A pressure sensor-free electric vehicle EMB system clamping force estimation method

Abstract

The application discloses a kind of based on pressure sensor-free electric vehicle EMB system clamping force estimation method, it is related to automobile electronic mechanical brake system technical field, constructs dynamic stiffness model to describe the change rule of clamping force;The description equation of clamping force dynamic stiffness model is discretized, and the discretized clamping force state space equation is established;Expansion state observer is constructed, and load torque is estimated in real time to obtain load torque observation value;Load torque observation value is converted into clamping force observation value;Using Kalman filtering algorithm, in combination with the discretized clamping force state space equation and clamping force observation value, and according to the optimal clamping force estimation value obtained at last time, the optimal clamping force estimation value at current time is calculated.The application solves the problems of high cost of pressure sensor, failure caused by high temperature and high pressure environment, low clamping force estimation accuracy in prior art and large external interference.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of automotive electro-mechanical brake (EMB), and in particular to an EMB system clamping force estimation method based on a pressure sensor. BACKGROUND

[0002] With the development of intelligent driving and chassis control technology, the electronic mechanical brake system becomes the future development direction. Compared with the traditional hydraulic brake system, the EMB system has the advantages of realizing overall chassis control, reducing the weight of the brake system, avoiding the delay effect of brake fluid, responding more quickly, and realizing precise control of braking force through intelligent algorithm control of the motor drive actuator to directly clamp the brake disc.

[0003] In the traditional method, the clamping force is usually directly measured by a pressure sensor installed on the brake caliper body. However, the pressure sensor has high cost and limited installation space, which is not conducive to system integration and cost control. In the braking process, the pressure sensor will be subjected to a large extrusion force and high temperature generated by friction, which will affect the measurement accuracy of the pressure sensor. Long-term high-pressure environment can also easily cause the pressure sensor to fail. To overcome the above problems, some studies attempt to use easily measured signals such as motor current and motor position to indirectly estimate the clamping force by establishing a dynamic model. However, the EMB system has non-linear friction, transmission clearance, temperature drift and other disturbance factors, and the traditional observation method is difficult to accurately estimate the clamping force, especially in the dynamic braking process, the estimation error is large, which affects the braking performance and safety.

[0004] Therefore, in order to avoid the failure of the pressure sensor and affect the safety, it is urgent to develop a clamping force estimation method that does not depend on the pressure sensor and has strong robustness to external disturbances to estimate the size of the generated clamping force in real time. SUMMARY

[0005] In order to overcome the defects in the prior art, the present application provides an EMB system clamping force estimation method based on a pressure sensor for an electric vehicle, which aims to solve the problems of high cost of pressure sensors, easy failure under high temperature and high pressure environment, low estimation accuracy of clamping force in the prior art, and large external interference, and to realize real-time estimation of clamping force without pressure sensor, high precision and strong robustness.

[0006] To achieve the above purpose, the present application adopts the following technical solutions, comprising:

[0007] An EMB system clamping force estimation method based on a pressure sensor for an electric vehicle, comprising the following steps:

[0008] S1, based on the viscous characteristics existing between the brake disc and the friction plate in the braking process, a dynamic stiffness model is constructed to describe the change rule of the clamping force, so as to realize the prediction of the dynamic response of the clamping force;

[0009] S2, the description equation of the clamping force dynamic stiffness model is discretized, and a discretized clamping force state space equation is established;

[0010] S3, the torque balance equation of the motor is defined as a third-order state space containing three variables of motor angle, motor angular velocity and extended state, an extended state observer is constructed for real-time estimation of the load torque in the motor braking process, so as to obtain the real-time observation value of the load torque; wherein the load torque, viscous damping and unmodeled friction are combined into the extended state; the extended state observer is designed with a nonlinear error feedback function, and the extended state observer has the properties of smaller gain for larger error and larger gain for smaller error by nonlinear weighting of the observation error;

[0011] S4, according to the relationship between the clamping force and the load torque of the electronic mechanical brake system (EMB system), the load torque observed by the extended state observer is converted into the observation value of the clamping force;

[0012] S5, the Kalman filtering algorithm is used, the discretized clamping force state space equation and the clamping force observation value are combined, and the optimal clamping force estimation value obtained at the last time is used to calculate the optimal clamping force estimation value at the current time, so as to realize the real-time optimal estimation of the clamping force.

[0013] Preferably, in step S1, the clamping force dynamic stiffness model is:

[0014] ;

[0015] In the formula, is the clamping force prediction value, is the motor angle, is the differential of the clamping force prediction value with respect to time ; , , , is the motor angle constant; is the time constant.

