Stability cooperative control method and system based on distributed drive-by-wire vehicle

Abstract

The invention is suitable for the technical field of vehicle stability control, and provides a stability cooperative control method and system based on a distributed drive-by-wire vehicle, and the method comprises the following steps: constructing a vehicle dynamics model; vehicle state information is collected based on the state identification layer and the driving intention identification layer, and an ideal yaw velocity and an ideal side slip angle are calculated and corrected; a control quantity weight is distributed through a KDPC algorithm; values of a rear wheel turning angle and an additional yawing moment of the whole vehicle control quantity are calculated; and wheel driving torque is distributed according to the whole vehicle force, the additional yawing torque, the front and rear axle load proportional relation, the wheel radius and the wheel track. Through an electronic and dynamic control strategy, multi-dimensional advantages are formed in the aspects of flexibility, stability, energy efficiency and intelligence, and the limit boundary performance of a vehicle chassis and the vehicle handling stability are both expanded.

Description

Technical Field

[0001] The present invention belongs to the technical field of vehicle stability control, and particularly relates to a stability cooperative control method and system for a distributed-by-wire vehicle. Background Art

[0002] With the increasing functions and requirements of intelligent driving, the distributed drive by-wire technology has received a great deal of attention due to its superior performance and safety redundancy capabilities. Intelligent driving is divided into four links: perception, decision-making, planning, and control. To ensure the driving safety of the vehicle, the implementation of the control link is of utmost importance. In recent years, with the in-depth research, people's understanding of the four-wheel drive form has gradually deepened, and more and more people have adopted the method of installing motors on the wheel hubs or at the wheel ends to optimize performance and save vehicle space, enabling it to have the function of four-wheel independent control. After many experiments, it has been verified that its ultimate boundary performance and handling stability have been expanded.

[0003] Traditional vehicles are restricted by the limitations of mechanical coupling and have great room for improvement in terms of flexibility, stability, energy efficiency, etc., and cannot fully utilize the full performance of the chassis. Vehicle chassis cooperative control refers to a technology that optimizes the dynamic performance, safety, and comfort of a vehicle by integrating various chassis subsystems and using a central control system to achieve data sharing and unified scheduling. It provides an important basic support for high-level autonomous driving and personalized driving experiences. Summary of the Invention

[0004] The purpose of the embodiments of the present invention is to provide a stability cooperative control method for a distributed-by-wire vehicle, aiming to solve the problems proposed in the above background art.

[0005] The embodiments of the present invention are implemented as follows. A stability cooperative control method for a distributed-by-wire vehicle includes the following steps:

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

[0007] Collect vehicle state information based on the state identification layer and the driving intention identification layer, and calculate and correct the ideal yaw rate and the ideal sideslip angle of the center of mass;

[0008] Allocate the control quantity weights through the KDPC algorithm;

[0009] Design a cost function and establish constraint conditions, and calculate the values of the rear wheel steering angle and the additional yaw moment of the vehicle control quantity based on the constraint conditions;

[0010] Solve for the vehicle force required to maintain the vehicle speed through the vehicle driving force - running resistance balance equation, and then allocate the wheel driving torque according to the vehicle force, the additional yaw moment, the front and rear axle load ratio relationship, the wheel radius, and the wheelbase.

[0011] Another object of the embodiments of the present invention is to provide a stability cooperative control system based on a distributed by-wire vehicle for implementing the above-mentioned stability cooperative control method for a distributed by-wire vehicle, including:

[0012] A model construction module for constructing a vehicle dynamics model and converting the dynamic equation of linear two-degree-of-freedom four-wheel steering of the vehicle into a state space equation;

[0013] An ideal reference value correction module for collecting vehicle state information based on the state identification layer and the driving intention identification layer, and calculating and correcting the ideal yaw angular velocity and the ideal sideslip angle of the center of mass;

[0014] A control quantity weight distribution module for distributing control quantity weights through the KDPC algorithm;

[0015] A vehicle control quantity solving module for 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 quantity based on the constraint conditions;

[0016] A wheel driving torque distribution module for solving the vehicle force required to maintain the vehicle speed through 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 front and rear axle load ratio relationship, the wheel radius, and the wheelbase.

