Automobile EMB system clamping force accurate control method based on expansion state observer

Abstract

The invention discloses an automobile EMB system clamping force accurate control method based on an extended state observer, and belongs to the technical field of electric automobile EMB systems, and the method comprises the following steps: S1, building a novel EMB system five-degree-of-freedom dynamic model containing a LuGre friction model by using an EMB system dynamic relationship; s2, simplifying the five-degree-of-freedom dynamic model of the novel EMB system, estimating the load torque of an EMB driving motor according to the extended state observer, and then estimating the clamping force of the EMB system according to the load torque and the simplified five-degree-of-freedom dynamic model of the novel EMB system; and S3, replacing the measured value to be a feedback value of clamping force closed-loop control of the EMB system according to the estimated EMB clamping force, and accurately controlling the clamping force by adopting fuzzy PID control. By the adoption of the automobile EMB system clamping force accurate control method based on the expansion state observer, the problems that a pressure sensor is high in cost and automobile braking performance is prone to being reduced are solved, and clamping force response is accurately controlled through a fuzzy PID closed loop.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle EMB systems, and in particular to a precise clamping force control method for an automotive EMB system based on an extended state observer. Background Technique

[0002] At present, as an important means of transportation in modern society, the safety performance of automobiles is particularly important, and among them, the braking performance is the core to ensure the safety of automobiles. In the context of the transformation of automobiles towards electrification and intelligence, the technical limitations of traditional braking systems are gradually emerging. The electro-mechanical brake (EMB) system realizes the output of braking torque through motor drive, and has the advantages of compact structure, rapid response, high control precision, etc., and is regarded as the ultimate solution for automotive braking systems.

[0003] With the increasing maturity of EMB actuator technology, its control algorithm has become a key factor determining braking performance. Research shows that the clamping force directly affects the friction force between the brake pads and the brake disc, and thus determines the magnitude of the braking torque. Therefore, the control effect of the clamping force directly affects the braking performance of the vehicle.

[0004] In the closed-loop control strategy of the clamping force of the EMB system, traditional solutions generally rely on high-precision pressure sensors to collect force feedback signals. However, the high cost and weak anti-interference ability of pressure sensors limit their application in engineering: the magnetic field environment at the vehicle wheel side is complex, and a large amount of heat is generated during the braking process, making it difficult to guarantee the measurement accuracy of the sensor and it is prone to failure.

[0005] To avoid the situation of reduced accuracy or failure of the pressure sensor, how to use a pressure-sensorless control strategy to accurately estimate the clamping force of the EMB system in real time is an urgent problem to be solved. Summary of the Invention

[0006] The purpose of the present invention is to provide a precise clamping force control method for an automotive EMB system based on an extended state observer, which can solve the problems of high cost of pressure sensors and easy reduction of automotive braking performance, and can accurately control the clamping force response.

[0007] To achieve the above object, the present invention provides a precise clamping force control method for an automotive EMB system based on an extended state observer, including the following steps:

[0008] S1. Establish a new five-degree-of-freedom dynamic model of the EMB system including the LuGre friction model by using the dynamic relationship of the EMB system;

[0009] S2. Simplify the five-degree-of-freedom dynamic model of the new EMB system, estimate the load torque of the EMB drive motor according to the extended state observer, and then estimate the clamping force of the EMB system based on the load torque and the simplified five-degree-of-freedom dynamic model of the new EMB system;

[0010] S3. According to the estimated EMB clamping force, replace the measured value as the feedback value of the EMB system clamping force closed-loop control, and use fuzzy PID control to accurately control the clamping force.

[0011] Preferably, in S1, specifically:

[0012] The motor friction torque T fm is expressed by the LuGre friction model, and the expression is:

[0013]

[0014] In the formula, z is the bristle deformation; ω m is the motor speed; ω s is the Stribeck speed; T s is the maximum static friction torque; T c is the Coulomb friction torque; σ0 is the bristle stiffness; σ1 is the microscopic damping coefficient; σ2 is the viscous friction coefficient;

[0015] The load-end clamping force model is:

[0016]

[0017] In the formula, F cl is the brake disc clamping force; A1, A2, and A3 are the system stiffness coefficients identified by the least squares method; x n is the ball screw displacement; x gap is the braking gap between the brake pad and the brake disc;

[0018] The overall dynamic equation of the system is:

[0019]

[0020] In the formula, b f1 , b f2 , b fs , b fn are the friction coefficients of the pinion, the big gear, the ball screw, and the screw nut respectively.

