Intelligent Vehicle Stability Determination Method and System

Abstract

This invention provides a method and system for determining the stability of intelligent vehicles. The method includes establishing a three-degree-of-freedom (DOF) dynamic model; constructing a three-dimensional phase space based on the DDF dynamic model; dividing the stability domain boundaries of the three-dimensional phase space to obtain an initial stability domain database; optimizing the initial stability domain data using RBF and nonlinear fitting based on the initial stability domain database; dividing the stability state into several instability states based on the optimized stability domain database model, combined with the three-degree-of-freedom dynamic model, the state parameters of yaw stability, the state parameters of roll stability, the yaw angular velocity, and the load transfer coefficient; establishing stability criteria based on these instability states; and using the stability criteria as the intervention condition for the stability controller. This invention solves the problem of traditional methods' difficulty in identifying coupled instability, improves the adaptability and judgment accuracy of stability boundaries, and helps improve the active safety performance of vehicles.

Description

Technical Field

[0001] This invention relates to the field of vehicle stability control technology, and in particular to an intelligent vehicle stability determination method and system. Background Technology

[0002] With increasing vehicle speeds and road complexity, vehicle instability accidents are becoming more frequent, especially those caused by yaw and roll instability, which seriously threaten driving safety. Currently, common vehicle stability assessment methods are mostly based on linear models or single state parameters, such as threshold judgments based on yaw rate or center-of-gravity sideslip angle. However, in actual driving, vehicle systems exhibit strong nonlinear characteristics, especially under extreme conditions. Factors such as tire sideslip characteristics, road adhesion conditions, and vehicle load transfer are coupled, making it difficult for traditional methods to accurately identify vehicle instability states, particularly the coupled state of yaw and roll instability.

[0003] While existing technologies employ phase space methods for stability analysis, most focus on two-dimensional state spaces, failing to adequately consider the impact of roll stability. Furthermore, stability boundary delineation often relies on experience or linear assumptions, resulting in limited adaptability. In addition, while data-driven stability identification methods have improved accuracy to some extent, their modeling and generalization capabilities for multivariable and nonlinear systems still require improvement. Summary of the Invention

[0004] Therefore, the purpose of this invention is to provide a method and system for determining the stability of intelligent vehicles, so as to overcome the shortcomings of the prior art.

[0005] In a first aspect, the present invention provides a method for determining the stability of an intelligent vehicle, the method comprising:

[0006] A three-degree-of-freedom dynamic model of vehicle lateral movement, vehicle yaw, and vehicle roll is established, and the state parameters of stability of yaw and roll are obtained.

[0007] Based on the aforementioned three-degree-of-freedom dynamic model, a three-dimensional phase space is constructed according to the center-of-mass sideslip angle, the center-of-mass sideslip angular velocity, and the load transfer coefficient.

[0008] Based on the three-dimensional phase space, and according to the spatial trajectory convergence type and load transfer coefficient threshold, the stability domain boundary of the three-dimensional phase space is initially divided to obtain an initial stability domain database.

[0009] Based on the initial stability domain database, by comparing the stability criteria results of the tire force method and the monorail model method with the changes in vehicle driving state parameters, and by using RBF to optimize the initial stability domain data and perform nonlinear fitting, an optimized stability domain database model is obtained.

[0010] Based on the optimized stability domain database model, and combined with the three-degree-of-freedom dynamic model, the state parameters of the yaw stability, the state parameters of the tilt stability, the angular velocity of the yaw, and the load transfer coefficient, the stability state is divided into several unstable states. Stability criteria are established based on several unstable states, and the stability criteria are used as the intervention conditions of the stability controller.

[0011] Compared with existing technologies, the beneficial effects of this invention are as follows: By constructing a three-dimensional phase space including the center of mass sideslip angle, the center of mass sideslip angular velocity, and LTR, it can simultaneously reflect the yaw and roll stability states of the vehicle, solving the problem that traditional methods are difficult to identify coupled instability. Based on phase trajectory convergence, the stability domain is initially divided by combining the load transfer coefficient threshold. The method is intuitive, computationally simple, and suitable for real-time control. The introduction of an RBF neural network for nonlinear optimization and fitting of the initial stability domain significantly improves the adaptability and judgment accuracy of the stability boundary, making it particularly suitable for driving environments with strong nonlinearity and complex operating conditions. The final established stability criterion can clearly distinguish multiple instability modes, providing accurate input basis for the vehicle stability control system and helping to improve the active safety performance of the vehicle.

[0012] Furthermore, the expressions for the lateral dynamics equations of the vehicle, the yaw dynamics equations of the vehicle, and the roll dynamics equations of the vehicle are respectively:

[0013] ;

[0014] ;

[0015] ;

[0016] In the formula, Indicates the overall vehicle weight. Indicates lateral acceleration. Indicates the longitudinal driving speed. Indicates yaw rate. Indicates the sprung mass. This represents the distance from the center of mass to the center of roll. , These represent the vehicle body roll rate and the vehicle body roll acceleration, respectively. , These represent the lateral forces of the front tire and the rear tire, respectively. , They represent the car body circling The moment of inertia of the axis represents the roll-yaw coupled inertia product. Indicates yaw acceleration. , These represent the distances from the front wheels to the center of gravity and the distances from the rear wheels to the center of gravity, respectively. Represents gravitational acceleration. Indicates the equivalent roll stiffness. This represents the equivalent roll damping coefficient.

