An adhesion capacity quantification method, device, electronic equipment and storage medium

Abstract

The application provides an adhesion capacity quantification method and device, electronic equipment and storage medium, and relates to the technical field of vehicles. The adhesion capacity quantification method comprises the following steps: determining the center of mass side slip angle and the road adhesion coefficient of a vehicle according to vehicle state parameters; constructing a stable region boundary model of the vehicle according to the center of mass side slip angle and the road adhesion coefficient; and determining an adhesion margin based on the stable region boundary model. The application can convert the traditional adhesion capacity perception problem relying on driving experience and subjective judgment into an objective quantification problem based on a vehicle dynamics model, so that the degree of the current distance of the vehicle from the instability boundary is intuitively reflected, thereby providing the driver with perceptible and understandable adhesion capacity information and providing a quantification basis for subsequent warning prompts or control strategies, and further improving the operation lag or excessive operation problem of the driver in low adhesion or extreme working conditions due to insufficient perception, and improving the vehicle driving safety.

Description

Technical Field

[0001] This invention relates to the field of vehicle technology, and more specifically, to a method, apparatus, electronic device, and storage medium for quantifying adhesion capability. Background Technology

[0002] Vehicle electronic stability control systems are widely used in the field of active automotive safety. Existing stability control systems typically collect signals such as steering wheel angle, yaw rate, lateral acceleration, and wheel speed. Based on a vehicle dynamics model, they calculate a reference yaw rate or lateral acceleration and compare the vehicle's actual motion with the reference value. When the deviation exceeds a set threshold, additional yaw torque is generated by applying braking force to individual wheels or limiting engine output torque, thereby suppressing understeer or oversteer tendencies and maintaining vehicle stability. This type of control method has a relatively mature structure and achieves good stability control performance on roads with medium coefficient of friction and when the tires are in their linear operating range.

[0003] However, under extreme conditions such as low-friction surfaces or emergency steering, tires are prone to entering the nonlinear region or even the saturation region, significantly reducing the vehicle's stable area. Existing stability control systems are mostly triggered based on yaw rate or lateral acceleration deviation thresholds, which can only determine whether the vehicle is showing signs of instability. When the vehicle tires enter the nonlinear region and are about to become unstable, the stability control system intervenes to correct the vehicle body. The driver cannot perceive the remaining traction in time, which affects the driver's handling feel and causes a deviation between the vehicle's handling actions and the expected target. In severe cases, this may affect the vehicle's driving safety. Summary of the Invention

[0004] The problem addressed by this invention is how to improve active safety and handling comfort on low-adhesion road surfaces.

[0005] To address the aforementioned problems, this invention provides a method, apparatus, electronic device, and storage medium for quantifying adhesion capability.

[0006] In a first aspect, the present invention provides a method for quantifying adhesion ability, comprising: The vehicle's center of gravity sideslip angle and road adhesion coefficient are determined based on the vehicle's condition parameters. The stable region boundary model of the vehicle is constructed based on the centroid sideslip angle and the road surface adhesion coefficient. The attachment margin is determined based on the stable region boundary model.

[0007] Optionally, determining the vehicle's center of gravity sideslip angle and road adhesion coefficient based on vehicle state parameters includes: Collect at least one of the following vehicle state parameters: steering wheel angle, yaw rate, longitudinal acceleration, lateral acceleration, and wheel speed; Based on the vehicle dynamics model, the centroid sideslip angle and the road surface adhesion coefficient are estimated in real time according to the vehicle state parameters.

[0008] Optionally, the real-time estimation of the centroid sideslip angle and the road surface adhesion coefficient based on the vehicle state parameters includes: State equations and observation equations are established based on the vehicle dynamics model, and the predicted lateral acceleration value is determined according to the vehicle state parameters. Based on the residual between the predicted lateral acceleration and the actual measured lateral acceleration, the predicted values ​​of the centroid sideslip angle and the road surface adhesion coefficient are corrected online to obtain estimated values ​​of the centroid sideslip angle and the road surface adhesion coefficient.

[0009] Optionally, constructing the stable region boundary model of the vehicle based on the centroid sideslip angle and the road surface adhesion coefficient includes: A phase plane is constructed based on the centroid sideslip angle and the centroid sideslip angular velocity, wherein the centroid sideslip angular velocity is determined by differentiating the centroid sideslip angle with respect to time. Within the phase plane, a boundary model of the stable region is constructed based on the road surface adhesion coefficient.

[0010] Optionally, constructing the stable region boundary model based on the road surface adhesion coefficient includes: The boundary parameters of the stable region boundary model are determined based on the road surface adhesion coefficient and vehicle speed. The stable region boundary curve is then generated in the phase plane based on the boundary parameters to form the stable region boundary model.

