Vehicle cooperative control system and method based on intelligent tire

Abstract

The application discloses a whole vehicle cooperative control system and method based on intelligent tires, wherein intelligent sensors arranged in the inner side of the tire tread of the vehicle acquire the air pressure, acceleration and rotation angle of each tire, the load and the center of mass position of the vehicle, the side stiffness parameter and the optimal rotation angle of the front wheel are acquired through processing, and the whole vehicle steering is controlled, when applied to a commercial cargo vehicle, the control precision can be improved, the rotation angle of the steering wheel is reduced to a certain extent, and the safety, comfort and stability of vehicle driving are improved.

Description

Technical Field

[0001] This invention relates to the field of vehicle control technology, specifically to a vehicle collaborative control system and method based on intelligent tires. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] Traditional vehicle control methods typically utilize parameters such as vehicle mass, lateral stiffness, and real-time vehicle load for control. Common real-time vehicle load detection methods include engine torque estimation, air suspension travel measurement, vehicle leaf spring deformation detection, and adhesive strain sensor-based detection. These methods involve complex equipment installation, high costs, and unsatisfactory load estimation accuracy. Commercial trucks, due to their cargo-carrying characteristics, experience significant load variations and frequent center of gravity shifts during driving. Using traditional methods to obtain load, center of gravity position, and lateral stiffness parameters for control is insufficient to meet their control accuracy requirements, especially for steering control. Summary of the Invention

[0004] To address the technical problems mentioned above, this invention provides a vehicle collaborative control system and method based on intelligent tires. The system acquires the air pressure, acceleration, and steering angle of each tire through intelligent sensors deployed on the inner side of the tire tread. After processing, the system obtains the vehicle's load and center of gravity position, lateral stiffness parameters, and the optimal steering angle of the front wheels, thereby controlling the vehicle's steering.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] The first aspect of the present invention provides a vehicle cooperative control system based on smart tires, including a smart tire sensor and an on-board terminal connected in communication. The smart tire sensor is located on the inner side of the tire tread and is used to acquire tire pressure, acceleration and rotation angle and send them to the on-board terminal. The on-board terminal uses a data processing unit to realize vehicle control based on the received information.

[0007] The data processing unit is configured as follows:

[0008] Based on the lateral forces acting on the front and rear wheels of the vehicle and the front wheel rotation angle, the resultant force in the lateral direction of the vehicle and the yaw moment about the vertical axis are determined, and the moment balance equation in the natural coordinate system is obtained.

[0009] The tire contact length is determined by the signal length C between adjacent positive and negative peaks in the acceleration signal, and the tire rotation number is determined by the time interval L between adjacent positive and negative peaks. The ratio between C and L is used as the actual load index. The position of the vehicle's center of gravity on the horizontal plane and the tire's lateral stiffness are obtained by using a pre-calibrated experiment.

[0010] Using information from any point on the vehicle's planned trajectory, the error equation between the actual point position vector and the planned point vector is determined. Using the planned trajectory as the coordinate axis in the natural coordinate system, the solution to the error equation is obtained using the torque balance equation in the natural coordinate system. After post-processing, different lateral errors after the change of the center of mass position are obtained, and the optimal steering angle of the front wheel is determined.

[0011] Furthermore, based on the lateral forces acting on the front and rear wheels of the vehicle and the front wheel rotation angle, the resultant force in the lateral direction of the vehicle and the yaw moment about the vertical axis are determined, resulting in the torque balance equation in the natural coordinate system. This includes using a two-degree-of-freedom model of the vehicle, based on the lateral forces acting on the front and rear wheels and the front wheel rotation angle, to obtain the resultant force in the lateral direction of the vehicle and the yaw moment about the vertical axis, and then transforming the coordinate system to obtain the torque balance equation in the natural coordinate system.