[0017] Furthermore, the calculation expressions for the front tire lateral force and the rear tire lateral force are as follows:

[0018] ;

[0019] ;

[0020] In the formula, , These represent the lateral forces of the front tire and the rear tire, respectively. , These represent the vertical load on the front tire and the vertical load on the rear tire, respectively. Indicates the road surface adhesion coefficient. , These represent the front tire stiffness factor and the rear tire stiffness factor, respectively. , These represent the front tire shape factor and the rear tire shape factor, respectively. , These represent the front tire slip angle and the rear tire slip angle, respectively.

[0021] Furthermore, prior to the step of constructing a three-dimensional phase space based on the three-degree-of-freedom dynamic model and according to the center-of-mass sideslip angle, the center-of-mass sideslip angular velocity, and the load transfer coefficient, the method further includes:

[0022] Based on the three-degree-of-freedom model, the equivalent calculation expression for the load transfer coefficient is obtained, and the equivalent calculation expression for the load transfer coefficient is as follows:

[0023] ;

[0024] In the formula, Indicates the load transfer factor. This represents the distance from the center of mass to the center of roll. Indicates the lateral acceleration of the vehicle body. Represents gravitational acceleration. Indicates the body roll angle. This indicates the wheelbase (left and right).

[0025] Furthermore, the step of initially dividing the stability domain boundary of the three-dimensional phase space based on the three-dimensional phase space and according to the spatial trajectory convergence type and load transfer coefficient threshold to obtain an initial stability domain database includes:

[0026] Based on the characteristics of the stable equilibrium point of the phase diagram of the centroid sideslip angle and the centroid sideslip angular velocity, the stable boundary of the phase diagram of the centroid sideslip angle and the centroid sideslip angular velocity is divided by a rhombic stable boundary.

[0027] By combining the load transfer coefficient and introducing the roll stability boundary, a three-dimensional phase space stability domain boundary composed of the load transfer coefficient is obtained, and a preliminary stability domain database for different longitudinal vehicle speeds, road adhesion coefficients, and front wheel steering angles is established.

[0028] Furthermore, the step of optimizing and nonlinearly fitting the initial stability domain data based on the initial stability domain database by comparing the stability criteria results of the tire force method and the monorail model method with the changes in vehicle driving state parameters, and using RBF, includes:

[0029] Based on the initial stable domain database and combined with the centroid sideslip angle and centroid sideslip angular velocity, the diagonal of the three-dimensional rhombic stable domain is scaled.

[0030] The boundary of the three-dimensional rhombic stability domain is adjusted by comparing the results of the tire force method and the single-track model method of vehicle state parameters, and by combining the changes in vehicle driving state parameters. The boundary data of the three-dimensional rhombic stability domain is then fitted by an RBF neural network.

[0031] Furthermore, the expression for the unstable state is:

[0032] ;

[0033] In the formula, The criterion value representing the unstable state. , These represent the reference yaw rate value of the linear model and the actual yaw rate state quantity of the vehicle, respectively. Indicates the load transfer factor. Indicates the LTR threshold. Represents the AND operator.

[0034] Secondly, the present invention also provides an intelligent vehicle stability determination system, the system comprising:

[0035] A module is established to create a three-degree-of-freedom dynamic model of the vehicle's lateral, yaw, and roll directions, and to obtain the state parameters of the stability of the yaw and the stability of the roll direction.

[0036] The construction module is used to construct a three-dimensional phase space based on the three-degree-of-freedom dynamic model and according to the center-of-mass sideslip angle, center-of-mass sideslip angular velocity and load transfer coefficient;

[0037] The partitioning module is used to initially partition the stability domain boundaries of the three-dimensional phase space based on the spatial trajectory convergence type and load transfer coefficient threshold, so as to obtain an initial stability domain database.

[0038] The optimization module is used to optimize the initial stable domain database based on the initial stable domain database by comparing the stability criteria results of the tire force method and the monorail model method with the changes in vehicle driving state parameters, and by using RBF to perform nonlinear fitting on the initial stable domain data, so as to obtain an optimized stable domain database model.

[0039] The determination module is used to divide the stability state into several unstable states based on the optimized stability domain database model, combined with the three-degree-of-freedom dynamic model, the state parameters of the yaw stability, the state parameters of the tilt stability, the angular velocity of the yaw, and the load transfer coefficient, and to establish a stability criterion based on the several unstable states, and to use the stability criterion as the intervention condition of the stability controller.

[0040] Thirdly, the present invention also 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 above-described intelligent vehicle stability determination method.

[0041] Fourthly, the present invention also provides a storage medium storing a computer program thereon, which, when executed by a processor, implements the above-described method for determining the stability of intelligent vehicles. Attached Figure Description

[0042] Figure 1 This is a flowchart of the intelligent vehicle stability determination method in the first embodiment of the present invention;

[0043] Figure 2 This is a schematic diagram of the stable region of the phase diagram provided in the first embodiment of the present invention;

[0044] Figure 3 This is a schematic diagram of the framework of the vehicle yaw-roll stability criterion method in the first embodiment of the present invention;

[0045] Figure 4 This is a trajectory comparison diagram of an exemplary vehicle test under extreme conditions;

[0046] Figure 5 A schematic diagram showing the comparison of yaw rates during vehicle testing;

[0047] Figure 6 This is a diagram showing the comparison of roll angles during vehicle testing.