[0011] Optionally, determining the attachment margin based on the stable region boundary model includes: The normalized distance between the vehicle's current state point and the stable region boundary is determined based on the stable region boundary model. The attachment margin is determined based on the normalized distance.

[0012] Optionally, the adhesion margin and the normalized distance are inversely related; the smaller the normalized distance, the larger the adhesion margin, and vice versa.

[0013] Optionally, the adhesion capability quantification method further includes: when the adhesion margin is in the intervention range, determining the front wheel steering angle correction amount based on the distance between the current state point of the vehicle and the boundary of the stable area, so as to correct or limit the front wheel steering angle requested by the driver, reduce the actual front wheel steering angle or limit the front wheel steering angle increment; The target yaw moment is determined based on the distance between the vehicle's current state point and the boundary of the stable region. The braking force of the inner rear wheel is then determined based on the target yaw moment, and braking is applied to the inner rear wheel of the vehicle.

[0014] Optionally, the adhesion capability quantification method further includes: when the adhesion margin is in the warning range, outputting a warning signal to the driver to indicate that the vehicle is approaching the boundary of the stable area, wherein the adhesion margin in the warning range is greater than the adhesion margin in the intervention range.

[0015] In a second aspect, the present invention provides an adhesion capacity quantification device, comprising: The first module is used to determine the vehicle's center of gravity sideslip angle and road adhesion coefficient based on vehicle state parameters. The second module is used to construct a stable region boundary model of the vehicle based on the centroid sideslip angle and the road surface adhesion coefficient. The third module is used to determine the attachment margin based on the stable region boundary model.

[0016] Thirdly, the present invention provides an electronic device, including a memory and a processor; The memory is used to store computer programs; The processor is configured to implement the adhesion capability quantification method as described in the first aspect when executing the computer program.

[0017] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the adhesion capability quantification method as described in the first aspect.

[0018] The beneficial effects of the adhesion quantification method of the present invention are as follows: Based on vehicle state parameters, the vehicle's center of gravity sideslip angle and road adhesion coefficient are determined. A vehicle stability zone boundary model is then constructed based on the center of gravity sideslip angle and road adhesion coefficient. Furthermore, the adhesion margin is determined based on the stability zone boundary model. This transforms the relationship between the vehicle's current operating state and the stability zone boundary into a calculable quantitative indicator. The remaining tire adhesion, which was previously difficult for the driver to perceive directly, is represented numerically. This transforms the traditional adhesion perception problem, which relies on driving experience and subjective judgment, into an objective quantitative problem based on vehicle dynamics models. It provides a direct reflection of the vehicle's current distance from the instability boundary, thus offering the driver perceptible and understandable adhesion information and providing a quantitative basis for subsequent warning prompts or control strategies. This improves the problem of driver lag or over-operation due to insufficient perception under low adhesion or extreme conditions, thereby enhancing vehicle driving safety. Attached Figure Description

[0019] Figure 1This is a flowchart illustrating the adhesion quantification method according to an embodiment of the present invention; Figure 2 This is a schematic diagram illustrating the principle of the adhesion quantification method according to an embodiment of the present invention; Figure 3 This is a schematic diagram illustrating the process of determining the vehicle's center of gravity sideslip angle and road surface adhesion coefficient according to an embodiment of the present invention. Figure 4 This is a schematic diagram of the real-time estimation process according to an embodiment of the present invention; Figure 5 This is a schematic diagram illustrating the process of constructing a stable region boundary model according to an embodiment of the present invention; Figure 6 This is a schematic diagram of the process for determining the adhesion margin according to an embodiment of the present invention; Figure 7 This is a schematic diagram of the process for quantifying adhesion capability according to an embodiment of the present invention; Figure 8 This is a system architecture diagram of the adhesion capacity quantification device according to an embodiment of the present invention; Figure 9 This is a system architecture diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0020] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Although some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the accompanying drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0021] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0022] The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to"; the term "based on" means "at least partially based on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments"; and the term "optionally" means "optional embodiments". Definitions of other terms will be given in the following description. It should be noted that the concepts of "first," "second," etc., mentioned in this invention are used only to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.

[0023] It should be noted that the terms "one" and "more" used in this invention are illustrative rather than restrictive. Those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0024] The names of the messages or information exchanged between the multiple devices in the embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0025] like Figure 1 As shown, an embodiment of the present invention provides a method for quantifying adhesion ability, comprising: S100: Determine the vehicle's center of gravity sideslip angle and road adhesion coefficient based on vehicle state parameters.

