Electric vehicle EMB system clamping force estimation method based on non-pressure sensor

Abstract

The invention discloses an electromobile EMB system clamping force estimation method based on a non-pressure sensor, and relates to the technical field of distributed electric drive-by-wire automobiles, the viscous effect between a brake disc and a friction plate during actual braking is considered, and a clamping force dynamic stiffness model is established for predicting the clamping force; discretizing a description equation of the clamping force dynamic stiffness model, and establishing a discretized clamping force state space equation; according to the motor torque balance equation, designing a sliding-mode observer of the motor load torque for observing the load torque of the motor in real time in the braking process; according to a transmission model of the EMB system, a load torque observation value observed by the sliding mode observer in real time is converted into a clamping force observation value; and calculating an optimal clamping force estimated value at the current moment according to an optimal clamping force estimated value obtained at the previous moment by using a Kalman filtering algorithm in combination with the discretized clamping force state space equation and the clamping force observed value, so as to realize real-time optimal estimation of the clamping force. The problems that under the control of the pressure sensor, due to high temperature and high pressure during braking, the measuring precision of the pressure sensor is reduced, and even the whole pressure sensor is invalid are solved.

Description

Technical Field

[0001] The invention relates to the technical field of distributed electric drive wire-controlled automobiles, in particular to a clamping force estimation method of an EMB system of an electric vehicle based on a pressure-free sensor. Background Art

[0002] Electromechanical Brake (EMB) is a new type of brake system introduced in recent years for electric vehicles. Compared with traditional hydraulic brakes, the EMB system adopts a full wire control method, replacing the mechanical system with wire control, saving a lot of space and reducing mass; in terms of response speed, EMB adopts intelligent wire control, eliminating the delay of the hydraulic brake system and greatly shortening the braking response time; finally, the EMB system can accurately achieve the target clamping force through relevant control algorithms to achieve precise braking.

[0003] However, in actual braking, when the friction pad presses the brake disc, a large amount of heat energy will be generated. The pressure sensor in a high temperature environment is very likely to be affected in measurement accuracy. At the same time, when the pressure sensor is installed in the brake disc, it will also be subject to huge squeezing force. During long-term fatigue use, the pressure sensor is easy to fail. When the pressure sensor has accuracy problems or even fails, the braking force of the vehicle will not reach the required braking force, which greatly affects personal safety.

[0004] In order to avoid the pressure sensor in the EMB system from experiencing a decrease in accuracy or even failure, which would result in the inability to provide the clamping force required by the electric vehicle, how to use a pressure sensor-free control strategy to estimate the clamping force generated in real time and replace the pressure sensor is an urgent problem to be solved. Summary of the invention

[0005] In order to overcome the above-mentioned defects in the prior art, the present invention provides a clamping force estimation method for an electric vehicle EMB system based on a pressure-free sensor, which solves the problem that the pressure sensor in the prior art is high in cost and prone to decreased accuracy or even overall failure in actual harsh braking environments, resulting in serious safety issues.

[0006] To achieve the above object, the present invention adopts the following technical solutions, including:

[0007] A method for estimating the clamping force of an EMB system of an electric vehicle based on a pressure-free sensor comprises the following steps:

[0008] S1, considering the viscosity effect between the brake disc and the friction pad during actual braking, a clamping force dynamic stiffness model is established to predict the clamping force;

[0009] S2, discretize the description equation of the clamping force dynamic stiffness model and establish the discretized clamping force state space equation;

[0010] S3, according to the motor torque balance equation, design a sliding mode observer of the motor load torque, which is used to observe the motor load torque in real time during the braking process and obtain the load torque observation value;

[0011] S4, according to the transmission model of the electronic mechanical brake system, namely, the EMB system, converting the load torque observation value observed in real time by the sliding mode observer into the clamping force observation value;

[0012] S5, using the Kalman filter algorithm, combined with the discretized clamping force state space equation and the clamping force observation value, and based on the optimal clamping force estimate value obtained at the previous moment, to calculate the optimal clamping force estimate value at the current moment, to achieve 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, F cl1 represents the predicted value of clamping force, θ m is the motor rotation angle, It represents the differential of the predicted value of clamping force with respect to time; Λ3, Λ2, Λ1, Λ0 are the motor angle constants; τ is the time constant.

