A coordinated stability control method and system based on distributed wire-controlled vehicles

Abstract

This invention is applicable to the field of vehicle stability control technology and provides a coordinated stability control method and system for distributed, drive-by-wire vehicles. The method comprises the following steps: constructing a vehicle dynamics model; collecting vehicle state information based on a state recognition layer and a driving intention recognition layer, calculating and correcting the ideal yaw rate and ideal center-of-mass sideslip angle; allocating control variable weights using a KDPC algorithm; calculating the values ​​of the vehicle's control variables, namely, the rear wheel angle and the additional yaw moment; and allocating wheel drive torque based on the vehicle force, the additional yaw moment, the ratio of front and rear axle loads, the wheel radius, and the wheelbase. Through its electronic and dynamic control strategy, the invention achieves multi-dimensional advantages in flexibility, stability, energy efficiency, and intelligence, extending the vehicle chassis' extreme performance and vehicle handling stability.

Description

Technical Field

[0001] The present invention belongs to the technical field of vehicle stability control, and in particular relates to a stability collaborative control method and system based on a distributed wire-controlled vehicle. Background Art

[0002] As the functionality and demand for intelligent driving grow, distributed drive-by-wire technology has garnered significant attention for its superior performance and safety redundancy. Intelligent driving encompasses four key components: perception, decision-making, planning, and control. Implementation of the control component is paramount to ensuring vehicle safety. In recent years, as research deepens, understanding of four-wheel drive has gradually deepened. Increasingly, wheel-mounted or hub-mounted motors are being adopted to optimize performance and save space, enabling independent four-wheel control. Numerous experiments have demonstrated that these technologies offer significantly improved performance and handling stability.

[0003] Traditional vehicles, constrained by the limitations of mechanical coupling, have significant room for improvement in flexibility, stability, and energy efficiency, hindering the full performance of the chassis. Collaborative vehicle chassis control integrates chassis subsystems and utilizes a central control system to achieve data sharing and unified scheduling, thereby optimizing vehicle dynamic performance, safety, and comfort. This technology provides critical foundational support for advanced autonomous driving and a personalized driving experience. Summary of the Invention

[0004] The purpose of the embodiments of the present invention is to provide a stability cooperative control method based on a distributed wire-controlled vehicle, aiming to solve the problems raised in the above background technology.

[0005] The embodiment of the present invention is implemented as follows: a method for coordinated stability control based on a distributed wire-controlled vehicle, comprising the following steps:

[0006] Construct a vehicle dynamics model and transform the vehicle's linear two-degree-of-freedom four-wheel steering dynamics equations into state-space equations;

[0007] The vehicle status information is collected based on the status recognition layer and the driving intention recognition layer, and the ideal yaw rate and the ideal sideslip angle of the center of mass are calculated and corrected;

[0008] Allocate control weights through the KDPC algorithm;

[0009] Design a cost function and establish constraints, and calculate the values ​​of the rear wheel angle and additional yaw moment of the vehicle control variables based on the constraints;

[0010] The vehicle driving force-driving resistance balance equation is used to solve the vehicle force required to maintain the vehicle speed, and then the wheel driving torque is distributed according to the vehicle force, additional yaw moment, the proportional relationship between the front and rear axle loads, the wheel radius and the wheel width.

[0011] Another object of an embodiment of the present invention is to provide a distributed wire-controlled vehicle-based stability cooperative control system, which is used to implement the above-mentioned distributed wire-controlled vehicle-based stability cooperative control method, including:

[0012] The model building module is used to build a vehicle dynamics model and convert the dynamic equations of the vehicle's linear two-degree-of-freedom four-wheel steering into state-space equations;

[0013] An ideal reference value correction module is used to collect vehicle state information based on the state recognition layer and the driving intention recognition layer, and calculate and correct the ideal yaw rate and the ideal center of mass sideslip angle;

[0014] The control quantity weight allocation module is used to allocate the control quantity weight through the KDPC algorithm;

[0015] The vehicle control variable solving module is used to design the cost function and establish the constraint conditions, and calculate the values ​​of the vehicle control variables such as the rear wheel angle and the additional yaw moment based on the constraint conditions;

[0016] The wheel driving torque distribution module is used to solve the vehicle force required to maintain the vehicle speed through the vehicle driving force-driving resistance balance equation, and then distribute the wheel driving torque according to the vehicle force, additional yaw moment, the proportional relationship between the front and rear axle loads, the wheel radius and the wheelbase.