[0012] Furthermore, based on pre-calibrated experiments, a regression model of load, actual load index and tire pressure is determined. The load of each wheel position and the position of the vehicle's center of mass on the horizontal plane are obtained using the air pressure and acceleration acquired by the intelligent tire sensor.

[0013] Furthermore, based on pre-calibration experiments, the relationship surface between lateral stiffness, air pressure, and load is obtained. Using the tire air pressure and corresponding load data acquired by the intelligent tire sensor, the lateral stiffness of the tire is determined by looking up a table.

[0014] Furthermore, using information from any point on the vehicle's planned trajectory, the error between the actual point position vector and the planned point vector is determined; this includes obtaining the error equation between the actual point position vector and the planned point vector based on the position, velocity, heading angle, and acceleration of any point on the planned trajectory.

[0015] Further post-processing includes obtaining the optimal control input based on a discrete linear quadratic regulator.

[0016] Further post-processing includes determining the position, velocity, and yaw angle information of the predicted point based on the feedforward.

[0017] Furthermore, if the planned trajectory is a continuous trajectory, the error of the discrete trajectory points is determined based on the matching points.

[0018] Furthermore, the point closest to the actual location of the discrete programming trajectory point is used as the matching point, and the projection point is determined by the geometric relationship between the projection point and the matching point.

[0019] A second aspect of the present invention provides a control method based on the above-described system, comprising the following steps:

[0020] Based on the lateral forces acting on the front and rear wheels of the vehicle and the front wheel rotation angle, the resultant force in the lateral direction of the vehicle and the yaw moment about the vertical axis are determined, and the moment balance equation in the natural coordinate system is obtained.

[0021] The tire contact length is determined by the signal length C between adjacent positive and negative peaks in the acceleration signal, and the tire rotation number is determined by the time interval L between adjacent positive and negative peaks. The ratio between C and L is used as the actual load index. The position of the vehicle's center of gravity on the horizontal plane and the tire's lateral stiffness are obtained by using a pre-calibrated experiment.

[0022] Using information from any point on the vehicle's planned trajectory, the error equation between the actual point position vector and the planned point vector is determined. The planned trajectory is used as the coordinate axis in the natural coordinate system. The error value is obtained using the torque balance equation in the natural coordinate system. After post-processing, different lateral errors after the change of the center of mass position are obtained. Based on the obtained lateral errors, the optimal front wheel steering angle of the vehicle is determined and control is achieved.

[0023] Compared with existing technologies, one or more of the above technical solutions have the following beneficial effects:

[0024] By using intelligent sensors installed on the inside of the vehicle's tire treads to obtain the air pressure, acceleration, and steering angle of each tire, the system processes the data to obtain the vehicle's load and center of gravity position, lateral stiffness parameters, and the optimal steering angle of the front wheels, thereby controlling the vehicle's steering. When applied to commercial freight vehicles, this can improve control precision, thereby reducing the steering wheel angle to some extent and improving the vehicle's safety, comfort, and stability. Attached Figure Description

[0025] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0026] Figure 1 A schematic diagram of a vehicle collaborative control system architecture based on smart tires provided for one or more embodiments of the present invention;

[0027] Figure 2 A schematic diagram of the hardware structure of a smart tire provided for one or more embodiments of the present invention;

[0028] Figure 3 A schematic diagram of a two-degree-of-freedom vehicle model provided in one or more embodiments of the present invention;

[0029] Figure 4A schematic diagram of a coordinate system provided for one or more embodiments of the present invention;

[0030] Figure 5 A schematic diagram of the acceleration signal of a tire under a 450kg load, provided for one or more embodiments of the present invention;

[0031] Figure 6 A schematic diagram of the acceleration signal of a tire under a load of 659 kg, provided for one or more embodiments of the present invention;

[0032] Figure 7 A schematic diagram of the three-dimensional relationship surface between lateral stiffness, air pressure, and load provided for one or more embodiments of the present invention;

[0033] Figure 8 A schematic diagram of the planned trajectory provided for one or more embodiments of the present invention;