[0016] Preferably, in step S2, the discretized clamping force state space equation is:

[0017] ;

[0018] In the formula, , are respectively time, Predicted clamping force at any given moment; This is the coefficient for the predicted clamping force. , , , The discretized motor rotation angle constant; for k The motor's rotation angle at any given moment.

[0019] Preferably, in step S3, the design of the load torque expansion state observer includes the following steps:

[0020] S31, the drive motor is set to a permanent magnet synchronous motor, and the motor torque balance equation is:

[0021] ;

[0022] In the formula, This is the moment of inertia of the motor. The angular velocity of the motor. Electromagnetic torque; For load torque, Friction torque;

[0023] S32, the motor rotation angle is derived based on the torque balance equation of the permanent magnet synchronous motor. and motor angular velocity Mechanical dynamics model:

[0024] ;

[0025] In the formula, This represents the expanded state of the sum of load torque and unmodeled disturbances; for The derivative, that is ; , Motor rotation angle Motor angular velocity The derivative;

[0026] S33, the formula for the extended state observer is as follows:

[0027] ;

[0028] In the formula, for The observed value is the estimated value. for The derivative; This is the difference between the actual value and the observed value of the motor rotation angle. ; for The observed values, is the derivative of , is the derivative of , is the derivative of ; , , are the gain coefficients of the three state variables in the extended state observer, corresponding to the estimation error feedback of the rotation angle, the angular velocity and the extended state respectively;

[0029] , are the nonlinear error feedback functions of the rotation speed and the extended state respectively:

[0030] ;

[0031] ;

[0032] wherein, and are the nonlinear indexes of and respectively; ; and are the linear thresholds of and respectively; is a sign function, when 0 , when 0 , when 0 =0;

[0033] S34, the error equation of the extended state observer is obtained by subtracting the formula in step S32 from the formula in step S33:

[0034] ;

[0035] wherein, = , , , , are the derivatives of , , respectively;

[0036] S35, the following is defined:

[0037] ;

[0038] wherein, It is a positive, bounded, nonlinear gain function. ;

[0039] According to step S32 Discretize the error equation of the extended state observer in step S34. The discretized error state equation is as follows:

[0040] ;

[0041] Will Treat it as 0;

[0042] The final form of the error state equation is as follows:

[0043] ;

[0044] S36, Definition:

[0045] ;

[0046] To ensure the stability of the extended state observer The eigenvalues ​​must be inside the unit circle. The characteristic polynomial is as follows:

[0047] ;

[0048] In the formula, The system matrix is ​​the error state equation. yes Characteristic polynomial, For the complex variable of the discrete domain stability criterion, Let be the identity matrix, by letting Find the system whose eigenvalues ​​lie within the unit circle. , , , , , and The value of ;

[0049] S37, using a bilinear transformation, Replace with After replacement, we get information about The cubic polynomial has the following characteristic equation:

[0050] ;

[0051] In the formula, This represents the continuous-domain virtual frequency variable introduced after the bilinear transformation;

[0052] According to the Routh-Hurwitz criterion, setting all coefficients to be positive, we obtain... Sufficient condition for stability:

[0053] ;

[0054] S38, according to Sufficient conditions for stability, and configuring appropriate gain coefficients for the extended state observer. , , ,as well as , Nonlinear exponent of a function , and linear threshold , Based on the extended state observer established in step S33, the load torque observation value is estimated in real time under braking conditions. .

[0055] Preferably, in step S4, the load torque observation value is observed in real time by the extended state observer. Calculated clamping force observations for:

[0056] ;

[0057] In the formula, It's the pitch of the ball screw. It is the planetary gear reduction ratio. It's gear efficiency. It refers to the transmission efficiency of the ball screw.

[0058] Preferably, in step S5, the real-time optimal estimation of the clamping force includes the following steps:

[0059] S51, the state-space model is constructed as follows:

[0060] ;

[0061] In the formula, , express k -1 moment, k The state vector at any given time; express k The input vector at time step; A Represents the state transition matrix; B Represents the control input matrix; express k The process noise matrix at time -1; express k The observation vector at time;H represents an observation matrix; represents k an observation noise matrix at time t;

[0062] S52, obtaining according to the discretized clamping force state space equation:

[0063]

[0064] wherein, represents the clamping force prediction value obtained by the discretized clamping force state space equation at time t-1; k is a clamping force prediction value coefficient; , , , , is a discretized motor angle constant; represents the motor angle at time t; k

[0065]

