A state estimation method for electronically controlled air suspension

Abstract

The application discloses a state estimation method for an electrically-controlled air suspension, and comprises the following steps: establishing an air spring charging and discharging circuit model; establishing an air suspension dynamics model; establishing an air suspension state equation according to the air suspension whole vehicle dynamics model; inputting parameters measured by an actual vehicle height sensor and an acceleration sensor into a state observer as measurement quantities; and calculating air spring internal pressure, vehicle spring mass speed and non-spring mass speed. The application considers the interaction of four corner air springs of a vehicle, sets a process noise covariance matrix and a measurement noise covariance matrix to consider model errors in the modeling process and measurement errors of sensors, and has good estimation effect.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of air suspension, and particularly relates to a state estimation method for an electronically controlled air suspension. BACKGROUND

[0002] The electronically controlled air suspension (ECAS) can realize active adjustment of suspension system stiffness, damping and vehicle body height, is of great significance to improvement of vehicle ride comfort, handling stability and fuel economy during driving, and has become a research hotspot in the field of vehicle engineering. Among them, the vehicle body height adjustment is one of the characteristic functions of the ECAS, and the system realizes active control of the vehicle body height by charging and discharging the air spring, thereby providing important technical support for improvement of the comprehensive performance of the vehicle.

[0003] The air spring is a very important component in the air suspension. In the vehicle body height adjustment, whether the value of the internal pressure of the air spring is accurate determines whether the vehicle body can be accurately adjusted to the desired height. However, it is difficult to install the pressure sensor into the air spring, and the internal pressure of the air spring cannot be obtained through the sensor. In addition, due to cost constraints, a vertical speed sensor is generally not mounted on the vehicle.

[0004] At present, there are mainly two methods for estimation of the state of the vehicle: one is to realize state estimation by establishing a mathematical model of the vehicle, and the other is to realize state estimation by collecting and analyzing various parameters in the running of the vehicle. The simplest way to realize state estimation by using the mathematical model is to integrate or differentiate the known state to obtain the unknown state, but this method will accumulate errors, and the final result is not accurate. For example, the article "Hyunsup Kim, Lee Hyeongcheol. Height and leveling control of automotive air suspension system using sliding mode approach [J]. IEEE transactions on vehicular technology, 2011, 60(5): 2027-2041." proposes to use a sliding mode observer to observe the internal pressure of the air spring, but this article only observes the internal pressure of a single air spring and does not consider the interaction of the four corner air springs of the vehicle, so the detection result is not accurate. By collecting various parameters in the running of the vehicle, the parameters can be fitted, and the regular curve of the state of the vehicle can also be summarized, but this method requires a large amount of data set and takes a long time, and is less used in actual application. SUMMARY

[0005] To at least solve one of the problems existing in the prior art, the application provides a state estimation method for an electronically controlled air suspension, which is used for parameters difficult to be measured by sensors in the air suspension, utilizes an unscented Kalman filtering algorithm, is combined with an air suspension dynamics model, takes the suspension deflection measured by a height sensor and the sprung mass acceleration measured by an acceleration sensor as system measurement, takes the mass flow of the air spring as an input, realizes state estimation of the air suspension, and obtains the sprung mass speed, the unsprung mass speed and the internal pressure of the air spring.

[0006] To achieve the object of the application, the application provides a state estimation method for an electronically controlled air suspension, which is realized according to the following steps:

[0007] (1) The components contained in the air spring charging and discharging circuit include an air tank, a compressor, an electromagnetic valve and an air spring, wherein the characteristics of the electromagnetic valve reflect the mass flow of the gas in the charging and discharging process, and the characteristics of the air spring reflect the relationship between the mass flow and the suspension deflection, so it is necessary to model the two. The selected electromagnetic valve is a high-speed on-off valve, and the flow characteristics of the valve are related to the size relationship of the pressure ratio and the critical pressure ratio. The air spring is analyzed by using the first law of thermodynamics, the charging and discharging process of the air spring is regarded as an adiabatic process, and the effective area of the air spring is regarded as a constant value, and the pressure gradient formula of the air spring is derived;

[0008] (2) An air suspension whole vehicle dynamics model is established, in the process of height adjustment of the air suspension, the vehicle performs linear motion, and the influence of the small pitch angle and roll angle can be ignored in the state estimation of the air suspension, so the air suspension whole vehicle dynamics model can be simplified as the superposition of four air suspension single wheel models, namely:

[0009]

[0010]

[0011]

[0012] Wherein, the subscript ij = fl, fr, rl, rr respectively represent the corresponding front left wheel, front right wheel, rear left wheel and rear right wheel; m sij represents the sprung mass; z sij represents the sprung mass displacement; z tij represents the unsprung mass displacement; represents the sprung mass speed, represents the unsprung mass speed; represents sprung mass acceleration; represents unsprung mass acceleration; ij represents damper damping; k tij represents tire stiffness; p ij represents air spring internal pressure; V 0ij represents air spring initial volume; A ij represents air spring effective area; k represents adiabatic index; R represents gas constant.

[0013] (3) The air suspension state equation is established according to the air suspension dynamics model. Since the actual vehicle in the application is only equipped with four height sensors and four sprung mass acceleration sensors, the state variable is selected as:

[0014]

[0015] The measurement variable is selected as:

[0016]

[0017] The system input is selected as:

[0018] u=[q mfl ,q mfr ,q mrl ,q mrr ] T (6)

[0019] The expression of the state space equation is:

[0020]

[0021] (4) The air suspension state observer based on the unscented Kalman filter algorithm is established

[0022] For a nonlinear system, the unscented Kalman filter algorithm filter can well handle the nonlinear characteristics of the air spring, and the values calculated by the state observer built have high precision, and the application uses the unscented Kalman filter algorithm to build the state observer. The implementation steps of the unscented Kalman filter algorithm are as follows:

[0023] a) Use UT transformation to construct 2n+1 Sigma points and their weights, where n is the state dimension

[0024] b) Calculate the 2n+1 Sigma point set prediction

[0025] c) One-step prediction of system state

[0026] d) Use UT transformation to generate a new Sigma point set

[0027] e) Obtain predicted measurements from the new set of Sigma points

[0028] f) Weighted sum to obtain system predicted mean and covariance

[0029] g) Calculate Kalman gain matrix

[0030] h) Calculate state update and covariance update of the system

[0031] (5) The parameters measured by the actual vehicle height sensor and acceleration sensor are input into the above state observer as measurement, combined with the input mass flow and system structure parameters, to calculate the estimation of the internal pressure of the air spring, the sprung mass speed and the unsprung mass speed.

[0032] Further, the input includes the mass flow of four air springs; the measurement includes the suspension dynamic deflection and the sprung mass acceleration of the four corners of the vehicle; the state includes the sprung mass speed, the unsprung mass speed, the suspension dynamic deflection, the tire dynamic deformation, and the internal pressure of the air spring of the four corners of the vehicle. The internal pressure of the air spring, the sprung mass speed and the unsprung mass speed are calculated according to the height sensor and the acceleration sensor on the vehicle.

[0033] Compared with the prior art, the present application has the following advantages:

[0034] 1) The model-based state estimation method realizes the estimation of the parameters difficult to measure directly, and reduces the use of vehicle speed sensors.

[0035] 2) The state observer based on the unscented Kalman filter algorithm can quickly approach the true value, and the time delay caused by software running can be ignored.

[0036] 3) The state observer can estimate the unmeasurable state of the system, which estimates the unmeasurable state based on the input and measurable state. The state observer for the air suspension is established, and the unmeasurable state can be obtained through the known input and the information of the vehicle sensor. Not only can the problem of difficult measurement of the internal pressure of the air spring be solved, but also the number of vehicle sensors can be reduced, greatly saving the cost.

[0037] 4) The unscented Kalman filter algorithm is used for state estimation in this paper, which not only uses the whole vehicle dynamics model, but also estimates the internal pressure of the air spring, the sprung mass speed and the unsprung mass speed.

[0038] 5) The state observer can estimate the unmeasurable state of the system, which estimates the unmeasurable state based on the input and measurable state. The state observer for the air suspension is established, and the unmeasurable state can be obtained through the known input and the information of the vehicle sensor. Not only can the problem of difficult measurement of the internal pressure of the air spring be solved, but also the number of vehicle sensors can be reduced, greatly saving the cost. BRIEF DESCRIPTION OF DRAWINGS

[0039] Figure 1 is a schematic diagram of a vehicle dynamics model of an air suspension in an embodiment of the present application.

[0040] Figure 2 is a flowchart of a state estimation method for an electronically controlled air suspension provided in an embodiment of the present application.