[0034] Figure 9 A schematic diagram showing the relationship between the position, velocity, and yaw angle of a prediction point provided for one or more embodiments of the present invention;

[0035] Figure 10 A schematic diagram of the geometric relationship between projection points and matching points provided for one or more embodiments of the present invention. Detailed Implementation

[0036] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0037] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0038] As described in the background section, the control accuracy, especially steering control accuracy, of traditional vehicle control methods is insufficient to meet the control requirements of commercial trucks. Therefore, the following embodiments present a vehicle cooperative control system and method based on intelligent tires. This system utilizes intelligent tires to obtain more direct parameter identification methods and real-time state parameter inputs, replacing traditional state observation algorithms based on vehicle body signals. For quantities that cannot be directly estimated through intelligent tires, the introduction of intelligent tires reduces the number of state variables, allowing for more accurate and rapid estimation of the remaining unknown quantities. By monitoring key parameters such as load and wear through intelligent tires, the vehicle control strategy can be effectively optimized, improving vehicle safety and fuel economy.

[0039] Smart tires refer to tires that are no longer passive rubber composites on a vehicle, but rather components within the vehicle's control system that can acquire data or execute control commands through hardware such as chips mounted on the tire. For example, they can automatically monitor and adjust tire temperature and pressure to maintain optimal operating conditions under different circumstances.

[0040] Example 1:

[0041] like Figures 1-10 As shown, the vehicle collaborative control system based on smart tires includes a smart tire sensor and an on-board terminal connected by communication. The smart tire sensor is located on the inner side of the tire tread and is used to obtain the tire pressure, acceleration and rotation angle and send them to the on-board terminal. The on-board terminal uses a data processing unit to realize vehicle control based on the received information.

[0042] The data processing unit is configured as follows:

[0043] Based on the lateral forces acting on the front and rear wheels of the vehicle and the front wheel rotation angle, the resultant force in the lateral direction of the vehicle and the yaw moment about the vertical axis are determined, and the moment balance equation in the natural coordinate system is obtained.

[0044] The tire contact length is determined by the signal length C between adjacent positive and negative peaks in the acceleration signal, and the tire rotation number is determined by the time interval L between adjacent positive and negative peaks. The ratio between C and L is used as the load, and the regression model of load and tire pressure determined by pre-calibrated experiments is used to obtain the position of the vehicle's center of gravity in the horizontal plane and the tire's lateral stiffness.

[0045] Using information from any point on the vehicle's planned trajectory, the error between the actual point position vector and the planned point vector is determined. Using the planned trajectory as the coordinate axis in the natural coordinate system, the error value is obtained using the torque balance equation in the natural coordinate system. After optimization, prediction, and simulation, different lateral errors after the change of the centroid position are obtained.

[0046] System architecture such as Figure 1 As shown, the device includes a smart tire sensor and an on-board terminal with communication connectivity. The smart tire sensor includes a power supply module, an MCU, a temperature sensor, a pressure sensor, an acceleration sensor, and a wireless signal transmitting unit. It can also integrate a cornering sensor. The on-board terminal includes a wireless signal receiving unit, a data processing unit (MCU computing unit), a GPS module, and an on-board power supply module.

[0047] Smart tire sensors such as Figure 2 As shown, the internal modules are integrated inside the housing and arranged on the inside of the tread of the commercial truck tire.

[0048] Data on tire ground contact deformation is collected. There are various methods for collecting this data, such as rim distance measurement, camera image processing, and acceleration signal analysis. To reduce the cost and power consumption of the smart hardware, this embodiment uses acceleration signal analysis to measure tire deformation data.