[0017] An embodiment of the present invention provides a coordinated stability control method based on a distributed wire-controlled vehicle. Through an electronic and dynamic control strategy, it forms multi-dimensional advantages in flexibility, stability, energy efficiency and intelligence, thereby expanding the ultimate boundary performance of the vehicle chassis and the vehicle's handling stability. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 A flowchart of a stability cooperative control method based on a distributed wire-controlled vehicle provided in an embodiment of the present invention;

[0019] Figure 2 A flowchart of allocating control variable weights using a KDPC algorithm in a distributed wire-controlled vehicle stability cooperative control method provided by an embodiment of the present invention;

[0020] Figure 3 A structural diagram of a stability cooperative control system based on a distributed wire-controlled vehicle provided by an embodiment of the present invention;

[0021] Figure 4 A comparison curve between the state quantity of the yaw angular velocity and the target value provided by an embodiment of the present invention;

[0022] Figure 5A comparison curve between the state quantity of the sideslip angle of the center of mass provided in an embodiment of the present invention and the target value. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0024] The specific implementation of the present invention is described in detail below with reference to specific embodiments.

[0025] like Figure 1 FIG. 1 is a flow chart of a method for coordinated stability control of a distributed wire-controlled vehicle according to an embodiment of the present invention, comprising the following steps:

[0026] Step 1: Construct a vehicle dynamics model. The dynamics equation of the vehicle's linear two-degree-of-freedom four-wheel steering is:

[0027] ;

[0028] ;

[0029] in, is the vehicle mass; 、 are the distances from the center of mass to the front and rear axles respectively; 、 are the front and rear axle lateral stiffness respectively; is the longitudinal speed; is the vehicle's center of mass sideslip angle; is the vehicle yaw angular velocity; is the front wheel turning angle; is the rear wheel turning angle; is the moment of inertia of the vehicle around the Z axis; is the additional yaw moment;

[0030] The above differential equation is transformed into a state space equation that is convenient for calculation:

[0031] ;

[0032] in, ; ; ; ; ; .

[0033] Step 2: Based on the state recognition layer and the driving intention recognition layer, the vehicle state information is collected to calculate and correct the ideal yaw rate and the ideal center of mass sideslip angle:

[0034] Step 2.1: After collecting vehicle state information based on the state recognition layer and the driving intention recognition layer, calculate and correct the ideal yaw rate and ideal center of mass sideslip angle, where the ideal center of mass sideslip angle is 0, and the calculation formula for the ideal yaw rate is:

[0035] ;

[0036] in, is the ideal yaw rate; 、 are differential torque and front wheel angle respectively; coefficient 、 The calculation formula is: ; ;

[0037] in, ; is the wheelbase;

[0038] Step 2.2: From the above formula, we know that the ideal yaw rate increases as the front wheel angle increases. However, in actual control, vehicle stability must be guaranteed. Therefore, the ideal yaw rate value needs to be limited based on road adhesion conditions. When the vehicle's sideslip angle is less than 3°, the following relationship is satisfied: ;

[0039] in, is the lateral acceleration of the vehicle's center of mass; is the road adhesion coefficient; is the acceleration due to gravity;

[0040] Therefore, the ideal upper limit of the vehicle's yaw rate can be determined as: ;

[0041] When using dynamic methods for this estimation, there are constraints such as poor real-time performance and difficulty in obtaining tire parameters. Therefore, this formula cannot be directly used to limit its ideal upper limit in engineering. However, since sensors can measure the lateral acceleration of the vehicle's center of mass in real time, it is possible to consider calculating the ideal upper limit approximate value of the vehicle's yaw rate from the lateral acceleration. This is called the safe yaw rate and is calculated using the following formula: ;