[0041] Figure 3 is a flowchart of an unscented Kalman filter algorithm used in an embodiment of the present application.

[0042] Figure 4 is a comparison diagram of sprung mass speed estimation results in an embodiment of the present application.

[0043] Figure 5 is a comparison diagram of unsprung mass speed estimation results in an embodiment of the present application.

[0044] Figure 6 is a comparison diagram of air spring internal pressure estimation results in an embodiment of the present application. DETAILED DESCRIPTION

[0045] To make the objectives, technical solutions and advantages of embodiments of the present application clearer, the following will be combined with the accompanying drawings of embodiments of the present application to make a clear and complete description of the technical solutions in embodiments of the present application. Obviously, the described embodiments are some, but not all, of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work are within the scope of protection of the present application.

[0046] Referring to Figure 2 , the present application provides a state estimation method for an air suspension, specifically comprising the following steps:

[0047] Step 1: Establishing an electromagnetic valve flow model and an air spring charging and discharging model.

[0048] In some embodiments of the present application, the components contained in the air spring charging and discharging circuit include an air tank, a compressor, an electromagnetic valve and an air spring, wherein the characteristics of the electromagnetic valve reflect the mass flow of the gas in the charging and discharging process, and the characteristics of the air spring reflect the relationship between the mass flow and the suspension deflection, so it is necessary to model both.

[0049] In some embodiments of the present application, the electromagnetic valve used is a high-speed on-off valve, and its flow characteristics are related to the size relationship of the pressure ratio and the critical pressure ratio.

[0050] The first law of thermodynamics is used to analyze the air spring, the charging and discharging process of the air spring is regarded as an adiabatic process, and the effective area of the air spring is regarded as a constant value, and the pressure gradient formula of the air spring is derived.

[0051] Step 1.1: The establishment process of electromagnetic valve flow model is as follows:

[0052] When the electromagnetic valve is opened for air spring charging and discharging, the electromagnetic valve is simplified as a throttle hole, and the mass flow rate q of the gas in the pipeline is m The characteristic can be expressed as:

[0053]

[0054] Wherein, A represents the flow area of the electromagnetic valve, p represents the upstream pressure of the electromagnetic valve, T represents the upstream temperature of the electromagnetic valve, k represents the adiabatic index, R represents the gas constant, p represents the upstream pressure of the electromagnetic valve, and σ represents the critical pressure ratio. v u u d

[0055] In some embodiments of the present application, when k = 1.4,

[0056] Step 1.2: The establishment process of air spring charging and discharging model is as follows:

[0057] According to the first law of thermodynamics, the energy exchange process of the gas inside the air spring is analyzed:

[0058] dU = dQ + dW (9)

[0059] Wherein, U represents the internal energy of the gas, Q represents the heat exchange amount between the internal gas and the outside world, and W represents the work amount of the gas.

[0060] If this process is regarded as an adiabatic process, then:

[0061] The heat exchange dQ per unit time = 0

[0062] The change amount of internal energy is:

[0063] The work of the air spring: dW = p1dV1

[0064]

[0065] According to R = c p -c v , after rearranging the formula, we can get:

[0066]

[0067] Wherein, T represents the temperature of the gas flowing through the air spring, ​​​​represents the mass flow rate of the air spring per unit time, p1represents the internal pressure of the air spring, V1represents the volume of the air spring, c p represents the specific heat capacity of the gas at constant pressure, c v represents the specific heat capacity of the gas at constant volume.

[0068] The effective area change rate of the air spring is small in the working height range, so the effective area of the air spring is regarded as a constant value, and the volume change rate of the air spring is equivalent to the effective area multiplied by the relative speed, i.e.

[0069]

[0070] wherein, represents the change of the volume of the air spring per unit time, z s represents the sprung mass displacement; z t represents the unsprung mass displacement; represents the sprung mass velocity; represents the unsprung mass velocity; A represents the effective area of the air spring, and t is time.

[0071] When the electromagnetic valve is opened, the mass flow rate of the electromagnetic valve should be equal to the mass flow rate of the air spring, and the pressure gradient of the air spring can be represented as:

[0072]

[0073] wherein V0is the initial volume of the air spring, which is determined by the effective area of the air spring and the initial pressure.

[0074] Step 2: Establish an air suspension vehicle dynamics model.