[0066] wherein, I represents a unit matrix, represents the clamping force observation value at time t converted from the load torque observation value observed by the extended state observer; k S53, fusing and estimating the clamping force prediction value and the clamping force observation value according to the Kalman filtering algorithm, as shown below:

[0067] S531, performing state prediction on the EMB system:

[0068] calculating the prior estimation of the EMB system state at time t:

[0069] k

[0070]

[0071] wherein, represents applying the optimal clamping force estimation value at time t-1 k to the prior state estimation value of the clamping force at time t; represents the optimal clamping force estimation value at time t-1 k ; k

[0072] calculating the prior estimation of the EMB system covariance at time t: k

[0073] ​​​​​​​​​​​

[0074] wherein, denotes the application k optimal covariance estimate of the EMB system at time to k prior state estimate of the EMB system covariance at time denotes k optimal covariance estimate of the EMB system at time denotes the transpose of the state transition matrix Q denotes the process noise covariance matrix

[0075] S532, measurement update of the EMB system:

[0076] calculate the EMB system Kalman gain:

[0077] ;

[0078] wherein, denotes k EMB system Kalman gain at time denotes the transpose of the observation matrix R denotes the measurement noise covariance matrix

[0079] combined with the clamping force observation value update the state estimate of the EMB system:

[0080] ;

[0081] wherein, denotes k optimal clamping force estimate at time ;

[0082] update the EMB system covariance matrix:

[0083] ;

[0084] wherein, denotes k covariance matrix estimate of the EMB system at time

[0085] Preferably, when the electric vehicle enters the braking state, the electronic control unit detects that the brake pedal is depressed, and calculates the required braking clamping force reference value in real time according to the pedal stroke ; then, the electronic control unit sends a control instruction to the drive motor of the vehicle electronic mechanical brake system, so that it enters the closed-loop control mode; in the pressure tracking stage, according to the preset algorithm, the extended state observer is used to observe the load torque at time k , and the load torque observation value is converted into the corresponding clamping force observation value ; at the same time, the clamping force observation value is observed by using a Kalman filter Optimization is carried out continuously, and optimal clamping force estimation values at each moment are iteratively generated; the optimal clamping force estimation values are used as inputs of the force feedback link, and replace the measured signals of the traditional pressure sensor, so that a clamping force closed-loop control strategy without a pressure sensor is realized.

[0086] The application further provides a readable storage medium, which has a computer program stored thereon, and the computer program is executed to realize the EMB system clamping force estimation method for an electric vehicle without a pressure sensor.

[0087] The application further provides an electronic device, which comprises a processor, a memory, and a computer program stored on the memory and executable on the processor, and the processor realizes the EMB system clamping force estimation method for an electric vehicle without a pressure sensor when the computer program is executed.

[0088] The application further provides a computer program product, which comprises computer programs / instructions, and the computer programs / instructions realize the EMB system clamping force estimation method for an electric vehicle without a pressure sensor when executed by a processor.

[0089] The application has the following advantages:

[0090] (1) The application provides a method for estimating the clamping force of an EMB system for an electric vehicle in real time without a pressure sensor, and compared with the prior art, the application uses an extended state observer to estimate (observe) the clamping force, and improves the clamping force estimation precision; at the same time, the Kalman filtering algorithm is used, the clamping force observation value obtained by the observer is combined with the clamping force prediction value, and through real-time updating iteration, the optimal clamping force estimation value is obtained. The application solves the problem that under extreme braking conditions, the observation precision of the pressure sensor is reduced or even fails due to the harsh environment and long-time use, thereby affecting the safety of drivers and pedestrians.

[0091] (2) The present application is based on the existence of the viscous characteristics between the brake disc and the friction plate in the braking process, a dynamic stiffness model is constructed to describe the change rule of the clamping force, so that the prediction of the dynamic response is realized; the description equation of the clamping force dynamic stiffness model is discretized, and the discretized state space equation is established; based on the torque balance equation of the motor, an extended state observer is constructed, and the observation value of the load torque of the motor is obtained in real time according to the q-axis current, the motor rotor angle and the motor angular velocity monitored by the sensor; according to the relationship between the output clamping force of the electromechanical brake system and the load torque, the observation value of the load torque is converted into the observation value of the clamping force in real time; finally, through Kalman filtering algorithm, the optimal clamping force estimation value at the current time is calculated by combining the predicted value and the observation value of the clamping force and according to the optimal clamping force estimation value obtained at the last time. Through the Kalman filtering algorithm, the present application considers the external noise, iteratively updates the optimal clamping force estimation value in real time, greatly improves the accuracy of the clamping force estimation, and can replace the pressure sensor.