[0042] Due to the steering hysteresis characteristic of the vehicle, the safe yaw rate calculated by the above formula often lags behind the actual yaw rate of the vehicle. In other words, the safe yaw rate cannot be directly used to limit it. Therefore, it is proposed to use the critical yaw rate as the stability control boundary of the ideal yaw rate. The formula is as follows:

[0043] ;

[0044] in, is the critical yaw rate; is the yaw rate compensation value; 、 are the understeering factor and oversteering factor respectively; is the one-dimensional lookup table value of vehicle speed; is the steering wheel angular velocity; 、 is the calibration parameter.

[0045] Step 2.3: Calculate and correct the target yaw rate by using the ideal yaw rate, the safe yaw rate, and the weight coefficient:

[0046] ;

[0047] in, is the target yaw rate; is the ideal yaw rate; is the safe yaw rate; is the weight factor, and the calculation formula is as follows:

[0048] ;

[0049] in, is the calibration coefficient, ranging from 0.5 to 0.9.

[0050] Step 3: Assign control weights through the KDPC algorithm (Kernel Density Peak Clustering). This involves dynamically clustering the vehicle state to identify different operating conditions and dynamically adjusting the control weights based on the similarity between the current state and the ideal state. Figure 2 As shown:

[0051] Step 3.1: After collecting vehicle dynamic parameters in real time, select features from the vehicle status information to construct a feature vector:

[0052] ;

[0053] in, is the eigenvector; is the longitudinal speed; is the vehicle yaw angular velocity; is the vehicle's center of mass sideslip angle; is the wheel angle; is the road adhesion coefficient; Represents the transpose operation of the matrix;

[0054] Normalize the characteristic parameters to eliminate dimensional differences: ;

[0055] in, is the normalization parameter, is the characteristic parameter, is the mean, is the standard deviation;

[0056] Use Gaussian kernel function (RBF) to map data into high-dimensional space to enhance clustering effect:

[0057] ;

[0058] in, , is the sample point; represents the kernel function; is the Euclidean distance between two points; is the kernel width parameter and needs to be adjusted according to the data distribution.

[0059] Step 3.2: Clustering and state classification:

[0060] Calculate the local density and define the local density of each sample point as the weighted sum of the number of samples in the neighborhood: ;

[0061] in, is the local density; For all sample points Calculate the kernel function value and accumulate it;

[0062] Calculate the minimum distance for each sample point , find all sample points with higher density than it, and calculate the minimum distance to these points : ;

[0063] Among them, the point with the highest density Set to maximum distance;

[0064] Based on this, a decision diagram is drawn, and samples with high local density and far away from other high-density points are selected as cluster centers. The states are divided into three categories according to the region: stable state, transition state, and unstable state;

[0065] According to the clustering results, all data points are divided into the category of the nearest cluster center.

[0066] Step 3.3: Dynamically calculate the weight factor:

[0067] Calculate the inter-class distance, that is, the Euclidean distance from the current state point to each cluster center:

[0068] ;

[0069] in, is the Euclidean distance, is the current state point, For the Cluster centers of classes;

[0070] Normalize the distance weight and convert the distance into a weight coefficient based on the principle that the closer the distance, the higher the weight : , as can be seen from the above formula, if the current state is closest to the center of the stable state, the active rear wheel steering ARS weight is high; on the contrary, if it is close to the unstable state, the weight of the direct yaw moment DYC is increased;

[0071] Allocate the control amount of ARS and DYC according to the weight coefficient:

[0072] ;

[0073] ;

[0074] in, is the steady-state weight coefficient; is the transition state weight coefficient; is the weight coefficient of the unstable state;

[0075] To prevent sudden changes in the control amount, weight limiting is added. In this embodiment of the present invention, the ARS weight is 0.3-0.8, and the DYC weight is 0.2-0.7. When sensor failure causes clustering anomalies, the system switches to rule-based backup weight allocation.