[0017] A stability cooperative control method for a distributed by-wire vehicle provided by the embodiments of the present invention forms multi-dimensional advantages in terms of flexibility, stability, energy efficiency, and intelligence through an electronic and dynamic control strategy, expanding both the ultimate boundary performance of the vehicle chassis and the vehicle handling stability. Description of the Drawings

[0018] Figure 1 It is a flowchart of a stability cooperative control method for a distributed by-wire vehicle provided by the embodiments of the present invention;

[0019] Figure 2 It is a flowchart of distributing control quantity weights by the KDPC algorithm in a stability cooperative control method for a distributed by-wire vehicle provided by the embodiments of the present invention;

[0020] Figure 3 It is a structural diagram of a stability cooperative control system for a distributed by-wire vehicle provided by the embodiments of the present invention;

[0021] Figure 4 It is a comparison curve of the state quantity and the target value of the yaw angular velocity provided by the embodiments of the present invention;

[0022] Figure 5The comparison curve between the state quantity and the target value of the centroid sideslip angle provided by the embodiment of the present invention. Detailed implementation manners

[0023] In order to make the objectives, technical solutions and advantages of the present invention clearer, 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, but not to limit the present invention.

[0024] The following describes the specific implementation of the present invention in detail with reference to specific embodiments.

[0025] As Figure 1 shown, it is a flowchart of a stability cooperative control method based on a distributed by-wire vehicle provided by an embodiment of the present invention, including the following steps:

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

[0027] ;

[0028] ;

[0029] Among them, is the vehicle mass; , are the distances from the centroid to the front axle and the rear axle respectively; , are the cornering stiffnesses of the front and rear axles respectively; is the longitudinal vehicle speed; is the centroid sideslip angle of the vehicle; is the yaw rate of the vehicle; is the front wheel steering angle; is the rear wheel steering angle; is the moment of inertia of the whole vehicle about the Z axis; is the additional yaw moment;

[0030] Convert the above differential equation into a state space equation for easy calculation:

[0031] ;

[0032] Among them, ; ; ; ; ; .

[0033] Step 2: Collect vehicle state information based on the state identification layer and the driving intention identification layer, and calculate and correct the ideal yaw rate and the ideal centroid sideslip angle:

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

[0035] ;

[0036] Wherein, is the ideal yaw rate; , are the differential torque and the front wheel steering angle respectively; the coefficients , are calculated by the following formulas: ; ;

[0037] Wherein, ; is the wheelbase;

[0038] Step 2.2: As can be seen from the above formula, the ideal yaw rate will increase as the front wheel steering angle increases. However, in actual control, the stability of the vehicle must be ensured. Therefore, it is necessary to limit the value of the ideal yaw rate based on the road surface adhesion condition. When the sideslip angle of the vehicle's center of mass is less than 3°, the following relationship is satisfied: ;

[0039] Wherein, is the lateral acceleration of the vehicle's center of mass; is the road surface adhesion coefficient; is the gravitational acceleration;

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

[0041] When using the dynamic method for this estimation, there will be constraints such as poor real-time performance and difficulty in obtaining tire parameters. Therefore, this formula cannot be directly used in engineering to limit its ideal upper limit value. Since the sensor can measure the lateral acceleration of the vehicle's center of mass in real time, the ideal upper limit approximation value of the vehicle yaw rate, called the safe yaw rate, can be calculated through the lateral acceleration. The calculation formula is: ;

[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. That is, the safe yaw rate cannot be directly used to limit it. Therefore, the critical yaw rate is proposed as the stability control boundary of the ideal yaw rate. The formula is as follows:

[0043] ;

[0044] Wherein, is the critical yaw rate; is the yaw rate compensation value; , are the understeer factor and oversteer factor respectively; is the one-dimensional look-up value of vehicle speed; is the angular velocity of the steering wheel; , are calibration parameters.

[0045] Step 2.3: Calculate and correct through the ideal yaw rate, safety yaw rate and weight coefficient together to obtain the target yaw rate:

[0046] ;

[0047] Among them, is the target yaw rate; is the ideal yaw rate; is the safety yaw rate; is the weight factor, and the calculation formula is as follows:

[0048] ;

[0049] Among them, is the calibration coefficient, which is between 0.5 and 0.9.