[0092] (3) In the present application, the torque balance equation of the motor is defined as a three-order state space of "angle- angular velocity-extended state", an extended state observer is constructed, and the real-time estimation of the load torque during the motor braking process is used to obtain the real-time observation value of the load torque; wherein the load torque, the viscous damping and the unmodeled friction are combined into the extended state; the extended state observer adopts the continuous nonlinear feedback of fal(e, alpha, delta), the small error segment gain is automatically amplified, and the large error segment gain is automatically reduced, which not only ensures the sensitivity in the low speed area, but also completely eliminates the high frequency flutter. And the extended state observer directly regards all unknown disturbances as extended states, and has stronger interference resistance. Provide high-credibility observation values for subsequent Kalman optimal estimation. BRIEF DESCRIPTION OF DRAWINGS

[0093] Figure 1 The method flowchart of the present application.

[0094] Figure 2 The comparison diagram of the target clamping force, the actual clamping force and the estimated clamping force of the experiment.

[0095] Figure 3 The error diagram of the actual clamping force and the estimated clamping force of the experiment. DETAILED DESCRIPTION

[0096] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0097] By Figure 1 As shown, the EMB system clamping force estimation method based on a pressure sensor for an electric vehicle according to the present application comprises the following steps:

[0098] S1, establishment of a clamping force dynamic stiffness model: in an actual braking process, due to the existence of a viscous effect between a brake disc and a friction plate, the clamping force size is not the same in the clamping-release process although the caliper and the friction plate are at the same motor angle, on this basis, the clamping force model based on the motor angle polynomial is improved, and a new clamping force dynamic stiffness model is established.

[0099] In braking, the motor drives the planetary gear to decelerate and increase torque, the planetary gear drives the ball screw to convert rotary motion into linear motion to push the friction plate to press the brake disc to decelerate; in releasing braking, the motor releases the friction plate through reverse rotation.

[0100] Due to the viscous effect between the friction plate and the brake disc during braking, the clamping force size at the same motor angle will be different in the process of pressing (or releasing) the friction plate.

[0101] The traditional clamping force estimation is to measure the actual clamping force and the actual motor angle position in the braking process, to obtain the relationship curve between the clamping force and the motor angle, then to fit the polynomial of the motor angle, and finally to estimate the clamping force size through the real-time motor angle. However, this method ignores the error caused by the viscous effect, which will lead to a large error between the estimated clamping force size and the actual clamping force size when pressing (or releasing) the friction plate.

[0102] Therefore, the present application establishes a more actual electronic mechanical brake (EMB) system clamping force dynamic stiffness model to estimate the clamping force, as shown below:

[0103] S11, considering the ideal condition that the clamping force size is linearly related to the motor angle, and considering the hysteresis of the clamping force caused by the viscous effect, the transfer function of the EMB system can be represented as:

[0104] ;

[0105] In the formula, is the transfer function; , are the clamping force prediction value and the form of the motor angle in the complex domain, respectively; is the linear relationship coefficient of the clamping force and the motor angle; is the time constant; is a complex number.

[0106] S12, the transfer function is converted into a representation in the real number field, and the expression of the clamping force is obtained as follows:

[0107]

[0108] wherein, is the clamping force prediction value, is the motor rotation angle, is the differential of the clamping force prediction value with respect to time.

[0109] S13, in practice, the clamping force is usually expressed in the form of a polynomial of the motor rotation angle, and thus a more accurate expression of the clamping force, i.e., a clamping force dynamic stiffness model, is obtained as follows:

[0110]

[0111] wherein, , , , are motor rotation angle constants determined through experiments.

[0112] S2, the state space equation is converted into a discrete form, and a discrete clamping force state space equation is established. The details are as follows:

[0113]

[0114] wherein, , are system state vectors at time t and t-1 respectively; k is a system input vector at time t; k is a state transition matrix; is a control input matrix. k A According to the above discrete form of the state space equation, the clamping force expression obtained in step S13 is converted into a discrete form, and the discrete clamping force state space equation is as follows: B

[0115]

[0116]

[0117] wherein, , are clamping force prediction values at time t and t-1 respectively; k is a clamping force prediction value coefficient; k , , , ​​​​​​​​​​The discretized motor rotation angle constant; for k The motor's rotation angle at any given moment.

[0118] in, ; ; ;

[0119] .

[0120] S3, Construction of the extended state observer: Based on the motor's torque balance equation, an extended state observer is constructed, based on the q-axis current monitored by the sensor. Motor rotation angle and motor angular velocity The value is used to monitor the load torque in real time during motor braking, thereby obtaining the real-time observed value of the load torque. .