[0026] Specifically, the vehicle controller collects vehicle status parameters in real time, such as the steering wheel angle δ. sw Yaw rate γ, vehicle longitudinal acceleration a x lateral acceleration of the vehicle a y and the wheel speed ω of each wheel i The controller estimates the vehicle state based on the vehicle dynamics model, and obtains the vehicle's center of gravity sideslip angle β and the road adhesion coefficient μ. For example, the vehicle dynamics model can adopt a two-degree-of-freedom vehicle model, and the vehicle's lateral dynamic equation can be established by combining the tire sideslip characteristic model. The center of gravity sideslip angle β and the road adhesion coefficient μ are estimated in real time through the state estimation algorithm, so that the system can obtain the vehicle's lateral motion state and road adhesion capability.

[0027] S200: Construct a stable region boundary model for the vehicle based on the centroid sideslip angle and the road surface adhesion coefficient.

[0028] Specifically, after obtaining the sideslip angle β, the controller further constructs a phase plane based on the sideslip angular velocity and builds a vehicle stability region boundary model within this phase plane. The shape of the stability region boundary model can be an elliptical boundary, a hyperbolic boundary, or a nonlinear boundary curve obtained through experimental calibration. The boundary parameters of the stability region boundary model are based on the road adhesion coefficient μ and the vehicle longitudinal velocity V. x Adjustments are made to create different stable region boundaries for different adhesion coefficients and vehicle speeds, so that the stable region boundary model can reflect the stability limits of the vehicle under different operating conditions.

[0029] S300: Determine the attachment margin based on the stable region boundary model.

[0030] Specifically, the controller calculates the distance between the vehicle's current state point and the boundary of the stable region based on the vehicle's current state point position in the phase plane, and normalizes this distance to obtain a normalized distance. The adhesion margin is then calculated based on this normalized distance. The adhesion margin characterizes the degree to which the vehicle's current state is close to the instability boundary; a smaller adhesion margin indicates that the vehicle is closer to the instability boundary. The adhesion margin can be divided into a safe zone, a warning zone, and an intervention zone. When the adhesion margin is in the intervention zone, coordinated control is executed, including front wheel steering angle correction and differential braking of the inner rear wheel. By reducing the actual front wheel steering angle or limiting the front wheel steering angle increment, the lateral force demand of the vehicle is reduced. Simultaneously, by applying braking force to the inner rear wheel of the vehicle, an additional yaw moment is generated, causing the vehicle state to return to the stable region, thereby improving vehicle stability.

[0031] Combination Figure 2 As shown, the vehicle controller can collect vehicle state parameters through the perception layer, such as the environmental perception unit (forward-facing camera), vehicle motion sensor (IMU), wheel speed sensor, and steering wheel angle sensor (inside the SBW). The vehicle controller can perform coordinated control through the control layer, such as implementing the state estimation algorithm through the state estimation module (UKF), determining the adhesion margin through the phase plane margin calculation module, and then performing front wheel angle correction and / or inner rear wheel differential braking through the mode decision and coordinated control module. Finally, coordinated control is achieved through the execution layer, such as the SBW road sensor motor (HMI vibration warning), SBW front wheel steering actuator (virtual limit), ESC hydraulic unit (differential braking), and HMI display unit (instrument / ambient lighting).

[0032] In this embodiment, the vehicle's center of gravity sideslip angle and road adhesion coefficient are determined based on vehicle state parameters. A vehicle stability zone boundary model is then constructed based on the center of gravity sideslip angle and road adhesion coefficient. Subsequently, the adhesion margin is determined based on the stability zone boundary model. This transforms the relationship between the vehicle's current operating state and the stability zone boundary into a calculable quantitative indicator, allowing the tire's remaining adhesion capacity, which was originally difficult for the driver to perceive directly, to be represented in numerical form. This transforms the traditional adhesion capacity perception problem, which relies on driving experience and subjective judgment, into an objective quantitative problem based on the vehicle dynamics model. It provides a direct reflection of the vehicle's current distance from the instability boundary, thereby providing the driver with perceptible and understandable adhesion capacity information and providing a quantitative basis for subsequent warning prompts or control strategies. This improves the problem of driver lag or over-operation due to insufficient perception under low adhesion or extreme conditions, thereby enhancing vehicle driving safety.

[0033] Optionally, determining the vehicle's center of gravity sideslip angle and road adhesion coefficient based on vehicle state parameters includes: S110: Collect at least one of the following vehicle state parameters: steering wheel angle, yaw rate, longitudinal acceleration, lateral acceleration, and wheel speed.

