A Dual-Chamber Air Spring Control Method Based on Vehicle State Recognition

Abstract

The present invention discloses a control method for a dual-chamber air spring based on vehicle state recognition, comprising the following steps: establishing a two-degree-of-freedom model of an automobile to calculate the lateral acceleration of the automobile under a steering condition; establishing a seven-degree-of-freedom model of a whole vehicle with a dual-chamber air spring to obtain the state space equation and output equation of its system; based on the signals collected by sensors, using the Kalman filtering algorithm to estimate the body roll angle and the displacement of the unsprung mass; according to the relationship between the estimated body roll angle and the unsprung mass roll angle, changing the switching state of the solenoid valve, and further determining the working state of the dual-chamber air spring, so as to effectively improve the ride comfort and handling stability of the automobile.

Description

Technical Field

[0001] The present invention relates to the technical field of automotive suspension systems, and particularly to a control method for a dual-chamber air spring based on vehicle state recognition. Background Art

[0002] With the rapid development of society and economy, the improvement of automotive manufacturing technology and the development of industrial level, people's requirements for automobiles are constantly increasing, especially the upgrading and improvement in aspects such as the ride comfort and handling stability of automobiles. For general passive suspensions, suspension development designers can, through reasonable matching, calculate the suspension spring stiffness and shock absorber damping coefficient, which can, to a certain extent, balance the handling stability and ride comfort during vehicle driving. However, during driving, in the face of different vehicle loads, vehicle speeds, road conditions, and special road conditions, it is difficult for passive suspensions to ensure that the vehicle can meet various situations.

[0003] Air suspensions have characteristics such as adjustable stiffness and adjustable vehicle body height, and their applications in the automotive field are increasing day by day. In order to further improve the performance of air springs, many domestic and foreign scholars have proposed dual-chamber air springs, which, compared with single air springs, add an additional air chamber and throttling elements (such as throttle valves). The additional air chamber can be used to expand the volume of the air spring, thereby reducing the stiffness of the air spring and improving the ride comfort of the vehicle under normal road conditions. However, at the same time, the decrease in the stiffness of the air suspension will lead to a decrease in the roll stiffness of the vehicle, thus affecting the handling stability of the vehicle.

[0004] Li Zhongxing et al. in the document "A lateral interconnected air suspension imitating skyhook interconnected state control system and control method" provided in CN106828004A proposed to set solenoid valves on the connecting pipelines of the lateral interconnected air springs, and control the opening and closing of the interconnected solenoid valves by obtaining the unsprung mass and the roll angle of the unsprung mass, and adjust the roll stiffness of the suspension to achieve the purpose of suppressing the roll movement of the sprung mass. However, existing sensors are difficult to directly collect small displacements, so it is difficult to accurately measure the displacement of the unsprung mass, and the control accuracy cannot meet the use requirements. Summary of the Invention

[0005] The purpose of the present invention is to overcome the defects of existing passive suspensions and air springs, and propose a control method for a dual-chamber air spring based on vehicle state recognition. First, the centroid acceleration and suspension dynamic deflection are measured by height sensors and acceleration sensors installed on the vehicle, a dual-chamber air spring model and a vehicle body vertical dynamics model are constructed, the displacements of the tires and the roll angles of the vehicle body are predicted under different working states of the air spring and different vehicle states, and the working state of the dual-chamber air spring is determined according to the relationship between the roll angle of the vehicle body and the tire displacement, so as to further determine the connection and disconnection of the solenoid valve, thereby realizing the coordinated control of the ride comfort and handling stability of the vehicle.

[0006] To achieve the object of the present invention, a dual-chamber air spring control method based on vehicle state recognition provided by the present invention includes the following steps:

[0007] (1) Establish a two-degree-of-freedom vehicle steering model considering yaw and lateral motion based on Newton's second law, and calculate the lateral acceleration of the vehicle based on the front wheel steering angle;

[0008] (2) Establish a dual-chamber air spring model and a seven-degree-of-freedom vehicle vibration model to obtain the state equation and output equation of the system. State variables of the system:

[0009]

[0010] Measured variables of the system:

[0011]

[0012] System input , the disturbance input is , where: the lateral

[0013] tilting moment ;

[0014]

[0015] (3) Based on the vehicle-related data collected by the sensor, use the Kalman filtering method to solve the state equation and output equation to obtain the estimated body roll angle and the roll angle of the unsprung mass.

