selected as Furthermore, roll stability can be measured by the lateral load transfer rate, therefore roll stability can be indirectly controlled by the tire's longitudinal force distribution. The expression for the lateral load transfer rate is as follows:

[0099]

[0100] in, F zl and F zr These represent the vertical forces on the left and right sides of the target vehicle, respectively. F z1 -F z4 These represent the vertical forces on the four wheels of the target vehicle, with roll as the primary instability dimension. When the target vehicle rolls, the vertical tire force on the roll side increases, leading to an increase in longitudinal force. Based on the elliptical nature of tire friction, the increase in longitudinal force results in a decrease in lateral force, thus increasing the risk of sideslip for the target vehicle. Therefore, a priori feedback algorithm is needed to compensate for the control input, specifically by adjusting the control allocation matrix. The parameters have been redesigned, and the expression is as follows:

[0101]

[0102] in To adjust the parameters, sgn represents the step function symbol. Hildreth quadratic programming is then used to optimize the control quantities of each actuator, as follows:

[0103] First, define the objective function for the constrained optimization problem:

[0104]

[0105] in, The control increment vector for each actuator during each sampling time interval is defined as follows:

[0106]

[0107] in yes The control increment vector at each time step, For each implementing agency in The control variable at time is defined as follows: , respectively corresponding to the first i The driving and braking torque of the wheels, the first i The stiffness and damping of the above-wheel active suspension, and the steering angle of the active steering mechanism. I m Represents the identity matrix.

[0108] definition For the control system coefficient matrix, Let the constraint vectors be defined as follows:

[0109]

[0110] , They are defined as follows:

[0111]

[0112] in, The weight matrix represents the control increment. The reference output vector for each sampling time interval is defined as follows:

[0113]

[0114] in, yes The reference output vector at time step is defined as:

[0115]

[0116] The output vectors in the above formula are longitudinal acceleration, lateral acceleration, four-wheel angular acceleration, yaw angular acceleration, and roll angular velocity, respectively.

[0117] Define separately and The expression is as follows:

[0118]

[0119] in , , , This is the coefficient matrix of the target vehicle's overall system.

[0120] Considering the maximum dynamic and kinematic constraints of the entire vehicle and the maximum actuation constraints of each actuator, the output constraints are defined as follows:

[0121]

[0122] In the formula, and These are the upper and lower limits of the output amplitude, respectively. As a state variable, it is defined as follows:

[0123]

[0124] Introducing the Lagrange operator The optimization problem can be rewritten as follows:

[0125]

[0126] According to the Kuhn-Tucker condition, the necessary condition for the existence of an optimal solution in the above equation is:

[0127]

[0128] The optimal problem is then expressed as follows:

[0129]

[0130] Taking the first-order partial derivative of the above equation, we obtain the optimal solution for the desired control quantity:

[0131]

[0132] Substituting the above formula into the objective function, we can obtain the original expression for... The problem of finding the optimal solution is transformed into finding the dual variables. Find the optimal solution, that is:

[0133]

[0134] In the formula, , ;

[0135] The Hildreth quadratic programming algorithm is used to solve the problem. , means as follows:

[0136]

[0137] In the formula, As an intermediate variable, Representation matrix L The i OK jColumn elements, Representing vectors K The i row element; will Substituting the value into the optimal solution formula yields the desired control quantity. The optimal solution.

[0138] In summary, this method helps vehicles comprehensively consider the impact of changes in dynamic parameters on stability states in different dimensions, and couples and adjusts the vehicle's stability states in different dimensions, improving the evaluation accuracy under multi-dimensional instability conditions. It also solves the problem in existing technologies where different dimensions of stability control cannot be compatible under extreme vehicle conditions. Furthermore, by using a collaborative controller with a multi-actuator coupling contradiction mechanism to optimize the control quantities of each actuator, it overcomes the problem in existing stability control methods where different actuators operate independently, resulting in poor overall vehicle stability control performance.