[0034] Specifically, in combination Figure 3 As shown, vehicle state parameters are obtained through vehicle sensors, including steering wheel angle measured by steering angle sensor, yaw rate measured by inertial measurement unit, longitudinal acceleration and lateral acceleration measured by acceleration sensor, and wheel speed measured by wheel speed sensor.

[0035] S120: Based on the vehicle dynamics model, the centroid sideslip angle and the road surface adhesion coefficient are estimated in real time according to the vehicle state parameters.

[0036] Specifically, a state estimator is established based on the vehicle dynamics model. The vehicle state parameters are used to estimate the centroid sideslip angle β and the road adhesion coefficient μ in real time, thereby obtaining information on the vehicle's lateral motion state and road adhesion capability. This provides a foundation for subsequent stable region boundary construction and adhesion margin calculation. An exemplary process is shown below: (1) Calculation formula:

[0037] Where the state vector x= , It is the current predicted value, which represents the predicted state at the current time (k) calculated by the system based on the previous state; It is the optimal value at the previous time step, representing the final output after filtering and correction at the previous time step (k-1) (i.e., the previous estimation result). The control input represents the external control quantity applied to the system at the current time (k); f(*) is the system state transition function, representing a nonlinear function used to describe the vehicle dynamics model, containing the physical laws describing how the vehicle moves (such as the two-degree-of-freedom vehicle differential equation, tire model). This function receives the position of the vehicle at the previous time step. and the driver's current operating intention As input, and then based on the laws of physics, it is calculated what the vehicle would be in now if there were no sensor measurements or corrections. .

[0038] (2) Observation update: Using the residual between the measured value (mainly the lateral acceleration ay) and the predicted value (ay calculated based on the tire model), the road adhesion coefficient μ and the centroid side slip angle β are corrected online through the Kalman gain matrix K.

[0039] + ; in, , The predicted state at the previous moment (centroid sideslip angle, road surface adhesion coefficient); in, These are predicted lateral acceleration values ​​calculated based on tire models (such as Dugoff's or the Magic Formula). Among them, K β K μ The gain coefficients of the corresponding state components in the Kalman gain matrix are determined in real time by the system covariance and the measurement noise covariance. Among them, residual Reflecting the degree of deviation between model prediction and actual measurement, this residual increases significantly when the tire enters the nonlinear saturation region, driving the Kalman filter to correct μ and β online, making the estimated value closer to the true adhesion limit.

[0040] When the tire force enters the saturation zone, the actual a y The growth rate of μ will be slower than the predicted value of the linear model based on the small angle assumption. The current peak adhesion coefficient μ can be identified in reverse through this residual.

[0041] In this optional embodiment, by collecting at least one vehicle state parameter among steering wheel angle, yaw rate, longitudinal acceleration, lateral acceleration, and wheel speed, and estimating the center of gravity sideslip angle and road adhesion coefficient in real time based on the vehicle dynamics model, the system can obtain key state parameters such as the vehicle center of gravity sideslip angle and road adhesion coefficient that are difficult to measure directly without adding additional complex sensors. This improves the system's engineering feasibility and environmental adaptability, and provides reliable state input for subsequent stable region boundary construction and adhesion margin calculation.

[0042] Optionally, the real-time estimation of the centroid sideslip angle and the road surface adhesion coefficient based on the vehicle state parameters includes: S121: Establish state equations and observation equations based on the vehicle dynamics model, and determine the predicted lateral acceleration value according to the vehicle state parameters.

[0043] Specifically, in combination Figure 4 As shown, state equations and observation equations are established based on the vehicle dynamics model, and the predicted value of lateral acceleration is determined according to the vehicle state parameters. The state equations are used to describe the changing relationship of the vehicle's lateral motion state, and the observation equations are used to describe the relationship between the sensor measurements and the vehicle state. S122: Based on the residual between the predicted lateral acceleration value and the actual measured lateral acceleration, the predicted values ​​of the centroid sideslip angle and the road surface adhesion coefficient are corrected online to obtain the estimated values ​​of the centroid sideslip angle and the road surface adhesion coefficient.

[0044] Specifically, the predicted lateral acceleration value is compared with the actual measured lateral acceleration to obtain the residual between the two. Based on this residual, the centroid sideslip angle β and the road adhesion coefficient μ are corrected online, so that the estimation results continuously approach the true values, improving the estimation accuracy of the centroid sideslip angle and the road adhesion coefficient. When the tire gradually approaches the adhesion limit, the growth trend of the actual lateral acceleration will be lower than the model prediction value. The change in the road adhesion coefficient can be identified by the change in this residual, thereby realizing the online estimation of the road adhesion coefficient.