[0076] Through the above steps, the KDPC algorithm can achieve dynamic and adaptive allocation of vehicle control weights, significantly improving vehicle stability and safety under complex operating conditions. In actual application, the clustering threshold and weighting rules need to be adjusted based on the specific vehicle model parameters.

[0077] Step 4: Design a cost function and establish constraints. Based on the constraints, calculate the values ​​of the rear wheel steering angle and additional yaw moment of the vehicle control variables:

[0078] The model predictive control method is used to solve the vehicle control quantity, and the cost function is:

[0079] ;

[0080] in, is the cost function; is the controlled output; For in time In the future The controlled output value of the step output; is the reference output; For in time In the future Reference output value of step output; is the prediction time domain; To control the time domain range; To control the increment; 、 The predicted time is The weight matrix of the controlled output and the control input;

[0081] The constraints are:

[0082] ;

[0083] ;

[0084] in, and is the input threshold, and is the input increment threshold;

[0085] Calculate the values ​​of the rear wheel angle and additional yaw moment of the vehicle control variable.

[0086] Step 5: Calculate the vehicle force required to maintain the vehicle speed by using the vehicle driving force-driving resistance balance equation. Then, distribute the wheel driving torque based on the vehicle force, additional yaw moment, the ratio of front and rear axle loads, wheel radius, and wheel width:

[0087] The vehicle force required to maintain the vehicle speed is solved by the vehicle driving force-driving resistance balance equation , and then according to the vehicle force , additional yaw moment , the load ratio between the front and rear axles 、 , wheel radius and front and rear wheelbases 、 , distribute wheel driving torque:

[0088] ;

[0089] ;

[0090] ;

[0091] ;

[0092] in, 、 、 、 The driving torque is distributed to the left front, right front, left rear and right rear wheels respectively; ;

[0093] ; 、 、 、 They are the vertical loads on the left front, right front, left rear and right rear wheels respectively.

[0094] like Figure 3 FIG. 1 is a structural diagram of a distributed wire-controlled vehicle stability cooperative control system according to an embodiment of the present invention, comprising:

[0095] The model building module 100 is used to build a vehicle dynamics model and convert the dynamic equations of the vehicle's linear two-degree-of-freedom four-wheel steering into state-space equations;

[0096] The ideal reference value correction module 200 is used to collect vehicle state information based on the state recognition layer and the driving intention recognition layer, and calculate and correct the ideal yaw rate and the ideal center of mass sideslip angle;

[0097] The control amount weight distribution module 300 is used to distribute the control amount weight using the KDPC algorithm;

[0098] The vehicle control variable solving module 400 is used to design a cost function and establish constraints, and calculate the values ​​of the vehicle control variables such as the rear wheel angle and the additional yaw moment based on the constraints;

[0099] The wheel driving torque distribution module 500 is used to solve the vehicle force required to maintain the vehicle speed through the vehicle driving force-driving resistance balance equation, and then distribute the wheel driving torque according to the vehicle force, additional yaw moment, the proportional relationship between the front and rear axle loads, the wheel radius and the wheelbase.

[0100] Effect test: A joint simulation experiment was conducted on a double lane change condition with a vehicle speed of 120 km / h and an adhesion coefficient of 0.85. The method proposed in the embodiment of the present invention was compared with that not using the control method. The results of the vehicle stability state quantity and the target value were obtained as follows: Figure 4 、 5 As shown;

[0101] according to Figure 4 It can be seen that when the vehicle does not use this stability cooperative control method, there is a significant gap between the yaw rate and the target value curve, with the maximum difference reaching about 15°. After using this method, the curve fitting degree is higher, and the maximum difference does not exceed 5°, which is an improvement of about 200% compared with the former. Figure 5 It can be seen that when the vehicle does not use the stability cooperative control method, the maximum side slip angle of the center of mass reaches about 4.5°. After using this method, the maximum value does not exceed 1.5°, which is an increase of about 200% compared with the former. This proves that after adopting the method of the embodiment of the present invention, the handling stability of the vehicle is greatly improved.

