Vehicle stability judgment method based on phase plane

Abstract

The invention provides a vehicle stability judgment method based on a phase plane, which comprises the following steps of: inputting different vehicle running condition parameters into a nonlinear two-degree-of-freedom vehicle model, drawing a vehicle stability phase plane graph under all working conditions, and dividing to obtain an initial stable region boundary; the working condition parameters correspond to the slope and the horizontal axis intercept of the initial stable region boundary, a random forest database is constructed, the working condition parameters serve as input, the slope and the horizontal axis intercept serve as output, and a random forest regression prediction model is obtained through training; inputting real-time vehicle driving condition parameters into the nonlinear two-degree-of-freedom vehicle model and the random forest regression prediction model to obtain coordinates of real-time vehicle state points and an initial stable boundary under the real-time vehicle driving condition, and mapping tire lateral force characteristics into a phase plane graph under the real-time condition; and judging a vehicle stability control mode according to the state point in the region where the phase plane graph is located. According to the method, the judgment accuracy of the vehicle stability control mode is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle stability control, and in particular to a vehicle stability judgment method based on a phase plane. Background Art

[0002] Safety, energy saving and environmental protection have become the main directions of development of automobile technology today. The stability of the vehicle has a great impact on the safety, comfort and fuel economy of the passengers. Therefore, it is of great significance to carry out research on vehicle stability criteria.

[0003] At present, there are three main methods for judging vehicle stability at home and abroad: 1. Center of mass slip angle-yaw rate phase plane method. Although this method can fully characterize the stability state of the vehicle under all working conditions, it is difficult to accurately describe the stability boundary and cannot accurately judge the stability of the vehicle under unstable conditions such as pure sideslip with small yaw rate fluctuations; 2. Center of mass slip angle-center of mass slip rate phase plane method. Most of these methods use the double-line method to simply divide the boundary of the stable area. When the vehicle speed is low, some unstable points in the first and third quadrants will be included in the stable area. In addition, although there are phase trajectories that can converge to the stable points in the upper left and lower right corners of the stable area boundary divided by the double-line method However, the center of mass sideslip angle values ​​of each point on the phase trajectory of these two areas are too large, which will result in a long time for the vehicle to return to the balance point, and the control effect is not obvious; in addition, the accuracy and effectiveness of the two-line method depend on benchmark criteria such as the tire force method, which means that if the accuracy of the benchmark criterion is affected, the effectiveness of the two-line method will also be reduced accordingly. Therefore, it is unreasonable to use the center of mass sideslip angle-center of mass sideslip angular velocity phase plane divided by the two-line method to judge the vehicle stability; third, the lateral velocity-yaw angular velocity phase plane method. This method is difficult to obtain the lateral velocity, and it cannot accurately judge the vehicle stability state under unstable conditions such as sideslip, so it is also unreasonable.

[0004] During vehicle driving, the front wheel steering angle, longitudinal speed and road adhesion coefficient of the vehicle are key parameters that affect the phase plane stability region. Most vehicle stability judgment methods fail to establish a nonlinear mapping relationship between the front wheel steering angle, longitudinal speed and road adhesion coefficient of the vehicle and the phase plane stability region and accurately divide the stability region. Most vehicle stability judgment methods use the database lookup method to determine the stability region under different vehicle driving condition parameters. Although this solution can meet the accuracy requirements of stability analysis, the database lookup method cannot handle complex multi-dynamic feature data sets well. Machine learning methods have the unique advantages of being not easily affected by noise, insensitive to outliers, resistant to overfitting, and able to handle high-dimensional data for processing such data. In addition, most vehicle stability judgment methods do not match the dynamic characteristics of tire lateral force with the vehicle stability control requirements, and fail to achieve an effective combination of the dynamic characteristics of tire lateral force and phase plane region division.