[0045] In this optional embodiment, by establishing state equations and observation equations, and by correcting the centroid sideslip angle and road adhesion coefficient online based on the residual between the predicted lateral acceleration value and the actual measured lateral acceleration, the system can correct model errors in real time according to the actual operating state of the vehicle. It can still maintain high state estimation accuracy when the tire enters the nonlinear region or the road adhesion coefficient changes, thereby improving the accuracy and stability of adhesion margin calculation.

[0046] Optionally, constructing the stable region boundary model of the vehicle based on the centroid sideslip angle and the road surface adhesion coefficient includes: S210: Construct a phase plane based on the centroid sideslip angle and the centroid sideslip angular velocity, wherein the centroid sideslip angular velocity is determined by differentiating the centroid sideslip angle with respect to time.

[0047] Specifically, in combination Figure 5As shown, a phase plane is constructed based on the sideslip angle and sideslip angular velocity (determined by differentiating the sideslip angle with respect to time), and a stable region boundary model is constructed within this phase plane. The stable region boundary model can be obtained through simulation experiments or real vehicle tests. The stable region boundary curves are calibrated under different road surface adhesion coefficients and different vehicle speeds, and stored as a lookup table. An example lookup table is shown below:

[0048] S220: Within the phase plane, construct the boundary model of the stable region based on the road surface adhesion coefficient.

[0049] Specifically, during vehicle operation, the controller determines the road surface adhesion coefficient μ and the vehicle's longitudinal speed V. x The corresponding stable region boundary parameters are selected from the lookup table to construct the stable region boundary model under the current operating condition. The stable boundary can usually be approximated by a set of hyperbolic or elliptical equations. Taking the elliptical equation as an example, by rotating the coordinates to align its principal axes with the coordinate axes, the elliptical equation can be transformed into a one-dimensional linear inequality, i.e. , where the coefficient , It is a function of the road surface adhesion coefficient μ and the vehicle speed Vx, i.e. , It is updated in real time by looking up a table or fitting a formula.

[0050] In this optional embodiment, by constructing a phase plane of the center of gravity sideslip angle and the center of gravity sideslip angular velocity, and constructing a stable region boundary model in the phase plane based on the road surface adhesion coefficient, the vehicle stable region can be intuitively represented in the form of a boundary, and the range of the stable region can be adaptively adjusted according to different road surface adhesion conditions. Thus, the stability determination no longer depends on a single state threshold, but is based on the overall dynamic state of the vehicle, thereby improving the accuracy of vehicle stability determination.

[0051] Optionally, constructing the stable region boundary model based on the road surface adhesion coefficient includes: The boundary parameters of the stable region boundary model are determined based on the road surface adhesion coefficient and vehicle speed. The stable region boundary curve is then generated in the phase plane based on the boundary parameters to form the stable region boundary model.

[0052] Specifically, the real-time estimated road adhesion coefficient and vehicle longitudinal speed are obtained, and the boundary parameters of the stable region boundary model are determined based on the road adhesion coefficient and vehicle longitudinal speed. The boundary parameters are used to characterize the shape and size of the stable region boundary curve. After obtaining the boundary parameters, the stable region boundary curve is generated in the phase plane composed of the centroid sideslip angle and the rate of change of the centroid sideslip angle. The stable region boundary curve is used to characterize the stable operating range of the vehicle under the current road adhesion conditions and the current vehicle speed conditions. When the vehicle speed is high or the road adhesion coefficient is low, the stable region boundary curve shrinks towards the origin of the phase plane. When the vehicle speed is low or the road adhesion coefficient is high, the stable region boundary curve expands outward from the phase plane, thus forming a stable region boundary model that changes with the operating conditions.

[0053] In this optional embodiment, the boundary parameters of the stable region boundary model are determined based on the road surface adhesion coefficient and vehicle speed, and a stable region boundary curve is generated in the phase plane. This allows the stable region boundary to be adaptively adjusted according to changes in road surface adhesion conditions and vehicle speed. Therefore, the stable region boundary model can better conform to the actual stability limit of the vehicle under different operating conditions. The adhesion capability quantification result is no longer based on a fixed boundary, but on a dynamic stable region that changes with adhesion conditions and vehicle speed, thereby improving the accuracy and adaptability of the adhesion capability quantification result.

[0054] Optionally, determining the attachment margin based on the stable region boundary model includes: S310: Determine the normalized distance between the vehicle's current state point and the stable region boundary based on the stable region boundary model.

[0055] Specifically, in combination Figure 6 As shown, first, the position of the vehicle's current state point (β, dβ / dt) in the phase plane is determined. Then, the shortest distance from this state point to the boundary of the stable region is calculated, and this distance is normalized to obtain the normalized distance D. The generalized distance D of the current state point P is defined as follows: D= ; When D < 1, the vehicle is in a stable region; when D = 1, it is at the boundary; when D > 1, it is unstable.