[0102] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A stability cooperative control method based on a distributed wire-controlled vehicle, characterized in that: The following steps are involved: Construct a vehicle dynamics model and transform the vehicle's linear two-degree-of-freedom four-wheel steering dynamics equations into state-space equations; The vehicle status information is collected based on the status recognition layer and the driving intention recognition layer, and the ideal yaw rate and the ideal sideslip angle of the center of mass are calculated and corrected; The control amount weights are assigned by the kernel density peak clustering algorithm; Design a cost function and establish constraints, and calculate the values ​​of the rear wheel angle and additional yaw moment of the vehicle control variables based on the constraints; The vehicle driving force-driving resistance balance equation is used to solve the vehicle force required to maintain the vehicle speed. The wheel driving torque is then distributed based on the vehicle force, additional yaw moment, the ratio of front and rear axle loads, wheel radius, and wheel width. The step of constructing a vehicle dynamics model and converting the dynamics equation of the vehicle's linear two-degree-of-freedom four-wheel steering into a state-space equation specifically includes: The dynamic equation of the vehicle's linear two-degree-of-freedom four-wheel steering is: ； ； in, is the vehicle mass; 、 are the distances from the center of mass to the front and rear axles respectively; 、 are the front and rear axle lateral stiffness respectively; is the longitudinal speed; is the vehicle's center of mass sideslip angle; is the vehicle yaw angular velocity; is the front wheel turning angle; is the rear wheel turning angle; is the moment of inertia of the vehicle around the Z axis; is the additional yaw moment; The state space equation is: ; in, ; ; ; ; ; ; The step of collecting vehicle state information based on the state recognition layer and the driving intention recognition layer, and calculating and correcting the ideal yaw rate and the ideal center of mass sideslip angle specifically includes: Based on the state recognition layer and the driving intention recognition layer, the vehicle state information is collected to calculate the ideal yaw rate and the ideal center of mass sideslip angle. The ideal center of mass sideslip angle is 0, and the calculation formula of the ideal yaw rate is: ； in, is the ideal yaw rate; 、 are differential torque and front wheel angle respectively; coefficient 、 The calculation formula is: ; ; in, ; is the wheelbase; Based on the road adhesion condition, the ideal yaw rate is limited. When the vehicle's sideslip angle is less than 3°, the following relationship is satisfied: ; in, is the lateral acceleration of the vehicle's center of mass; is the road adhesion coefficient; is the acceleration due to gravity; The ideal upper limit approximation of the vehicle's yaw rate is calculated by lateral acceleration, which is called the safe yaw rate. The calculation formula is: ; The critical yaw rate is used as the stability control boundary of the ideal yaw rate, and the formula is as follows: ； in, is the critical yaw rate; is the yaw rate compensation value; 、 are the understeering factor and oversteering factor respectively; is the one-dimensional lookup table value of vehicle speed; is the steering wheel angular velocity; 、 is the calibration parameter; The target yaw rate is obtained by calculating and correcting the ideal yaw rate, the safe yaw rate and the weight coefficient: ； in, is the target yaw rate; is the ideal yaw rate; is the safe yaw rate; is the weight factor, and the calculation formula is as follows: ； in, is the calibration coefficient, ranging from 0.5 to 0.

9.

2. The method for coordinated stability control of a distributed wire-controlled vehicle according to claim 1, characterized in that: The step of allocating control amount weights by using the kernel density peak clustering algorithm specifically includes: Select features from vehicle status information to construct feature vectors: ； in, is the eigenvector; is the longitudinal speed; is the vehicle yaw angular velocity; is the vehicle's center of mass sideslip angle; is the wheel angle; is the road adhesion coefficient; Represents the transpose operation of the matrix; Normalize the feature parameters; Use Gaussian kernel function to map data into high-dimensional space; Perform clustering and state classification; The weight factors are calculated dynamically.