[0005] In summary, the current methods for judging vehicle stability at home and abroad do not take the following factors into consideration at the same time: First, there are unreasonable aspects in the stable area of ​​the phase plane obtained by dividing the phase plane using the double-line method; Second, most vehicle stability judgment methods do not simultaneously consider the nonlinear mapping relationship between the three parameters of the vehicle's longitudinal speed, front wheel steering angle and road adhesion coefficient and the stable area of ​​the phase plane, resulting in a one-sided division of the stable area of ​​the phase plane; Third, most studies use the database lookup method to determine the stable area under different vehicle driving condition parameters, and cannot handle complex multi-dynamic feature data sets well; Fourth, the dynamic characteristics of the tire lateral force are not effectively combined with the phase plane area division. Summary of the invention

[0006] In view of this, the present invention provides a vehicle stability judgment method based on a phase plane to solve the above problem.

[0007] The present invention provides a vehicle stability judgment method based on phase plane, comprising: inputting different vehicle driving condition parameters into the nonlinear two-degree-of-freedom vehicle model to draw a vehicle stability phase plane diagram under all working conditions; dividing the vehicle stability phase plane diagram under all working conditions by using a quadrilateral method to obtain the boundary of an initial stable region of the phase plane under all working conditions, and recording the slope and horizontal axis intercept of the boundary; correspondingly combining the different vehicle driving condition parameters and the slope and horizontal axis intercept of the boundary of the initial stable region of the phase plane under all working conditions to construct a random forest database; using the different vehicle driving condition parameters in the random forest database as input and the slope and horizontal axis intercept of the boundary of the initial stable region of the phase plane under all working conditions as output, and using a random forest method to judge the vehicle stability of the vehicle under all working conditions. Model training is performed to obtain a random forest regression prediction model; real-time vehicle driving condition parameters are input into the nonlinear two-degree-of-freedom vehicle model and the random forest regression prediction model to obtain the coordinates of the real-time vehicle state point under the real-time vehicle driving condition and the slope and horizontal intercept of the initial stable boundary of the phase plane under the real-time vehicle driving condition; the dynamic characteristics of the tire lateral force are mapped to the vehicle stability phase plane diagram under the real-time vehicle driving condition in combination with the slope and horizontal intercept of the initial stable boundary of the phase plane under the real-time vehicle driving condition, and the vehicle stability phase plane diagram is divided into regions according to the mapping results; the vehicle stability control mode is judged according to the region where the real-time vehicle state point is located in the vehicle stability phase plane diagram to obtain a judgment result.

[0008] In another implementation of the present invention, it also includes: establishing a nonlinear two-degree-of-freedom vehicle model by introducing a nonlinear tire lateral force model into the two-degree-of-freedom vehicle model.

[0009] In another implementation of the present invention, the nonlinear tire lateral force model is established by the magic tire formula, which is expressed as:

[0010]

[0011] Among them, F f is the lateral force of the front wheel; F r is the rear wheel lateral force; μ is the road adhesion coefficient; F zf is the vertical load on the front wheel; F zr is the vertical load on the rear wheel; α f is the front wheel slip angle; α r is the rear wheel slip angle; C f , C r , B f , B r 、E f 、E r are the Magic Tire model fitting coefficients and are determined by the tire vertical load and camber angle.

[0012] In another implementation of the present invention, the two-degree-of-freedom vehicle model is expressed as:

[0013]

[0014] Where, β is the side slip angle of the vehicle center of mass; m is the vehicle mass; u is the longitudinal speed; δ is the front wheel turning angle; γ is the vehicle yaw rate; a is the distance from the vehicle center of mass to the front axle; b is the distance from the vehicle center of mass to the rear axle; I z is the moment of inertia of the vehicle mass around the z-axis.

[0015] In another implementation of the present invention, the vehicle stability phase plane diagram is a phase plane diagram of the center of mass sideslip angle and the center of mass sideslip angular velocity; wherein the center of mass sideslip angle is used as the horizontal coordinate, and the center of mass sideslip angular velocity is used as the vertical coordinate.

[0016] In another implementation of the present invention, the vehicle driving condition parameters include longitudinal vehicle speed, front wheel steering angle and road adhesion coefficient; the longitudinal vehicle speed, front wheel steering angle and road adhesion coefficient are combined to obtain the different vehicle driving condition parameters.

[0017] In another implementation of the present invention, the random forest regression prediction model is expressed as:

[0018]

[0019] Among them, x represents the input of the random forest model; y represents the output of the random forest model; m represents that the input value is divided into m units; c i Represents a fixed output value on each unit; R m Indicates the mth unit into which the input value is divided.