[0048] Figure 7This is a trajectory comparison diagram of an exemplary vehicle test under extreme conditions;

[0049] Figure 8 A schematic diagram showing the comparison of yaw rates during vehicle testing;

[0050] Figure 9 This is a diagram showing the comparison of roll angles during vehicle testing.

[0051] Figure 10 A comparison chart of roll angles for an example of a vehicle under extreme operating conditions test;

[0052] Figure 11 This is a structural block diagram of the intelligent vehicle stability determination system in the second embodiment of the present invention;

[0053] Figure 12 This is a schematic diagram of the structure of the electronic device in the third embodiment of the present invention.

[0054] Explanation of key component symbols:

[0055] 10. Establish module; 20. Build module; 30. Divide module; 40. Optimize module; 50. Decision module;

[0056] 60. Bus; 61. Processor; 62. Memory; 63. Communication interface.

[0057] The following detailed description, in conjunction with the accompanying drawings, will further illustrate the present invention. Detailed Implementation

[0058] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Several embodiments of the invention are illustrated in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0059] It should be noted that when a component is said to be "fixed to" another component, it can be directly on the other component or there may be an intervening component. When a component is said to be "connected to" another component, it can be directly connected to the other component or there may be an intervening component. The terms "vertical," "horizontal," "left," "right," and similar expressions used in this document are for illustrative purposes only.

[0060] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0061] Example 1

[0062] Please see Figure 1 The figure shows a method for determining the stability of an intelligent vehicle in the first embodiment of the present invention, the method comprising steps S1 to S5:

[0063] S1. Establish a three-degree-of-freedom dynamic model of vehicle lateral movement, vehicle yaw, and vehicle roll, and obtain the state parameters of stability of the yaw and the state parameters of stability of the roll.

[0064] In this embodiment, the dynamic equations for the vehicle's lateral movement, yaw, and roll are respectively:

[0065] ;

[0066] ;

[0067] ;

[0068] In the formula, Indicates the overall vehicle weight. Indicates lateral acceleration. Indicates the longitudinal driving speed. Indicates yaw rate. Indicates the sprung mass. This represents the distance from the center of mass to the center of roll. , This indicates the vehicle body roll rate and roll acceleration. , These represent the lateral forces of the front tire and the rear tire, respectively. , Indicates the car body is around The moment of inertia of the axis represents the roll-yaw coupled inertia product. Indicates yaw acceleration. , These represent the distances from the front wheels to the center of gravity and the distances from the rear wheels to the center of gravity, respectively. Represents gravitational acceleration. Indicates the equivalent roll stiffness. This represents the equivalent roll damping coefficient.

[0069] S2, Based on the three-degree-of-freedom dynamic model, a three-dimensional phase space is constructed according to the center of mass sideslip angle, the center of mass sideslip angular velocity, and the load transfer coefficient;

[0070] It is understandable that the lateral force in the vehicle dynamics model... , Given the influence of tire lateral characteristics, the tire lateral force needs to be calculated based on a nonlinear tire model. Furthermore, vehicle instability typically occurs within the tire's nonlinear operating region; therefore, the Magic Formula tire model is chosen, and its calculation expression is as follows:

[0071] ;

[0072] In the formula, Indicates the lateral force of the tire. Indicates vertical load. Indicates the road surface adhesion coefficient. Represents the stiffness factor. Represents the shape factor. Indicates the tire slip angle;

[0073] In this embodiment, the calculation expressions for the lateral force of the front tire and the lateral force of the rear tire are as follows:

[0074] ;

[0075] ;

[0076] In the formula, , These represent the lateral forces of the front tire and the rear tire, respectively. , These represent the vertical load on the front tire and the vertical load on the rear tire, respectively. Indicates the road surface adhesion coefficient. , These represent the front tire stiffness factor and the rear tire stiffness factor, respectively. , These represent the front tire shape factor and the rear tire shape factor, respectively. , These represent the front tire slip angle and the rear tire slip angle, respectively.

[0077] The expressions for the front tire slip angle and the rear tire slip angle are as follows:

[0078] ;

[0079] In the formula, Indicates the centroid sideslip angle. , These represent the distances from the front wheels to the center of gravity and the distances from the rear wheels to the center of gravity, respectively. Indicates the longitudinal driving speed. Indicates yaw rate;

[0080] It should be noted that the lateral load transfer ratio (LTR) is used as the roll stability condition in phase space, and the expression for calculating LTR is as follows:

[0081] ;

[0082] In the formula, This indicates the vertical load on the left wheel. Indicates the vertical load on the right wheel;

[0083] Furthermore, based on the aforementioned three-degree-of-freedom model, an equivalent calculation expression for the load transfer coefficient is obtained, which is:

[0084] ;

[0085] In the formula, Indicates the load transfer factor. This represents the distance from the center of mass to the center of roll. Indicates the lateral acceleration of the vehicle body. Represents gravitational acceleration. Indicates the body roll angle. This represents the left and right wheelbase. Understandably, based on the established vehicle dynamics model, a series of vehicle state variables are established at different vehicle speeds, road adhesion coefficients, and front wheel steering angles, including the center of gravity sideslip angle, center of gravity sideslip angular velocity, and LTR, to create a three-dimensional state phase space.