[0021] Preferably, in S2, the simplification of the five-degree-of-freedom dynamic model of the new EMB system is specifically:

[0022] Set the drive motor as a permanent magnet synchronous motor, adopt the control strategy of id = 0, and obtain according to the motor torque balance equation:

[0023]

[0024] In the formula, J m is the moment of inertia; θ m is the rotation angle of the motor; is the second derivative of the motor rotor rotation angle with respect to time; T e is the electromagnetic torque of the motor; T L is the load torque; T fm is the friction torque;

[0025]

[0026] In the formula, P n is the number of pole pairs, ψ f is the motor magnetic flux linkage, i q is the motor q-axis current; K t represents the torque coefficient of the motor;

[0027] After simplifying the five-degree-of-freedom dynamic model of the new EMB system, is expressed as:

[0028]

[0029] In the formula, B f represents the simplified friction coefficient.

[0030] Preferably, in S2, the specific method for estimating the load torque of the EMB drive motor according to the extended state observer is:

[0031] Estimate the motor load through the extended state observer equation, rewrite the EMB model into the state space form, and define the extended state variables x1 and x2 to represent the motor speed and load respectively:

[0032]

[0033] The state equation of the extended state system is obtained as:

[0034]

[0035] In the formula, h(t) is an unknown bounded quantity;

[0036] The design objective of the extended state observer is to estimate the states x1 and x2, and its dynamic equation is designed as:

[0037]

[0038] In the formula, represents the estimated values of x1 and x2; l1 and l2 are the observer gains used to ensure convergence and control the convergence speed;

[0039] Using the forward Euler method with a discrete time step of T s , the ESO equation is discretized as follows:

[0040]

[0041] where represents the estimated value of the rotational speed at time k + 1, ω m (k) represents the motor rotational speed at time k, i q (k) represents the motor current at time k, represents the estimated value of the load torque at time k + 1.

[0042] Preferably, in S2, the EMB system clamping force is estimated according to the load torque and the simplified five-degree-of-freedom dynamic model of the new EMB system as follows:

[0043] The EMB clamping force is estimated in real time according to the gear connected to the motor shaft, the transmission ratio of the ball screw, the transmission efficiency, and the estimated load torque:

[0044]

[0045] where is the estimated clamping force, i g is the transmission ratio of the transmission gear; η g is the efficiency of the transmission gear; η s is the efficiency of the ball screw; l is the lead of the ball screw.

[0046] Preferably, in S3, it is specifically as follows:

[0047] During vehicle braking, the driver steps on the brake pedal. The BCU analyzes the target braking force based on the pedal sensor signal and generates a control command for the target clamping force F cl_target . After the BCU transmits the control signal to the ECU, the ECU drives the permanent magnet synchronous motor to rotate through a pulse width modulation signal; the torque of the motor output shaft is reduced by a stage of reduction gears and then drives the ball screw to rotate to drive the linear motion of the nut, pushing the brake pads inside the brake caliper towards the brake disc; when the brake pads contact the brake disc, elastic deformation occurs to compress the brake disc to generate a clamping force, thereby generating a frictional force and a braking torque;

[0048] At this time, the q-axis current i q measured by the sensor and the motor rotational speed ω m are input into the ECU, and the motor load torque and the EMB clamping force

[0049] The estimated clamping force As the closed-loop feedback value of control, the clamping force of the EMB is precisely controlled by fuzzy PID, and the expected braking effect is generated to achieve EMB automotive braking without a pressure sensor.

[0050] Therefore, the present invention adopts the above-mentioned precise control method for the clamping force of an automotive EMB system based on an extended state observer. By designing an algorithm without a pressure sensor to replace the pressure sensor, only the motor current and speed signals are required to estimate the clamping force generated during the operation of the EMB system in real time. This solves the problems of high cost of the pressure sensor and the decrease in accuracy or even overall failure of the clamping force sensor in the complex environment near the wheel and under severe braking conditions, which may lead to a decline in the braking performance of the vehicle. And the clamping force response is accurately controlled through the fuzzy PID closed-loop.