[0075] In the process of height adjustment of the air suspension, the vehicle performs straight line motion, and the influence of the small pitch angle and roll angle can be ignored in the state estimation of the air suspension, so the air suspension vehicle dynamics model can be simplified as the superposition of four air suspension single wheel models, i.e.

[0076]

[0077]

[0078]

[0079] wherein the subscript ij = fl, fr, rl, rr respectively represents the corresponding front left wheel, front right wheel, rear left wheel, and rear right wheel; m sij represents the sprung mass; z sij represents the sprung mass displacement; m tij represents the unsprung mass; z tijdenotes unsprung mass displacement; denotes sprung mass velocity; denotes unsprung mass velocity; denotes sprung mass acceleration; denotes unsprung mass acceleration; rij denotes displacement due to road excitation; ij denotes damper force; tij denotes tire stiffness; ij denotes air spring internal pressure; 0ij denotes air spring initial volume; ij denotes air spring effective area; k denotes adiabatic index; R denotes gas constant; p0 denotes atmospheric pressure at standard state; g denotes gravitational acceleration; mij denotes mass flow rate of each air spring. It should be noted that the displacement here is the displacement after air suspension motion, and does not include the initial compression amount due to load.

[0080] Step 3: Establish the air suspension state equation according to the air suspension dynamics model.

[0081] Since the actual vehicle is provided with four height sensors and four sprung mass acceleration sensors, the velocity of each sprung mass, the velocity of each unsprung mass, the suspension dynamic deflection, the tire dynamic deformation and the internal pressure of each air spring are selected as state variables; the suspension dynamic deflection and the acceleration of each sprung mass are selected as measurement variables, and the mass flow rate of each air spring is selected as input, to establish a state equation, that is:

[0082]

[0083]

[0084]

[0085] The expression of the state equation is:

[0086]

[0087] where x is the state variable, y is the measurement variable, u is the system input, ω is the process noise, v is the measurement noise, is the first derivative of x, f(x, u) is the function matrix of x and u, g(ω) is the function matrix of ω, h(x) is the function matrix of x.

[0088]

[0089]

[0090]

[0091]

[0092]

[0093]

[0094]

[0095]

[0096] f1, f2, f3, f4, f5 are components in column vector f

[0097] x1, x2, x3, x4, x5, x6, x7, x8, x9, x 10 , x 11 , x 11 , x 13 , x 14 , x 15 , x 16 , x 17 , x 18 , x 19 , x 20 All are components in column vector x, u1, u2, u3, u4 are components in column vector u

[0098] Step 4: establish an air suspension state observer based on an unscented Kalman filtering algorithm.

[0099] For a nonlinear system, the unscented Kalman filtering algorithm is used to build a state observer, and the unscented Kalman filtering algorithm filtering can well handle the nonlinear characteristics of the air spring, and the values calculated by the built state observer have high precision.

[0100] The specific process of the unscented Kalman filtering algorithm is as follows:

[0101] The nonlinear system can be described by the following equation:

[0102]

[0103] X(k+1) represents the state vector at the next time; f[X(k), W(k)] represents a nonlinear state equation function; Z(k) represents an observation vector; X(k) represents the state vector at the current time; h[X(k), V(k)] represents a nonlinear observation equation function; W(k) represents process noise with mean 0 and covariance matrix Q; V(k) represents measurement noise with mean 0 and covariance matrix R

[0104] a) Construct 2n+1 Sigma points and their weights using UT transformation, where n is the state dimension:

[0105]

[0106]

[0107] where λ is the scaling parameter, λ = α 2 (n + κ) - n, α controls the distribution of sampling points in state, β is a non-negative weight coefficient. X (i) (k|k) represents the Sigma point set constructed after UT transformation; represents the mean of the initial state; P(k|k) represents the variance of the initial state; represents the mean or covariance of the Sigma point, subscript m is the mean, c is the covariance, superscript is the Sigma point.

[0108] b) Calculate 2n+1 Sigma point set prediction

[0109] X (i) (k+1|k) = f[k, X (i) (k|k)] (25)

[0110] f[k, X (i) (k|k)] is the state equation function described above; X (i) (k+1|k) represents the Sigma point set at time k+1;

[0111] c) One step prediction of system state

[0112]

[0113]

[0114] represents the mean of the Sigma point set at time k+1; P(k+1|k) represents the variance of the Sigma point set at time k+1.