[0121] In this invention, the torque balance equation of the motor is defined as a third-order state space containing three variables: motor rotation angle, motor angular velocity, and extended state, and an extended state observer is constructed. The load torque, viscous damping, and unmodeled friction are combined into the extended state. The extended state observer is designed with a nonlinear error feedback function. By nonlinearly weighting the observation error, the extended state observer has the property that the larger the error, the smaller the gain, and the smaller the error, the larger the gain.

[0122] Step S3 is detailed below:

[0123] S31, the drive motor is set to a permanent magnet synchronous motor, and the motor torque balance equation is:

[0124] ;

[0125] In the formula, This is the moment of inertia of the motor. The angular velocity of the motor. Electromagnetic torque; For load torque, This is the frictional torque.

[0126] The electromagnetic torque equation of the permanent magnet synchronous motor is as follows:

[0127] ;

[0128] In the formula, This represents the number of pole pairs of the motor. For motor magnetic flux, This represents the magnitude of the q-axis current of the motor.

[0129] S32, the motor rotation angle is derived based on the motor torque balance equation of the permanent magnet synchronous motor. and the motor angular velocity of the mechanical dynamics model:

[0130] ;

[0131] where is the extended state of the sum of the load torque and unmodeled disturbances. is the derivative of , in the clamping and releasing actions, the related uncertainty and disturbance are concentrated in , once the unmodeled disturbance changes rapidly, the size of will increase, for this, the derivative of , is assumed to be bounded uncertainty. The friction torque is obtained by establishing an empirical model, such as the Stribeck, LuGre, etc. models, the Stribeck model can cover static friction, Coulomb friction, viscous friction and Stribeck effect at the same time, so the Stribeck model is adopted to simulate the actual running situation to obtain the friction torque of the system. is the derivative of the motor angle , and is the derivative of the motor angular velocity .

[0132] The formula of the extended state observer is as follows:

[0133] ;

[0134] where is the observed (estimated) value of , and is the derivative of ; is the difference between the actual value of the motor angle and the observed (estimated) value of the motor angle, ; is the estimated value of , and is the derivative of ; is defined as the observed (estimated) value of the extended state , so as to track , is the derivative of .

[0135] , , The gain coefficients of the three state variables in the extended state observer are respectively corresponding to the estimation error feedback of the rotation angle, the angular velocity and the extended state, and the clamping force can be estimated correctly in a limited time;

[0136] 、 are the nonlinear error feedback functions of the rotation speed and the extended state respectively, and the extended state observer has large gain in small error and small gain in large error by nonlinear weighting of the observation error to improve the observation accuracy, which is shown as follows:

[0137] ;

[0138] ;

[0139] In the formula, and are the nonlinear indexes of and respectively, ; and are the linear thresholds of and respectively, is a sign function, when 0, when 0, when 0, =0.

[0140] S34, the error equation of the extended state observer is obtained by subtracting the formula in step S32 from the formula in step S33:

[0141] ;

[0142] In the formula, , , ; , , are the derivatives of , , respectively.

[0143] S35, define:

[0144] ;

[0145] wherein, is a positive, bounded nonlinear gain function, ; at the discrete time k , once Sampling, After calculation, it can be regarded as a fixed constant gain, denoted as . It will not change within the calculation cycle of this step;

[0146] According to step S32 Discretize the error equation of the extended state observer in step S34. The discretized error state equation is as follows:

[0147] ;

[0148] Because the controller has a high sampling frequency, the expansion state The change between two adjacent sampling times is very small and can be ignored. Consider it as 0.

[0149] Therefore, the final form of the error state equation is as follows:

[0150] ;

[0151] S36, Definition:

[0152] ;

[0153] To ensure the stability of the extended state observer The eigenvalues ​​must be inside the unit circle. The characteristic polynomial is as follows:

[0154] ;

[0155] In the formula, The system matrix is ​​the error state equation. for The characteristic polynomial, where For the complex variable of the discrete domain stability criterion, Let be the identity matrix, by letting Find the system whose eigenvalues ​​lie within the unit circle. , , , , , and The value of .

[0156] S37, Solve using the Juli criterion The roots are all inside the unit circle, which is quite cumbersome and complex. A bilinear transformation can be used instead. Mapping discrete characteristic polynomials to continuous polynomials The field, after replacement, yields information about The cubic polynomial can be used to determine the stability of the system by Routh criterion, and the characteristic equation is as follows:

[0157]

[0158] In the formula, ω represents the continuous domain virtual frequency variable introduced after the bilinear transformation; According to Routh criterion, the sufficient condition for stability is that all the coefficients are positive.

