Electric vehicle EMB system clamping force estimation method based on pressure sensor-free control strategy

Abstract

The invention relates to an electric vehicle EMB system clamping force estimation method based on a pressure-sensor-free control strategy. Compared with the prior art, the defect that a pressure sensor of an EMB system cannot meet the actual use requirement is overcome. The method comprises the following steps: estimating load torque; torque estimation balance force is estimated according to the load torque and a transmission model; estimating a characteristic curve estimated clamping force through a characteristic curve dynamic stiffness model; and carrying out fusion estimation on clamping force of the EMB system. According to the method, a non-pressure sensor algorithm is designed to replace a pressure sensor, the clamping force generated when the EMB system works is optimally estimated in real time, and the problems that the cost of the pressure sensor is high, and the braking performance of the electric vehicle is reduced due to the fact that the precision of clamping force measurement is reduced or even the whole is invalid under the severe braking working condition are solved.

Description

Technical Field

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

[0002] With the continuous development of motors, batteries, and electric drive technologies, more and more wire control technologies have emerged to replace traditional mechanical control technologies. The Electromechanical Brake (EMB) system has gradually become the first choice for the next generation of electric vehicle braking technology due to its advantages such as intelligence, lightweight, and high safety. Compared with traditional hydraulic brakes, the EMB system adopts a mechatronics design, eliminates complex hydraulic pipelines, has a simpler structure, and is clean and environmentally friendly. Since it does not rely on brake fluid, the EMB system avoids the resulting braking delay and has a faster response speed. In addition, the EMB system can accurately control the required braking force, significantly improving the safety and reliability of the vehicle.

[0003] However, under actual braking conditions, the strain gauge of the pressure sensor will be frequently squeezed. Under long-term fatigue use, the strain gauge may break, causing the pressure sensor to fail, and its reliability cannot be guaranteed. Secondly, a large amount of heat is generated when the friction plate contacts the brake disc. Under extremely high temperatures, the clamping force measured by the pressure sensor may be different from the actual one, resulting in failure to meet braking requirements and affecting 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] The purpose of the present invention is to solve the defect that the pressure sensor of the EMB system in the prior art cannot meet the actual use needs, and to provide a clamping force estimation method of the EMB system of an electric vehicle based on a pressure sensor-free control strategy to solve the above problem.

[0006] In order to achieve the above object, the technical solution of the present invention is as follows:

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

[0008] Estimation of load torque: Based on the inherent parameters of the electric vehicle motor and the angular velocity ω of the motor during braking m , q-axis current i qThrough the dynamic torque balance equation, the external load torque T L Make estimates;

[0009] The torque estimation balance force is estimated based on the load torque and transmission model: according to the transmission ratio, transmission efficiency and estimated load torque T of the planetary gear and ball screw connected to the external rotating shaft of the motor L , estimate the torque estimation balance force F1 in real time;

[0010] The characteristic curve estimated clamping force is estimated by the characteristic curve dynamic stiffness model: When the EMB system is working, the characteristic curve dynamic stiffness model between the clamping force and the motor angle is used to estimate the clamping force F2(k-1) through the value returned by the motor rotor position encoder and the characteristic curve calculated at the previous moment, and the value of the characteristic curve estimated clamping force F2(k) at this moment is calculated in real time;

[0011] Fusion estimation of the clamping force of the EMB system: Under the vehicle braking condition, the ECU receives the signal of the brake pedal being pressed and calculates the required target clamping force F based on the degree to which the brake pedal is pressed. ref At this time, the EMB system starts to work, and the ball screw is driven by the driving motor to make the friction plate press the brake disc to generate clamping force. The balance force F1 is estimated according to the torque, and the clamping force F2 is estimated by the characteristic curve. The variance of the torque-estimated balance force F1 and the characteristic curve-estimated clamping force F2 is set. The fusion estimates the optimal clamping force, i.e., the clamping force of the EMB system of the electric vehicle.