[0020] In another implementation of the present invention, the dynamic characteristics of the tire lateral force are mapped to the vehicle stability phase plane diagram in combination with the slope and the horizontal axis intercept of the initial stable boundary of the phase plane under the real-time vehicle driving condition, and the vehicle stability phase plane diagram is divided into regions according to the mapping result, including: mapping the dynamic characteristics of the tire lateral force to the vehicle stability phase plane diagram in combination with the slope and the horizontal axis intercept of the initial stable boundary of the phase plane to obtain a phase plane stable boundary and a phase plane unstable boundary; taking the stable point of the vehicle stability phase plane diagram as the center of the circle, making inscribed circles of the phase plane stable boundary and the phase plane unstable boundary respectively to obtain a stable circle and an unstable circle; dividing the vehicle stability phase plane diagram into regions according to the phase plane stable boundary, the stable circle, and the unstable circle.

[0021] In another implementation of the present invention, the area division includes: the inner area of ​​the stability circle is defined as the stability domain; the area between the stability circle and the phase plane stability boundary is defined as the manipulation control domain; the area between the phase plane stability boundary and the unstable circle is defined as the coordination control domain; and the area outside the unstable circle is defined as the stable control domain.

[0022] In another implementation of the present invention, the vehicle stability control mode is judged according to the area where the real-time vehicle state point is located in the vehicle stability phase plane diagram to obtain a judgment result, including: when the real-time vehicle state point is in the stable domain, no control algorithm intervention is required; when the real-time vehicle state point is in the manipulation control domain, only the manipulation control algorithm intervention is required; when the real-time vehicle state point is in the coordinated control domain, the control ratio of the vehicle stability control algorithm is ρ, and the control ratio of the manipulation control algorithm is 1-ρ; when the real-time vehicle state point is in the stable control domain, the vehicle stability control algorithm is mainly used, ρ=1; wherein ρ is the weight coefficient of the vehicle stability control algorithm, 0≤ρ≤1, expressed as:

[0023]

[0024] Where R is the distance between the real-time vehicle state point and the stable point; assuming that the intersection of the line between the real-time vehicle state point and the origin O and the phase plane stable boundary quadrilateral in the same quadrant is point ε, then D S is the distance between the real-time vehicle status point and point ε; R U is the radius of the unstable circle in the phase plane.

[0025] The vehicle stability judgment method based on the phase plane of the present invention takes into account that the three parameters of the vehicle longitudinal speed, the front wheel turning angle and the road adhesion coefficient have a greater impact on the vehicle stability. According to the influence of the above parameters on the vehicle stability under the condition of change, a phase plane diagram of the vehicle stability under all working conditions under different vehicle driving condition parameters is drawn; a random forest regression prediction model trained by the random forest method is used to process complex multi-dynamic feature data sets; a new quadrilateral method is used to divide the vehicle stability phase plane diagram into more reasonable phase plane areas; the dynamic characteristics of the tire lateral force are effectively combined with the phase plane area division, the phase plane area is further divided, and a more accurate vehicle stability control mode is proposed. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. By reading the detailed description of the following implementations, the advantages and benefits of the solutions become clear to those skilled in the art. The drawings are only used to illustrate the preferred implementations and are not to be considered as limiting the present invention.

[0027] In the attached picture:

[0028] Figure 1 The figure is a flow chart of a vehicle stability determination method based on a phase plane according to an embodiment of the present invention.

[0029] Figure 2 Schematic diagram of a nonlinear two-degree-of-freedom vehicle model according to an embodiment of the present invention.

[0030] Figure 3 A schematic diagram of a method for finding an initial stable boundary line of a phase plane according to an embodiment of the present invention.

[0031] Figure 4 Schematic diagram of the phase plane initial stable boundary quadrilateral method according to an embodiment of the present invention.

[0032] Figure 5 A schematic diagram of the mapping relationship between input and output of a random forest regression prediction model according to an embodiment of the present invention.