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

[0017]

[0018] In the formula, F cl1 (k), F cl1 (k-1) represents the predicted clamping force at time k and time k-1 respectively; α represents the predicted clamping force coefficient; α3, α2, α1, α0 represent the motor rotation angle constant after discretization; θ m (k) represents the motor rotation angle at time k.

[0019] Preferably, in step S3, the sliding mode observer of the load torque is:

[0020]

[0021] in, ε>0;

[0022] In the formula, is the observed value of the motor angular velocity; is the observed value of the motor angular velocity Differentiation of time; is the observed value of load torque; is the observed value of load torque The differential with respect to time; g is the feedback gain of the sliding mode observer; U is the input control variable; ε is the gain of the symbolic function sgn(·) in U; ​​J represents the moment of inertia of the motor; p n Indicates the number of motor pole pairs; ψ f represents the permanent magnet flux; i q represents the q-axis current.

[0023] Preferably, in step S3, the design of the sliding mode observer of the load torque includes the following steps:

[0024] S31, the motor torque balance equation is:

[0025]

[0026] Where, J represents the rotational inertia of the motor; Represents the differential of the motor angular velocity with respect to time; T L represents the load torque; B represents the damping coefficient; ω m Indicates the motor angular velocity; T e represents electromagnetic torque;

[0027] in,

[0028]

[0029] In the formula, p n Indicates the number of motor pole pairs; ψ f represents the permanent magnet flux; i q represents the q-axis current;

[0030] S32, with motor angular velocity ω m With load torque T L As a state variable, since the actual sampling frequency of the controller is much higher than the frequency of load torque change, its derivative can be regarded as a constant of 0, and the state equation is obtained:

[0031]

[0032] S33, the motor angular velocity observation value The actual value of the motor angular velocity ω m The difference between

[0033] S34, select the reaching law of the sliding mode observer as the constant speed reaching law:

[0034]

[0035] In the formula, is the sliding surface variable; γ is The gain of the sign function sgn(·) in ; sgn(·) is the sign function;

[0036] S35, the motor angular velocity ω m With load torque T L As the observation object, a sliding mode observer is established:

[0037]

[0038] in, ε>0;

[0039] In the formula, is the observed value of the motor angular velocity Differentiation of time; is the observed value of load torque; is the observed value of load torque The differential with respect to time; g is the feedback gain of the sliding mode observer; U is the input control variable; ε is the gain of the sign function sgn(·) in U;

[0040] S36, subtract the formula in step S35 from the formula in step S32 to obtain the error equation of the sliding mode observer:

[0041]

[0042] Where, e1 represents the observation error of the motor angular velocity; It represents the differential of the observed error e1 of the motor angular velocity with respect to time; e2 represents the observed error of the load torque; Represents the differential of the observed error e2 of the load torque with respect to time;

[0043] S37, according to the Lyapunov stability principle, the Lyapunov function is taken as Its derivative must satisfy Right now

[0044] The value range of gain ε is

[0045] Where V is the Lyapunov function variable, is the differential of the Lyapunov function variable with respect to time;

[0046] S38, when the sliding surface variable S reaches the sliding surface S = 0, it satisfies Right now Then the formula in step S36 can be simplified as:

[0047]

[0048] According to the stability criterion, if e2 is to approach 0, the value range of gain g is g<0.

[0049] S39, according to the value range of gain ε and g, configure appropriate gain ε and g, and obtain the load torque observation value in real time under the braking condition according to the synovial observer established in step S35

[0050] Preferably, in step S4, according to the planetary gear ratio i of the mechanical transmission structure g , ball screw lead L0, planetary gear transmission efficiency η p , ball screw transmission efficiency η s , the load torque observation value observed in real time by the sliding mode observer Convert to observed clamping force value F cl2 :

[0051]

[0052] Preferably, in step S5, the real-time optimal estimation of the clamping force comprises the following steps:

[0053] S51, establish the state space equation:

[0054]

[0055] Where X(k-1) and X(k) represent the state vectors at time k-1 and time k; U(k) represents the input vector at time k; A represents the state transfer matrix; B represents the control input matrix; w(k-1) represents the process noise matrix at time k-1; Z(k) represents the observation vector at time k; H represents the observation matrix; v(k) represents the observation noise matrix at time k;

[0056] S52, according to the discretized clamping force state space equation, we get:

[0057]

[0058] In the formula, F cl1 (k-1) represents the predicted value of the clamping force at time k-1 obtained by the discretized clamping force state space equation; α represents the clamping force coefficient; α3, α2, α1, α0 represent the discretized motor angle constants; θ m (k) represents the motor rotation angle at time k.