[0016] (4) Estimate the body roll angle and the displacement of the unsprung mass of the vehicle from the Kalman filtering algorithm in step (3), and output the on-off control strategy of the solenoid valve according to the magnitudes of the body roll angle and the roll angle of the unsprung mass.

[0017] Furthermore, the dual-chamber air spring includes an upper cover plate, a bladder, a piston, and a solenoid valve. The bladder, the upper cover plate, and the outer surface of the piston form the main air chamber, and an additional air chamber is formed inside the piston. The two air chambers are controlled to be connected or disconnected by the solenoid valve.

[0018] Furthermore, in step (1):

[0019] The two-degree-of-freedom vehicle steering model is

[0020]

[0021] In the formula, is the vehicle mass; v is the vehicle lateral velocity; is v the first derivative of; is the vehicle driving speed; is the yaw rate; are the cornering stiffnesses of the front and rear tires respectively; is the sideslip angle of the center of mass; is the front wheel steering angle; 、 are the distances from the front axle and the rear axle to the center of mass respectively; is the yaw rate of the vehicle.

[0022] Furthermore, the construction process of the dual-chamber air spring model described in step (2) is as follows:

[0023] The spring force expression of the air spring is:

[0024]

[0025] According to the first law of thermodynamics, the rate of change of the internal pressure in the main chamber is:

[0026]

[0027] When the solenoid valve is opened, the rate of change of the internal air pressure in the additional chamber is:

[0028]

[0029] Among them, the relationship between the air pressures in the main chamber and the additional chamber is:

[0030]

[0031] In the formula, is the volume of the main chamber, is the first derivative of, is the throttling damping coefficient, is the volume of the main chamber, is the mass flow rate of the gas in the main chamber, is the pressure of the additional chamber, is the first derivative of, is the temperature of the additional chamber, k is the polytropic process index, is the temperature of the main chamber, is the gas constant, is the pressure of the main chamber, is the ambient atmospheric pressure, is the effective area of the air spring.

[0032] Furthermore, the seven-degree-of-freedom vibration model of the whole vehicle in step (2) is:

[0033] The vertical motion equation at the center of mass of the vehicle body is:

[0034]

[0035] Equation of vehicle body pitching motion

[0036]

[0037] Vehicle body roll motion

[0038]

[0039] The vertical motion equations of the unsprung mass are respectively as follows:

[0040]

[0041] The vertical displacements at the four end points of the vehicle body and the vertical displacement of the vehicle body have the following relationship:

[0042]

[0043] Among them, is the sprung mass; is the unsprung mass; is the vehicle body roll angle; is the vehicle body pitching angle; is the second derivative of; F i is the air spring force, i = 1, 2, 3, 4, respectively representing the left front, right front, left rear and right rear positions; c si is the shock absorber damping; is the wheel stiffness; is the distance from the center of mass to the front axle; b is the distance from the center of mass to the rear axle; is the roll center height; B is the vehicle track; K ti is the stiffness of the four tires; is the vertical displacement of the vehicle body center of mass; is the second derivative of, and is the vertical acceleration of the vehicle body; is the vertical displacement of the four corners of the vehicle body; is the vertical displacement of the wheel; is the pitching moment of inertia; is the roll moment of inertia.

[0044] Furthermore, in step (3), the steps of obtaining the estimated vehicle body roll angle and the unsprung mass roll angle include: The first step: Discretize the state equation and output equation of the system to obtain the state space equations of the discretized state observation system and parameter estimation system.