[0051] The technical solution of the present invention will be further described in detail below with reference to the drawings and embodiments. Description of the Drawings

[0052] Figure 1 is a step diagram of the precise control method for the clamping force of an automotive EMB system based on an extended state observer of the present invention;

[0053] Figure 2 is a schematic diagram of a five-degree-of-freedom dynamic model of a novel EMB system in an embodiment of the precise control method for the clamping force of an automotive EMB system based on an extended state observer of the present invention;

[0054] Figure 3 is a schematic diagram of the clamping force estimation method in an embodiment of the precise control method for the clamping force of an automotive EMB system based on an extended state observer of the present invention. Detailed Embodiments

[0055] The technical solution of the present invention will be further described below with reference to the drawings and embodiments.

[0056] Embodiment 1

[0057] As Figure 1 shown, the present invention provides a precise control method for the clamping force of an automotive EMB system based on an extended state observer, including the following steps:

[0058] S1. Establish a five-degree-of-freedom dynamic model of a novel EMB system including the LuGre friction model by using the dynamic relationship of the EMB system. As Figure 2 shown in the figure, where J m is the rotational inertia of the electronic rotor, T e is the motor torque, k m is the torsional stiffness of the motor shaft, c m is the torsional damping of the motor shaft, T1 is the torsional moment of the motor shaft, J1 is the rotational inertia of the pinion, k gis the torsional stiffness of the gear, c g is the torsional damping of the gear, T2 is the torsional moment of the gear, J2 is the moment of inertia of the large gear, k s is the torsional stiffness of the lead screw shaft, c s is the torsional damping of the lead screw shaft, T s is the torsional moment of the lead screw shaft, J s is the moment of inertia of the lead screw shaft, k n is the torsional stiffness of the lead screw, c n is the torsional damping of the lead screw, T n is the torsional moment of the lead screw, m n is the mass of the nut and the brake pad, F cl is the clamping force of the brake disc.

[0059] The motor frictional torque T fm is expressed by the LuGre friction model, and the expression is:

[0060]

[0061] In the formula, z is the deformation of the bristles; ω m is the motor speed; ω s is the Stribeck speed; T s is the maximum static frictional torque; T c is the Coulomb frictional torque; σ0 is the bristle stiffness; σ1 is the microscopic damping coefficient; σ2 is the viscous friction coefficient.

[0062] The load end clamping force model is:

[0063]

[0064] In the formula, F cl is the clamping force of the brake disc; A1, A2, A3 are the system stiffness coefficients identified by the least squares method; x n is the displacement of the ball screw; x gap is the braking gap between the brake pad and the brake disc.

[0065] The overall dynamic equation of the system is:

[0066]

[0067] In the formula, b f1 、b f2 、b fs 、b fn are the friction coefficients of the pinion, large gear, ball screw, and screw nut respectively.

[0068] S2. Simplify the five-degree-of-freedom dynamic model of the new EMB system, estimate the load torque of the EMB drive motor according to the extended state observer, and then estimate the clamping force of the EMB system according to the load torque and the simplified five-degree-of-freedom dynamic model of the new EMB system.

[0069] Among them, the specific form of the simplified five-degree-of-freedom dynamic model of the new EMB system is as follows:

[0070] Set the drive motor as a permanent magnet synchronous motor, adopt the control strategy of id = 0, and obtain according to the motor torque balance equation:

[0071]

[0072] In the formula, J m is the moment of inertia; θ m is the rotation angle of the motor; is the second derivative of the motor rotor rotation angle with respect to time; T e is the electromagnetic torque of the motor; T L is the load torque; T fm is the friction torque.

[0073]

[0074] In the formula, P n is the number of pole pairs, ψ f is the motor magnetic flux, i q is the motor q-axis current; K t represents the torque coefficient of the motor.

[0075] After simplifying the five-degree-of-freedom dynamic model of the new EMB system, express as:

[0076]

[0077] In the formula, B f represents the simplified friction coefficient.