[0059]

[0060] In the formula, I represents the unit matrix, F cl2(k) represents the clamping force observation value at time k converted from the load torque observation value observed by the sliding mode observer;

[0061] S53, according to the Kalman filter algorithm, the clamping force prediction value and the clamping force observation value are fused and estimated, as shown below:

[0062] S531, status prediction of EMB system:

[0063] Calculate the a priori estimate of the EMB system state at time k:

[0064] X(k|k-1)=AX(k-1|k-1)+BU(k);

[0065] Where X(k|k-1) represents the optimal clamping force estimate F at the k-1 moment of application. cl (k-1) is the prior state estimate of the clamping force at time k; X(k-1|k-1) represents the optimal clamping force estimate F at time k-1 cl (k-1);

[0066] Calculate the a priori estimate of the EMB system covariance at time k:

[0067] P(k|k-1)=AP(k-1|k-1)A T +Q;

[0068] Where P(k|k-1) represents the prior state estimate of the covariance of the EMB system at time k using the optimal covariance estimate P(k-1|k-1) of the EMB system at time k-1; P(k-1|k-1) represents the optimal covariance estimate of the EMB system at time k-1; A T represents the transpose of the state transfer matrix; Q represents the process noise covariance matrix;

[0069] S532, measure and update the EMB system:

[0070] Calculate the Kalman gain of the EMB system:

[0071] K(k)=[P(k|k-1)H T ]·[HP(k|k-1)H T +R] -1 ;

[0072] Where K(k) represents the Kalman gain of the EMB system at time k; H T represents the transpose of the observation matrix; R represents the measurement noise covariance matrix;

[0073] Combined with the clamping force observation Z(k), the state estimate of the EMB system is updated:

[0074] X(k|k)=X(k|k-1)+K(k)[Z(k)-HX(k|k-1)];

[0075] Where X(k|k) represents the optimal clamping force estimate F at time k cl (k);

[0076] Update the EMB system covariance matrix:

[0077] P(k|k)=(IK(k)H)·P(k|k-1);

[0078] Where P(k|k) represents the estimated value of the covariance matrix of the EMB system at time k.

[0079] A readable storage medium stores a computer program, which, when executed, implements the above-mentioned method for estimating the clamping force of an EMB system of an electric vehicle based on a pressure-free sensor.

[0080] An electronic device comprises a processor, a memory and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned method for estimating the clamping force of an electric vehicle EMB system based on a pressure-free sensor is implemented.

[0081] A computer program product includes a computer program / instruction, which, when executed by a processor, implements the above-mentioned method for estimating the clamping force of an EMB system of an electric vehicle based on a pressure-free sensor.

[0082] The advantages of the present invention are:

[0083] (1) Compared with the prior art, the present invention uses the Kalman filter algorithm, combines the clamping force prediction value obtained by the spatial state equation of the clamping force dynamic stiffness model and the clamping force observation value obtained by the sliding mode observer, and obtains the optimal clamping force estimation value through real-time updating and iteration. The present invention solves the problem that under extreme braking conditions, the pressure sensor is in a high temperature and high pressure environment and the number of times of use increases, resulting in a decrease in the accuracy of clamping force observation, and in severe cases, even the entire sensor fails, thus affecting safety.