[0045] Step 2: Based on the state - space equations of the discrete - form state - observation system and parameter - estimation system, the Kalman filtering algorithm is used for state observation and parameter estimation to obtain the state - estimation results of the vehicle - body roll angle and the displacement of the unsprung mass.

[0046] The observation process of the Kalman filter for the system is as follows:

[0047] ① Design the prediction - estimation equation:

[0048]

[0049] ;

[0050] ② Recursively predict the error - covariance matrix of the predicted value and the true value:

[0051]

[0052] is the covariance matrix of the process error. Establish a road - excitation model, and obtain the covariance matrix of the process error based on the road unevenness of different roads and the vehicle's driving speed;

[0053] ③ Calculate the filtering - gain matrix:

[0054]

[0055] R is the covariance matrix of the measurement error. Take the historical - data variances of the height sensor and the acceleration sensor as R.

[0056] ④ Calculate the optimal state - estimation value at time k:

[0057]

[0058] ⑤ Update the filtering - error covariance matrix:

[0059]

[0060] Calculate the roll angle of the unsprung mass:

[0061] Further, the roll angle of the unsprung mass at the front axle:

[0062]

[0063] The roll angle of the unsprung mass at the rear axle:

[0064]

[0065] Furthermore, the sensors for measuring data include four body height sensors and one acceleration sensor. The four body height sensors are respectively used to measure the dynamic deflections of four suspensions, and the acceleration sensor is used to measure the vertical acceleration of the vehicle body.

[0066] Furthermore, the front and rear axle dual-chamber air springs are controlled separately, and the switching control strategy is as follows:

[0067] Taking the front axle (the method for the rear axle is the same) as an example:

[0068] If , the control system outputs a signal to close the solenoid valve to the front axle, outputs a high level to the normally open solenoid valves in the left and right two dual-chamber air springs of the front axle, and disconnects the connection between the main air chamber and the additional air chamber;

[0069] If , the control system outputs a signal to open the solenoid valve to the front axle, outputs a low level to the normally open solenoid valves in the left and right two dual-chamber air springs of the front axle, and connects the main air chamber and the additional air chamber.

[0070] Compared with the prior art, the present invention can at least achieve the following beneficial effects:

[0071] (1) By using the measurable dynamic deflections of the suspensions and the vertical acceleration of the vehicle body, the present invention estimates the displacement of the unsprung mass and the roll angle of the sprung mass by using the Kalman filtering algorithm, and finally performs on-off control on the solenoid valves in the air springs, with high control accuracy.

[0072] (2) The present invention establishes a state observer and uses the Kalman filtering algorithm to estimate the displacement signal of the unsprung mass required in the control strategy, and can obtain an accurate displacement signal.

[0073] (3) By controlling the solenoid valves connecting the main and auxiliary air chambers in the air springs, the present invention changes the stiffness of the air springs to meet the performance requirements under different working conditions. For example, when in a straight-line working condition, the solenoid valve is opened to connect the two air chambers, reducing the stiffness of the air spring and improving the ride comfort of the vehicle; when in a steering working condition, the solenoid valve is closed to cut off the two air chambers, increasing the stiffness of the air spring and improving the handling stability of the vehicle. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 is a schematic diagram of a dual-chamber air spring provided by an embodiment of the present invention;

[0075] In the figure: 1. upper cover plate, 2. bladder, 3. main air chamber, 4. normally open solenoid valve, 5. piston, 6. additional air chamber;

[0076] Figure 2 is a schematic diagram of a whole vehicle with a dual-chamber air spring based on vehicle state recognition in an embodiment of the present invention.

[0077] Figure 3 It is a flowchart of a dual-chamber air spring control method based on vehicle state recognition provided by an embodiment of the present invention;

[0078] Figure 4 It is a schematic diagram of the simulation analysis result based on matlab / simulink in an embodiment of the present invention. Specific embodiments

[0079] To make the purpose, technical solutions and advantages of the present invention clearer and more definite, the following takes embodiments in conjunction with the accompanying drawings to further describe the present invention in detail.