[0033] Figure 6 The figure is a schematic diagram of the dynamic characteristics of the tire lateral force according to an embodiment of the present invention.

[0034] Figure 7 It is a schematic diagram of the division of vehicle stability phase plane regions according to an embodiment of the present invention.

[0035] Figure 8 The figure is a schematic diagram of calculating the dynamic weight coefficient of vehicle stability according to an embodiment of the present invention. DETAILED DESCRIPTION

[0036] In order to enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be described clearly and in detail below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in the field based on the embodiments in the embodiments of the present invention should fall within the scope of protection of the embodiments of the present invention.

[0037] Figure 1 A schematic flow chart of a vehicle stability determination method based on a phase plane provided in an embodiment of the present invention is shown in FIG. Figure 1 As shown, this embodiment mainly includes:

[0038] Step 1: Input different vehicle driving condition parameters into the nonlinear two-degree-of-freedom vehicle model, and draw a vehicle stability phase plane diagram under all conditions.

[0039] Step 2: Use the quadrilateral method to divide the vehicle stability phase plane diagram under all working conditions, obtain the initial stable region boundary of the phase plane under all working conditions, and record the slope and horizontal axis intercept of the boundary.

[0040] For example, Figure 3 As shown in , according to the drawn vehicle stability phase plane diagram, a coordinate axis centered on the stable point is established, and tangents to the outermost phase trajectory back to the stable point are drawn through the two saddle points B1 (-β1, 0) and B2 (-β2, 0) in the vehicle stability phase plane diagram. Figure 4 As shown, the one with the smaller horizontal intercept of the two tangent lines is selected as the boundary line of the initial stable boundary of the phase plane, and the slope and horizontal intercept of the boundary of the initial stable region of the phase plane in the third quadrant can be obtained. Then, the boundary line is rotated 90°, 180° and 270° clockwise with the stable point as the center, and the boundary lines of the initial stable boundaries in the other three quadrants can be obtained. Finally, the four boundary lines form a closed quadrilateral, which is the boundary of the initial stable region of the phase plane.

[0041] Step 3: Correspondingly combine the different vehicle driving condition parameters and the slope and horizontal intercept of the initial stable region boundary of the phase plane under the full working conditions to construct a random forest database.

[0042] Step 4: Using different vehicle driving condition parameters in the random forest database as input, and the slope and horizontal intercept of the initial stable region boundary of the phase plane under all working conditions as output, the model is trained through the random forest method to obtain a random forest regression prediction model.

[0043] Step 5: Input the real-time vehicle driving condition parameters into the nonlinear two-degree-of-freedom vehicle model and the random forest regression prediction model to obtain the coordinates of the real-time vehicle state point under the real-time vehicle driving condition and the slope and horizontal intercept of the initial stable boundary of the phase plane under the real-time vehicle driving condition.

[0044] Exemplarily, the real-time vehicle driving condition parameters are longitudinal vehicle speed, front wheel steering angle and road adhesion coefficient.

[0045] Step six: map the dynamic characteristics of the tire lateral force into the vehicle stability phase plane diagram under the real-time vehicle driving condition in combination with the slope and the horizontal axis intercept of the initial stable boundary of the phase plane under the real-time vehicle driving condition, and divide the vehicle stability phase plane diagram into regions according to the mapping results.

[0046] Step seven: judging the vehicle stability control mode according to the area where the real-time vehicle state point is located in the vehicle stability phase plane diagram to obtain a judgment result.

[0047] The vehicle stability judgment method based on the phase plane of the present invention takes into account that the three parameters of the vehicle longitudinal speed, the front wheel turning angle and the road adhesion coefficient have a greater impact on the vehicle stability. According to the influence of the above parameters on the vehicle stability under the condition of change, a phase plane diagram of the vehicle stability under all working conditions under different vehicle driving condition parameters is drawn; a random forest regression prediction model trained by the random forest method is used to process complex multi-dynamic feature data sets; a new quadrilateral method is used to divide the vehicle stability phase plane diagram into more reasonable phase plane areas; the dynamic characteristics of the tire lateral force are effectively combined with the phase plane area division, the phase plane area is further divided, and a more accurate vehicle stability control mode is proposed.