[0086] S3. Based on the three-dimensional phase space, and according to the spatial trajectory convergence type and load transfer coefficient threshold, the stability domain boundary of the three-dimensional phase space is initially divided to obtain an initial stability domain database.

[0087] Specifically, step S3 includes steps S31 to S32:

[0088] S31, based on the characteristics of the stable equilibrium point of the phase diagram of the centroid side slip angle and the centroid side slip angular velocity, the stable boundary of the centroid side slip angle and the centroid side slip angular velocity of the phase diagram is divided by a rhombic stable boundary.

[0089] S32, combining the load transfer coefficient and introducing the roll stability boundary, to obtain the three-dimensional phase space stability domain boundary formed by the load transfer coefficient, and to establish a preliminary stability domain database for different longitudinal vehicle speeds, road surface adhesion coefficients and front wheel steering angles.

[0090] It should be noted that you should refer to [link / reference]. Figure 2Based on the stable equilibrium point characteristics of the phase diagram of the sideslip angle-sidelip angular velocity phase diagram, a diamond-shaped stable boundary is used to divide the phase diagram into stable boundaries for the sideslip angle and the sidelip angular velocity phase diagram. Then, combined with the LTR value, a roll stability boundary is introduced, thus obtaining a stable equilibrium point based on the sideslip angle. angular velocity of center of mass deflection A three-dimensional phase space stability domain composed of LTR is then established for different longitudinal vehicle speeds. Road surface adhesion coefficient and front wheel steering angle A preliminary three-dimensional stability domain database in phase space;

[0091] Among them, the phase space trajectory is divided into stable states according to the convergence status and the LTR threshold:

[0092] ;

[0093] ;

[0094] In the formula, , , These represent the coordinates of the phase trajectory endpoint, the coordinates of the phase diagram stable equilibrium point, and the centroid sideslip angle convergence tolerance, respectively.

[0095] Based on the stable equilibrium point of the phase diagram The corresponding maximum and minimum centroid sideslip angles , The maximum and minimum values ​​of the lateral angular velocity of the center of mass. , Then consider the roll stability constraints. Delineate stable regions:

[0096] ;

[0097] Combining the phase trajectory convergence condition and the above constraints, the rhombic hexahedral stable region in the stability criterion is finally obtained.

[0098] S4. Based on the initial stability domain database, by comparing the stability criteria results of the tire force method and the monorail model method with the changes in vehicle driving state parameters, and by using RBF to optimize and nonlinearly fit the initial stability domain data, an optimized stability domain database model is obtained.

[0099] Specifically, step S4 includes steps S41 to S42:

[0100] S41, Based on the initial stable domain database and combined with the centroid sideslip angle and centroid sideslip angular velocity, the diagonal of the three-dimensional rhombic stable domain is scaled.

[0101] S42, compare the results of the tire force method and the single-track model method of vehicle state parameters, and adjust the boundary of the three-dimensional rhombic stability domain by combining the changes in vehicle driving state parameters, and fit the boundary data of the three-dimensional rhombic stability domain by RBF neural network.

[0102] It should be noted that, based on the obtained preliminary stability domain database and the convenience of using the rhombus method to delineate the boundaries of the centroid sideslip angle and centroid sideslip angular velocity, the diagonal of the three-dimensional rhombus stability domain is scaled. The results of the tire force method and the monorail model method with vehicle state parameters as reference are compared, and the boundaries of the three-dimensional rhombus stability domain are adjusted in combination with the changes in vehicle running stability parameters.

[0103] The single-track model method, based on a linear 2-DOF vehicle dynamics model, identifies the vehicle state by the difference between the actual vehicle state variables and the state variables of the linear 2-DOF vehicle model. In this embodiment, the expression for the linear 2-DOF vehicle model is:

[0104] ;

[0105] ;

[0106] ;

[0107] In the formula, Indicates the overall vehicle weight. Indicates lateral acceleration. Indicates the longitudinal driving speed. This indicates the vertical load on the left wheel. This indicates the vertical load on the right wheel. Indicates the car body is around Moment of inertia of the shaft Indicates yaw acceleration. , These represent the distances from the front wheels to the center of gravity and the distances from the rear wheels to the center of gravity, respectively. Indicates the front wheel lateral stiffness. Indicates the front wheel slip angle. Indicates the rear wheel lateral stiffness. Indicates the rear wheel slip angle; by adjusting a series of different longitudinal speeds Road surface adhesion coefficient and front wheel steering angle The initial stable domain database is constructed, and new three-dimensional stable domain boundary data are obtained.

[0108] Then, by using an RBF neural network to fit the stable domain boundary data, an optimized stable domain database model is obtained. Specifically, to obtain stable region boundary data under different vehicle speeds, road adhesion coefficients, and front wheel steering angles using the RBF network, a nonlinear mapping relationship from vehicle speed, road adhesion coefficient, and front wheel steering angle to the vertices of the stable region is first established:

[0109] ;

[0110] In the formula, , , Each endpoint represents a separate endpoint. axis coordinate values, , Each endpoint represents a separate endpoint. axis coordinate values, , Each endpoint represents a separate endpoint. The axis coordinate values ​​are derived from The coordinates of the vertices of the rhombic hexahedron are: , , , , , , , ;

[0111] ;

[0112] ;

[0113] in, The output of the RBF neural network, The connection weights from the hidden layer to the output layer. This is the output layer bias. For radial basis functions, As the center of the kernel function, This is the width parameter.