[0084] (2) The present invention takes into account the viscous effect between the friction plate and the brake disc, establishes a clamping force dynamic stiffness model; converts the established clamping force dynamic stiffness model into a discretized clamping force state space equation; establishes a sliding mode observer of the motor torque, and receives the q-axis current magnitude i fed back from the sensor. q 、Motor rotor angle position θ m and the motor rotor angular velocity ω m To obtain the observed value of the motor's external load torque in real time Through the transmission mechanism parameters of the EMB system, the load torque observation value The invention uses the Kalman filter algorithm to take into account external noise, and updates and iterates the optimal clamping force value in real time, which greatly improves the accuracy of clamping force estimation, effectively prevents the inaccuracy of a single model caused by noise, and can replace pressure sensors. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] Figure 1 The figure is a flow chart of the method of the present invention.

[0086] Figure 2 It is a schematic diagram of the experimental results of the present invention. DETAILED DESCRIPTION

[0087] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0088] Depend on Figure 1 As shown, the method for estimating the clamping force of an electric vehicle EMB system based on a pressure-free sensor according to the present invention comprises the following steps:

[0089] S1. Establishment of the dynamic stiffness model of clamping force: In the actual braking process, the clamping forces of the caliper and the friction pad at the same motor angle during the clamping-release process are not the same. Therefore, considering the viscosity effect of the brake disc during actual braking, the traditional clamping force model based on the motor angle polynomial is improved to establish a new dynamic stiffness model of clamping force.

[0090] During braking, the motor in the electronic mechanical brake (EMB) system drives the planetary gear to decelerate and increase torque. The planetary gear drives the ball screw to convert rotational motion into linear motion, pushing the friction plate to press the brake disc to decelerate. When releasing the brake, the motor releases the friction plate by reversing.

[0091] Due to the viscous effect between the friction plate and the brake disc during the braking process, the clamping force values ​​at the same motor rotation angle will be different during the process of pressing (or releasing) the friction plate.

[0092] The traditional method of estimating the clamping force is to measure the relationship curve between the clamping force and the motor angle, then express the clamping force as a polynomial of the motor angle, and finally estimate the clamping force by the real-time motor angle. However, this method ignores the error caused by the viscosity effect, which will lead to a large error between the estimated clamping force and the actual clamping force when the friction plate is pressed (or released).

[0093] Therefore, the present invention establishes a clamping force dynamic stiffness model of an electronic mechanical brake (EMB) system that is more in line with reality, and estimates the clamping force, as shown below:

[0094] S11, considering the ideal situation that the clamping force is linearly related to the motor angle, and considering the hysteresis of the clamping force caused by the viscosity effect, the transfer function of the EMB system can be expressed as:

[0095]

[0096] Where G(s) is the transfer function, F cl1 (s),θ m (s) are the predicted value of clamping force and the form of motor angle in the complex domain, K s is the linear relationship coefficient between the clamping force and the motor angle, τ is the time constant, and s is a complex number.

[0097] S12, converting the transfer function into a real number domain, the expression of the clamping force can be obtained as:

[0098]

[0099] In the formula, F cl1 represents the predicted value of clamping force, θ m is the motor rotation angle, Represents the differential of the predicted clamping force with respect to time.

[0100] S13, in practice, the clamping force is usually expressed as a polynomial of the motor angle, from which a more accurate clamping force expression can be obtained, namely, the clamping force dynamic stiffness model:

[0101]

[0102] Wherein, Λ3, Λ2, Λ1, and Λ0 are the motor rotation angle constants to be determined experimentally.

[0103] S2, transformation of state space equation: transform the description equation of the clamping force dynamic stiffness model taking into account the viscosity effect into a discretized clamping force state space equation. Specifically, it is as follows:

[0104] The state space equation in discrete form is expressed as:

[0105] X(k)=AX(k-1)+BU(k);

[0106] In the formula, X(k-1) and X(k) represent the system state vector at time k-1 and time k respectively; U(k) represents the system input vector at time k; A represents the state transfer matrix; and B represents the control input matrix.

[0107] According to the above-mentioned discrete state space equation, the clamping force expression obtained in step S13 is converted into a discrete form, and the discretized clamping force state space equation is:

[0108]

[0109] In the formula, F cl1 (k), F cl1 (k-1) represents the predicted clamping force values ​​at time k and time k-1 respectively; α represents the predicted clamping force coefficient; α3, α2, α1, α0 represent the motor rotation angle constants to be determined experimentally after discretization; θ m (k) represents the motor rotation angle at time k.