[0080] For ease of understanding, the dual-chamber air spring will be specifically introduced first. Please refer to Figure 1 , the dual-chamber air spring used in the embodiment of the present invention includes an upper cover plate 1, a bladder 2, a piston 5, and a normally open solenoid valve 4. Among them, the outer surfaces of the bladder 2, the upper cover plate 1, and the piston 5 form a main air chamber 3; an additional air chamber 6 is formed inside the piston 5; the main air chamber 3 and the additional air chamber 6 are controlled to be connected or disconnected through the normally open solenoid valve 4.

[0081] Figure 3 It is a simplified seven-degree-of-freedom vehicle model. This three-dimensional model mainly considers 4 wheel masses, 7 degrees of freedom including vertical, pitch, and roll.

[0082] Please refer to Figure 3 , a dual-chamber air spring control method based on vehicle body state estimation provided by the present invention includes the following steps:

[0083] Step 1: Establish a two-degree-of-freedom vehicle model considering yaw motion and lateral motion according to Newton's second law, and calculate the lateral acceleration during vehicle steering based on the front wheel angle.

[0084] Among them, the vehicle two-degree-of-freedom model is

[0085]

[0086] In the formula, m is the vehicle mass; v is the vehicle lateral velocity, is v the first derivative of; is the vehicle driving speed; is the yaw angular velocity; are the cornering stiffnesses of the front and rear tires respectively; is the sideslip angle of the center of mass; is the front wheel angle; , are the distances from the front axle and the rear axle to the center of mass respectively; is the vehicle yaw angular velocity.

[0087] Step 2: Establish a double-chamber air spring model and a seven-degree-of-freedom vehicle vibration model to obtain the state equation and output equation of the system.

[0088] In some embodiments of the present invention, this step specifically includes the following sub-steps:

[0089] Step 2.1: The double-chamber air spring model is as follows: The spring force expression of the air spring is:

[0090]

[0091] In the formula, is the main chamber pressure, is the external atmospheric pressure, is the effective area of the air spring.

[0092] According to the first law of thermodynamics, the change rate of the internal pressure of the main chamber is:

[0093]

[0094] In the formula, is the main chamber volume, is the first derivative of, is the gas mass flow rate in the main chamber, k is the polytropic process index, is the main chamber temperature. is the gas constant.

[0095] When the solenoid valve is opened, the change rate of the internal air pressure of the additional chamber is:

[0096]

[0097] In the formula, is the additional chamber volume, is the gas mass flow rate in the main chamber, Pa is the additional chamber pressure, is the first derivative of, is the additional chamber temperature.

[0098] The solenoid valve uses a throttle orifice model:

[0099]

[0100] In the formula, = / is the air pressure ratio of the two chambers, where the upstream air pressure: = max( , );Downstream air pressure =min( , ); T1 is the upstream gas temperature; A is the solenoid valve flow area. If the solenoid valve is closed, the solenoid valve flow area A is 0.

[0101] The relationship between the air pressures in the main air chamber and the additional air chamber is:

[0102]

[0103] is the throttling damping coefficient;

[0104] Step 2.2: As Figure 2 The seven-degree-of-freedom vibration model of the whole vehicle is:

[0105] The vertical motion equation at the center of mass of the vehicle body is:

[0106]

[0107] The pitching motion equation of the vehicle body

[0108]

[0109] The roll motion of the vehicle body

[0110]

[0111] The vertical motion equations of the unsprung mass are respectively:

[0112]

[0113] The relationship between the vertical displacements at the four endpoints of the vehicle body and the vertical displacement of the vehicle body is as follows:

[0114]

[0115] Among them, is the sprung mass; is the unsprung mass; is the roll angle of the vehicle body; is the pitch angle of the vehicle body, is the second derivative of; F i is the air spring force, i = 1, 2, 3, 4, which respectively represent the left front, right front, left rear and right rear positions; csi is the shock absorber damping; is the wheel stiffness; is the distance from the center of mass to the front axle; b is the distance from the center of mass to the rear axle; is the roll center height; B is the vehicle track width; K ti is the stiffness of the four tires; is the vertical displacement of the vehicle body center of mass; is the second derivative of, which is the vertical acceleration of the vehicle body; is the vertical displacement of the four corners of the vehicle body; is the vertical displacement of the wheel; is the pitch moment of inertia; is the roll moment of inertia.