[0049] This embodiment selects a linear two-degree-of-freedom vehicle model as the ideal model to realize steering control. This model reflects the lateral and yaw degrees of freedom of the vehicle during the turning process, and considers the yaw motion of the vehicle about the z-axis. The model is simple and easy to analyze. At the same time, compared with the kinematic equations, the two-degree-of-freedom vehicle dynamics can be combined with the natural coordinate system, decoupling the lateral and longitudinal control of the vehicle, which facilitates the analysis of the lateral control of the vehicle separately.

[0050] like Figure 3 Force analysis is performed as shown. The resultant force along the y-axis and the yaw moment about the z-axis can be expressed by the following equations:

[0051] ∑F y =ma y =F yf cosδ+F yr (1)

[0052]

[0053] Where F yf and F yr Let δ represent the lateral force acting on the front and rear wheels, and δ be the front wheel steering angle. Considering that the front wheel steering angle δ is relatively small, the above formula can be simplified to:

[0054] ∑F y =F yf +F yr =C af a f +C ar a r (3)

[0055]

[0056] Next, the governing equations in the natural coordinate system are obtained, as shown in the coordinate system below. Figure 4 As shown:

[0057]

[0058] The equations for torque balance in the natural coordinate system are:

[0059]

[0060] In the formula: F yf F yrC represents the force in the y-direction acting on the front and rear wheels; a and b represent the distances from the front and rear axles to the center of mass; af C ar Let be the lateral stiffness of the front and rear wheels; I be the moment of inertia. The centroid sideslip angle; δ f This refers to the steering angle of the front wheels.

[0061] Figure 5 , Figure 6 These are the tire's internal x-direction acceleration signals under 450kg and 650kg loads, respectively. The data length C between the positive and negative peaks reflects the tire's contact patch length. The higher the load on the tire, the longer the contact patch length.

[0062] Furthermore, the interval L between two positive peaks or two negative peaks represents one revolution of the tire. Therefore, tire stiffness is also affected by speed, meaning speed also affects the value C. Therefore, the index CLR (contact length ratio) = C / L is chosen as the actual load index.

[0063] Therefore, Load = f(air pressure, CLR). A regression model of load versus CLR and air pressure was obtained through laboratory calibration.

[0064] During actual vehicle operation, the CLR (Carrier Center of Gravity) can be obtained through acceleration signals, and air pressure can be acquired through air pressure sensors. This allows for the determination of the load at each wheel position, and further, the position of the center of gravity in the x and y planes.

[0065] Figure 7 This is a surface representing the relationship between lateral stiffness, tire pressure, and load, calibrated in the laboratory. The tire's lateral stiffness can be obtained in real-time by looking up tables using load and tire pressure data returned by intelligent tire sensors. The load is obtained using the method described above, while the tire pressure is acquired in real-time by sensors.

[0066] make u = δ f , We can obtain:

[0067]

[0068] That is, the y-axis can be controlled by the front wheel steering angle. Control.

[0069] Establish the lateral error equation: Given the position information x of any point on the planned trajectory r y r speed v r heading angle θ r acceleration a r Write the error equation between the actual point position vector and the planned point vector:

[0070]

[0071] Since the actual point position satisfies equation (7), we can obtain:

[0072]

[0073] Using the planned trajectory as the coordinate axes in the natural coordinate system, specific information is as follows: Figure 8 As shown.

[0074] Let the lateral error be d / e d ; heading error e θ The magnitude of the velocity of the projection point is It is a unit vector. (By...) Figure 8 Equation (10) can be obtained:

[0075]

[0076] By differentiating d, we can obtain:

[0077]

[0078] Known k is the curvature. We can obtain the following formula:

[0079]

[0080] Substituting equation (12) into equations (5) and (6) and rearranging the resulting equations, we obtain the linear differential equation:

[0081]

[0082]

[0083] As can be seen from equation (13), the vehicle load, center of gravity position, and vehicle lateral stiffness significantly affect the vehicle's lateral error.

[0084] The optimal control input is then obtained through a discrete LQR (linear quadratic regulator):

[0085] u k =-ke rr (k) (15)

[0086] In the formula: k is the optimal feedback matrix of the system.