[0115] d) Use UT transformation again to generate a new Sigma point set

[0116]

[0117] e) Get the predicted observation from the new Sigma point set

[0118] Z (i) (k+1|k) = h[X (i) (k+1|k)] (29)

[0119] h[X (i)(k+1|k) represents the predicted measurement at time k+1; Z(k+1) represents the actual measurement at time k+1; P(k+1|k+1) represents the variance of the state at time k+1. (i) (k+1|k) represents the predicted measurement at time k+1; Z(k+1) represents the actual measurement at time k+1; P(k+1|k+1) represents the variance of the state at time k+1.

[0120] f) Weighted summation to obtain the system predicted mean and covariance

[0121]

[0122]

[0123]

[0124] (k+1|k) represents the predicted measurement at time k+1; Z(k+1) represents the actual measurement at time k+1; P(k+1|k+1) represents the variance of the state at time k+1. (k+1|k) represents the predicted measurement at time k+1; Z(k+1) represents the actual measurement at time k+1; P(k+1|k+1) represents the variance of the state at time k+1. (k+1|k) represents the predicted measurement at time k+1; Z(k+1) represents the actual measurement at time k+1; P(k+1|k+1) represents the variance of the state at time k+1.

[0125] g) Calculate the Kalman gain matrix

[0126]

[0127] h) Calculate the state update and covariance update of the system

[0128]

[0129]

[0130] (k+1|k) represents the predicted measurement at time k+1; Z(k+1) represents the actual measurement at time k+1; P(k+1|k+1) represents the variance of the state at time k+1.

[0131] Step 5: The parameters measured by the actual vehicle height sensor and the acceleration sensor are input into the above state observer as measurement, combined with the input mass flow and system structure parameters, and through software calculation, the estimation of the air spring internal pressure, vehicle sprung mass speed and unsprung mass speed can be realized.

[0132] In some embodiments of the present application, a state observer for air suspension is built in Matlab / Simulink, and a model of air spring and its charging and discharging circuit is built in Amesim. The height sensor and acceleration sensor in Amesim transmit the suspension deflection and sprung mass acceleration information of four corners of the vehicle to Matlab / Simulink, and the state observer calculates the sprung mass speed, unsprung mass speed and the internal air pressure of the four air springs through the input and measured values. The results calculated by the state observer are compared with the data collected by the sensor, as shown in FIG. 9, the calculated estimated value and the collected true value are highly coincident, verifying the effectiveness of the method of the present application. Figures 4 to 6

[0133] The above description of disclosed embodiments enables those skilled in the art to carry out or use the present application. Various modifications to these embodiments will be apparent 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 application. Therefore, the present application will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.​

Claims

1. A state estimation method for electronically controlled air suspension, characterized in that, Includes the following steps: (1) Establish the flow model of the solenoid valve and the air spring charging and discharging model; (2) Establish the whole vehicle dynamics model of air suspension: simplified to the superposition of four single-wheel models of air suspension, that is: (1) (2) (3) Subscript These represent the corresponding front left wheel, front right wheel, rear left wheel, and rear right wheel, respectively. Indicates the sprung mass; Indicates the displacement of the sprung mass; Indicates unsprung mass; Indicates the displacement of the unsprung mass; Indicates the speed of the sprung mass; Indicates the velocity of the unsprung mass; Indicates the acceleration of the sprung mass; Indicates the acceleration of unsprung mass; This indicates the displacement caused by road surface excitation; Indicates the damping of the shock absorber; Indicates tire stiffness; Indicates the internal pressure of the air spring; This indicates the initial volume of the air spring; Indicates the effective area of ​​the air spring; Indicates the adiabatic index; Represents the gas constant; Indicates atmospheric pressure under standard conditions; Represents gravitational acceleration; Indicates the mass flow rate of each air spring; Indicates the temperature of the gas flowing through the air spring; (3) Establish the air suspension state equation based on the air suspension dynamics model. Since the actual vehicle is equipped with only four height sensors and four sprung mass acceleration sensors, the state variables are selected as follows: (4) The measurement variables are selected as follows: (5) The system input is selected as: (6) The state-space equations are expressed as follows: (7) in, For state variables, For measurement variables, For system input, For process noise, To measure noise, for The first derivative, for and The function matrix, for The function matrix, for The function matrix, with the superscript T indicating transpose; (4) Establish an air suspension state observer based on the unscented Kalman filter algorithm. The implementation steps of the unscented Kalman filter algorithm are as follows: a) Construct 2 using UT transformation n +1 Sigma points and their weights, where n Let be the dimension of the state; b) Calculate 2 n +1 Sigma point set prediction; c) One-step prediction of system state; d) Use UT transformation to generate a new Sigma point set; e) Obtain the predicted observations from the new Sigma point set; f) Weighted summation yields the system prediction mean and covariance; g) Calculate the Kalman gain matrix; h) Compute the state update and covariance update of the system; (5) Input the parameters measured by the actual vehicle height sensor and acceleration sensor into the air suspension state observer as the measurement quantity, and calculate the internal pressure of the air spring, the sprung mass velocity and the unsprung mass velocity of the vehicle by combining the input mass flow rate and the system structural parameters.