[0078] The specific method for estimating the load torque of the EMB drive motor according to the extended state observer is as follows:

[0079] Estimate the motor load through the extended state observer equation, rewrite the EMB model into the state space form, and define the extended state variables x1 and x2 to represent the motor speed and load respectively:

[0080]

[0081] The state equation of the extended state system is obtained as:

[0082]

[0083] where \(h(t)\) is an unknown bounded quantity, \(x_1 = \omega\) m is measurable, and \(x_2\) is the total disturbance;

[0084] The design objective of the extended state observer is to estimate the states \(x_1\) and \(x_2\), and its dynamic equation is designed as:

[0085]

[0086] where denotes the estimated values of \(x_1\) and \(x_2\); \(l_1\) and \(l_2\) are the observer gains, which are used to ensure convergence and control the convergence speed.

[0087] Using the forward Euler method with a discrete time step of \(T\) s , the ESO equation is discretized as:

[0088]

[0089] where denotes the estimated value of the rotational speed at time \(k + 1\), \(\omega\) m (k) denotes the motor rotational speed at time \(k\), \(i\) q (k) denotes the motor current at time \(k\), denotes the estimated value of the load torque at time \(k + 1\).

[0090] The clamping force of the EMB system is estimated according to the load torque and the simplified five - degree - of - freedom dynamic model of the new EMB system, specifically:

[0091] According to the gear ratio, ball screw transmission ratio, transmission efficiency of the motor shaft connection, and the estimated load torque, the EMB clamping force is estimated in real - time:

[0092]

[0093] where is the estimated clamping force, \(i\) g is the transmission gear ratio; \(\eta\) g is the transmission gear efficiency; \(\eta\) s is the ball screw efficiency; \(l\) is the ball screw lead.

[0094] S3. According to the estimated EMB clamping force, replace the measured value as the feedback value of the EMB system clamping force closed - loop control, and use fuzzy PID control to accurately control the clamping force.

[0095] Specifically:

[0096] When braking the vehicle, the driver steps on the brake pedal, and the BCU analyzes the target braking force based on the pedal sensor signal and generates a control command for the target clamping force \(F\) cl_targetAfter the BCU transmits the control signal to the ECU, the ECU drives the permanent magnet synchronous motor to rotate through a pulse width modulation signal. After the torque of the motor output shaft is decelerated by a reduction gear, it drives the ball screw to rotate and drives the linear motion of the nut, pushing the brake pads inside the brake caliper towards the brake disc. When the brake pads come into contact with the brake disc, elastic deformation occurs to press the brake disc to generate a clamping force, and then frictional force and braking torque are generated.

[0097] At this time, the q-axis current i measured by the sensor q , and the motor speed ω m are input into the ECU, and the motor load torque is estimated through the built-in extended state observer and the EMB clamping force

[0098] The estimated clamping force is used as the control closed-loop feedback value, and the EMB clamping force is accurately controlled through fuzzy PID to generate the expected braking effect, realizing the EMB automotive braking without a pressure sensor.

[0099] Therefore, the present invention adopts the above-mentioned method for accurately controlling the clamping force of an automotive EMB system based on an extended state observer. By designing an algorithm without a pressure sensor to replace the pressure sensor, only the motor current and speed signals are required to estimate the clamping force generated during the operation of the EMB system in real time, solving the problems of high cost of the pressure sensor and the decrease in accuracy or even overall failure of the clamping force sensor in the complex environment at the wheel end and under harsh braking conditions, resulting in a decrease in the braking performance of the vehicle, and accurately controlling the clamping force response through a fuzzy PID closed loop.

[0100] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that they can still modify or equivalently replace the technical solutions of the present invention, and these modifications or equivalent replacements cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. An accurate clamping force control method for an automotive EMB system based on an extended state observer, characterized in that: It includes the following steps: S1. Establish a new five-degree-of-freedom dynamic model of the EMB system including the LuGre friction model by using the dynamic relationship of the EMB system; S2. Simplify the five-degree-of-freedom dynamic model of the new EMB system, estimate the load torque of the EMB drive motor according to the extended state observer, and then estimate the clamping force of the EMB system according to the load torque and the simplified five-degree-of-freedom dynamic model of the new EMB system; S3. According to the estimated EMB clamping force, replace the measured value as the feedback value of the closed-loop control of the EMB system clamping force, and use fuzzy PID control to accurately control the clamping force.