[0012] The load moment estimation comprises the following steps:

[0013] The drive motor is set to a permanent magnet synchronous motor, using the d-axis current i d =0 control strategy;

[0014] According to the torque balance equation of the motor, we get:

[0015]

[0016] Where, J represents the rotational inertia of the motor; ω m represents the angular velocity of the motor, Represents the derivative of the motor rotor angular velocity with respect to time; T e Represents the electromagnetic torque of the motor, and its magnitude is where p n is the number of motor pole pairs, ψ f represents the motor flux, i q Indicates the magnitude of the motor q-axis current, T L Denotes load moment, T f Indicates the friction torque inside the motor;

[0017] Simplifying the internal friction torque of the motor and expressing it in the form of load torque, we get:

[0018]

[0019] Where μ represents the Coulomb friction coefficient, A represents the offset coefficient, F1 represents the torque estimation balance force, sgn represents the sign function, and θ m represents the motor rotor angle, It represents the differential of the rotor angle with respect to time.

[0020] The method of estimating the torque balancing force according to the load torque and the transmission model comprises the following steps:

[0021] In the actual braking process of electric vehicles, the motor decelerates and increases torque through the planetary gear, and then the ball screw presses the brake disc to achieve braking.

[0022] According to the planetary gear ratio, planetary gear and ball screw transmission efficiency, and ball screw lead of the EMB mechanical transmission system, the relationship between the clamping force and the load torque is obtained:

[0023]

[0024] Where, T L represents the load moment, F1 represents the torque estimation balance force, L0 represents the ball screw lead, i g represents the planetary gear ratio, η p represents the planetary gear transmission efficiency, η s Indicates the ball screw transmission efficiency;

[0025] The expression in the form of load moment is Get an estimate of the balancing force:

[0026]

[0027] In the formula, i q represents the q-axis current; ψ f Represents the motor flux; p n represents the number of motor pole pairs; J represents the motor moment of inertia; represents the differential of the motor rotor angular velocity with respect to time; A represents the offset coefficient; sgn represents the sign function; represents the differential of the rotor angle with respect to time; μ represents the Coulomb friction coefficient;

[0028] Express it in discretized form:

[0029]

[0030] Where F1(k) represents the estimated value of the torque estimation balance force at time k, ω m (k-1),ω m (k) represents the angular velocity of the motor rotor at time k-1 and time k respectively, θ m (k-1),θ m (k) represents the angular position of the motor rotor at time k-1 and time k.

[0031] The method of estimating the clamping force by using the characteristic curve dynamic stiffness model comprises the following steps:

[0032] When the EMB is working, the clamping force F2 is estimated by estimating the characteristic curve using the characteristic curve dynamic stiffness model, taking into account the viscous effect between the friction pad and the brake disc during braking;

[0033] During the caliper loading process, if the clamping force and the motor rotor angle θ m It is a linear relationship, and taking into account the "viscous effect", the transfer function of the characteristic curve dynamic stiffness model is obtained:

[0034]

[0035] Where G(s) represents the transfer function, F2(s) represents the expression of the clamping force in the complex domain, and θ m (s) represents the expression of the motor rotor angle position in the complex domain, K s Indicates the clamping force F2 and the motor rotor angle θ m The linear coefficient, τ represents the time constant, and s represents the complex function;

[0036] From the transfer function of the characteristic curve dynamic stiffness model, the expression for estimating the clamping force F2 using the characteristic curve in the real number domain is obtained:

[0037]

[0038] In the formula, θ m Indicates the motor rotor angular position, represents the differential of the clamping force with respect to time;

[0039] During the actual braking process, the characteristic curve estimates the clamping force expressed as the motor rotor angle position θ m The polynomial relationship of the clamping force is obtained:

[0040]

[0041] In the formula, Λ3, Λ2, Λ1, and Λ0 are polynomial coefficients.