[0159]

[0160]

[0161] S38, according to the sufficient condition for stability, the gain coefficient of the extended state observer is configured , , , , , The nonlinear index , and the linear threshold , of the function are configured according to the extended state observer established in step S33, and the load torque observation value is estimated in real time under the braking condition.

[0162] S4, according to the relationship between the load torque and the clamping force, the load torque observation value observed by the extended state observer is converted into the clamping force observation value :

[0163]

[0164] In the formula, p is the pitch of the ball screw, is the planetary gear reduction ratio, is the gear efficiency, is the transmission efficiency of the ball screw. S5, the Kalman filtering algorithm is used, the discretized clamping force state space equation and the clamping force observation value are combined, and the optimal clamping force estimation value at the last moment is used to calculate the optimal clamping force estimation value at the current moment, so as to realize the real-time optimal estimation of the clamping force.

[0165] In step S5, the real-time optimal estimation of the clamping force includes the following steps:

[0166] S51, the state space model is constructed as follows:

[0167] ​​​​​​

[0168] ;

[0169] In the formula, 、 represents k the state vector at time -1; k the state vector at time t; represents k the input vector at time -1; A represents the state transition matrix; B represents the control input matrix; represents k the process noise matrix at time -1; represents k the observation vector at time t; H represents the observation matrix; represents k the observation noise matrix at time t.

[0170] S52, according to the discretized clamping force state space equation, obtain:

[0171] ;

[0172] In the formula, represents the clamping force prediction value at time -1 obtained by the discretized clamping force state space equation; k is a clamping force prediction value coefficient; 、 、 、 、 is a motor rotation angle constant after discretization; represents the motor rotation angle at time t. k

[0173] ;

[0174] In the formula, I represents the unit matrix, represents the clamping force observation value at time -1 converted from the load torque observation value observed by the extended state observer; k S53, according to the Kalman filtering algorithm, the clamping force prediction value and the clamping force observation value are fused and estimated, as shown below:

[0175] S531, the state of the EMB system is predicted:

[0176] The prior estimate of the EMB system state at time t is calculated:

[0177] k

[0178] ;​​​

[0179] In the formula, Indicates application k The optimal clamping force estimate at time -1 right k The prior state estimate of the clamping force at any given moment; express k The optimal clamping force estimate at time -1 ;

[0180] calculate k Prior estimates of the covariance of the EMB system at time points:

[0181] ;

[0182] In the formula, Indicates application k The optimal covariance estimate of the EMB system at time -1 right k Prior state estimation of the covariance of the EMB system at time t; express k The optimal covariance estimate of the EMB system at time -1; This represents the transpose of the state transition matrix; Q Represents the process noise covariance matrix;

[0183] S532, Measurement Update for EMB System:

[0184] Calculate the Kalman gain of the EMB system:

[0185] ;

[0186] In the formula, express k Kalman gain of the EMB system at time t; This represents the transpose of the observation matrix; R Represents the measurement noise covariance matrix;

[0187] Combined with clamping force observations Update the state estimate of the EMB system:

[0188] ;

[0189] In the formula, This represents the estimated optimal clamping force at time k. ;

[0190] Update the EMB system covariance matrix:

[0191] ;

[0192] In the formula, Indicates k The covariance matrix estimation value of the EMB system at the moment.

[0193] The implementation principle of the pressure sensor-free clamping force closed-loop control strategy of the present application is as follows: when the electric vehicle enters the braking state, the ECU (electronic control unit) detects that the brake pedal is depressed, and calculates the required braking clamping force reference value in real time according to the pedal stroke . Then, the ECU sends a control instruction to the drive motor of the EMB system, so that it enters the closed-loop control mode. In the pressure tracking stage, the system estimates (observes) the load torque at the moment k using the extended state observer according to the preset algorithm, and converts it into the corresponding clamping force observation value . At the same time, the Kalman filter built in the ECU continuously optimizes the observation value, and iteratively generates the optimal clamping force estimation value at each moment. The optimal clamping force estimation value is used as the input of the force feedback link, replacing the measured signal of the traditional pressure sensor, so as to realize the pressure sensor-free clamping force closed-loop control strategy.