[0114] To eliminate the influence of different parameter dimensions on the fitting of the stable region and improve the fitting effect of the RBF neural network, the vehicle speed, road adhesion coefficient, and front wheel steering angle data are normalized:

[0115] ;

[0116] ;

[0117] ;

[0118] in, , , for , , The normalized value, , , , , , for minimum and maximum values minimum and maximum values The minimum and maximum values.

[0119] Normalize the boundary parameter data of the stable region:

[0120] ;

[0121] ;

[0122] ;

[0123] ;

[0124] ;

[0125] ;

[0126] ;

[0127] in, , They represent The minimum and maximum values, , They represent The minimum and maximum values, , They represent The minimum and maximum values, , They represent The minimum and maximum values, , They represent The minimum and maximum values, , They represent The minimum and maximum values, , They represent The minimum and maximum values, , , , , , , They represent , , , , , , The normalized value; , , , , , , Let x and y represent the x-coordinates of the upper and lower endpoints, the y-coordinates of the upper and lower endpoints, the x-coordinate of the left and right endpoints, the z-coordinate of the upper surface of the rhombus, and the z-coordinate of the lower surface of the rhombus, respectively. Indicates the multiplication sign;

[0128] Therefore, the boundary parameters of the stable region obtained by the RBF neural network are inversely normalized:

[0129] ;

[0130] in, , , , , , , for , , , , , , The value after inverse normalization.

[0131] Through the endpoints of the stable region obtained above, a rhombic hexahedral stable region in three-dimensional space is obtained. Since the obtained stable region is a convex hexahedron, combined with the geometric properties of convex polyhedra, the normal vector is used to determine whether a point is located inside the hexahedron:

[0132] For point :

[0133] ;

[0134] in, For the equations of each plane, For planar parameters, It is the plane normal vector.

[0135] S5. Based on the optimized stability domain database model, and combined with the three-degree-of-freedom dynamic model, the state parameters of the yaw stability, the state parameters of the tilt stability, the angular velocity of the yaw, and the load transfer coefficient, the stability state is divided into several unstable states. Stability criteria are established based on several unstable states, and the stability criteria are used as the intervention conditions of the stability controller.

[0136] Understandably, by combining the yaw rate reference value and LTR threshold of the vehicle dynamics model, the specific instability types of the vehicle are classified into understeer, oversteer, roll instability, understeer and roll instability, and oversteer and roll instability. Corresponding yaw and roll controllers are then activated according to different instability types. In this embodiment, the expression for the instability state is:

[0137] ;

[0138] In the formula, The criterion value representing the unstable state. , These represent the reference yaw rate value of the linear model and the actual yaw rate state quantity of the vehicle, respectively. Indicates the load transfer factor. Indicates the LTR threshold. The AND operator is represented.

[0139] In this design, front-wheel steering control (AFS) is used as the yaw stability controller, and active suspension force control is used as the roll stability controller. Stability criteria serve as the intervention conditions for each stability controller, while simultaneously preventing excessive stabilizer intervention that could lead to stability degradation. The intervention mechanisms of the yaw and roll stability controllers are as follows:

[0140] ;

[0141] Indicates the control type;

[0142] Criterion framework such as Figure 3 As shown, the verification case is as follows: a vehicle model was built in the CarSim / Simulink co-simulation platform, and tests were conducted on double lane change and side roll conditions.

[0143] Please see Figures 4 to 6As shown, under this low-speed, low-coefficient-of-adhesion condition, the vehicle becomes unstable without stability control. When AFS (Adaptive Front-Side Steering) front-wheel steering control is used, the vehicle's condition improves compared to the uncontrolled condition. However, due to excessive controller intervention, the vehicle body shakes from side to side. After applying the stability criterion and combining it with the AFS controller, the vehicle reaches a steady state in 10.4 seconds. It can be seen that using this method of criterion, combined with a stability controller, can effectively improve the vehicle's stability under low coefficients of road adhesion.

[0144] Please see Figures 7 to 9 Under high-speed, high-adhesion road surface conditions, the vehicle becomes unstable without stability control. While the AFS controller improves trajectory tracking, a significant tendency towards instability still exists. However, by employing a criterion and combining it with the AFS controller, the trajectory tracking is further improved, closely approximating the reference trajectory, with both yaw rate and roll angle amplitudes lower than those without the criterion. This demonstrates that using this criterion, combined with a stability controller, effectively enhances vehicle stability under high road surface adhesion coefficients.

[0145] like Figure 10 As shown, under roll conditions, the criterion of this method, combined with the roll stability controller, can effectively reduce the vehicle roll angle under extreme conditions and improve the vehicle roll stability.

[0146] The results show that, compared with the traditional threshold-based judgment method, it has higher accuracy and timeliness in identifying yaw-roll coupling instability, and maintains good performance under extreme conditions such as low-adhesion road surfaces and high-speed steering.