[0116] Step 2.3: Further establish the state equation and output equation of the Kalman filter:

[0117]

[0118] The measurement variables of the system:

[0119]

[0120] System input , disturbance input , where the roll moment

[0121] ; obtain the state equation and output equation of the system as:

[0122]

[0123] where A is the state transition matrix, B is the control input matrix, H is the state observation matrix, w is the process noise (disturbance input), v is the measurement noise, x is the state variable, and y is the measurement variable.

[0124] Step 3: Based on the vehicle body acceleration and the four suspension dynamic deflections collected by the sensors, use the Kalman filter method to solve the state equation and output equation, and obtain the estimated vehicle body roll angle and the displacement of the unsprung mass.

[0125] In some embodiments of the present invention, Step 3 specifically includes the following sub-steps:

[0126] Step 3.1: Discretize the state equation and output equation of the system to obtain the state - space equations of the discrete - form state - observation system and parameter - estimation system.

[0127]

[0128]

[0129]

[0130]

[0131] Among them, T denotes the sampling time, and \(k\) is the sampling moment of discrete time; is the discretized state - transition matrix; is the discretized control - input matrix; is the discretized control - input matrix; , are the system - state variables at the \(k\) - th moment and \((k - 1)\) - th moment respectively; is the system - state variable at the \(k\) - th moment; is the process noise at the \((k - 1)\) - th moment; is the input vector at the \((k - 1)\) - th moment; is the process noise at the \((k - 1)\) - th moment, is the covariance matrix of the process error. Establish a road - excitation model, and obtain the covariance matrix of the process error based on the road unevenness of different roads and the driving speed of the vehicle.

[0132] Step 3.2: Based on the state - space equations of the discrete - form state - observation system and parameter - estimation system, use the Kalman - filtering algorithm for state observation and parameter estimation to obtain the state - estimation results of the vehicle - body roll angle and the displacement of the unsprung mass.

[0133] The observation process of the Kalman filter for the system is as follows:

[0134] ① Design the prediction - estimation equation:

[0135] ;

[0136] is the optimal - estimation value at the \((k - 1)\) - th moment; is the estimated value predicted through at the \((k - 1)\) - th moment.

[0137] ② Recursively calculate the error - covariance matrix of the predicted value and the true value :

[0138] ;

[0139] is the predicted value error covariance matrix at time k-1, is the covariance matrix of the process error. A road surface excitation model is established, and the covariance matrix of the process error is obtained based on the road unevenness of different road surfaces and the driving speed of the vehicle.

[0140] ③ Calculate the filter gain matrix :

[0141] ;

[0142] R is the covariance matrix of the measurement error. Take the historical data variances of the height sensor and the acceleration sensor as R.

[0143] ④ Calculate the optimal state estimate value at time k :

[0144]

[0145] ⑤ Update the filter error covariance matrix:

[0146] ;

[0147] is the identity matrix.

[0148] Through the above recursive calculation, the body roll angle and the displacement of the unsprung mass of the vehicle at each discrete time k can be estimated in real time.

[0149] Step 4: Estimate the body roll angle and the displacement of the unsprung mass of the vehicle from the Kalman filter algorithm in Step 3, and output the on-off control strategy of the solenoid valve according to the magnitudes of the body roll angle and the unsprung mass roll angle.

[0150] ① Calculate the unsprung mass roll angle based on the displacement of the unsprung mass estimated in Step 3

[0151] Unsprung mass roll angle of the front axle:

[0152]

[0153] Unsprung mass roll angle of the rear axle:

[0154]

[0155] ② Take the front axle (the control strategy for the rear axle is the same) as an example to give the control strategy of the double-chamber air spring:

[0156] If the control system outputs a closing solenoid valve signal to the front axle, it outputs a high level to the normally open solenoid valves in the left and right double-chamber air springs of the front axle, disconnects the communication between the main air chamber and the auxiliary air chamber, increases the air spring stiffness, thereby increasing the roll stiffness of the front axle and improving the vehicle handling stability.