3. The stability cooperative control method based on a distributed wire-controlled vehicle according to claim 2, characterized in that: The steps of clustering and state classification specifically include: Calculate the local density and define the local density of each sample point as the weighted sum of the number of samples in the neighborhood; Calculate the minimum distance. For each sample point, find all sample points with higher density than it, calculate the minimum distance to these points, and set the minimum distance of the point with the highest density as the maximum distance. Based on this, a decision diagram is drawn, and samples with high local density and far away from other high-density points are selected as cluster centers. The states are divided into three categories according to the region: stable state, transition state, and unstable state; According to the clustering results, all data points are divided into the category of the nearest cluster center.

4. The method for coordinated stability control of a distributed wire-controlled vehicle according to claim 2, characterized in that: The step of dynamically calculating the weight factor specifically includes: Calculate the inter-cluster distance, that is, the Euclidean distance from the current state point to each cluster center; Normalize the distance weight and convert the distance into a weight coefficient based on the principle that the closer the distance, the higher the weight. If the current state is closest to the stable state center, the active rear wheel steering ARS weight is high; on the contrary, if it is close to the unstable state, the direct yaw moment DYC weight is increased. Allocate the control amount of ARS and DYC according to the weight coefficient: ； ； in, is the steady-state weight coefficient; is the transition state weight coefficient; is the instability weight coefficient.

5. The stability cooperative control method based on distributed wire-controlled vehicles according to claim 2, characterized in that: The steps of designing a cost function and establishing constraint conditions, and calculating the values ​​of the rear wheel steering angle and the additional yaw moment of the vehicle control variables based on the constraint conditions, specifically include: The cost function is: ； in, is the cost function; is the controlled output; For in time In the future The controlled output value of the step output; is the reference output; For in time In the future Reference output value of step output; is the prediction time domain; To control the time domain range; To control the increment; 、 The predicted time is The weight matrix of the controlled output and the control input; The constraints are: ； ； in, and is the input threshold, and is the input increment threshold.

6. The method for coordinated stability control of a distributed wire-controlled vehicle according to claim 5, characterized in that: The step of solving the vehicle force required to maintain the vehicle speed by using the vehicle driving force-driving resistance balance equation, and then distributing the wheel driving torque according to the vehicle force, the additional yaw moment, the proportional relationship between the front and rear axle loads, the wheel radius, and the wheel width specifically includes: The vehicle force required to maintain the vehicle speed is solved by the vehicle driving force-driving resistance balance equation , and then according to the vehicle force , additional yaw moment , the load ratio between the front and rear axles 、 , wheel radius and front and rear wheelbases 、 , distribute wheel driving torque: ； ； ； ； in, 、 、 、 The driving torque is distributed to the left front, right front, left rear and right rear wheels respectively; ; , 、 、 、 They are the vertical loads on the left front, right front, left rear and right rear wheels respectively.

7. A distributed wire-controlled vehicle stability cooperative control system, used to implement the distributed wire-controlled vehicle stability cooperative control method according to any one of claims 1 to 6, characterized in that: include: The model building module is used to build a vehicle dynamics model and convert the dynamic equations of the vehicle's linear two-degree-of-freedom four-wheel steering into state-space equations; An ideal reference value correction module is used to collect vehicle state information based on the state recognition layer and the driving intention recognition layer, and calculate and correct the ideal yaw rate and the ideal center of mass sideslip angle; A control quantity weight allocation module is used to allocate control quantity weights through a kernel density peak clustering algorithm; The vehicle control variable solving module is used to design the cost function and establish the constraint conditions, and calculate the values ​​of the vehicle control variables such as the rear wheel angle and the additional yaw moment based on the constraint conditions; The wheel driving torque distribution module is used to solve the vehicle force required to maintain the vehicle speed through the vehicle driving force-driving resistance balance equation, and then distribute the wheel driving torque according to the vehicle force, additional yaw moment, the proportional relationship between the front and rear axle loads, the wheel radius and the wheelbase.

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