[0050] Step 3: Allocate the control quantity weight through the KDPC algorithm (Kernel Density Peak Clustering algorithm), that is, identify different working condition categories through the dynamic clustering of vehicle states, and dynamically adjust the control weight according to the similarity between the current state and the ideal state. The process is as Figure 2 shown:

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

[0052] ;

[0053] Among them, is the feature vector; is the longitudinal vehicle speed; is the vehicle yaw rate; is the vehicle center of mass sideslip angle; is the wheel steering angle; is the road surface adhesion coefficient; represents the transpose operation of the matrix;

[0054] Normalize the feature parameters to eliminate the dimension difference: ;

[0055] Among them, is the normalization parameter, is the feature parameter, is the mean value, is the standard deviation;

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

[0057] ;

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

[0059] Step 3.2. Perform 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] Among them, is the local density; represents calculating the kernel function values for all sample points and accumulating them;

[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, for the point with the highest density, is set as the maximum distance;

[0064] Based on this, draw a decision graph, select the sample points with high local density and far from other high-density points as the clustering centers, and the states are divided into three categories: stable state, transition state, and unstable state according to the region;

[0065] According to the clustering results, divide all data points into the category of the nearest clustering 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 clustering center:

[0068] ;

[0069] Among them, is the Euclidean distance, is the current state point, is the clustering center of the th class;

[0070] Normalize the distance weights and convert the distance into a weight coefficient according to the principle that the closer the distance, the higher the weight : , as can be seen from the above formula, the current state is closest to the steady state center, so the weight of the active rear wheel steering ARS is high; on the contrary, when approaching the unstable state, the weight of the direct yaw moment DYC increases;

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

[0072] ;

[0073] ;

[0074] where is the steady state weight coefficient; is the transition state weight coefficient; is the unstable state weight coefficient;

[0075] To prevent sudden changes in the control amount, a weight limit is added. In the embodiment of the present invention, the ARS weight is between 0.3 - 0.8, and the DYC weight is between 0.2 - 0.7; when the sensor fails and causes abnormal clustering, it switches to the rule-based backup weight allocation;

[0076] Through the above steps, the KDPC algorithm can achieve dynamic adaptive allocation of the vehicle control amount weights, significantly improving the vehicle stability and safety under complex working conditions. In practical applications, it is necessary to adjust the clustering threshold and weight rules in combination with specific vehicle model parameters.

[0077] Step 4: Design a cost function and establish constraint conditions, and calculate the values of the rear wheel steering angle and additional yaw moment of the vehicle control amount based on the constraint conditions:

[0078] Use the model predictive control method to solve the vehicle control amount, and the cost function is:

[0079] ;

[0080] where is the cost function; is the controlled output; is at time the future step output value of the controlled output; is the reference output; is at time the future step output value of the reference output; is the prediction time domain range; is the control time domain range; is the control increment; 、 are the weight matrices of the controlled output and the control input at the prediction time of respectively;

[0081] The constraint conditions are:

[0082] ;

[0083] ;

[0084] where, and are the input thresholds, and are the input increment thresholds;

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

[0086] Step 5: 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, front and rear axle load ratio relationship, wheel radius, and wheelbase:

[0087] Solve the vehicle force required to maintain the vehicle speed from the vehicle driving force - driving resistance balance equation , and then according to the vehicle force , additional yaw moment , front and rear axle load ratio relationship 、 , wheel radius and front and rear wheelbases 、 , distribute the wheel driving torque:

[0088] ;

[0089] ;

[0090] ;

[0091] ;

[0092] where, 、 、 、 are the wheel driving torque distributions of the left front, right front, left rear, and right rear wheels respectively; ;

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

[0094] As Figure 3 shown, it is a structural diagram of a stability cooperative control system based on a distributed by-wire vehicle provided by an embodiment of the present invention, including:

[0095] The model construction module 100 is used to construct a vehicle dynamics model and transform the dynamic equation of linear two-degree-of-freedom four-wheel steering of the vehicle into a state space equation;

[0096] The ideal reference value correction module 200 is used to collect vehicle state information based on the state identification layer and the driving intention identification layer, and calculate and correct the ideal yaw rate and the ideal centroid side slip angle;

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

[0098] The vehicle control quantity solving module 400 is used to design a cost function and establish constraint conditions, and calculate the values of the vehicle control quantity rear wheel steering angle and the additional yaw moment based on the constraint conditions;

[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, the additional yaw moment, the front and rear axle load ratio relationship, the wheel radius, and the wheelbase.