[0147] In summary, the intelligent vehicle stability determination method in the above embodiments of the present invention, by constructing a three-dimensional phase space including the center of mass sideslip angle, the center of mass sideslip angular velocity, and LTR, can simultaneously reflect the yaw and roll stability states of the vehicle, solving the problem that traditional methods are difficult to identify coupled instability. Based on phase trajectory convergence, the stability domain is initially divided by combining the load transfer coefficient threshold. The method is intuitive, computationally simple, and suitable for real-time control. The introduction of the RBF neural network for nonlinear optimization and fitting of the initial stability domain significantly improves the adaptability and judgment accuracy of the stability boundary, making it particularly suitable for driving environments with strong nonlinearity and complex operating conditions. The final established stability criterion can clearly distinguish multiple instability modes, providing accurate input basis for the vehicle stability control system and helping to improve the active safety performance of the vehicle.

[0148] Example 2

[0149] The second embodiment of the present invention also provides an intelligent vehicle stability determination system, please refer to [link / reference]. Figure 11 The figure shows an intelligent vehicle stability determination system according to a second embodiment of the present invention. The system includes:

[0150] Module 10 is used to establish a three-degree-of-freedom dynamic model of the vehicle's lateral, yaw, and roll directions, and to obtain the state parameters for the stability of the yaw and roll directions. The dynamic equations for the vehicle's lateral, yaw, and roll directions are as follows:

[0151] ;

[0152] ;

[0153] ;

[0154] In the formula, Indicates the overall vehicle weight. Indicates lateral acceleration. Indicates the longitudinal driving speed. Indicates yaw rate. Indicates the sprung mass. This represents the distance from the center of mass to the center of roll. , These represent the vehicle body roll rate and the vehicle body roll acceleration, respectively. , These represent the lateral forces of the front tire and the rear tire, respectively. , They represent the car body circling The moment of inertia of the axis represents the roll-yaw coupled inertia product. Indicates yaw acceleration. , These represent the distances from the front wheels to the center of gravity and the distances from the rear wheels to the center of gravity, respectively. Represents gravitational acceleration. Indicates the equivalent roll stiffness. The equivalent roll damping coefficient is represented by the formulas for calculating the lateral force of the front tire and the lateral force of the rear tire, respectively:

[0155] ;

[0156] ;

[0157] In the formula, , These represent the lateral forces of the front tire and the rear tire, respectively. , These represent the vertical load on the front tire and the vertical load on the rear tire, respectively. Indicates the road surface adhesion coefficient. , These represent the front tire stiffness factor and the rear tire stiffness factor, respectively. , These represent the front tire shape factor and the rear tire shape factor, respectively. , These represent the front tire slip angle and the rear tire slip angle, respectively.

[0158] Module 20 is used to construct a three-dimensional phase space based on the three-degree-of-freedom dynamic model and according to the center-of-mass sideslip angle, center-of-mass sideslip angular velocity and load transfer coefficient;

[0159] The partitioning module 30 is used to initially partition the stability domain boundary of the three-dimensional phase space based on the three-dimensional phase space and according to the spatial trajectory convergence type and load transfer coefficient threshold, so as to obtain an initial stability domain database.

[0160] The optimization module 40 is used to optimize the initial stable domain database based on the initial stable domain database by comparing the stability criteria results of the tire force method and the monorail model method with the changes in vehicle driving state parameters, and by using RBF to perform nonlinear fitting on the initial stable domain data, so as to obtain an optimized stable domain database model.

[0161] The determination module 50 is used to classify the stability state into several unstable states based on the optimized stability domain database model, combined with the three-degree-of-freedom dynamic model, the state parameters of the yaw stability, the state parameters of the roll stability, the angular velocity of the yaw, and the load transfer coefficient. It then establishes stability criteria based on these unstable states and uses these criteria as the intervention conditions for the stability controller. The expression for each unstable state is:

[0162] ;

[0163] In the formula, The criterion value representing the unstable state. , These represent the reference yaw rate value of the linear model and the actual yaw rate state quantity of the vehicle, respectively. Indicates the load transfer factor. Indicates the LTR threshold. Represents the AND operator.

[0164] Specifically, in some optional embodiments, the building module 20 includes:

[0165] The acquisition unit is used to obtain an equivalent calculation expression for the load transfer coefficient based on the three-degree-of-freedom model. The equivalent calculation expression for the load transfer coefficient is as follows:

[0166] ;

[0167] In the formula, Indicates the load transfer factor. This represents the distance from the center of mass to the center of roll. Indicates the lateral acceleration of the vehicle body. Represents gravitational acceleration. Indicates the body roll angle. This indicates the wheelbase (left and right).

[0168] Specifically, in some optional embodiments, the partitioning module 30 includes:

[0169] The dividing unit is used to divide the stable boundary of the phase diagram of the centroid side slip angle and the centroid side slip angular velocity according to the characteristics of the stable equilibrium point of the phase diagram, and by means of a rhombic stable boundary.

[0170] A setup unit is introduced to combine the load transfer coefficient and introduce the roll stability boundary to obtain the three-dimensional phase space stability domain boundary formed by the load transfer coefficient, and to establish a preliminary stability domain database for different longitudinal vehicle speeds, road adhesion coefficients and front wheel steering angles.