[0110] Where X(k) = F cl1 (k); X(k-1) = F cl1 (k-1); A = α;

[0111]

[0112] S3, construction of sliding mode observer: according to the motor torque balance equation, design sliding mode control algorithm, under braking condition, according to the q-axis current i measured by the sensor in real time q 、Motor angle (motor rotor angle position) θ m 、Motor (rotor) angular velocity ω m The value of the external load torque of the drive motor is calculated in real time The details are as follows:

[0113] S31, the torque balance equation of the motor is expressed as:

[0114]

[0115] Where, J represents the rotational inertia of the motor; Represents the motor angular velocity ω m Differentiation with respect to time; T L represents the load torque; B represents the damping coefficient; ω m Indicates the motor angular velocity; T e represents electromagnetic torque;

[0116] The type of drive motor selected is a surface-mounted permanent magnet synchronous motor, using a d-axis current i d =0 control mode, its electromagnetic torque T e for:

[0117]

[0118] In the formula, p n Indicates the number of motor pole pairs; ψ f represents the permanent magnet flux; i q represents the q-axis current.

[0119] S32, with motor angular velocity ω m With load torque T L As a state variable, since the actual sampling frequency of the controller is much higher than the frequency of load torque change, its derivative can be regarded as a constant of 0, and the state equation is obtained:

[0120]

[0121] In the formula, since the actual sampling frequency of the controller is much higher than the load torque T L The frequency of the change, and therefore its derivative Can be considered as a constant 0.

[0122] S33, the motor angular velocity observation value The actual value of the motor angular velocity ω m The difference between

[0123] S34, select the reaching law of the sliding mode observer as the constant speed reaching law:

[0124]

[0125] In the formula, is the sliding surface variable; γ is The gain of the sign function sgn(·) in ; sgn(·) is the sign function.

[0126] S35, the motor angular velocity ω m With load torque T L As the observation object, a sliding mode observer is established:

[0127]

[0128] in, ε>0;

[0129] In the formula, is the observed value of the motor angular velocity Differentiation of time; is the observed value of load torque; is the observed value of load torque is the time derivative; g is the feedback gain of the observer; U is the input control variable; ε is the gain of the sign function sgn(·) in U.

[0130] S36, subtract the two equations in step S35 and step S32 to obtain the error equation of the sliding mode observer:

[0131]

[0132] Where, e1 represents the motor angular velocity observation error; It represents the differential of the motor angular velocity observation error with respect to time; e2 represents the load torque observation error; Represents the differential of the load torque observation error with respect to time.

[0133] S37, according to the Lyapunov stability principle, the Lyapunov function is taken as Its derivative must satisfy Right now:

[0134] The range of sliding mode gain ε is

[0135] Where V is the Lyapunov function variable, is the time derivative of the Lyapunov function variable.

[0136] S38, when the observed quantity follows the sliding surface, it satisfies Right now Then the formula in step S36 can be simplified as:

[0137]

[0138] Substitute the relationship between U and e2 into the first formula of step S36, and then express U in the second formula of step S36 with e2 to obtain the above formula.

[0139] According to the stability criterion, if e2 is to approach 0, the value range of gain g is g<0.

[0140] S39, according to the value range of gain ε and g, configure appropriate gain ε and g, and observe the motor load torque in real time under braking conditions according to the synovial observer established in step S35

[0141] S4, conversion of load torque observation value and clamping force observation value: Based on the transmission model of the EMB system, according to the planetary gear connected to the external rotating shaft of the motor, the reduction ratio of the ball screw, the transmission efficiency and the load torque observation value obtained by the sliding mode observer Real-time calculation of clamping force observation value F cl2 The details are as follows:

[0142] The mechanical transmission structure used is the motor shaft-planetary gear-ball screw structure. According to the planetary gear transmission ratio i g , ball screw lead L0, planetary gear transmission efficiency η p , ball screw transmission efficiency η s , the load torque observation value observed in real time by the sliding mode observer can be Convert to observed clamping force value F cl2 :

[0143]

[0144] S5, based on Kalman filtering, data fusion is performed to obtain the optimal clamping force: the Kalman filtering algorithm is used to combine the clamping force prediction value and the clamping force observation value, and the optimal clamping force value obtained at the previous moment is used to calculate the optimal clamping force estimate at the current moment. The details are as follows:

[0145] S51, establish the state space equation:

[0146]

[0147] Where X(k-1) and X(k) represent the state vectors at time k-1 and time k; U(k) represents the input vector at time k; A represents the state transfer matrix; B represents the control input matrix; w(k-1) represents the process noise matrix at time k-1; Z(k) represents the observation vector at time k; H represents the observation matrix; v(k) represents the observation noise matrix at time k.