[0056] S320: Determine the adhesion margin based on the normalized distance.

[0057] Specifically, the adhesion margin M is then determined based on the normalized distance D. The adhesion margin is used to indicate how close the vehicle's current state is to the boundary of the stable region. The closer the normalized distance is to the boundary, the smaller the adhesion margin. When the normalized distance exceeds the boundary, it indicates that the vehicle has entered the unstable region.

[0058] Define the attachment margin M (expressed as a percentage): M=(1 D)×100%; M is used to characterize the distance from the current state point to the instability boundary. M=50% means there is still half the distance to the boundary; M=0% means it is just on the boundary; a negative M value indicates that it has become unstable.

[0059] In this optional embodiment, by calculating the normalized distance between the vehicle's current state point and the boundary of the stable region, and determining the adhesion margin based on the normalized distance, the vehicle stability can be quantitatively characterized in the form of a continuous variable. This reflects the remaining stability margin of the vehicle from the instability boundary, providing a quantitative basis for vehicle stability control and driver warning, and improving the precision of the control system.

[0060] Optionally, the adhesion margin and the normalized distance are inversely related; the smaller the normalized distance, the larger the adhesion margin, and vice versa.

[0061] Specifically, after obtaining the normalized distance from the vehicle's current state point to the boundary of the stable region, the adhesion margin is determined based on the normalized distance. The adhesion margin is used to characterize the degree of proximity of the vehicle's current state point to the boundary of the stable region. The adhesion margin and the normalized distance have an inverse relationship. That is, as the vehicle's current state point gradually approaches the boundary of the stable region, the normalized distance gradually increases and the adhesion margin gradually decreases. Conversely, as the vehicle's current state point moves away from the boundary of the stable region, the normalized distance gradually decreases and the adhesion margin gradually increases.

[0062] In this optional embodiment, the adhesion margin is inversely related to the normalized distance from the vehicle's current state point to the boundary of the stable region. This allows the adhesion margin to decrease continuously as the vehicle's state approaches the boundary of the stable region, thereby enabling the vehicle's remaining adhesion capability to be characterized by a continuously changing quantitative index. This allows for a continuous quantitative representation of the vehicle's remaining adhesion capability, enabling the driver or vehicle control system to more intuitively perceive the degree to which the vehicle is approaching the adhesion limit, thus providing a quantitative basis for driver warnings or vehicle stability control.

[0063] Optionally, the adhesion capability quantification method further includes: when the adhesion margin is within the intervention range, S330: Determine the front wheel steering angle correction amount based on the distance between the vehicle's current state point and the boundary of the stable area, so as to correct or limit the front wheel steering angle requested by the driver, reduce the actual front wheel steering angle or limit the front wheel steering angle increment.

[0064] Specifically, in combination Figure 7As shown, when the vehicle's adhesion margin is in the intervention range, the front wheel angle correction amount is determined based on the distance between the vehicle's current state point (β, dβ / dt) and the boundary of the stable region (saddle point). The front wheel angle correction amount is used to correct or limit the front wheel angle requested by the driver, so that the actual front wheel angle is reduced or the front wheel angle increment is limited, thereby reducing the vehicle's lateral force demand.

[0065] The goal of SBW virtual limit (front wheel correction) is to pull the state point back to the stable region, that is, to reduce the D value, by correcting the front wheel steering angle. This makes Dnew≈0.9 (returning to the warning zone); through vehicle dynamics sensitivity analysis, a system can be established. Approximate linear relationship with ΔD: ; in, It is related to vehicle speed and The relevant gain coefficient (obtained through calibration); the final actual steering angle applied to the front wheels is ,because A negative value (reducing the turning angle) achieves a virtual limit effect.

[0066] S340: Determine the target yaw moment based on the distance between the vehicle's current state point and the boundary of the stable region, and determine the braking force of the inner rear wheel based on the target yaw moment to apply braking to the inner rear wheel of the vehicle.

[0067] Specifically, the target yaw moment is determined based on the distance between the vehicle's current state point and the boundary of the stable region. The target yaw moment is then converted into braking force on the inner rear wheel based on parameters such as the vehicle's track width and wheel radius. This braking force is applied to the inner rear wheel of the vehicle, thereby generating a corrective yaw moment that stabilizes the vehicle's yaw motion.

[0068] Since recovery by simply reducing the steering angle is slow, additional yaw moment is required. Therefore, applying braking force to the inner rear wheel during steering can be chosen. A simple PD controller is used, with the distance D from the state point to the boundary as the control target, and the target yaw moment M... z It can be represented as: ; in, Set it to 0.9.