[0042] Express it and simplify it into discretized form:

[0043]

[0044] In the formula, θ m (k) represents the angular position of the motor rotor at time k, α3, α2, α1, α0, and α represent the discretized polynomial coefficients; F2(k-1) and F2(k) represent the estimated values ​​of the clamping force at time k-1 and k, respectively.

[0045] The fusion estimation of the clamping force of the EMB system includes the following steps:

[0046] Under the vehicle braking condition, the ECU receives the signal of the brake pedal being pressed and calculates the required target clamping force F based on the degree to which the brake pedal is pressed. ref At this time, the EMB system starts to work, driving the ball screw through the planetary gear by the driving motor to make the friction plate press the brake disc to generate clamping force;

[0047] At this time, the q-axis current i measured by the sensor in real time q 、Motor rotor angle position θ m 、Motor rotor angular velocity ω m The data is input into the ECU, and the ECU calculates the torque estimated balancing force F1 and the characteristic curve estimated clamping force F2 in real time through the code written according to the torque dynamic equation method and the characteristic curve dynamic stiffness model method;

[0048] Considering the external interference and the inaccuracy of the sensor, there is Gaussian noise in the process of real-time calculation of the torque estimation balance force F1 and characteristic curve estimation clamping force F2. Suppose the variances of the estimated values ​​F1 and F2 are

[0049] According to the minimum variance principle, and by configuring in advance in the algorithm The optimal clamping force F is calculated cl The value of

[0050]

[0051] Under braking conditions, the optimal clamping force F is calculated in real time by the ECU. cl As the feedback value of the force closed loop, it is related to the required target clamping force F re f is subtracted to obtain the input of the force closed loop, and through subsequent PI control, the required q-axis current value i is obtained. q It is input into the controller to make the motor rotate, and the motor shaft is decelerated and torque increased through the planetary gears. Finally, the ball screw converts the rotational motion into linear motion so that the friction plate presses the brake disc to brake, realizing smooth braking of electric vehicles without pressure sensors.

[0052] Beneficial Effects

[0053] The invention discloses a method for estimating the clamping force of an electric vehicle EMB system based on a pressure sensor-free control strategy. Compared with the prior art, a pressure sensor-free algorithm is designed to replace a pressure sensor, so as to optimally estimate the clamping force generated when the EMB system is working in real time, thereby solving the problem that the pressure sensor is high in cost and the clamping force measurement under harsh braking conditions may suffer from reduced accuracy or even overall failure, thereby causing reduced braking performance of the electric vehicle.

[0054] The pressure-free sensor designed by the present invention firstly considers the dynamic torque balance model of the motor, and receives the q-axis current magnitude i fed back by the sensor. q 、Motor rotor angle position θ m and the motor rotor angular velocity ω m To estimate the external load T of the motor in real time L , and then the torque estimation balance force F1 is obtained through the transmission mechanism parameters of EMB; secondly, considering the "viscous effect" between the friction plate and the brake disc, the characteristic curve dynamic stiffness model is established, and the motor rotor angle position θ fed back by the sensor is m The characteristic curve at the previous moment estimates the clamping force F2(k-1) and the characteristic curve at this moment estimates the clamping force F2(k); finally, the variance size of the two models is configured to make the best estimate of the clamping force.

[0055] The present invention greatly improves the accuracy of clamping force estimation by fusing two models of the EMB system, and effectively prevents the inaccuracy problem caused by noise in a single model, and can replace the pressure sensor. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 is a method sequence diagram of the present invention;

[0057] Figure 2 This is a diagram for verifying the experimental structure of the present invention. DETAILED DESCRIPTION

[0058] In order to have a further understanding and recognition of the structural features and the effects achieved by the present invention, a preferred embodiment and accompanying drawings are used for detailed description as follows:

[0059] like Figure 1 As shown, the method for estimating the clamping force of an electric vehicle EMB system based on a pressure sensor-free control strategy according to the present invention comprises the following steps:

[0060] The first step is to estimate the load torque: according to the inherent parameters of the electric vehicle motor and the angular velocity ω of the motor during braking m , q-axis current i qThrough the dynamic torque balance equation, the external load torque T L The realization of this technology requires the installation of current sensors and speed encoders, and the programming of load torque calculation in the ECU in advance. In the actual braking process, the current sensor and speed encoder transmit the motor q-axis current and rotor angular velocity information to the ECU, and the load torque value is obtained through the calculation of the ECU.