[0087] When performing lateral control, if the control system only has feedback control, the system will have steady-state error. In order to eliminate the steady-state error, feedforward control needs to be introduced.

[0088] Finally, using relevant mathematical software, the feedforward can be obtained as follows:

[0089]

[0090] Next, a prediction module is added to obtain the true result. Figure 9 Information such as the predicted point position, velocity, and yaw angle can be obtained:

[0091]

[0092]

[0093] v xpre =v x +a x t s (19)

[0094] v ypre =v y +a y t s (20)

[0095]

[0096] In the formula: x and y are the current positions of the vehicle; x pre y pre To predict the vehicle's location after time t; v x v y v represents the lateral velocity of the vehicle along the x and y axes. pre v pre To predict the lateral velocity of the vehicle along the x and y axes after time t; t is time.

[0097] If the planned trajectory is continuous, the projection may not be unique. Therefore, error calculation is performed using discrete trajectory points.

[0098] The specific steps are as follows:

[0099] 1. Find the point on the discrete programming trajectory that is closest to the actual position (x,y), and call this point the matching point (machpoint).

[0100] 2. Because the trajectory is discrete, the matching point is not the same as the projected point, but it can be determined through... Figure 10 The projection point is calculated by considering the relationship between the matching point and the projection point.

[0101]

[0102] The above process uses intelligent tire sensors to acquire the vehicle's load, center of gravity position, and lateral stiffness parameters in real time, and calculates the optimal steering angle of the front wheels in real time to control the vehicle's steering and improve control precision. This enhances the vehicle's safety, comfort, and stability.

[0103] By employing the Lagrange multiplier method, compared with other control algorithms, the algorithm space complexity and number of iterations are reduced, and the computation speed is improved without sacrificing control accuracy.

[0104] Example 2:

[0105] The vehicle collaborative control method based on smart tires includes the following steps:

[0106] Based on the lateral forces acting on the front and rear wheels of the vehicle and the front wheel rotation angle, the resultant force in the lateral direction of the vehicle and the yaw moment about the vertical axis are determined, and the moment balance equation in the natural coordinate system is obtained.

[0107] The tire contact length is determined by the signal length C between adjacent positive and negative peaks in the acceleration signal, and the tire rotation number is determined by the time interval L between adjacent positive and negative peaks. The ratio between C and L is used as the actual load index. The position of the vehicle's center of gravity on the horizontal plane and the tire's lateral stiffness are obtained by using a pre-calibrated experiment.

[0108] Using information from any point on the vehicle's planned trajectory, the error equation between the actual point position vector and the planned point vector is determined. The planned trajectory is used as the coordinate axis in the natural coordinate system. The error value is obtained using the torque balance equation in the natural coordinate system. After post-processing, different lateral errors after the change of the center of mass position are obtained. Based on the obtained lateral errors, the optimal front wheel steering angle of the vehicle is determined and control is achieved.

[0109] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A vehicle collaborative control system based on intelligent tires, characterized in that, It includes a smart tire sensor and an on-board terminal with communication connection. The smart tire sensor is located on the inside of the tire tread and is used to obtain the tire pressure, acceleration and rotation angle and send them to the on-board terminal. The on-board terminal uses a data processing unit to realize the whole vehicle control based on the received information. The data processing unit is configured as follows: Based on the lateral forces acting on the front and rear wheels of the vehicle and the front wheel rotation angle, the resultant force in the lateral direction of the vehicle and the yaw moment about the vertical axis are determined, and the moment balance equation in the natural coordinate system is obtained. The tire contact length is determined by the signal length C between adjacent positive and negative peaks in the acceleration signal, and the tire rotation number is determined by the time interval L between adjacent positive and negative peaks. The ratio between C and L is used as the actual load index. The position of the vehicle's center of gravity on the horizontal plane and the tire's lateral stiffness are obtained by using a pre-calibrated experiment. Using information from any point on the vehicle's planned trajectory, the error equation between the actual point position vector and the planned point vector is determined. Using the planned trajectory as the coordinate axis in the natural coordinate system, the solution to the error equation is obtained using the torque balance equation in the natural coordinate system. After post-processing, different lateral errors after the change of the center of mass position are obtained, and the optimal steering angle of the front wheel is determined.