[0139] It should be noted that the steps shown in the above process or in the flowchart of the accompanying figures can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0140] This invention also provides a vehicle multi-dimensional stability control device for implementing the above embodiments and preferred embodiments; details already described will not be repeated. The terms "module," "unit," "subunit," etc., used below refer to combinations of software and / or hardware that perform a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0141] Figure 6 This is a structural block diagram of the vehicle multi-dimensional stability control device of the present invention, as shown below. Figure 6 As shown, the device includes:

[0142] The index determination module 601 is used to establish a multi-degree-of-freedom dynamic model and determine the comprehensive stability evaluation index of the target vehicle in multiple dimensions based on the multi-degree-of-freedom dynamic model; the dimensions include at least two of longitudinal slip, sideslip and roll.

[0143] The data processing module 602 acquires the real-time dynamic parameters of the target vehicle, and combines them with the comprehensive stability evaluation index of the target vehicle in multiple dimensions to determine the expected control quantities of the dynamic parameters of the target vehicle in different dimensions.

[0144] The control execution module 603 performs vehicle control on the target vehicle according to the desired control quantity under different dimensions.

[0145] In this device, firstly, a multi-degree-of-freedom dynamic model is established through the index determination module 601. This model includes dynamic parameters of the vehicle in different dimensions, characterizing the stability state of the target vehicle under different dynamic parameters in the corresponding dimensions. By comprehensively considering the impact of changes in the dynamic parameters of the target vehicle in different dimensions on its stability state, a comprehensive stability evaluation index is determined. This index comprehensively considers the impact of changes in dynamic parameters on the stability state in different dimensions, facilitating the determination of optimal values ​​for the dynamic parameters that maintain vehicle stability. Then, the real-time dynamic parameters of the target vehicle are substituted into the comprehensive stability evaluation index, combined with the road parameters of the target vehicle's current operating environment and the target vehicle's own structural parameters, to determine the real-time stability state of the target vehicle. Furthermore, the data processing module 602, based on the real-time stability state of the target vehicle, determines optimal values ​​for the dynamic parameters that allow the target vehicle to adjust to or maintain a stable state. Combining these optimal values ​​with the real-time dynamic parameters of the target vehicle, the desired control quantities for the target vehicle's dynamic parameters in different dimensions can be determined. Finally, the control execution module 603 performs vehicle control on the target vehicle according to the determined desired control quantity, issuing the optimal dynamic parameters to the actuators controlling the stability of the target vehicle in various dimensions. This allows the vehicle to adjust to or maintain a stable state in a timely manner with optimal dynamic parameters. This device helps the vehicle comprehensively consider the impact of changes in dynamic parameters on the stability state in different dimensions, coupling and adjusting the vehicle's stability state in different dimensions. This solves the problem in existing technologies where different dimensions of stability control cannot be compatible under extreme vehicle operating conditions.

[0146] It should be noted that the above modules can be functional modules or program modules, and can be implemented through software or hardware. For modules implemented through hardware, the above modules can reside in the same processor; or the above modules can be located in different processors in any combination.

[0147] The present invention also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0148] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0149] Optionally, in one embodiment, the processor described above may be configured to perform the following steps via a computer program:

[0150] S1. Establish a multi-degree-of-freedom dynamic model and determine the comprehensive stability evaluation index of the target vehicle in multiple dimensions based on the multi-degree-of-freedom dynamic model; the dimensions include at least two of longitudinal slip, sideslip and roll.

[0151] S2: Obtain the real-time dynamic parameters of the target vehicle, and combine them with the comprehensive stability evaluation index of the target vehicle in multiple dimensions to determine the expected control quantities of the dynamic parameters of the target vehicle in different dimensions.

[0152] S3 performs vehicle control on the target vehicle based on the desired control quantity under different dimensions.