[0061] (1) The drive motor is set to a permanent magnet synchronous motor and the d-axis current i d =0 control strategy.

[0062] (2) According to the torque balance equation of the motor, we get:

[0063]

[0064] Where, J represents the rotational inertia of the motor; ω m represents the angular velocity of the motor, Represents the derivative of the motor rotor angular velocity with respect to time; T e Represents the electromagnetic torque of the motor, and its magnitude is where p n is the number of motor pole pairs, ψ f represents the motor flux, i q Indicates the magnitude of the motor q-axis current, T L Denotes load moment, T f Represents the friction torque inside the motor.

[0065] (3) Simplify the internal friction torque of the motor and express it in the form of load torque to obtain:

[0066]

[0067] Where μ represents the Coulomb friction coefficient, A represents the offset coefficient, F1 represents the torque estimation balance force, sgn represents the sign function, and θ m represents the motor rotor angle, It represents the differential of the rotor angle with respect to time.

[0068] The second step is to estimate the torque balance force based on the load torque and transmission model: 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 estimated load torque T L , and estimate the torque balance force F1 in real time. Here, the mechanical transmission structure outside the EMB drive motor shaft is clarified, and the load torque T is calculated through the transmission ratio and transmission efficiency information of the transmission structure. L Converted into balancing force.

[0069] The method of estimating the torque balancing force according to the load torque and the transmission model comprises the following steps:

[0070] (1) During the actual braking process of an electric vehicle, the motor decelerates and increases torque through the planetary gear, and then the ball screw presses the brake disc to achieve braking.

[0071] According to the planetary gear ratio, planetary gear and ball screw transmission efficiency, and ball screw lead of the EMB mechanical transmission system, the relationship between the clamping force and the load torque is obtained:

[0072]

[0073] Where, T L represents the load moment, F1 represents the torque estimation balance force, L0 represents the ball screw lead, i g represents the planetary gear ratio, η p represents the planetary gear transmission efficiency, η s Indicates the ball screw transmission efficiency.

[0074] (2) Expression in the form of load moment Get an estimate of the balancing force:

[0075]

[0076] In the formula, i q represents the q-axis current; ψ f Represents the motor flux; p n represents the number of motor pole pairs; J represents the motor moment of inertia; represents the differential of the motor rotor angular velocity with respect to time; A represents the offset coefficient; sgn represents the sign function; represents the differential of the rotor angle with respect to time; μ represents the Coulomb friction coefficient;

[0077] Express it in discretized form:

[0078]

[0079] Where F1(k) represents the estimated value of the torque estimation balance force at time k, ω m (k-1),ω m (k) represents the angular velocity of the motor rotor at time k-1 and time k respectively, θ m (k-1),θ m (k) represents the angular position of the motor rotor at time k-1 and time k.

[0080] The third step is to estimate the clamping force estimated by the characteristic curve through the dynamic stiffness model of the characteristic curve: when the EMB system is working, the characteristic curve dynamic stiffness model between the clamping force and the motor angle is used to estimate the clamping force F2(k-1) through the value returned by the motor rotor position encoder and the characteristic curve calculated at the previous moment, and the value of the characteristic curve estimated clamping force F2(k) at this moment is calculated in real time. This technology fully considers the "viscous effect" between the friction pad and the brake disc during the actual braking process, and by determining the parameters of the dynamic stiffness model of the characteristic curve in advance through experiments, a more accurate estimate of the actual brake clamping force is obtained.