[0048] In another implementation of the present invention, it also includes: establishing a nonlinear two-degree-of-freedom vehicle model by introducing a nonlinear tire lateral force model into the two-degree-of-freedom vehicle model.

[0049] In another implementation of the present invention, the nonlinear tire lateral force model is established by the magic tire formula, which is expressed as:

[0050]

[0051] Among them, F f is the lateral force of the front wheel; F r is the rear wheel lateral force; μ is the road adhesion coefficient; F zf is the vertical load on the front wheel; F zr is the vertical load on the rear wheel; α f is the front wheel slip angle; α r is the rear wheel slip angle; C f , C r , B f , B r 、E f 、E r are the Magic Tire model fitting coefficients and are determined by the tire vertical load and camber angle.

[0052] In another implementation of the present invention, Figure 2 As shown, a two-degree-of-freedom vehicle model with the sideslip angular velocity and yaw angular velocity of the center of mass as state variables is established, which is expressed as:

[0053]

[0054] Where, β is the side slip angle of the vehicle center of mass; m is the vehicle mass; u is the longitudinal speed; δ is the front wheel turning angle; γ is the vehicle yaw rate; a is the distance from the vehicle center of mass to the front axle; b is the distance from the vehicle center of mass to the rear axle; I z is the moment of inertia of the vehicle mass around the z-axis.

[0055] In another implementation of the present invention, the vehicle stability phase plane diagram is a phase plane diagram of the center of mass sideslip angle and the center of mass sideslip angular velocity; wherein the center of mass sideslip angle is used as the horizontal coordinate, and the center of mass sideslip angular velocity is used as the vertical coordinate.

[0056] In another implementation of the present invention, the vehicle driving condition parameters include longitudinal vehicle speed, front wheel steering angle and road adhesion coefficient; the longitudinal vehicle speed, front wheel steering angle and road adhesion coefficient are combined to obtain the different vehicle driving condition parameters.

[0057] Exemplarily, the vehicle driving condition parameters include the longitudinal vehicle speed u, the front wheel steering angle δ and the road adhesion coefficient μ. Different vehicle driving condition parameters refer to: the longitudinal vehicle speed interval is set to 20 km / h, ranging from 40 km / h to 100 km / h, the front wheel steering angle interval is set to 0.1°, ranging from 0.1° to 0.5°, and the road adhesion coefficient interval is set to 0.1, ranging from 0.3 to 0.9. The above longitudinal vehicle speed, front wheel steering angle and road adhesion coefficient are combined to obtain different vehicle driving condition parameters.

[0058] In another implementation of the present invention, the random forest regression prediction model is expressed as:

[0059]

[0060] Among them, x represents the input of the random forest model; y represents the output of the random forest model; m represents that the input value is divided into m units; c i Represents a fixed output value on each unit; R m Indicates the mth unit into which the input value is divided.

[0061] For example, Figure 5 As shown in the figure, the longitudinal speed, front wheel steering angle and road adhesion coefficient under different vehicle driving condition parameters in the random forest database are taken as input, and the slope and horizontal intercept of the initial stable region boundary of the phase plane are taken as output. The random forest regression prediction model is obtained by training with the random forest method.

[0062] In another implementation of the present invention, the dynamic characteristics of the tire lateral force are mapped to the vehicle stability phase plane diagram under the real-time vehicle driving condition in combination with the slope and the horizontal axis intercept of the initial stable boundary of the phase plane under the real-time vehicle driving condition, and the vehicle stability phase plane diagram is divided into regions according to the mapping result, including: mapping the dynamic characteristics of the tire lateral force to the vehicle stability phase plane diagram in combination with the slope and the horizontal axis intercept of the initial stable boundary of the phase plane to obtain a phase plane stable boundary and a phase plane unstable boundary; taking the stable point of the vehicle stability phase plane diagram as the center of the circle, making inscribed circles of the phase plane stable boundary and the phase plane unstable boundary respectively to obtain a stable circle and an unstable circle; dividing the vehicle stability phase plane diagram into regions according to the phase plane stable boundary, the stable circle, and the unstable circle.