[0069] The braking force distribution is expressed as follows: ; Where d is the wheel track. The braking force is applied to the inner rear wheel, and this braking force is precisely and gently established through the ESC hydraulic unit to avoid abrupt deceleration.

[0070] In this optional embodiment, the front wheel steering angle correction is determined based on the distance between the vehicle's current state point and the boundary of the stable region, and the target yaw moment is determined based on the distance between the vehicle's current state point and the boundary of the stable region. This allows for the control of the front wheel steering angle and the inner rear wheel braking force, enabling the steering control and braking control to be coordinated and adjusted according to the vehicle's stability margin. This reduces the vehicle's lateral force requirement while providing additional yaw moment, allowing the vehicle to return to the stable region more smoothly, reducing the abruptness caused by single braking control, and improving the smoothness of human-machine collaborative control and the vehicle's handling stability.

[0071] Optionally, the adhesion capability quantification method further includes: when the adhesion margin is in the warning range, outputting a warning signal to the driver to indicate that the vehicle is approaching the boundary of the stable area, wherein the adhesion margin in the warning range is greater than the adhesion margin in the intervention range.

[0072] Specifically, the adhesion margin is divided into a safe range (30% ≤ M), a warning range (10% ≤ M < 30%), and an intervention range (M < 10%). When the adhesion margin is in the warning range, a warning signal is output to the driver to indicate that the vehicle is approaching the boundary of the stable area. The warning signal can be one or more of the following: steering wheel vibration warning, instrument warning, audible warning, or head-up display warning. When the adhesion margin further decreases from the warning range to the intervention range, the controller performs front wheel steering angle correction and inner rear wheel differential braking to control vehicle stability. Through the above-mentioned graded warning and coordinated control mechanism, the driver can perceive in advance that the vehicle is approaching the adhesion limit and the control system can provide auxiliary intervention when necessary, thereby improving the driving safety of the vehicle on low-adhesion road surfaces.

[0073] In this optional embodiment, a warning signal is output to the driver when the adhesion margin is in the warning range, so that the driver can receive a risk warning in advance when the vehicle is approaching the boundary of the stable area but has not yet entered the unstable state. This allows the driver to actively adjust the steering or speed, reduce the probability of the vehicle entering the unstable state, and form a graded warning and graded control mechanism with the subsequent active coordinated control, thereby improving the active safety of the vehicle.

[0074] like Figure 8 As shown, an adhesion quantification device 800 provided in an embodiment of the present invention includes: The first module 810 is used to determine the vehicle's center of gravity sideslip angle and road adhesion coefficient based on the vehicle's state parameters. The second module 820 is used to construct a stable region boundary model of the vehicle based on the centroid sideslip angle and the road surface adhesion coefficient. The third module 830 is used to determine the attachment margin based on the stable region boundary model.

[0075] like Figure 9 As shown, an electronic device 900 provided in this embodiment of the invention includes a memory 920 and a processor 910; the memory 920 is used to store a computer program; the processor 910 is used to implement the adhesion capability quantification method as described above when the computer program is executed.

[0076] Alternatively, an electronic device 900 includes a memory 920 and a processor 910 coupled to the memory 920; the memory 920 is configured to store a computer program; and the processor 910 is configured to perform the following operations when the computer program is executed: The vehicle's center of gravity sideslip angle and road adhesion coefficient are determined based on the vehicle's condition parameters. The stable region boundary model of the vehicle is constructed based on the centroid sideslip angle and the road surface adhesion coefficient. The attachment margin is determined based on the stable region boundary model.

[0077] This invention provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the adhesion capability quantification method described above.

[0078] Alternatively, a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, causes the processor to perform the following operations: The vehicle's center of gravity sideslip angle and road adhesion coefficient are determined based on the vehicle's condition parameters. The stable region boundary model of the vehicle is constructed based on the centroid sideslip angle and the road surface adhesion coefficient. The attachment margin is determined based on the stable region boundary model.

[0079] The present invention will now be described an electronic device 900 that can serve as a server or client of the present invention, which is an example of a hardware device that can be applied to various aspects of the present invention. Electronic device 900 is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic device 900 can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0080] Electronic device 900 includes a computing unit that can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) or a computer program loaded from a storage unit into random access memory (RAM). The RAM may also store various programs and data required for device operation. The computing unit, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0081] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc. In this application, the units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments of the present invention according to actual needs. Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units can be implemented in hardware or as software functional units.

[0082] While the present invention has been disclosed above, its scope of protection is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, and all such changes and modifications will fall within the scope of protection of the present invention.