[0100] Effect test: Select a double lane change condition with a vehicle speed of 120 km / h and an adhesion coefficient of 0.85 for a co-simulation experiment, compare and verify the method proposed in the embodiment of the present invention with the case where the control method is not used, and obtain the results of the vehicle stability state quantity and the target value as Figure 4 , 5 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 relatively obvious gap between the yaw rate and the target value curve, and the maximum difference reaches about 15°. After using this method, the curve fitting degree is relatively high, and the maximum difference does not exceed 5°. The improvement ratio compared with the former is about 200%; According to Figure 5 it can be seen that when the vehicle does not use this stability cooperative control method, the maximum value of the centroid side slip angle reaches about 4.5°. After using this method, the maximum value does not exceed 1.5°. The improvement ratio compared with the former is about 200%, which proves that after adopting the method of the embodiment of the present invention, the handling stability of the vehicle has been greatly improved.

[0102] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A stability cooperative control method for a distributed by-wire vehicle, characterized in that It includes the following steps: Construct a vehicle dynamics model and transform the dynamic equation of linear two-degree-of-freedom four-wheel steering of the vehicle into a state-space equation; Based on the state identification layer and the driving intention identification layer, collect vehicle state information, and calculate and correct the ideal yaw rate and the ideal centroid side slip angle; Allocate the control quantity weights through the KDPC algorithm; Design a cost function and establish constraint conditions, and calculate the values of the rear wheel steering angle and the additional yaw moment of the vehicle control quantity based on the constraint conditions; Solve the vehicle force required to maintain the vehicle speed through the vehicle driving force - running resistance balance equation, and then distribute the wheel driving torque according to the vehicle force, the additional yaw moment, the front and rear axle load ratio relationship, the wheel radius, and the wheelbase.

2. The stability cooperative control method for a distributed by-wire vehicle according to claim 1, characterized in that, The step of constructing a vehicle dynamics model and transforming the dynamic equation of linear two-degree-of-freedom four-wheel steering of the vehicle into a state-space equation specifically includes: The dynamic equation of linear two-degree-of-freedom four-wheel steering of the vehicle is: ； ； wherein, is the vehicle mass; , are the distances from the center of mass to the front axle and the rear axle respectively; , are the cornering stiffnesses of the front and rear axles respectively; is the longitudinal vehicle speed; is the sideslip angle of the vehicle center of mass; is the yaw rate of the vehicle; is the front wheel steering angle; is the rear wheel steering angle; is the moment of inertia of the whole vehicle about the Z-axis; is the additional yaw moment; The state space equation is as follows: ; Among them, ; ; ; ; ; .

3. The stability cooperative control method for a distributed by-wire vehicle according to claim 2, wherein The step of collecting vehicle state information based on the state identification layer and the driving intention identification layer, and calculating and correcting the ideal yaw rate and the ideal centroid side slip angle specifically includes: Collect vehicle state information based on the state identification layer and the driving intention identification layer, and calculate the ideal yaw rate and the ideal centroid side slip angle, where the ideal centroid side slip angle is 0, and the calculation formula for the ideal yaw rate is: ； Among them, is the ideal yaw rate; , are the differential torque and the front wheel steering angle respectively; the calculation formulas for the coefficients , are: ; ; Among them, ; is the wheelbase; Based on the road surface adhesion condition, the value of the ideal yaw rate is restricted. When the sideslip angle of the vehicle's center of mass is less than 3°, the following relationship is satisfied: ; wherein, is the lateral acceleration of the vehicle's center of mass; is the road surface adhesion coefficient; is the acceleration due to gravity; Calculating the ideal upper limit approximation of the vehicle's yaw rate through lateral acceleration, which is called the safe yaw rate, and the calculation formula is: ; Take the critical yaw rate as the stability control boundary of the ideal yaw rate, and the formula is as follows: ； Among them, is the critical yaw rate; is the yaw rate compensation value; , are the understeer factor and oversteer factor respectively; is the one-dimensional look-up value of vehicle speed; is the angular velocity of the steering wheel; , are calibration parameters; Calculate and correct through the ideal yaw rate, the safe yaw rate, and the weight coefficient to obtain the target yaw rate: ； Among them, is the target yaw rate; is the ideal yaw rate; is the safety yaw rate; is the weight factor, and its calculation formula is as follows: ； Among them, is the calibration coefficient, which is between 0.5 and 0.