[0063] For example, Figure 6 As shown in the figure, the linear region, nonlinear region and saturation region in the dynamic characteristics of the tire lateral force are mapped to the vehicle stability phase plane diagram under the real-time vehicle driving condition, and the first quadrant part with the equilibrium point as the coordinate axis is used to obtain the phase plane stable boundary and the phase plane unstable boundary. Among them, the maximum value of the tire side slip angle in the linear region corresponds to the boundary line of the phase plane stable boundary, the minimum value of the tire side slip angle in the saturation region corresponds to the boundary line of the phase plane unstable boundary, and the nonlinear region corresponds to the area between the phase plane stable boundary and the unstable boundary. The final division result is shown in the figure below. Figure 7 As shown; then, taking the stable point of the vehicle stability phase plane diagram as the center, draw the inscribed circles of the phase plane stable boundary and the phase plane unstable boundary respectively, and obtain the phase plane stable circle and the phase plane unstable circle.

[0064] Calculate the front wheel slip angle α through the tire model f and rear wheel slip angle α r :

[0065]

[0066] According to the nonlinear tire lateral force model curve, the maximum value α1 of the linear part of the lateral force and the minimum value α2 of the saturated part are obtained. The intersection points of the boundary line of the first quadrant of the coordinate axis centered on the stable point in the vehicle stability phase plane diagram and the horizontal axis are (β1,0) and (β3,0), which can be calculated by the following formula:

[0067]

[0068] The slopes of the phase plane stable boundary and the phase plane unstable boundary are the same as the slopes of the boundaries of the initial stable region of the phase plane obtained in step 2, and the formation process of the phase plane stable boundary and the phase plane unstable boundary is similar to that of the initial stable boundary of the phase plane: by rotating the two boundary lines in the first quadrant by 90°, 180° and 270° clockwise respectively, six boundary lines in the other three quadrants can be obtained. Finally, these eight boundary lines form closed phase plane stable boundaries and phase plane unstable boundaries respectively.

[0069] With the stable point as the center, draw the inscribed circles of the stable boundary of the phase plane and the unstable boundary of the phase plane respectively. These two inscribed circles are the stable circle and the unstable circle respectively.

[0070] In another implementation of the present invention, Figure 8 As shown, the vehicle stability phase plane diagram is divided into regions according to the phase plane stable boundary, stable circle and unstable circle, and the divided regions are: the inner area of ​​the stable circle is defined as the stable domain; the area between the stable circle and the phase plane stable boundary is defined as the steering control domain; the area between the phase plane stable boundary and the unstable circle is defined as the coordination control domain; and the area outside the unstable circle is defined as the stable control domain.

[0071] In another implementation of the present invention, the vehicle stability control mode is judged according to the area where the real-time vehicle state point is located in the vehicle stability phase plane diagram to obtain a judgment result, including: when the real-time vehicle state point is in the stable domain, no control algorithm intervention is required; when the real-time vehicle state point is in the manipulation control domain, only the manipulation control algorithm intervention is required; when the real-time vehicle state point is in the coordinated control domain, the control ratio of the vehicle stability control algorithm is ρ, and the control ratio of the manipulation control algorithm is 1-ρ; when the real-time vehicle state point is in the stable control domain, the vehicle stability control algorithm is mainly used, and ρ=1.

[0072] Among them, Figure 8 As shown, the vehicle stability control mode is defined when the real-time vehicle state point is in each area, ρ is the weight coefficient of the vehicle stability control algorithm, 0≤ρ≤1, expressed as:

[0073]

[0074] Where R is the distance between the real-time vehicle state point and the stable point; assuming that the intersection of the line between the real-time vehicle state point and the origin O and the phase plane stable boundary quadrilateral in the same quadrant is point ε, then D S is the distance between the real-time vehicle status point and point ε; R U is the radius of the unstable circle in the phase plane.

[0075] For example, the real-time vehicle status point is introduced The calculation formula of the distance R from the stable point O(0,0) is:

[0076]

[0077] Where β0 is the horizontal coordinate of the real-time vehicle state point; It is the ordinate of the real-time vehicle status point.

[0078] The present invention can more fully characterize the stability state of a vehicle under all operating conditions, and provide a more accurate basis for vehicle stability judgment and vehicle stability control mode selection.