[0153] It should be noted that specific examples of this electronic device can be found in the embodiments and optional implementations of the above method, and will not be repeated in this embodiment.

[0154] Furthermore, in conjunction with the vehicle multi-dimensional stability control method provided in this invention, a storage medium can also be provided for implementation. This storage medium stores a computer program; when executed by a processor, the computer program implements any of the vehicle multi-dimensional stability control methods described in the above embodiments.

[0155] It should be understood that the specific embodiments described herein are merely illustrative of the application and not intended to limit it. All other embodiments derived by those skilled in the art based on the embodiments provided in this application without inventive effort are within the scope of protection of this application.

[0156] Obviously, the accompanying drawings are merely some examples or embodiments of this application. Those skilled in the art can apply this application to other similar situations based on these drawings without any creative effort. Furthermore, it is understood that although the work done in this development process may be complex and lengthy, for those skilled in the art, certain design, manufacturing, or production modifications made based on the technical content disclosed in this application are merely conventional technical means and should not be considered as insufficient disclosure of this application.

[0157] The term "embodiment" in this application refers to a specific feature, structure, or characteristic described in connection with an embodiment that may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily imply the same embodiment, nor does it imply that it is mutually exclusive with or independent of other embodiments. It will be clearly or implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments without conflict.

Claims

1. A vehicle multi-dimensional stability control method, characterized by, include: A multi-degree-of-freedom dynamic model is established, and a comprehensive stability evaluation index for the target vehicle in multiple dimensions is determined based on the multi-degree-of-freedom dynamic model; the dimensions include at least two of longitudinal slip, sideslip, and roll. The real-time dynamic parameters of the target vehicle are obtained, and combined with the comprehensive stability evaluation index of the target vehicle in multiple dimensions, the expected control quantities of the dynamic parameters of the target vehicle in different dimensions are determined. Vehicle control is performed on the target vehicle based on the desired control values ​​under different dimensions; The multi-degree-of-freedom dynamic model includes: lateral motion model, yaw motion model, longitudinal motion model, roll motion model, and wheel rotation motion model; Wherein, the wheel rotation motion model and the longitudinal motion model correspond to the longitudinal slip dimension; The lateral motion model and the yaw motion model correspond to the sideslip dimension; The tilt motion model corresponds to the tilt dimension; Based on the aforementioned multi-degree-of-freedom dynamic model, the comprehensive stability evaluation index of the target vehicle in multiple dimensions is determined, including: The stability boundaries of the target vehicle under different stability conditions are determined based on the multi-degree-of-freedom dynamic model. The stability boundaries of the target vehicle under different stability conditions are fitted to determine the stability domain of the target vehicle in different dimensions. Coordinate and couple the stability domains of the target vehicle in different dimensions to determine the comprehensive stability evaluation index of the target vehicle in multiple dimensions; Based on the aforementioned multi-degree-of-freedom dynamic model, the stability boundaries of the target vehicle under different stability conditions are determined, including: A Jacobian matrix is ​​constructed, and the eigenvalues ​​of the Jacobian matrix are solved. Based on the solution results, the bifurcation values ​​of each dynamic parameter of the target vehicle under different stability conditions and instability in different dimensions are determined. The Jacobian matrix contains the dynamic parameters in the multi-degree-of-freedom dynamic model. Based on the bifurcation values ​​of various dynamic parameters of the target vehicle when it becomes unstable in different dimensions, the stability boundaries of the target vehicle under different stability conditions are determined.

2. The vehicle multi-dimensional stability control method according to claim 1, characterized by, Fitting the stability boundaries of the target vehicle under different stability conditions to determine the stability domain of the target vehicle in different dimensions includes: The stability boundary of the target vehicle under different stability conditions is fitted using a nonlinear numerical method and a linearized analytical method to determine the stability domain boundary of the target vehicle. The stability domain boundary is used to divide the target vehicle into a stability domain and a danger domain in different dimensions. The stability domain includes an absolute stability domain and a relative stability domain.