Claims

1. A method for quantifying adhesion ability, characterized in that, include: The vehicle's center of gravity sideslip angle and road adhesion coefficient are determined based on the vehicle's condition parameters. The stable region boundary model of the vehicle is constructed based on the centroid sideslip angle and the road surface adhesion coefficient. The adhesion margin is determined based on the stable region boundary model, and the adhesion margin is used to characterize the degree to which the current state of the vehicle is far from the instability boundary; The determination of the attachment margin based on the stable region boundary model includes: The normalized distance between the vehicle's current state point and the stable region boundary is determined based on the stable region boundary model. The attachment margin is determined based on the normalized distance.

2. The adhesion quantification method according to claim 1, characterized in that, The determination of the vehicle's center of gravity sideslip angle and road adhesion coefficient based on vehicle state parameters includes: Collect at least one of the following vehicle state parameters: steering wheel angle, yaw rate, longitudinal acceleration, lateral acceleration, and wheel speed; Based on the vehicle dynamics model, the centroid sideslip angle and the road surface adhesion coefficient are estimated in real time according to the vehicle state parameters.

3. The adhesion quantification method according to claim 2, characterized in that, The real-time estimation of the centroid sideslip angle and the road surface adhesion coefficient based on the vehicle state parameters includes: State equations and observation equations are established based on the vehicle dynamics model, and the predicted lateral acceleration value is determined according to the vehicle state parameters. Based on the residual between the predicted lateral acceleration and the actual measured lateral acceleration, the predicted values ​​of the centroid sideslip angle and the road surface adhesion coefficient are corrected online to obtain estimated values ​​of the centroid sideslip angle and the road surface adhesion coefficient.

4. The adhesion quantification method according to claim 1, characterized in that, The step of constructing the stable region boundary model of the vehicle based on the centroid sideslip angle and the road surface adhesion coefficient includes: A phase plane is constructed based on the centroid sideslip angle and the centroid sideslip angular velocity, wherein the centroid sideslip angular velocity is determined by differentiating the centroid sideslip angle with respect to time. Within the phase plane, a boundary model of the stable region is constructed based on the road surface adhesion coefficient.

5. The adhesion quantification method according to claim 4, characterized in that, The construction of the stable region boundary model based on the road surface adhesion coefficient includes: The boundary parameters of the stable region boundary model are determined based on the road surface adhesion coefficient and vehicle speed. The stable region boundary curve is then generated in the phase plane based on the boundary parameters to form the stable region boundary model.

6. The adhesion quantification method according to claim 1, characterized in that, The adhesion margin and the normalized distance are inversely related; the smaller the normalized distance, the larger the adhesion margin, and vice versa.

7. The adhesion quantification method according to claim 1, characterized in that, Also includes: When the adhesion margin is in the intervention range, the front wheel steering angle correction amount is determined based on the distance between the current state point of the vehicle and the boundary of the stable area, so as to correct or limit the front wheel steering angle requested by the driver, reduce the actual front wheel steering angle or limit the front wheel steering angle increment. And / or, when the adhesion margin is in the intervention range, a target yaw moment is determined based on the distance between the vehicle's current state point and the boundary of the stable region, and the braking force of the inner rear wheel is determined based on the target yaw moment to apply braking to the inner rear wheel of the vehicle.

8. The method for quantifying adhesion ability according to claim 7, characterized in that, Also includes: When the adhesion margin is within the warning range, a warning signal is output to the driver to indicate that the vehicle is approaching the boundary of the stable area. The adhesion margin in the warning range is greater than the adhesion margin in the intervention range.

9. An adhesion capacity quantification device, characterized in that, include: The first module is used to determine the vehicle's center of gravity sideslip angle and road adhesion coefficient based on vehicle state parameters. The second module is used to construct a stable region boundary model of the vehicle based on the centroid sideslip angle and the road surface adhesion coefficient. The third module is used to determine the adhesion margin based on the stable region boundary model, wherein the adhesion margin is used to characterize the degree to which the current state of the vehicle is far from the instability boundary; The determination of the attachment margin based on the stable region boundary model includes: The normalized distance between the vehicle's current state point and the stable region boundary is determined based on the stable region boundary model. The attachment margin is determined based on the normalized distance.

10. An electronic device, characterized in that, Including memory and processor; The memory is used to store computer programs; The processor is configured to implement the adhesion capability quantification method as described in any one of claims 1 to 8 when executing the computer program.

11. A computer-readable storage medium, characterized in that, The storage medium stores a computer program that, when executed by a processor, implements the adhesion capability quantification method as described in any one of claims 1 to 8.

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