[0079] In another aspect of the present invention, an electronic device includes: a processor, a memory, a communication bus, and a communication interface.

[0080] in:

[0081] The processor, memory and communication interface communicate with each other through a communication bus.

[0082] Communication interface, used to communicate with other electronic devices or servers.

[0083] The processor is used to execute the program, and specifically can execute the steps of any one of the vehicle stability judgment methods based on the phase plane in the above-mentioned embodiments.

[0084] Specifically, the program may include program codes including computer operation instructions.

[0085] The processor may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application. The one or more processors included in the smart device may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0086] The memory is used to store programs. The memory may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory.

[0087] The program can be specifically used to enable the processor to execute to implement the steps of any one of the vehicle stability judgment methods based on the phase plane described in the embodiment. The specific implementation of each step in the program can refer to the corresponding descriptions in the steps and units executed by any one of the vehicle stability judgment methods based on the phase plane in the above steps, and will not be repeated here. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described devices and modules can refer to the corresponding process description in the aforementioned method embodiment.

[0088] The method according to the embodiment of the present invention may be implemented in a server equipped with a central processing unit (CPU) and an image processing unit (COU).

[0089] Thus far, specific embodiments of the present invention have been described. Other embodiments are within the scope of the appended claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results.

[0090] It should be noted that all directional indications in the embodiments of the present invention (such as up, down, left, right, back, etc.) are only used to explain the relative position relationship between the components in a certain specific order (as shown in the accompanying drawings). If the specific order changes, the directional indication will also change accordingly.

[0091] In the description of the present invention, the terms "first" and "second" are only used to facilitate the description of different components or names, and cannot be understood as indicating or implying a sequential relationship, relative importance, or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features.

[0092] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0093] It should be noted that although the specific embodiments of the present invention are described in detail in conjunction with the accompanying drawings, it should not be understood as limiting the scope of protection of the present invention. Within the scope described in the claims, various modifications and variations that can be made by those skilled in the art without creative work still belong to the scope of protection of the present invention.

[0094] The examples of the embodiments of the present invention are intended to concisely illustrate the technical features of the embodiments of the present invention so that those skilled in the art can intuitively understand the technical features of the embodiments of the present invention, and are not intended to be improper limitations of the embodiments of the present invention.

[0095] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A vehicle stability judgment method based on phase plane, characterized in that: include: Inputting different vehicle driving condition parameters into the nonlinear two-degree-of-freedom vehicle model, and drawing a vehicle stability phase plane diagram under all conditions; The vehicle stability phase plane diagram under the full working condition is divided by a quadrilateral method to obtain the initial stable region boundary of the phase plane under the full working condition, and the slope and the horizontal axis intercept of the boundary are recorded; The different vehicle driving condition parameters and the slope and the horizontal axis intercept of the initial stable region boundary of the phase plane under the full working condition are correspondingly combined to construct a random forest database; Using different vehicle driving condition parameters in the random forest database as input, and the slope and horizontal intercept of the initial stable region boundary of the phase plane under all conditions as output, the model is trained by the random forest method to obtain a random forest regression prediction model; Inputting the real-time vehicle driving condition parameters into the nonlinear two-degree-of-freedom vehicle model and the random forest regression prediction model to obtain the coordinates of the real-time vehicle state point under the real-time vehicle driving condition and the slope and horizontal axis intercept of the initial stable boundary of the phase plane under the real-time vehicle driving condition; In combination with the slope and the horizontal axis intercept of the initial stable boundary of the phase plane under the real-time vehicle driving condition, the dynamic characteristics of the tire lateral force are mapped into the vehicle stability phase plane diagram under the real-time vehicle driving condition, and the vehicle stability phase plane diagram is divided into regions according to the mapping result; The vehicle stability control mode is judged according to the area where the real-time vehicle state point is located in the vehicle stability phase plane diagram to obtain a judgment result.

2. The method according to claim 1, characterized in that Also includes: A nonlinear two-degree-of-freedom vehicle model is established by introducing a nonlinear tire lateral force model into the two-degree-of-freedom vehicle model.