[0194] Figure 2 And Figure 3 shows the actual effect of the present application, Figure 2 is a comparison diagram of the target clamping force, the actual clamping force and the estimated clamping force of the experiment, Figure 3 is an error diagram of the actual clamping force and the estimated clamping force of the experiment. Under the demand of different clamping force sizes, the present application configures the weight coefficient (process noise covariance matrix Q and the measurement noise covariance matrix R ) for calculating the Kalman gain of the EMB system at the moment k , reduces the influence of external noise on the clamping force estimation, and can replace the pressure sensor to realize accurate estimation of the clamping force.

[0195] The above is only a preferred embodiment of the present application, and does not limit the present application. Any modification, equivalent replacement and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for estimating clamping force in an electric vehicle EMB system based on a pressure sensorless system, characterized in that, Includes the following steps: S1. Based on the viscous characteristics between the brake disc and the friction pad during braking, a dynamic stiffness model is constructed to describe the variation law of the clamping force, thereby realizing the prediction of the dynamic response of the clamping force. S2, discretize the description equation of the clamping force dynamic stiffness model and establish the discretized clamping force state space equation; S3 defines the motor torque balance equation as a third-order state space containing three variables: motor angle, motor angular velocity, and extended state. An extended state observer is constructed to estimate the load torque in real time during motor braking, thereby obtaining the real-time observed value of the load torque. Here, the load torque, viscous damping, and unmodeled friction are combined into an extended state. The extended state observer is designed with a nonlinear error feedback function. By nonlinearly weighting the observation error, the extended state observer has the property that the larger the error, the smaller the gain, and the smaller the error, the larger the gain. S4, based on the relationship between the clamping force and the load torque of the electromechanical braking system (EMB system), convert the load torque observed in real time by the expansion state observer into the observed value of the clamping force. S5 uses the Kalman filter algorithm, combining the discretized clamping force state space equation and clamping force observations, and calculates the optimal clamping force estimate for the current moment based on the optimal clamping force estimate obtained at the previous moment, thus achieving real-time optimal estimation of the clamping force.

2. The method for estimating clamping force in an electric vehicle EMB system based on a pressure sensorless system according to claim 1, characterized in that, In step S1, the dynamic stiffness model of the clamping force is: In the formula, This is the predicted clamping force value. For the motor rotation angle, Predicted clamping force versus time The differential; , , , The motor rotation angle constant; is the time constant.

3. The method for estimating clamping force in an electric vehicle EMB system based on a pressure sensorless system according to claim 1, characterized in that, In step S2, the discretized state-space equation for the clamping force is: In the formula, , They are respectively time, Predicted clamping force at any given moment; This is the coefficient for the predicted clamping force. , , , The discretized motor rotation angle constant; for k The motor's rotation angle at any given moment.

4. The clamping force estimation method for an electric vehicle EMB system based on a pressure sensor-free method according to claim 1, wherein the design of the load torque expansion state observer in step S3 includes the following steps: S31, the drive motor is set to a permanent magnet synchronous motor, and the motor torque balance equation is: In the formula, This is the moment of inertia of the motor. The angular velocity of the motor. Electromagnetic torque; For load torque, Friction torque; S32, the motor rotation angle is derived based on the torque balance equation of the permanent magnet synchronous motor. and motor angular velocity Mechanical dynamics model: In the formula, This represents the expanded state of the sum of load torque and unmodeled disturbances; for The derivative, that is ; , Motor rotation angle Motor angular velocity The derivative; S33, the formula for the extended state observer is as follows: In the formula, for The observed value is the estimated value. for The derivative; This is the difference between the actual value and the observed value of the motor rotation angle. ; for The observed values, for The derivative, In an expanding state The observed values, for The derivative; , , These are the gain coefficients of the three state variables in the extended state observer, corresponding to the estimation error feedback of the rotation angle, angular velocity, and extended state, respectively. , The nonlinear error feedback functions for rotational speed and expansion state are respectively: In the formula, and They are respectively and The nonlinear exponent, ; and They are respectively and The linear threshold; It is a sign function, when 0 o'clock ,when 0 o'clock ,when 0 o'clock =0; S34, Subtract the formulas from steps S32 and S33 to obtain the error equation for the extended state observer: In the formula, = , , , , They are respectively , , The derivative; S35, Definition: in, It is a positive, bounded, nonlinear gain function. ; According to step S32 Discretize the error equation of the extended state observer in step S34. The discretized error state equation is as follows: Will Treat it as 0; The final form of the error state equation is as follows: S36, Definition: To ensure the stability of the extended state observer The eigenvalues ​​must be inside the unit circle. The characteristic polynomial is as follows: In the formula, The system matrix is ​​the error state equation. yes Characteristic polynomial, For the complex variable of the discrete domain stability criterion, Let be the identity matrix, by letting Find the system whose eigenvalues ​​lie within the unit circle. , , , , , and The value of ; S37, using a bilinear transformation, Replace with After replacement, we get information about The cubic polynomial has the following characteristic equation: In the formula, This represents the continuous-domain virtual frequency variable introduced after the bilinear transformation; According to the Routh-Hurwitz criterion, setting all coefficients to be positive, we obtain... Sufficient condition for stability: S38, according to Sufficient conditions for stability, and configuring appropriate gain coefficients for the extended state observer. , , ,as well as , Nonlinear exponent of a function , and linear threshold , Based on the extended state observer established in step S33, the load torque observation value is estimated in real time under braking conditions. .