9.

4. The stability cooperative control method for a distributed by-wire vehicle according to claim 3, wherein The step of allocating the control quantity weights through the KDPC algorithm specifically includes: Select the features in the vehicle state information to construct a feature vector: ； Among them, is the feature vector; is the longitudinal vehicle speed; is the vehicle yaw rate; is the vehicle sideslip angle at the center of mass; is the wheel steering angle; is the road surface adhesion coefficient; represents the transpose operation on the matrix; Normalize the feature parameters; Use the Gaussian kernel function to map the data to a high-dimensional space; Perform clustering and state classification; Dynamically calculate the weight factors.

5. The stability cooperative control method for a distributed by-wire vehicle according to claim 4, wherein The step of performing clustering and state classification specifically includes: 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 from these points, and stipulate that the minimum distance of the point with the highest density is set as the maximum distance; Based on this, draw a decision diagram, select the sample points with high local density and far from other high-density points as the clustering centers, and divide the states into three categories: stable state, transition state, and unstable state according to the region; According to the clustering results, divide all data points into the category of the nearest clustering center.

6. The stability cooperative control method for a distributed by-wire vehicle according to claim 4, wherein The step of dynamically calculating the weight factors specifically includes: Calculate the inter-class distance, that is, the Euclidean distance from the current state point to each clustering center; Perform normalized distance weights, 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 weight of the active rear-wheel steering ARS is high; on the contrary, if it is close to the unstable state, the weight of the direct yaw moment DYC is increased; Allocate the control quantities of ARS and DYC according to the weight coefficient: ； ； Among them, is the steady-state weight coefficient; is the transition-state weight coefficient; is the unstable-state weight coefficient.

7. The stability cooperative control method for a distributed by-wire vehicle according to claim 4, characterized in that The step 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 quantity based on the constraint conditions specifically includes: The cost function is: ； Among them, is the cost function; is the controlled output; is at time the future step output value of the controlled output; is the reference output; is at time the future step output value of the reference output; is the prediction horizon; is the control horizon; is the control increment; , are respectively the weight matrices of the controlled output and the control input at the prediction time ; The constraint conditions are: ； ； Among them, and are input thresholds, and are input increment thresholds.

8. The stability cooperative control method for a distributed by-wire vehicle according to claim 7, characterized in that The steps of solving for the vehicle force required to maintain the vehicle speed through the vehicle driving force - driving resistance balance equation and then distributing the wheel driving torque based on the vehicle force, additional yaw moment, front and rear axle load ratio relationship, wheel radius, and wheelbase are specifically as follows: The vehicle force required to maintain the vehicle speed is obtained by solving the vehicle driving force - running resistance balance equation , and then based on the vehicle force , additional yaw moment , the front and rear axle load ratio , , wheel radius and the front and rear wheel track , , the wheel driving torque is distributed as follows: ； ； ； ； Among them, , , , are the driving torque distributions of the left front, right front, left rear, and right rear wheels respectively; ; ; , , , are the vertical loads of the left front, right front, left rear, and right rear wheels, respectively.

9. A stability cooperative control system for a distributed by-wire vehicle, which is used to implement the stability cooperative control method for a distributed by-wire vehicle as described in any one of claims 1-8, characterized in that, Including: A model construction module, used to construct a vehicle dynamics model and transform the dynamic equation of linear two-degree-of-freedom four-wheel steering of the vehicle into a state space equation; An ideal reference value correction module, used to collect vehicle state information based on the state identification layer and driving intention identification layer, and calculate and correct the ideal yaw angular velocity and ideal centroid side slip angle; A control quantity weight distribution module, used to distribute the control quantity weight through the KDPC algorithm; A vehicle control quantity solution module, used to design a cost function and establish constraint conditions, and calculate the values of the vehicle control quantity rear wheel steering angle and additional yaw moment based on the constraint conditions; A wheel driving torque distribution module, used to solve for 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 based on the vehicle force, additional yaw moment, front and rear axle load ratio relationship, wheel radius, and wheelbase.

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