[0171] Specifically, in some optional embodiments, the optimization module 40 includes:

[0172] The scaling unit is used to scale the diagonal of the three-dimensional rhombic stable domain based on the initial stable domain database and in combination with the centroid sideslip angle and the centroid sideslip angular velocity.

[0173] The adjustment unit is used to adjust the boundary of the three-dimensional rhombic stability domain by comparing the results of the tire force method and the single-track model method of vehicle state parameters, and by combining the changes in vehicle driving state parameters, and to fit the boundary data of the three-dimensional rhombic stability domain through an RBF neural network.

[0174] The functions or operation steps implemented by the above modules and units are largely the same as those in the above method embodiments, and will not be repeated here.

[0175] The intelligent vehicle stability determination system provided in this embodiment of the invention has the same implementation principle and technical effects as the aforementioned method embodiment. For the sake of brevity, any parts not mentioned in the system embodiment can be referred to the corresponding content in the aforementioned method embodiment.

[0176] Example 3

[0177] The third embodiment of the present invention also proposes an electronic device, please refer to [link / reference]. Figure 12 The image shows an electronic device according to a third embodiment of the present invention.

[0178] The electronic device may include a processor 61 and a memory 62 storing computer program instructions.

[0179] Specifically, the processor 61 may include a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the present application.

[0180] The memory 62 may include a large-capacity storage device for data or instructions. For example, and not limitingly, the memory 62 may include a hard disk drive (HDD), a floppy disk drive, a solid-state drive (SSD), flash memory, an optical disk drive, a magneto-optical disk drive, magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 62 may include removable or non-removable (or fixed) media. Where appropriate, the memory 62 may be internal or external to a data processing device. In a particular embodiment, the memory 62 is non-volatile memory. In a particular embodiment, the memory 62 includes read-only memory (ROM) and random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), an electrically alterable read-only memory (EAROM), or flash memory, or a combination of two or more of these. Where appropriate, the RAM can be Static Random-Access Memory (SRAM) or Dynamic Random-Access Memory (DRAM). DRAM can be Fast Page Mode Dynamic Random-Access Memory (FPMDRAM), Extended Data Out Dynamic Random-Access Memory (EDODRAM), Synchronous Dynamic Random-Access Memory (SDRAM), etc.

[0181] The memory 62 can be used to store or cache various data files that need to be processed and / or communicated, as well as possible computer program instructions executed by the processor 61.

[0182] The processor 61 reads and executes the computer program instructions stored in the memory 62 to implement the intelligent vehicle stability determination method of the above embodiment 1.

[0183] In some embodiments, the electronic device may further include a communication interface 63 and a bus 60. For example, Figure 12 As shown, the processor 61, memory 62, and communication interface 63 are connected through bus 60 and complete communication with each other.

[0184] The communication interface 63 is used to enable communication between the various modules, devices, units, and / or equipment in this application. The communication interface 63 can also enable data communication with other components such as external devices, image / data acquisition devices, databases, external storage, and image / data processing workstations.

[0185] Bus 60 includes hardware, software, or both, that couples components of a device together. Bus 60 includes, but is not limited to, at least one of the following: data bus, address bus, control bus, expansion bus, and local bus. For example, and not as a limitation, bus 60 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, bus 60 may include one or more buses. Although this application describes and illustrates a specific bus, this application considers any suitable bus or interconnection.

[0186] The electronic device can acquire the intelligent vehicle stability determination system and execute the intelligent vehicle stability determination method of this embodiment.

[0187] Furthermore, in conjunction with the intelligent vehicle stability determination method in Embodiment 1 above, this application can provide a storage medium for implementation. This storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement the intelligent vehicle stability determination method of Embodiment 1 above.

[0188] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0189] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for determining the stability of an intelligent vehicle, characterized in that, The method includes: Establish a three-degree-of-freedom dynamic model of vehicle lateral movement, vehicle yaw, and vehicle roll, and obtain the state parameters of stability of the yaw and the state parameters of stability of the roll. Based on the aforementioned three-degree-of-freedom dynamic model, a three-dimensional phase space is constructed according to the center-of-mass sideslip angle, the center-of-mass sideslip angular velocity, and the load transfer coefficient. Based on the three-dimensional phase space, and according to the spatial trajectory convergence type and load transfer coefficient threshold, the stability domain boundary of the three-dimensional phase space is initially divided to obtain an initial stability domain database. Based on the initial stability domain database, by comparing the stability criteria results of the tire force method and the monorail model method with the changes in vehicle driving state parameters, and by using RBF to optimize the initial stability domain data and perform nonlinear fitting, an optimized stability domain database model is obtained. Based on the optimized stability domain database model, and combined with the three-degree-of-freedom dynamic model, the state parameters of the yaw stability, the state parameters of the tilt stability, the angular velocity of the yaw, and the load transfer coefficient, the stability state is divided into several unstable states. Stability criteria are established based on several unstable states, and the stability criteria are used as the intervention conditions of the stability controller.