2. The vehicle collaborative control system based on intelligent tires as described in claim 1, characterized in that, Based on the lateral forces acting on the front and rear wheels of the vehicle and the front wheel rotation angle, the resultant force in the lateral direction of the vehicle and the yaw moment about the vertical axis are determined, and the moment balance equation in the natural coordinate system is obtained. This includes using a two-degree-of-freedom vehicle model to obtain the resultant force in the lateral direction of the vehicle and the yaw moment about the vertical axis based on the lateral forces on the front and rear wheels and the front wheel rotation angle. After coordinate system transformation, the torque balance equation in the natural coordinate system is obtained.

3. The vehicle collaborative control system based on intelligent tires as described in claim 1, characterized in that, Based on pre-calibrated experiments, a regression model of load, actual load index and tire pressure is determined. The load of each wheel position and the position of the vehicle's center of mass on the horizontal plane are obtained by using the air pressure and acceleration acquired by the intelligent tire sensor.

4. The vehicle collaborative control system based on intelligent tires as described in claim 1, characterized in that, Based on pre-calibration experiments, the relationship surface between lateral stiffness, air pressure, and load was obtained. The lateral stiffness of the tire was determined by looking up tables using tire pressure and corresponding load data obtained from intelligent tire sensors.

5. The vehicle collaborative control system based on intelligent tires as described in claim 1, characterized in that, Using information from any point on the vehicle's planned trajectory, determine the error between the actual point position vector and the planned point vector; This includes obtaining the error equation between the actual point position vector and the planned point vector based on the position, velocity, heading angle, and acceleration of any point on the planned trajectory.

6. The vehicle collaborative control system based on intelligent tires as described in claim 1, characterized in that, The post-processing includes obtaining the optimal control quantity based on a discrete linear quadratic regulator.

7. The vehicle collaborative control system based on intelligent tires as described in claim 1, characterized in that, The post-processing also includes determining the position, velocity, and yaw angle information of the prediction point based on the feedforward.

8. The vehicle collaborative control system based on intelligent tires as described in claim 1, characterized in that, If the planned trajectory is a continuous trajectory, the error of the discrete trajectory points is determined based on the matching points.

9. The vehicle collaborative control system based on intelligent tires as described in claim 1, characterized in that, The point closest to the actual location of the discrete planning trajectory point is taken as the matching point, and the projection point is determined by the geometric relationship between the projection point and the matching point.

10. A method for vehicle control based on the system according to any one of claims 1-9, characterized in that, Includes the following steps: Based on the lateral forces acting on the front and rear wheels of the vehicle and the front wheel rotation angle, the resultant force in the lateral direction of the vehicle and the yaw moment about the vertical axis are determined, and the moment balance equation in the natural coordinate system is obtained. The tire contact length is determined by the signal length C between adjacent positive and negative peaks in the acceleration signal, and the tire rotation number is determined by the time interval L between adjacent positive and negative peaks. The ratio between C and L is used as the actual load index. The position of the vehicle's center of gravity on the horizontal plane and the tire's lateral stiffness are obtained by using a pre-calibrated experiment. Using information from any point on the vehicle's planned trajectory, the error equation between the actual point position vector and the planned point vector is determined. The planned trajectory is used as the coordinate axis in the natural coordinate system. The error value is obtained using the torque balance equation in the natural coordinate system. After post-processing, different lateral errors after the change of the center of mass position are obtained. Based on the obtained lateral errors, the optimal front wheel steering angle of the vehicle is determined and control is achieved.

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