[0081] The method of estimating the clamping force by using the characteristic curve dynamic stiffness model comprises the following steps:

[0082] (1) When the EMB is working, the clamping force F2 is estimated by estimating the characteristic curve using the characteristic curve dynamic stiffness model, taking into account the viscous effect between the friction pad and the brake disc during braking;

[0083] During the caliper loading process, if the clamping force and the motor rotor angle θ m It is a linear relationship, and taking into account the "viscous effect", the transfer function of the characteristic curve dynamic stiffness model is obtained:

[0084]

[0085] Where G(s) represents the transfer function, F2(s) represents the expression of the clamping force in the complex domain, and θ m (s) represents the expression of the motor rotor angle position in the complex domain, K s Indicates the clamping force F2 and the motor rotor angle θ m is the linear coefficient, τ represents the time constant, and s represents the complex function.

[0086] (2) Based on the transfer function of the characteristic curve dynamic stiffness model, the expression for estimating the clamping force F2 using the characteristic curve in the real number domain is obtained:

[0087]

[0088] In the formula, θ m Indicates the motor rotor angular position, Represents the differential of clamping force with respect to time.

[0089] (3) During the actual braking process, the characteristic curve estimates the clamping force as the motor rotor angle position θ m The polynomial relationship of the clamping force is obtained:

[0090]

[0091] In the formula, Λ3, Λ2, Λ1, and Λ0 are polynomial coefficients.

[0092] Express it and simplify it into discretized form:

[0093]

[0094] In the formula, θ m (k) represents the angular position of the motor rotor at time k, α3, α2, α1, α0, and α represent the discretized polynomial coefficients; F2(k-1) and F2(k) represent the estimated values ​​of the clamping force at time k-1 and k, respectively.

[0095] Step 4: Fusion estimation of the clamping force of the EMB system: Under the vehicle braking condition, the ECU receives the signal of the brake pedal being pressed and calculates the required target clamping force F based on the degree to which the brake pedal is pressed. ref At this time, the EMB system starts to work, and the ball screw is driven by the driving motor to make the friction plate press the brake disc to generate clamping force. The balance force F1 is estimated according to the torque, and the clamping force F2 is estimated by the characteristic curve. The variance of the torque-estimated balance force F1 and the characteristic curve-estimated clamping force F2 is set. The fusion estimates the optimal clamping force, i.e., the clamping force of the EMB system of the electric vehicle.

[0096] (1) Under vehicle braking conditions, the ECU receives the brake pedal pressing signal and calculates the required target clamping force F based on the degree to which the brake pedal is pressed. ref At this time, the EMB system starts to work, driving the ball screw through the drive motor to make the friction plate press the brake disc to generate clamping force.

[0097] (2) At this time, the q-axis current i measured by the sensor in real time q 、Motor rotor angle position θ m 、Motor rotor angular velocity ω m The data is input into the ECU, which calculates the torque estimated balancing force F1 and the characteristic curve estimated clamping force F2 in real time through the code written according to the torque dynamic equation method and the characteristic curve dynamic stiffness model method.

[0098] (3) Considering the external interference and the inaccuracy of the sensor, there is Gaussian noise in the process of real-time calculation of the torque estimation balance force F1 and characteristic curve estimation clamping force F2. Let the variance of the estimated values ​​F1 and F2 be

[0099] According to the minimum variance principle, and by configuring in advance in the algorithm The optimal clamping force F is calculated cl The value of

[0100]

[0101] (4) Under braking conditions, the optimal clamping force F is calculated in real time by the ECU cl As the feedback value of the force closed loop, it is related to the required target clamping force F ref Subtract the input of the force closed loop, and through subsequent PI control, the required q-axis current value i is obtained. q It is input into the controller to make the motor rotate, and the motor shaft is decelerated and torque increased through the planetary gears. Finally, the ball screw converts the rotational motion into linear motion so that the friction plate presses the brake disc to brake, realizing smooth braking of electric vehicles without pressure sensors.

[0102] Figure 2 The actual effect of the invention is demonstrated. Under full braking conditions, the invention estimates the balancing force F1 by integrating the torque, estimates the clamping force F2 by the characteristic curve, and configures the variance coefficient, fully considering the external noise signal, thereby achieving a more accurate estimation of the clamping force.