2. The method for determining the stability of intelligent vehicles according to claim 1, characterized in that, The expressions for the lateral dynamics of the vehicle, the yaw dynamics of the vehicle, and the roll dynamics of the vehicle are as follows: ； ； ； In the formula, Indicates the overall vehicle weight. Indicates lateral acceleration. Indicates the longitudinal driving speed. Indicates yaw rate. Indicates the sprung mass. This represents the distance from the center of mass to the center of roll. , These represent the vehicle body roll rate and the vehicle body roll acceleration, respectively. , These represent the lateral forces of the front tire and the rear tire, respectively. , They represent the car body circling The moment of inertia of the axis represents the roll-yaw coupled inertia product. Indicates yaw acceleration. , These represent the distances from the front wheels to the center of gravity and the distances from the rear wheels to the center of gravity, respectively. Represents gravitational acceleration. Indicates the equivalent roll stiffness. This represents the equivalent roll damping coefficient.

3. The method for determining the stability of intelligent vehicles according to claim 2, characterized in that, The calculation expressions for the lateral force of the front tire and the lateral force of the rear tire are as follows: ； ； In the formula, , These represent the lateral forces of the front tire and the rear tire, respectively. , These represent the vertical load on the front tire and the vertical load on the rear tire, respectively. Indicates the road surface adhesion coefficient. , These represent the front tire stiffness factor and the rear tire stiffness factor, respectively. , These represent the front tire shape factor and the rear tire shape factor, respectively. , These represent the front tire slip angle and the rear tire slip angle, respectively.

4. The intelligent vehicle stability determination method according to claim 1, characterized in that, Before the step of constructing a three-dimensional phase space based on the three-degree-of-freedom dynamic model and according to the center-of-mass sideslip angle, the center-of-mass sideslip angular velocity, and the load transfer coefficient, the method further includes: Based on the aforementioned three-degree-of-freedom model, an equivalent calculation expression for the load transfer coefficient is obtained. The equivalent calculation expression for the load transfer coefficient is as follows: ； In the formula, Indicates the load transfer factor. This represents the distance from the center of mass to the center of roll. Indicates the lateral acceleration of the vehicle body. Represents gravitational acceleration. Indicates the body roll angle. This indicates the wheelbase (left and right).

5. The method for determining the stability of intelligent vehicles according to claim 1, characterized in that, The step of initially dividing the stability domain boundary of the three-dimensional phase space based on the spatial trajectory convergence type and load transfer coefficient threshold to obtain an initial stability domain database includes: Based on the characteristics of the stable equilibrium point of the phase diagram of the centroid sideslip angle and the centroid sideslip angular velocity, the stable boundary of the phase diagram of the centroid sideslip angle and the centroid sideslip angular velocity is divided by a rhombic stable boundary. By combining the load transfer coefficient and introducing the roll stability boundary, a three-dimensional phase space stability domain boundary composed of the load transfer coefficient is obtained, and a preliminary stability domain database for different longitudinal vehicle speeds, road surface adhesion coefficients, and front wheel steering angles is established.

6. The method for determining the stability of intelligent vehicles according to claim 1, characterized in that, The steps of optimizing and performing nonlinear fitting on the initial stability domain data based on the initial stability domain database, by comparing the stability criteria results of the tire force method and the monorail model method with the changes in vehicle driving state parameters, and using RBF, include: Based on the initial stable domain database and combined with the centroid sideslip angle and centroid sideslip angular velocity, the diagonal of the three-dimensional rhombic stable domain is scaled. The boundary of the three-dimensional rhombic stability domain is adjusted by comparing the results of the tire force method and the single-track model method of vehicle state parameters, and by combining the changes in vehicle driving state parameters. The boundary data of the three-dimensional rhombic stability domain is then fitted by an RBF neural network.

7. The intelligent vehicle stability determination method according to claim 1, characterized in that, The expression for the unstable state is: ； In the formula, The criterion value representing the unstable state. , These represent the reference yaw rate value of the linear model and the actual yaw rate state quantity of the vehicle, respectively. Indicates the load transfer factor. Indicates the LTR threshold. Represents the AND operator.

8. An intelligent vehicle stability determination system, characterized in that, The system includes: A module is established to create a three-degree-of-freedom dynamic model of the vehicle's lateral, yaw, and roll directions, and to obtain the state parameters of the stability of the yaw and the stability of the roll direction. The construction module is used to construct a three-dimensional phase space based on the three-degree-of-freedom dynamic model and according to the center-of-mass sideslip angle, center-of-mass sideslip angular velocity and load transfer coefficient; The partitioning module is used to initially partition the stability domain boundaries of the three-dimensional phase space based on the spatial trajectory convergence type and load transfer coefficient threshold, so as to obtain an initial stability domain database. The optimization module is used to optimize the initial stable domain database based on the initial stable domain database by comparing the stability criteria results of the tire force method and the monorail model method with the changes in vehicle driving state parameters, and by using RBF to perform nonlinear fitting on the initial stable domain data, so as to obtain an optimized stable domain database model. The determination module is used to divide the stability state into several unstable states based on the optimized stability domain database model, combined with the three-degree-of-freedom dynamic model, the state parameters of the yaw stability, the state parameters of the tilt stability, the angular velocity of the yaw, and the load transfer coefficient, and to establish a stability criterion based on the several unstable states, and to use the stability criterion as the intervention condition of the stability controller.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the intelligent vehicle stability determination method as described in any one of claims 1 to 7.

10. A storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the intelligent vehicle stability determination method as described in any one of claims 1 to 7.

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