[0148] S52, in order to be able to merge the clamping force, the state space equation is rewritten to obtain the state space equation:

[0149]

[0150] In the formula, F cl1 (k-1) represents the predicted value of the clamping force at time k-1 obtained by the discretized clamping force state space equation; α represents the clamping force coefficient; α3, α2, α1, α0 represent the motor rotation angle constants to be determined experimentally after discretization; θ m (k) represents the motor rotation angle at time k.

[0151]

[0152] In the formula, I represents the unit matrix, F cl2 (k) represents the clamping force observation value at time k converted from the load torque observation value observed by the sliding mode observer.

[0153] S53, after the predicted value and the observed value of the clamping force are obtained, the two are fused and estimated according to the Kalman filter theory, as shown below:

[0154] S531, status prediction of EMB system:

[0155] Calculate the a priori estimate of the EMB system state at time k:

[0156] X(k|k-1)=AX(k-1|k-1)+BU(k);

[0157] Where X(k|k-1) represents the optimal clamping force estimate F at the k-1 moment of application. cl (k-1) is the prior state estimate of the clamping force at time k; X(k-1|k-1) represents the optimal clamping force estimate F at time k-1 cl (k-1).

[0158] Calculate the a priori estimate of the EMB system covariance at time k:

[0159] P(k|k-1)=AP(k-1|k-1)A T +Q;

[0160] Where P(k|k-1) represents the prior state estimate of the covariance of the EMB system at time k using the optimal covariance estimate P(k-1|k-1) of the EMB system at time k-1; P(k-1|k-1) represents the optimal covariance estimate of the EMB system at time k-1; A T represents the transpose of the state transfer matrix; Q represents the process noise covariance matrix, and its parameters need to be configured according to actual conditions.

[0161] S532, measure and update the EMB system:

[0162] Calculate the Kalman gain of the EMB system:

[0163] K(k)=[P(k|k-1)H T ]·[HP(k|k-1)H T +R] -1 ;

[0164] Where K(k) represents the Kalman gain of the EMB system at time k; H T represents the transpose of the observation matrix; R represents the measurement noise covariance matrix, and its parameters need to be configured according to actual conditions.

[0165] Combined with the observations, update the state estimate of the EMB system:

[0166] X(k|k)=X(k|k-1)+K(k)[Z(k)-HX(k|k-1)];

[0167] Where X(k|k) represents the optimal clamping force estimate F at time k cl (k).

[0168] Update the covariance estimate of the EMB system:

[0169] P(k|k)=(IK(k)H)·P(k|k-1);

[0170] Where P(k|k) represents the optimal covariance estimate of the EMB system at time k.

[0171] S54, when the electric vehicle enters the braking condition, the ECU (electronic control unit) receives a signal that the brake pedal is stepped on, and calculates the clamping force reference value F required for braking in real time according to the degree of stepping on the brake pedal ref And send a command to the drive motor of the EMB system to enter the closed-loop working mode. When entering the pressure tracking stage, the ECU obtains the clamping force observation value F converted from the load torque observation value observed by the sliding mode observer at time k in real time according to the written code. cl2 (k) and, by programming the Kalman filter algorithm code into the ECU, continuously iterate to obtain the fusion estimation value of the optimal clamping force at different times, and use this estimation value as the feedback of the force closed loop to replace the feedback of the clamping force measured by the pressure sensor, thus realizing a control strategy without a pressure sensor.

[0172] Figure 2 The actual effect of the present invention is shown. Under the requirements of different clamping force sizes, the invention reduces the influence of external noise on the clamping force estimation by configuring the noise coefficient. At the same time, it can replace the pressure sensor to achieve accurate estimation of the clamping force.