2. The precise clamping force control method for an automotive EMB system based on an extended state observer according to claim 1, characterized in that: In S1, specifically: The motor frictional torque T fm is expressed by the LuGre friction model, and the expression is: where z is the deformation of the bristle; ω m is the motor speed; ω s is the Stribeck speed; T s is the maximum static friction torque; T c is the Coulomb friction torque; σ0 is the bristle stiffness; σ1 is the microscopic damping coefficient; σ2 is the viscous friction coefficient; The clamping force model at the load end is: Where, F cl is the clamping force of the brake disc; A1, A2, A3 are the system stiffness coefficients identified by the least squares method; x n is the displacement of the ball screw; x gap is the braking gap between the brake pad and the brake disc; The overall dynamic equation of the system is: where b f1 , b f2 , b fs , b fn are the friction coefficients of the pinion, the gear, the ball screw, and the lead screw nut, respectively.

3. The precise clamping force control method for an automotive EMB system based on an extended state observer according to claim 2, characterized in that: In S2, the simplification of the five-degree-of-freedom dynamic model of the new EMB system is specifically: Set the drive motor as a permanent magnet synchronous motor, adopt the control strategy of id = 0, and obtain according to the motor torque balance equation: Where, J m is the moment of inertia; θ m is the rotation angle of the motor; is the second derivative of the rotation angle of the motor rotor with respect to time; T e is the electromagnetic torque of the motor; T L is the load torque; T fm is the frictional torque; Where, P n is the number of pole pairs, ψ f is the motor magnetic flux linkage, i q is the motor q-axis current, K t represents the torque coefficient of the motor; After simplifying the five-degree-of-freedom dynamic model of the new EMB system, it will be expressed as: Where B f represents the simplified friction coefficient.

4. According to the method for accurately controlling the clamping force of an automotive EMB system based on an extended state observer described in claim 3, in S2, the estimation of the load torque of the EMB drive motor according to the extended state observer is specifically: Estimate the motor load through the extended state observer equation, rewrite the EMB model into the state space form, and define the extended state variables x1 and x2 to represent the motor speed and load respectively: The state equation of the extended state system is obtained as: In the formula, h(t) is an unknown bounded quantity; The design goal of the extended state observer is to estimate the states x1 and x2, and its dynamic equation is designed as: In the formula, represents the estimated values of x1 and x2; l1 and l2 are observer gains used to ensure convergence and control the convergence rate; Using the forward Euler method with a discrete time step of T s , the ESO equation is discretized as follows: In the formula, represents the estimated value of the rotational speed at the (k + 1)-th moment, ω m (k) represents the motor rotational speed at the k-th moment, i q (k) represents the motor current at the k-th moment, represents the estimated value of the load torque at the (k + 1)-th moment.

5. According to the method for accurately controlling the clamping force of an automotive EMB system based on an extended state observer described in claim 1, in S2, the estimation of the clamping force of the EMB system according to the load torque and the simplified five-degree-of-freedom dynamic model of the new EMB system is specifically: According to the gear connected to the motor shaft, the transmission ratio of the ball screw, the transmission efficiency, and the estimated load torque, the EMB clamping force is estimated in real time: In the formula, is the estimated clamping force, and i g is the transmission ratio of the transmission gear; η g is the transmission gear efficiency; η s is the efficiency of the ball screw; l is the lead of the ball screw.

6. According to the method for accurately controlling the clamping force of an automotive EMB system based on an extended state observer described in claim 5, in S3, specifically: During service braking, the driver steps on the brake pedal. The BCU analyzes the target braking force based on the pedal sensor signal and generates a control command for the target clamping force F cl_target , after the BCU transmits the control signal to the ECU, the ECU drives the permanent magnet synchronous motor to rotate through a pulse width modulation signal; after the torque of the motor output shaft is decelerated by the reduction gear for the first stage, it drives the ball screw to rotate to drive the linear motion of the nut, and pushes the brake pads inside the brake caliper towards the brake disc; when the brake pads contact the brake disc, elastic deformation occurs to press the brake disc to generate a clamping force, thereby generating a frictional force and a braking torque; At this time, the q-axis current i measured by the sensor q , and the motor speed ω m are input into the ECU, and the motor load torque and the EMB clamping force Estimate the clamping force As the closed-loop feedback value of the control, the clamping force of the EMB is precisely controlled by fuzzy PID, and the expected braking effect is generated to achieve EMB automotive braking without a pressure sensor.

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