2. The state estimation method for an electronically controlled air suspension according to claim 1, characterized in that, The flow model for a solenoid valve is: When the solenoid valve opens to charge and deflate the air spring, the solenoid valve can be simplified as a throttling orifice, representing the mass flow rate of the gas in the pipeline. The characteristics can be expressed as follows based on the pressure characteristics at both ends of the solenoid valve: (8) in, Indicates the flow area of ​​the solenoid valve. Indicates the upstream pressure of the solenoid valve. Indicates the upstream temperature of the solenoid valve. Indicates the adiabatic index. Represents the gas constant. This indicates the downstream pressure of the solenoid valve. This indicates the critical pressure ratio.

3. The state estimation method for an electronically controlled air suspension according to claim 1, characterized in that, The process of establishing the air spring inflation / deflation model includes: Based on the first law of thermodynamics, analyze the energy exchange process of the gas inside the air spring: (9) in, Indicates the internal energy of the gas inside. This indicates the amount of heat exchanged between the internal gas and the external environment. Indicates the amount of work done by the gas; If this process is considered adiabatic, then: Heat exchange per unit time Change in internal energy: Work done by the air spring: (10) according to After rearranging the formula, we get: (11) in, This indicates the temperature of the gas flowing through the air spring. This represents the change in mass of the air spring per unit time, i.e., the mass flow rate of the gas. This indicates the internal pressure of the air spring. Indicates the volume of the air spring. This represents the specific heat capacity of a gas at constant pressure. This indicates the specific heat capacity of a gas at constant volume. Treating the effective area of ​​the air spring as a constant, the rate of change of the air spring's volume is equivalent to the effective area multiplied by the relative velocity, i.e.: (12) in, This indicates the change in the volume of an air spring per unit time. Indicates the displacement of the sprung mass; Indicates the displacement of the unsprung mass; Indicates the speed of the sprung mass; Indicates the velocity of the unsprung mass; This represents the effective area of ​​the air spring, where t is time. When the solenoid valve is open, the mass flow rate of the solenoid valve is equal to the mass flow rate of the air spring, and the pressure gradient of the air spring can be expressed as: (13) in, This is the initial volume of the air spring. This indicates the mass flow rate of the gas.

4. The state estimation method for an electronically controlled air suspension according to claim 1, characterized in that, In step (3), the state equation for (14) In the formula: (15) (16) (17) (18) (19) in, , , , , , , , , , , , , , , , , , , , All of them are components in the x column vector. , , , These are the components in the column vector u.

5. The state estimation method for an electronically controlled air suspension according to claim 1, characterized in that, In the state equation for: (20) The superscript T indicates transpose.

6. The state estimation method for an electronically controlled air suspension according to claim 1, characterized in that, In the state equation for (21) in, , , , , , , , , , , , , , , , All of them are components in the x column vector.

7. The state estimation method for an electronically controlled air suspension according to claim 1, characterized in that, Air The mass flow rate is the same for the spring model and the solenoid valve model.

8. The state estimation method for an electronically controlled air suspension according to claim 1, characterized in that, Input quantities include the mass flow rates of the four air springs; measured quantities include the suspension dynamic deflection and sprung mass acceleration at the four corners of the vehicle; state quantities include the sprung mass velocity, unsprung mass velocity, suspension dynamic deflection, and tire dynamic deformation at the four corners of the vehicle. Internal pressure of the air spring.

9. The state estimation method for an electronically controlled air suspension according to claim 1, characterized in that, The internal pressure of the air spring, the sprung mass velocity, and the unsprung mass velocity are calculated based on the vehicle's height and acceleration sensors.

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