[0157] If the control system outputs an opening solenoid valve signal to the front axle, it outputs a low level to the normally open solenoid valves in the left and right double-chamber air springs of the front axle, connects the main air chamber and the auxiliary air chamber, reduces the air spring stiffness, thereby reducing the roll stiffness of the front axle and improving the vehicle ride comfort.

[0158] Step 5: Conduct simulation verification.

[0159] In some embodiments of the present invention, a step excitation condition of the front wheel angle is set in matlab / simulink, the simulation time is 10 s, the vehicle speed is 20 m / s, and the front wheel angle is set to 5°, and the simulation is carried out. The simulation results are as Figure 4 shown. It can be seen from the figure that the control method provided by the present invention can effectively reduce the body roll angle of the vehicle under the emergency steering condition and improve the vehicle handling stability and safety.

[0160] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A dual-chamber air spring control method based on vehicle state recognition, characterized in that It includes the following steps: (1) Establish a two-degree-of-freedom vehicle steering model considering yaw and lateral motion, and calculate the lateral acceleration of the vehicle based on the front wheel steering angle; (2) Establish a double-chamber air spring model and a seven-degree-of-freedom vehicle vibration model to obtain the state equation and output equation of the system; In the formula: is the state variable of the system: Measurement variables for the system: System input , i = 1, 2, 3, 4, representing the left front, right front, left rear, and right rear positions respectively, F i is the air spring force, is the roll moment, and the disturbance input is , is the state transition matrix, is the control input matrix, H is the state observation matrix, v is the measurement noise, is the vertical displacement of the vehicle body center of mass, are the vertical displacements of the four corners of the vehicle body; is the vertical displacement of the wheel, is the vehicle body roll angle; is the vehicle body pitch angle, is the transpose; (3) Based on the body acceleration and four suspension dynamic deflections of the vehicle collected by sensors, use the Kalman filtering method to solve the state equation and output equation, and obtain the estimated body roll angle and unsprung mass displacement; (4) Obtain the unsprung mass roll angle based on the unsprung mass displacement. The unsprung mass roll angle includes the unsprung mass roll angle of the front axle and the unsprung mass roll angle of the rear axle, and output the on-off control strategy of the solenoid valve according to the magnitudes of the body roll angle and the unsprung mass roll angle; Among them, in step (1): The two-degree-of-freedom vehicle steering model is: In the formula, is the vehicle mass; v is the lateral velocity of the vehicle; is v the first derivative of; is the vehicle driving speed; is the yaw rate; are the cornering stiffnesses of the front and rear tires respectively; is the sideslip angle of the center of mass; is the front wheel steering angle; , are the distances from the front axle and the rear axle to the center of mass respectively; is the yaw rate of the vehicle; In step (3), the steps to obtain the estimated body roll angle and unsprung mass roll angle include: Discretize the state equation and output equation of the system to obtain the state space equations of the discrete state observation system and parameter estimation system; Based on the state space equations of the discrete state observation system and parameter estimation system, use the Kalman filtering algorithm for state observation and parameter estimation to obtain the state estimation results of the body roll angle and unsprung mass displacement.

2. The dual-chamber air spring control method based on vehicle state recognition according to claim 1, wherein, The double-chamber air spring includes an upper cover plate, a bladder, a piston, and a solenoid valve. The bladder, upper cover plate, and the outer surface of the piston form the main air chamber, and an additional air chamber is formed inside the piston. The two air chambers are controlled to be connected or disconnected by the solenoid valve.