[0173] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for estimating the clamping force of an electric vehicle EMB system based on a pressure-free sensor, characterized in that: The following steps are involved: S1, considering the viscosity effect between the brake disc and the friction pad during actual braking, a clamping force dynamic stiffness model is established to predict 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, according to the motor torque balance equation, design a sliding mode observer of the motor load torque, which is used to observe the motor load torque in real time during the braking process and obtain the load torque observation value; S4, according to the transmission model of the electronic mechanical brake system, namely, the EMB system, converting the load torque observation value observed in real time by the sliding mode observer into the clamping force observation value; S5, using the Kalman filter algorithm, combined with the discretized clamping force state space equation and the clamping force observation value, and based on the optimal clamping force estimate value obtained at the previous moment, to calculate the optimal clamping force estimate value at the current moment, to achieve real-time optimal estimation of the clamping force.

2. The method for estimating the clamping force of an electric vehicle EMB system based on a pressure-free sensor according to claim 1, characterized in that: In step S1, the clamping force dynamic stiffness model is: In the formula, F cl1 represents the predicted value of clamping force, θ m is the motor rotation angle, It represents the differential of the predicted value of clamping force with respect to time; Λ3, Λ2, Λ1, Λ0 are the motor angle constants; τ is the time constant.

3. The method for estimating the clamping force of an electric vehicle EMB system based on a pressure-free sensor according to claim 1, characterized in that: In step S2, the discretized clamping force state space equation is: In the formula, F cl1 (k), F cl1 (k-1) represents the predicted clamping force at time k and time k-1 respectively; α represents the predicted clamping force coefficient; α3, α2, α1, α0 represent the motor rotation angle constant after discretization; θ m (k) represents the motor rotation angle at time k.

4. According to the method for estimating the clamping force of an electric vehicle EMB system based on a pressure sensor-free method as claimed in claim 1, in step S3, the sliding mode observer of the load torque is: in, ε>0; In the formula, is the observed value of the motor angular velocity; is the observed value of the motor angular velocity Differentiation of time; is the observed value of load torque; is the observed value of load torque The differential with respect to time; g is the feedback gain of the sliding mode observer; U is the input control variable; ε is the gain of the symbolic function sgn(·) in U; ​​J represents the moment of inertia of the motor; p n Indicates the number of motor pole pairs; ψ f represents the permanent magnet flux; i q represents the q-axis current.

5. According to the method for estimating the clamping force of an electric vehicle EMB system based on a pressure sensor-free method as claimed in claim 1, in step S3, the design of the sliding mode observer of the load torque comprises the following steps: S31, the motor torque balance equation is: Where, J represents the rotational inertia of the motor; Represents the differential of the motor angular velocity with respect to time; T L represents the load torque; B represents the damping coefficient; ω m Indicates the motor angular velocity; T e represents electromagnetic torque; in, In the formula, p n Indicates the number of motor pole pairs; ψ f represents the permanent magnet flux; i q represents the q-axis current; S32, with motor angular velocity ω m With load torque T L As the state variable, the state equation is obtained: S33, the motor angular velocity observation value The actual value of the motor angular velocity ω m The difference between S34, select the reaching law of the sliding mode observer as the constant speed reaching law: In the formula, is the sliding surface variable; γ is The gain of the sign function sgn(·) in ; sgn(·) is the sign function; S35, the motor angular velocity ω m With load torque T L As the observation object, a sliding mode observer is established: in, ε>0; In the formula, is the observed value of the motor angular velocity Differentiation of time; is the observed value of load torque; is the observed value of load torque The differential with respect to time; g is the feedback gain of the sliding mode observer; U is the input control variable; ε is the gain of the sign function sgn(·) in U; S36, subtract the formula in step S35 from the formula in step S32 to obtain the error equation of the sliding mode observer: Where, e1 represents the observation error of the motor angular velocity; It represents the differential of the observed error e1 of the motor angular velocity with respect to time; e2 represents the observed error of the load torque; Represents the differential of the observed error e2 of the load torque with respect to time; S37, according to the Lyapunov stability principle, the Lyapunov function is taken as Its derivative must satisfy Right now The value range of gain ε is Where V is the Lyapunov function variable, is the differential of the Lyapunov function variable with respect to time; S38, when the sliding surface variable S reaches the sliding surface S = 0, it satisfies Right now Then the formula in step S36 can be simplified as: According to the stability criterion, if e2 is to be approached to 0, the value range of gain g is g<0; S39, according to the value range of gain ε and g, configure appropriate gain ε and g, and obtain the load torque observation value in real time under the braking condition according to the synovial observer established in step S35 6. The method for estimating the clamping force of an electric vehicle EMB system based on a pressure sensor-free method according to claim 1, characterized in that: In step S4, according to the planetary gear ratio i of the mechanical transmission structure, g , ball screw lead L0, planetary gear transmission efficiency η p , ball screw transmission efficiency η s , the load torque observation value observed in real time by the sliding mode observer Convert to observed clamping force value F cl2 :