3. The method according to claim 2, characterized in that The nonlinear tire lateral force model is established by the magic tire formula and is expressed as: Among them, F f is the lateral force of the front wheel; F r is the rear wheel lateral force; μ is the road adhesion coefficient; F zf is the vertical load on the front wheel; F zr is the vertical load on the rear wheel; α f is the front wheel slip angle; α r is the rear wheel slip angle; C f , C r , B f , B r 、E f 、E r are the Magic Tire model fitting coefficients and are determined by the tire vertical load and camber angle.

4. The method according to claim 3, characterized in that The two-degree-of-freedom vehicle model is expressed as: Where, β is the side slip angle of the vehicle center of mass; m is the vehicle mass; u is the longitudinal speed; δ is the front wheel turning angle; γ is the vehicle yaw rate; a is the distance from the vehicle center of mass to the front axle; b is the distance from the vehicle center of mass to the rear axle; I z is the moment of inertia of the vehicle mass around the z-axis.

5. The method according to claim 1, characterized in that The vehicle stability phase plane diagram is a phase plane diagram of the center of mass sideslip angle and the center of mass sideslip angular velocity; The center of mass sideslip angle is used as the horizontal coordinate, and the center of mass sideslip angular velocity is used as the vertical coordinate.

6. The method according to claim 1, characterized in that The vehicle driving condition parameters include longitudinal vehicle speed, front wheel steering angle and road adhesion coefficient; The longitudinal vehicle speed, the front wheel steering angle and the road adhesion coefficient are combined to obtain the different vehicle driving condition parameters.

7. The method according to claim 1, characterized in that The random forest regression prediction model is expressed as: Among them, x represents the input of the random forest model; y represents the output of the random forest model; m represents that the input value is divided into m units; c i Represents a fixed output value on each unit; R m Indicates the mth unit into which the input value is divided.

8. The method according to claim 1, characterized in that The step of mapping the tire lateral force dynamic characteristics to the vehicle stability phase plane diagram in combination with the slope of the initial stable boundary of the phase plane under the real-time vehicle driving condition and the horizontal axis intercept, and dividing the vehicle stability phase plane diagram into regions according to the mapping result, comprises: In combination with the slope and the horizontal axis intercept of the initial stable boundary of the phase plane, the dynamic characteristics of the tire lateral force are mapped to the vehicle stability phase plane diagram to obtain the phase plane stable boundary and the phase plane unstable boundary; Taking the stable point of the vehicle stability phase plane diagram as the center of the circle, inscribed circles are drawn on the stable boundary of the phase plane and the unstable boundary of the phase plane to obtain a stable circle and an unstable circle; The vehicle stability phase plane diagram is divided into regions according to the phase plane stability boundary, the stability circle, and the unstable circle.

9. The method according to claim 8, characterized in that The regional divisions include: The inner area of ​​the stability circle is defined as the stability domain; The area between the stability circle and the phase plane stability boundary is defined as a manipulation control domain; The coordination control domain is defined between the phase plane stable boundary and the unstable circle; The area outside the unstable circle is defined as the stable control area.

10. The method according to claim 9, characterized in that The determining of the vehicle stability control mode according to the region where the real-time vehicle state point is located in the vehicle stability phase plane diagram to obtain a determination result includes: When the real-time vehicle state point is in the stable domain, no control algorithm intervention is required; When the real-time vehicle state point is within the maneuvering control domain, only the maneuvering control algorithm needs to intervene; When the real-time vehicle state point is in the coordinated control domain, the control ratio of the vehicle stability control algorithm is ρ, and the control ratio of the handling control algorithm is 1-ρ; When the real-time vehicle state point is in the stability control domain, the vehicle stability control algorithm is mainly used, ρ=1; Among them, ρ is the weight coefficient of the vehicle stability control algorithm, 0≤ρ≤1, expressed as: Where R is the distance between the real-time vehicle state point and the stable point; assuming that the intersection of the line between the real-time vehicle state point and the origin O and the phase plane stable boundary quadrilateral in the same quadrant is point ε, then D S is the distance between the real-time vehicle status point and point ε; R U is the radius of the unstable circle in the phase plane.

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