3. A dual-chamber air spring control method based on vehicle state recognition according to claim 1, characterized in that, The construction process of the double-chamber air spring model described in step (2) is: The spring force expression of the air spring is: According to the first law of thermodynamics, the internal pressure change rate of the main air chamber is: When the solenoid valve is opened, the internal air pressure change rate of the additional air chamber is: Among them, the relationship between the air pressures of the main air chamber and the additional air chamber is: In the formula, is the volume of the main air chamber, is 's first derivative, is the throttling damping coefficient, is the volume of the main air chamber, is the gas mass flow rate in the main air chamber, is the additional air chamber pressure, is 's first derivative, is the additional air chamber temperature, k is the polytropic process index, is the main air chamber temperature, is the gas constant, is the main air chamber pressure, is the external atmospheric pressure, is the effective area of the air spring.

4. A dual-chamber air spring control method based on vehicle state recognition according to claim 1, characterized in that The seven-degree-of-freedom vehicle vibration model in step (2) is: The vertical motion equation at the center of mass of the body is: The pitch motion equation of the body: The roll motion of the body: The vertical motion equations of the unsprung mass are respectively: The relationship between the vertical displacements at the four endpoints of the body and the vertical displacement of the body is as follows: Wherein, is the unsprung mass; is the sprung mass; is the body roll angle; is the body pitch angle; is the second derivative of; F i is the air spring force, i = 1, 2, 3, 4, respectively representing the left front, right front, left rear and right rear positions; c si is the shock absorber damping; is the wheel stiffness; is the distance from the center of mass to the front axle; b is the distance from the center of mass to the rear axle; is the roll center height; B is the vehicle track width; K ti is the stiffness of the four tires; is the vertical displacement of the vehicle body center of mass; is the second derivative of, which is the vertical acceleration of the vehicle body; is the vertical displacement of the four corners of the vehicle body; is the vertical displacement of the wheel; is the pitch moment of inertia; is the roll moment of inertia.

5. A dual-chamber air spring control method based on vehicle state recognition according to claim 1, characterized in that, The observation process of the system by the Kalman filter is as follows: Design the prediction estimation equation: Error covariance matrix of recursive predicted value and true value : The calculated filtering gain matrix : Calculate the optimal state estimate at time k : Update the filter error covariance matrix: Wherein, is the discretized state transition matrix, is the discretized control input matrix, is the optimal estimated value at the (k - 1)th moment, is the estimated value predicted through at the (k - 1)th moment, is the prediction error covariance matrix at the (k - 1)th moment, is the covariance matrix of the process error, is the covariance matrix of the measurement error, is the state observation matrix, and the superscript T represents the transpose, is the system state variable at the kth moment, is the input vector at the (k - 1)th moment, is the identity matrix; Through the above recursive calculation, the body roll angle and unsprung mass displacement of the vehicle at each discrete time k can be estimated in real time.

6. A dual-chamber air spring control method based on vehicle state recognition according to claim 1, characterized in that The unsprung mass roll angle of the front axle described in step (4) is: The unsprung mass roll angle of the rear axle is: is the vertical displacement of the wheel, i = 1, 2, 3, 4, which respectively represent the left front, right front, left rear and right rear positions, B is the vehicle track.

7. A dual-chamber air spring control method based on vehicle state recognition according to claim 1, characterized in that The sensors for measuring data include 4 body height sensors and 1 acceleration sensor. The 4 body height sensors are respectively used to measure 4 suspension dynamic deflections, and the acceleration sensor is used to measure the vertical acceleration of the body.

8. A dual-chamber air spring control method based on vehicle state recognition according to any one of claims 1-7, characterized in that, Control the front and rear axle double-chamber air springs separately, and the on-off control strategy is: The on-off control strategy of the front axle is: If , the control system outputs a closing solenoid valve signal to the front axle, outputs a high level to the normally open solenoid valves in the two double-chamber air springs on the left and right of the front axle, and disconnects the communication between the main air chamber and the additional air chamber; If , the control system outputs an opening solenoid valve signal to the front axle, outputs a low level to the normally open solenoid valves in the two double-chamber air springs on the left and right of the front axle, and connects the main air chamber and the additional air chamber; The on-off control strategy of the rear axle is the same as that of the front axle.

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