[0103] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions only describe the principles of the present invention. The present invention may be subject to various changes and improvements without departing from the spirit and scope of the present invention. These changes and improvements fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the attached claims and their equivalents.

Claims

1. A clamping force estimation method for an electric vehicle EMB system based on a pressure sensor-free control strategy, characterized in that: The following steps are involved: 11) Estimation of load torque: Based on the inherent parameters of the electric vehicle motor and the angular velocity ω of the motor during braking m , q-axis current i q Through the dynamic torque balance equation, the external load torque T L Make estimates; 12) Estimate the torque balance force based on the load torque and transmission model: According to the transmission ratio, transmission efficiency and estimated load torque T of the planetary gear and ball screw connected to the external rotating shaft of the motor L , estimate the torque estimation balance force F1 in real time; 13) Estimating the clamping force by the characteristic curve dynamic stiffness model: When the EMB system is working, the characteristic curve dynamic stiffness model between the clamping force and the motor angle is used to estimate the clamping force F2(k-1) by the value returned by the motor rotor position encoder and the characteristic curve calculated at the previous moment, and the value of the characteristic curve estimated clamping force F2(k) at this moment is calculated in real time; 14) Fusion estimation of clamping force of EMB system: Under the braking condition of the vehicle, the ECU receives the signal of the brake pedal being pressed and calculates the required target clamping force F based on the degree of the brake pedal being pressed. ref At this time, the EMB system starts to work, and the ball screw is driven by the driving motor to make the friction plate press the brake disc to generate clamping force. The balance force F1 is estimated according to the torque, and the clamping force F2 is estimated by the characteristic curve. The variance of the torque-estimated balance force F1 and the characteristic curve-estimated clamping force F2 is set. The fusion estimates the optimal clamping force, i.e., the clamping force of the EMB system of the electric vehicle.

2. The method for estimating the clamping force of an electric vehicle EMB system based on a pressure sensor-free control strategy according to claim 1 is characterized in that: The load moment estimation comprises the following steps: 21) Set the drive motor to a permanent magnet synchronous motor and use the d-axis current i d =0 control strategy; 22) According to the torque balance equation of the motor, we get: Where, J represents the rotational inertia of the motor; ω m represents the angular velocity of the motor, Represents the derivative of the motor rotor angular velocity with respect to time; T e Represents the electromagnetic torque of the motor, and its magnitude is where p n is the number of motor pole pairs, ψ f represents the motor flux, i q Indicates the magnitude of the motor q-axis current, T L Denotes load moment, T f Indicates the friction torque inside the motor; 23) Simplify the internal friction torque of the motor and express it in the form of load torque to obtain: Where μ represents the Coulomb friction coefficient, A represents the offset coefficient, F1 represents the torque estimation balance force, sgn represents the sign function, and θ m represents the motor rotor angle, It represents the differential of the rotor angle with respect to time.

3. The method for estimating the clamping force of an electric vehicle EMB system based on a pressure sensor-free control strategy according to claim 1 is characterized in that: The method of estimating the torque balancing force according to the load torque and the transmission model comprises the following steps: 31) During the actual braking process of an electric vehicle, the motor decelerates and increases torque through the planetary gear, and then the ball screw presses the brake disc to achieve braking. According to the planetary gear ratio, planetary gear and ball screw transmission efficiency, and ball screw lead of the EMB mechanical transmission system, the relationship between the clamping force and the load torque is obtained: Where, T L represents the load moment, F1 represents the torque estimation balance force, L0 represents the ball screw lead, i g represents the planetary gear ratio, η p represents the planetary gear transmission efficiency, η s Indicates the ball screw transmission efficiency; 32) Combined with the load moment expression Get an estimate of the balancing force: In the formula, i q represents the q-axis current; ψ f Represents the motor flux; p n represents the number of motor pole pairs; J represents the motor moment of inertia; represents the differential of the motor rotor angular velocity with respect to time; A represents the offset coefficient; sgn represents the sign function; represents the differential of the rotor angle with respect to time; μ represents the Coulomb friction coefficient; Express it in discretized form: Where F1(k) represents the estimated value of the torque estimation balance force at time k, ω m (k-1),ω m (k) represents the angular velocity of the motor rotor at time k-1 and time k respectively, θ m (k-1),θ m (k) represents the angular position of the motor rotor at time k-1 and time k.