7. The method for estimating the clamping force of an electric vehicle EMB system based on a pressure-free sensor according to claim 1, characterized in that: In step S5, the real-time optimal estimation of the clamping force includes the following steps: S51, establish the state space equation: Where X(k-1) and X(k) represent the state vectors at time k-1 and time k; U(k) represents the input vector at time k; A represents the state transfer matrix; B represents the control input matrix; w(k-1) represents the process noise matrix at time k-1; Z(k) represents the observation vector at time k; H represents the observation matrix; v(k) represents the observation noise matrix at time k; S52, according to the discretized clamping force state space equation, we get: In the formula, F cl1 (k-1) represents the predicted value of the clamping force at time k-1 obtained by the discretized clamping force state space equation; α represents the clamping force coefficient; α3, α2, α1, α0 represent the discretized motor rotation angle constants; θ m (k) represents the motor rotation angle at time k; In the formula, I represents the unit matrix, F cl2 (k) represents the clamping force observation value at time k converted from the load torque observation value observed by the sliding mode observer; S53, according to the Kalman filter algorithm, the clamping force prediction value and the clamping force observation value are fused and estimated, as shown below: S531, status prediction of EMB system: Calculate the a priori estimate of the EMB system state at time k: X(k|k-1)=AX(k-1|k-1)+BU(k); Where X(k|k-1) represents the optimal clamping force estimate F at the k-1 moment of application. cl (k-1) is the prior state estimate of the clamping force at time k; X(k-1|k-1) represents the optimal clamping force estimate F at time k-1 cl (k-1); Calculate the a priori estimate of the EMB system covariance at time k: P(k|k-1)=AP(k-1|k-1)A T +Q; Where P(k|k-1) represents the prior state estimate of the covariance of the EMB system at time k using the optimal covariance estimate P(k-1|k-1) of the EMB system at time k-1; P(k-1|k-1) represents the optimal covariance estimate of the EMB system at time k-1; A T represents the transpose of the state transfer matrix; Q represents the process noise covariance matrix; S532, measure and update the EMB system: Calculate the Kalman gain of the EMB system: K(k)=[P(k|k-1)H T ]·[HP(k|k-1)H T +R] -1 ; Where K(k) represents the Kalman gain of the EMB system at time k; H T represents the transpose of the observation matrix; R represents the measurement noise covariance matrix; Combined with the clamping force observation Z(k), the state estimate of the EMB system is updated: X(k|k)=X(k|k-1)+K(k)[Z(k)-HX(k|k-1)]; Where X(k|k) represents the optimal clamping force estimate F at time k cl (k); Update the EMB system covariance matrix: P(k|k)=(IK(k)H)·P(k|k-1); Where P(k|k) represents the estimated value of the covariance matrix of the EMB system at time k.

8. A readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed, a method for estimating the clamping force of an electric vehicle EMB system based on a pressure-free sensor as described in any one of claims 1 to 6 is implemented.

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 a clamping force estimation method for an electric vehicle EMB system based on a pressure-free sensor as described in any one of claims 1 to 6.

10. A computer program product, characterized in that It includes a computer program / instruction, which, when executed by a processor, implements a clamping force estimation method for an electric vehicle EMB system based on a pressure-free sensor as described in any one of claims 1 to 6.

Images