3. The vehicle multi-dimensional stability control method according to claim 1, characterized by, Based on the comprehensive stability evaluation index of the target vehicle across multiple dimensions, the desired control quantities of the target vehicle's dynamic parameters under different dimensions are determined, including: The phase sequence of the target vehicle's instability in different dimensions is determined based on the dynamic parameters of the target vehicle at the time of instability. Simulate the instability conditions of the target vehicle in each single dimension according to the phase sequence, collect the dynamic parameters corresponding to other dimensions, and determine the weight ratio of the influence of different dynamic parameters on the stability state of the target vehicle. The desired control quantity of the dynamic parameters of the target vehicle is determined based on the comprehensive evaluation index of the target vehicle in multiple dimensions and the weight ratio of the influence of different dynamic parameters on the stability state of the target vehicle.

4. The vehicle multi-dimensional stability control method according to claim 1, characterized by, Performing vehicle control on the target vehicle according to the desired control quantity includes: Determine the degree of influence of the actuators of the target vehicle in different dimensions on the stability of the target vehicle in other dimensions, and obtain the degree of influence results; The desired control quantity is optimized and compensated based on the results of the degree of influence. The actuators of the target vehicle in the corresponding dimension are controlled according to the expected control quantity after optimization and compensation.

5. A vehicle multi-dimensional stability control device characterized by comprising: include: The index determination module is used to establish a multi-degree-of-freedom dynamic model and determine the comprehensive stability evaluation index of the target vehicle in multiple dimensions based on the multi-degree-of-freedom dynamic model; the dimensions include at least two of longitudinal slip, sideslip, and roll. The multi-degree-of-freedom dynamic model includes: lateral motion model, yaw motion model, longitudinal motion model, roll motion model, and wheel rotation motion model; Wherein, the wheel rotation motion model and the longitudinal motion model correspond to the longitudinal slip dimension; The lateral motion model and the yaw motion model correspond to the sideslip dimension; The tilt motion model corresponds to the tilt dimension; Based on the aforementioned multi-degree-of-freedom dynamic model, the comprehensive stability evaluation index of the target vehicle in multiple dimensions is determined, including: The stability boundaries of the target vehicle under different stability conditions are determined based on the multi-degree-of-freedom dynamic model. The stability boundaries of the target vehicle under different stability conditions are fitted to determine the stability domain of the target vehicle in different dimensions. Coordinate and couple the stability domains of the target vehicle in different dimensions to determine the comprehensive stability evaluation index of the target vehicle in multiple dimensions; Based on the aforementioned multi-degree-of-freedom dynamic model, the stability boundaries of the target vehicle under different stability conditions are determined, including: A Jacobian matrix is ​​constructed, and the eigenvalues ​​of the Jacobian matrix are solved. Based on the solution results, the bifurcation values ​​of each dynamic parameter of the target vehicle under different stability conditions and instability in different dimensions are determined. The Jacobian matrix contains the dynamic parameters in the multi-degree-of-freedom dynamic model. Based on the bifurcation values ​​of each dynamic parameter of the target vehicle when it becomes unstable in different dimensions, the stability boundary of the target vehicle under different stability conditions is determined. The data processing module acquires the real-time dynamic parameters of the target vehicle, and combines them with the comprehensive stability evaluation index of the target vehicle in multiple dimensions to determine the expected control quantities of the dynamic parameters of the target vehicle in different dimensions. The control execution module performs vehicle control on the target vehicle according to the desired control quantity.

6. An electronic device comprising a memory and a processor, characterized in that The memory stores a computer program, and the processor is configured to run the computer program to perform the vehicle multidimensional stability control method according to any one of claims 1 to 4.

7. A computer-readable storage medium having stored thereon a computer program, characterized in that When the computer program is executed by the processor, it implements the steps of the vehicle multidimensional stability control method according to any one of claims 1 to 4.

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