5. The method for estimating clamping force in an electric vehicle EMB system based on a pressure sensorless system according to claim 1, characterized in that, In step S4, the load torque observation value is observed in real time by the extended state observer. Calculated clamping force observations for: In the formula, It's the pitch of the ball screw. It is the planetary gear reduction ratio. It's gear efficiency. It refers to the transmission efficiency of the ball screw.

6. The method for estimating clamping force in an electric vehicle EMB system based on a pressure sensorless system according to claim 1, characterized in that, In step S5, the real-time optimal estimation of the clamping force includes the following steps: S51, the state-space model is constructed as follows: In the formula, , express k -1 moment, k The state vector at any given time; express k The input vector at time step; A Represents the state transition matrix; B Represents the control input matrix; express k The process noise matrix at time -1; express k The observation vector at time; H Represents the observation matrix; express k The observation noise matrix at time step; S52, based on the discretized state-space equation of clamping force, we obtain: In the formula, The expression obtained from the discretized state-space equation of clamping force k Predicted clamping force at time -1; This is the coefficient for the predicted clamping force. , , , The discretized motor rotation angle constant; express k The motor's rotation angle at any given moment; In the formula, I Represents the identity matrix. This is expressed as the result of converting the load torque observations from the extended state observer. k The clamping force observation at any given moment; S53, Based on the Kalman filter algorithm, the predicted clamping force value and the observed clamping force value are fused and estimated, as shown below: S531, performing state prediction for the EMB system: calculate k Prior estimates of the EMB system state at time: In the formula, Indicates application k The optimal clamping force estimate at time -1 right k The prior state estimate of the clamping force at any given moment; express k The optimal clamping force estimate at time -1 ; calculate k Prior estimates of the covariance of the EMB system at time points: In the formula, Indicates application k The optimal covariance estimate of the EMB system at time -1 right k Prior state estimation of the covariance of the EMB system at time t; express k The optimal covariance estimate of the EMB system at time -1; This represents the transpose of the state transition matrix; Q Represents the process noise covariance matrix; S532, Measurement update for EMB system: Calculate the Kalman gain of the EMB system: In the formula, express k Kalman gain of the EMB system at time t; This represents the transpose of the observation matrix; R Represents the measurement noise covariance matrix; Combined with clamping force observations Update the state estimate of the EMB system: In the formula, express k Optimal clamping force estimate at time t ; Update the EMB system covariance matrix: In the formula, express k The estimated value of the covariance matrix of the EMB system at time t.

7. The method for estimating clamping force in an electric vehicle EMB system based on a pressure sensorless system according to claim 1, characterized in that, When an electric vehicle enters braking mode, the electronic control unit detects that the brake pedal has been depressed and calculates the required brake clamping force reference value in real time based on the pedal travel. Subsequently, the electronic control unit sends control commands to the drive motor of the vehicle's electromechanical braking system, causing it to enter closed-loop control mode; during the pressure tracking phase, according to a preset algorithm, the extended state observer is used at time... k The load torque was observed, and the observed load torque values ​​were converted into corresponding clamping force observation values. Simultaneously, the clamping force observations were analyzed using a Kalman filter. Continuous optimization is performed to iteratively generate the optimal clamping force estimate at each moment. The optimal clamping force estimate is used as the input of the force feedback loop to replace the measured signal of the traditional pressure sensor, thereby realizing a pressure sensorless clamping force closed-loop control strategy.

8. A readable storage medium, characterized in that, It stores a computer program, which, when executed, implements the clamping force estimation method for an electric vehicle EMB system based on a pressure sensorless system as described in any one of claims 1 to 7.

9. An electronic device, characterized in that, It includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the clamping force estimation method for an electric vehicle EMB system based on any one of claims 1 to 7.

10. A computer program product, characterized in that, It includes a computer program / instruction that, when executed by a processor, implements the clamping force estimation method for an electric vehicle EMB system based on any one of claims 1 to 7.

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