4. The method for estimating the clamping force of an electric vehicle EMB system based on a pressure sensor-free control strategy according to claim 1 is characterized in that: The method of estimating the clamping force by using the characteristic curve dynamic stiffness model comprises the following steps: 41) When the EMB is working, the clamping force F2 is estimated by estimating the characteristic curve using the characteristic curve dynamic stiffness model, taking into account the viscous effect between the friction pad and the brake disc during braking; During the caliper loading process, if the clamping force and the motor rotor angle θ m It is a linear relationship, and taking into account the "viscosity effect", the transfer function of the characteristic curve dynamic stiffness model is obtained: Where G(s) represents the transfer function, F2(s) represents the expression of the clamping force in the complex domain, θ m (s) represents the expression of the motor rotor angle position in the complex domain, K s Indicates the clamping force F2 and the motor rotor angle θ m The linear coefficient, τ represents the time constant, and s represents the complex function; 42) Based on the transfer function of the characteristic curve dynamic stiffness model, the expression for estimating the clamping force F2 using the characteristic curve in the real number domain is obtained: In the formula, θ m Indicates the motor rotor angular position, represents the differential of the clamping force with respect to time; 43) During the actual braking process, the characteristic curve estimates the clamping force expressed as the motor rotor angle position θ m The polynomial relationship of the clamping force is obtained: In the formula, Λ3, Λ2, Λ1, and Λ0 are polynomial coefficients. Express it and simplify it into discretized form: In the formula, θ m (k) represents the angular position of the motor rotor at time k, α3, α2, α1, α0, and α represent the discretized polynomial coefficients; F2(k-1) and F2(k) represent the estimated values ​​of the clamping force at time k-1 and k, respectively.

5. The method for estimating the clamping force of an electric vehicle EMB system based on a pressure sensor-free control strategy according to claim 1 is characterized in that: The fusion estimation of the clamping force of the EMB system includes the following steps: 51) Under the braking condition of the vehicle, the ECU receives the signal of the brake pedal being pressed and calculates the required target clamping force F based on the degree to which the brake pedal is pressed. ref At this time, the EMB system starts to work, driving the ball screw through the planetary gear by the driving motor to make the friction plate press the brake disc to generate clamping force; 52) At this time, the q-axis current i measured by the sensor in real time q 、Motor rotor angle position θ m 、Motor rotor angular velocity ω m The data is input into the ECU, and the ECU calculates the torque estimated balancing force F1 and the characteristic curve estimated clamping force F2 in real time through the code written according to the torque dynamic equation method and the characteristic curve dynamic stiffness model method; 53) Considering the external interference and the inaccuracy of the sensor, there is Gaussian noise in the process of real-time calculation of the torque estimation balance force F1 and characteristic curve estimation clamping force F2. Let the variance of the estimated values ​​F1 and F2 be According to the minimum variance principle, and by configuring in advance in the algorithm The optimal clamping force F is calculated cl The value of 54) Under braking conditions, the optimal clamping force F is calculated in real time by the ECU cl As the feedback value of the force closed loop, it is related to the required target clamping force F ref Subtract the input of the force closed loop, and through subsequent PI control, the required q-axis current value i is obtained. q It is input into the controller to make the motor rotate, and the motor shaft is decelerated and torque increased through the planetary gears. Finally, the ball screw converts the rotational motion into linear motion so that the friction plate presses the brake disc to brake, realizing smooth braking of electric vehicles without pressure sensors.

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